# XlongLab · Milvus 中文文档完整索引 > 共 4810 篇，覆盖 v3.0.x、v2.6.x、v2.5.x、v2.4.x。 ## v3.0.x（最新版） ### 概览与基础概念 - [比较](https://www.xlonglab.com/docs/about/comparison.md): 本文对 Milvus 与其他向量搜索解决方案进行了比较。 - [Milvus 限制](https://www.xlonglab.com/docs/about/limitations.md): 了解使用 Milvus 时的限制。 - [Milvus 用户](https://www.xlonglab.com/docs/about/milvus_adopters.md): 了解哪些公司在生产环境中采用了 Milvus。 - [什么是 Milvus](https://www.xlonglab.com/docs/about/overview.md): Milvus 是一款高性能、高度可扩展的向量数据库，可在从笔记本电脑到大规模分布式系统等各种环境中高效运行。它既提供开源软件版本，也提供云服务版本。 - [Milvus 路线图](https://www.xlonglab.com/docs/about/roadmap.md): Milvus 是一个专为支持人工智能应用而构建的开源向量数据库。以下是我们的路线图，用于指导我们的开发工作。 ### 管理与运维 - [为 Kubernetes 上的 Milvus 分配资源](https://www.xlonglab.com/docs/adminGuide/allocate.md): 了解如何在 Kubernetes 上为 Milvus 分配资源。 - [Milvus 用户认证](https://www.xlonglab.com/docs/adminGuide/authenticate.md): 配置 Milvus 用户认证：启用 Authentication，使用默认 root/Milvus 凭据通过 Python、Java、Node.js、Go 和 cURL 连接，并管理用户密码。 - [配置块缓存](https://www.xlonglab.com/docs/adminGuide/chunk_cache.md) - [在 EC2 上部署 Milvus 集群](https://www.xlonglab.com/docs/adminGuide/clouds/aws): 了解如何在 AWS EC2 上部署 Milvus 集群。 - [在 AWS 上为 Milvus 配置 Layer-7 Load Balancer](https://www.xlonglab.com/docs/adminGuide/clouds/aws/aws_layer7.md): 了解如何在 AWS 上的 Layer-7 负载均衡器后面部署 Milvus 集群。 - [在 EKS 上部署 Milvus 集群](https://www.xlonglab.com/docs/adminGuide/clouds/aws/eks.md): 了解如何在 EKS 上部署 Milvus 集群 - [按 IAM 角色配置 S3 访问权限](https://www.xlonglab.com/docs/adminGuide/clouds/aws/s3.md): 了解如何使用 IAM 角色配置 s3。 - [通过工作负载身份配置 Blob 存储访问](https://www.xlonglab.com/docs/adminGuide/clouds/azure/abs.md): 了解如何使用 Workload Identity 配置 Blob Storage。 - [使用 Kubernetes 在 Microsoft Azure 上部署 Milvus](https://www.xlonglab.com/docs/adminGuide/clouds/azure): 了解如何在 Azure 上部署 Milvus 集群。 - [为 Milvus 配置 ingress-nginx](https://www.xlonglab.com/docs/adminGuide/clouds/azure/ingress.md): 了解如何使用 Milvus 配置 ingress nginx。 - [在 GKE 上部署 Milvus 集群](https://www.xlonglab.com/docs/adminGuide/clouds/gcp): 了解如何在 GKE 上部署 Milvus 集群。 - [在 GCP 上为 Milvus 配置 Layer-7 Load Balancer](https://www.xlonglab.com/docs/adminGuide/clouds/gcp/gcp_layer7.md): 了解如何在 GCP 上的 Layer-7 负载均衡器后面部署 Milvus 集群。 - [使用 Workload Identity 配置 GCS 访问](https://www.xlonglab.com/docs/adminGuide/clouds/gcp/gcs.md): 了解如何使用工作负载身份配置 gcs。 - [在 OpenShift 上部署 Milvus 集群](https://www.xlonglab.com/docs/adminGuide/clouds/openshift): 了解如何在 OpenShift 上部署 Milvus 集群。 - [配置 Jaeger Tracing](https://www.xlonglab.com/docs/adminGuide/config_jaeger_tracing.md): 本指南介绍如何使用 Jaeger 收集 Milvus 的 Trace 数据。 - [使用 Docker Compose 配置 Milvus](https://www.xlonglab.com/docs/adminGuide/configure-docker.md): 使用 Docker Compose 配置 Milvus。 - [使用 Helm Chart 配置 Milvus](https://www.xlonglab.com/docs/adminGuide/configure-helm.md): 使用 Helm Chart 配置 Milvus。 - [使用本地磁盘配置 Milvus QueryNode](https://www.xlonglab.com/docs/adminGuide/configure-querynode-localdisk.md): 了解如何配置 Milvus QueryNode 以使用本地磁盘。 - [配置访问日志](https://www.xlonglab.com/docs/adminGuide/configure_access_logs.md) - [使用 Milvus Operator 配置 Milvus](https://www.xlonglab.com/docs/adminGuide/configure_operator.md): 了解如何使用 Milvus Operator 配置 Milvus。 - [使用 SASL/SSL 连接到 Kafka](https://www.xlonglab.com/docs/adminGuide/connect_kafka_ssl.md): 本指南列出了几种连接 Milvus 和 Kafka 的方法，从不带 SASL/SSL 的最简单方法到带 SASL/SSL 的完全安全方法。 - [数据基础设施](https://www.xlonglab.com/docs/adminGuide/data-infra-integration-overview.md): 概述 Milvus 使用的 metadata storage、object storage 和 message queue。 - [使用 Docker Compose 或 Helm 配置 Meta storage](https://www.xlonglab.com/docs/adminGuide/deploy_etcd.md): 了解如何使用 Docker Compose/Helm 为 Milvus 配置 Meta storage。 - [使用 Docker Compose 或 Helm 配置消息存储](https://www.xlonglab.com/docs/adminGuide/deploy_pulsar.md): 了解如何使用 Docker Compose 或 Helm 配置消息存储。 - [使用 Docker Compose 或 Helm 配置 Object Storage](https://www.xlonglab.com/docs/adminGuide/deploy_s3.md): 了解如何使用 Docker Compose 或 Helm 为 Milvus 设置 S3 存储。 - [删除 User 与 Role](https://www.xlonglab.com/docs/adminGuide/drop_users_roles.md): 为确保数据安全，建议删除不再使用的用户和角色。本指南介绍如何删除用户和角色。 - [动态配置 Milvus](https://www.xlonglab.com/docs/adminGuide/dynamic_config.md): 了解 Milvus 的动态配置。 - [etcd](https://www.xlonglab.com/docs/adminGuide/etcd.md) - [为 Role 授予 Privilege 或 Privilege Group](https://www.xlonglab.com/docs/adminGuide/grant_privileges.md): 创建角色后，就可以向角色授予权限。本指南将介绍如何向角色授予权限或权限组。 - [向 User 授予 Role](https://www.xlonglab.com/docs/adminGuide/grant_roles.md): 创建角色并授予角色权限后，就可以将角色授予用户，这样用户就可以访问资源并执行角色所定义的操作。可以向一个用户授予多个角色，也可以向多个用户授予一个角色。本指南将介绍如何向用户授予角色。 - [为 Milvus 配置水平 Pod 自动扩展 (HPA)](https://www.xlonglab.com/docs/adminGuide/hpa.md): 了解如何配置水平 Pod 自动扩展（HPA），以动态扩展 Milvus 集群。 - [设置 Collection 数量限制](https://www.xlonglab.com/docs/adminGuide/limit_collection_counts.md) - [使用 Milvus Operator 配置消息存储](https://www.xlonglab.com/docs/adminGuide/message_storage_operator.md): 了解如何使用 Milvus Operator 配置消息存储。 - [使用 Milvus Operator 配置 Meta storage](https://www.xlonglab.com/docs/adminGuide/meta_storage_operator.md): 了解如何使用 Milvus Operator 配置 Meta storage。 - [创建警报](https://www.xlonglab.com/docs/adminGuide/monitor/alert.md): 了解如何在 Grafana 中为 Milvus 服务创建警报。 - [配置 Grafana Loki](https://www.xlonglab.com/docs/adminGuide/monitor/configure_grafana_loki.md): 本主题介绍如何使用 Loki 收集日志，并使用 Grafana 查询 Milvus 集群的日志。 - [Milvus 指标控制面板](https://www.xlonglab.com/docs/adminGuide/monitor/metrics_dashboard.md): 本主题介绍 Milvus 仪表板中显示的监控指标。 - [部署监控服务](https://www.xlonglab.com/docs/adminGuide/monitor): 了解如何使用 Prometheus 为 Milvus 集群部署监控服务。 - [监控概述](https://www.xlonglab.com/docs/adminGuide/monitor/monitor_overview.md): 了解如何在 Milvus 中使用 Prometheus 和 Grafana 进行监控和警报服务。 - [可视化指标](https://www.xlonglab.com/docs/adminGuide/monitor/visualize.md): 了解如何在 Grafana 中可视化 Milvus 指标。 - [Kafka](https://www.xlonglab.com/docs/adminGuide/mq_kafka.md) - [Pulsar](https://www.xlonglab.com/docs/adminGuide/mq_pulsar.md) - [RocksMQ](https://www.xlonglab.com/docs/adminGuide/mq_rocksmq.md) - [Message Queue 概览](https://www.xlonglab.com/docs/adminGuide/mqtype-overview.md): 概述 Milvus 支持的 Message Queue (mqType) 选项，以及 standalone 与 distributed 部署应如何选择。 - [Object Storage](https://www.xlonglab.com/docs/adminGuide/object-storage.md) - [使用 Milvus Operator 配置 Object Storage](https://www.xlonglab.com/docs/adminGuide/object_storage_operator.md): 了解如何使用 Milvus Operator 配置 Object Storage。 - [创建 Privilege Group](https://www.xlonglab.com/docs/adminGuide/privilege_group.md): 为了简化授予权限的流程，建议将多个权限合并为一个权限组。 - [Milvus RBAC](https://www.xlonglab.com/docs/adminGuide/rbac.md): Milvus RBAC（Role-Based Access Control）通过 User、Role、Privilege 和资源范围控制 Database、Collection 与实例级操作，适用于生产环境权限管理。 - [管理 Resource Group](https://www.xlonglab.com/docs/adminGuide/resource_group.md): 了解如何管理资源组。 - [扩展 Milvus 依赖项](https://www.xlonglab.com/docs/adminGuide/scale-dependencies.md) - [扩展 Milvus Standalone](https://www.xlonglab.com/docs/adminGuide/scale-standalone.md): Milvus Standalone 是一种单机服务器部署方式。Milvus Standalone 的所有组件都打包到一个 Docker 镜像中，部署起来非常方便。本主题介绍如何扩展在此模式下运行的 Milvus 实例。 - [扩展 Milvus 集群](https://www.xlonglab.com/docs/adminGuide/scaleout.md): 了解如何在 Milvus 集群中手动或自动缩放和扩展。 - [在 Kafka 和 Woodpecker 之间切换](https://www.xlonglab.com/docs/adminGuide/switch-kafka-woodpecker.md): 使用 Helm 或 Milvus Operator 在 Kafka 和 Woodpecker 之间切换 Milvus 集群的消息队列。 - [切换 Message Queue](https://www.xlonglab.com/docs/adminGuide/switch-mq-type.md): 在不停机的情况下，将现有 Milvus 部署的 Message Queue 在 Woodpecker 与其他 Message Queue 之间切换。 - [在 Pulsar 和 Woodpecker 之间切换](https://www.xlonglab.com/docs/adminGuide/switch-pulsar-woodpecker.md): 使用 Helm 或 Milvus Operator，在 Pulsar 和 Woodpecker 之间切换 Milvus 集群的 message queue。 - [在 RocksMQ 与 Woodpecker 之间切换](https://www.xlonglab.com/docs/adminGuide/switch-rocksmq-woodpecker.md): 将 Milvus Standalone（Docker Compose）部署的 message queue 在 RocksMQ 与 Woodpecker 之间切换。 - [传输中的加密](https://www.xlonglab.com/docs/adminGuide/tls.md): 了解如何在 Milvus 启用 TLS Proxy。 - [将 Milvus 的 Pulsar 从 V2 升级到 V3](https://www.xlonglab.com/docs/adminGuide/upgrade-pulsar-v3.md): 了解如何在 Milvus 中将 Pulsar 从 V2 升级到 V3，以便使用最新版本的 Milvus v2.5.x。 - [使用 Helm Chart 升级 Milvus 集群](https://www.xlonglab.com/docs/adminGuide/upgrade_milvus_cluster-helm.md): 了解如何使用 Helm Chart 升级 Milvus 集群。 - [使用 Milvus Operator 升级 Milvus 集群](https://www.xlonglab.com/docs/adminGuide/upgrade_milvus_cluster-operator.md): 了解如何使用 Milvus Operator 对 Milvus 集群进行升级。 - [使用 Docker Compose 升级 Milvus Standalone](https://www.xlonglab.com/docs/adminGuide/upgrade_milvus_standalone-docker.md): 了解如何使用 Docker Compose 升级 Milvus Standalone。 - [使用 Helm Chart 升级 Milvus Standalone](https://www.xlonglab.com/docs/adminGuide/upgrade_milvus_standalone-helm.md): 了解如何使用 Helm Chart 升级 Milvus Standalone。 - [使用 Milvus Operator 升级 Milvus Standalone](https://www.xlonglab.com/docs/adminGuide/upgrade_milvus_standalone-operator.md): 了解如何使用 Milvus Operator 升级 Milvus Standalone。 - [在 Milvus v2.5.x 中使用 Pulsar v2](https://www.xlonglab.com/docs/adminGuide/use-pulsar-v2.md): Milvus 建议您将 Pulsar 升级到适用于 Milvus v2.5.x 的 v3 版本。不过，如果您更喜欢使用 Pulsar v2，本文将指导您完成在 Milvus v2.5.x 中继续使用 Pulsar v2 的步骤。 - [创建 User 与 Role](https://www.xlonglab.com/docs/adminGuide/users_and_roles.md): Milvus 通过 RBAC 实现细粒度访问控制。您可以从创建用户和角色开始，然后为角色分配权限或权限组，最后通过向用户授予角色来管理访问控制。这种方法可确保访问管理的效率和安全性。本页将介绍如何在 Milvus 中创建用户和角色。 - [Woodpecker 部署与配置](https://www.xlonglab.com/docs/adminGuide/woodpecker.md): 了解 Woodpecker 如何作为 Milvus 默认消息队列（WAL）工作，以及如何以 embedded 或 service mode 运行。 ### AI 工具 - [AGENTS.md for Milvus](https://www.xlonglab.com/docs/aiTools/agents_overview.md): 使用 PyMilvus 生成、审查或调试 Milvus 向量数据库代码的 AI coding agent 的规则和模式。 - [Prompt：Milvus 索引选择](https://www.xlonglab.com/docs/aiTools/index_selection.md): AI coding assistant 选择和配置 Milvus 索引的规则。 - [面向 AI Agent 的 Milvus](https://www.xlonglab.com/docs/aiTools/milvus_for_agents.md): 了解 AI Agent 如何将 Milvus 作为向量数据库，用于 RAG、语义搜索和长期记忆。 - [Prompt：Milvus Python SDK](https://www.xlonglab.com/docs/aiTools/python_sdk.md): AI coding assistant 使用 MilvusClient 编写正确的 Milvus Python 代码的规则。 - [RAG Pipeline](https://www.xlonglab.com/docs/aiTools/rag_pipeline.md): AI coding assistant 使用 Milvus 构建 RAG Pipeline 的规则。 - [Prompt：Milvus Schema 设计](https://www.xlonglab.com/docs/aiTools/schema_design.md): AI coding assistant 设计正确的 Milvus Collection Schema 的规则。 - [Prompt：Milvus 搜索模式](https://www.xlonglab.com/docs/aiTools/search_patterns.md): AI coding assistant 在 Milvus 中实现搜索、混合搜索和全文搜索的规则。 ### Embedding 模型 - [BGE M3](https://www.xlonglab.com/docs/embeddings/embed-with-bgm-m3.md): BGE-M3 因其在多语言、多功能和多地域性方面的能力而得名。 - [Cohere Embedding](https://www.xlonglab.com/docs/embeddings/embed-with-cohere.md): 本文介绍如何使用 CohereEmbeddingFunction 使用 Cohere Embedding 模型对文档和查询进行编码。 - [Gemini](https://www.xlonglab.com/docs/embeddings/embed-with-gemini.md): Milvus 通过 GeminiEmbeddingFunction 类与 OpenAI 的模型集成。 - [Instructor](https://www.xlonglab.com/docs/embeddings/embed-with-instructor.md): 本文介绍如何使用 InstructorEmbeddingFunction 使用 Instructor Embedding 模型对文档和查询进行编码。 - [Jina AI - Embedding](https://www.xlonglab.com/docs/embeddings/embed-with-jina.md): 本文介绍如何使用 JinaEmbeddingFunction 使用 Jina AI Embedding 模型对文档和查询进行编码。 - [mGTE](https://www.xlonglab.com/docs/embeddings/embed-with-mgte.md): 本文介绍如何使用 MGTEEmbeddingFunction 使用 mGTE Embedding 模型对文档和查询进行编码。 - [Mistral AI](https://www.xlonglab.com/docs/embeddings/embed-with-mistral-ai.md): 本文介绍如何使用 MistralAIEmbeddingFunction 使用 Mistral AI Embedding 模型对文档和查询进行编码。 - [Model2Vec](https://www.xlonglab.com/docs/embeddings/embed-with-model2vec.md): Milvus 通过 Model2VecEmbeddingFunction 类与 Model2Vec 的模型集成。 - [Nomic](https://www.xlonglab.com/docs/embeddings/embed-with-nomic.md): 本文介绍如何使用 NomicEmbeddingFunction 使用 Nomic Embedding 模型对文档和查询进行编码。 - [OpenAI](https://www.xlonglab.com/docs/embeddings/embed-with-openai.md): Milvus 通过 OpenAIEmbeddingFunction 类与 OpenAI 的模型集成。 - [Sentence Transformers](https://www.xlonglab.com/docs/embeddings/embed-with-sentence-transform.md): 本文演示了如何使用 Milvus 中的 Sentence Transformers 将文档和查询编码为稠密向量。 - [SPLADE](https://www.xlonglab.com/docs/embeddings/embed-with-splade.md): 本文介绍如何使用 SpladeEmbeddingFunction 使用 SPLADE 模型对文档和查询进行编码。 - [Voyage](https://www.xlonglab.com/docs/embeddings/embed-with-voyage.md): 本文介绍如何使用 VoyageEmbeddingFunction 使用 Voyage 模型对文档和查询进行编码。 - [Embedding Overview](https://www.xlonglab.com/docs/embeddings): 了解如何为数据生成 Embedding。 ### 常见问题 - [操作常见问题](https://www.xlonglab.com/docs/faq/operational_faq.md): 查找有关 Milvus Operator 常见问题的答案。 - [性能常见问题](https://www.xlonglab.com/docs/faq/performance_faq.md): 查找有关搜索性能、性能增强和其他性能相关问题的常见问题答案。 - [产品常见问题](https://www.xlonglab.com/docs/faq/product_faq.md): 查找有关世界上最先进的向量数据库常见问题的答案。 - [故障排除](https://www.xlonglab.com/docs/faq/troubleshooting.md): 了解使用 Milvus 时可能遇到的常见问题，以及如何克服这些问题。 ### 安装与入门 - [连接 Milvus 服务器](https://www.xlonglab.com/docs/getstarted/connect-to-milvus-server.md): 本主题介绍如何建立客户端与 Milvus 服务器的连接，并配置常用的连接选项。 - [Milvus 部署选项概述](https://www.xlonglab.com/docs/getstarted/install-overview.md): Milvus 是一个高性能、可扩展的向量数据库。它支持各种规模的用例，从在 Jupyter Notebooks 中本地运行的演示到处理数百亿向量的大规模 Kubernetes 集群。目前，有三种 Milvus 部署选项：Milvus Lite、Milvus Standalone 和 Milvus Distributed。 - [安装 Milvus Go SDK](https://www.xlonglab.com/docs/getstarted/install_SDKs/install-go.md): 了解如何安装 Milvus 的 GO SDK。 - [安装 Milvus Java SDK](https://www.xlonglab.com/docs/getstarted/install_SDKs/install-java.md): 了解如何安装 Milvus 的 Java SDK。 - [安装 Milvus Nodejs SDK](https://www.xlonglab.com/docs/getstarted/install_SDKs/install-node.md): 了解如何安装 Milvus 的 Node.js SDK。 - [安装 Milvus Python SDK](https://www.xlonglab.com/docs/getstarted/install_SDKs/install-pymilvus.md): 了解如何安装 Milvus 的 Python SDK。 - [本地运行 Milvus Lite](https://www.xlonglab.com/docs/getstarted/milvus_lite.md): 开始使用 Milvus Lite。 - [快速入门](https://www.xlonglab.com/docs/getstarted/quickstart.md): 开始使用 Milvus。 - [使用 RPM/DEB 软件包安装 Milvus Standalone](https://www.xlonglab.com/docs/getstarted/run-milvus-docker/install_standalone-binary.md): 了解如何使用预编译的 RPM/DEB 软件包安装 Milvus Standalone。 - [使用 Docker Compose 运行 Milvus (Linux)](https://www.xlonglab.com/docs/getstarted/run-milvus-docker/install_standalone-docker-compose.md): 了解如何使用 Docker Compose 独立安装 Milvus。 - [在 Docker 中运行 Milvus（Linux）](https://www.xlonglab.com/docs/getstarted/run-milvus-docker/install_standalone-docker.md): 在 Linux 上使用官方脚本通过 Docker 运行 Milvus Standalone，涵盖前置条件、端口、Milvus WebUI、配置、升级、停止和删除。 - [在 Docker 中运行 Milvus（Windows）](https://www.xlonglab.com/docs/getstarted/run-milvus-docker/install_standalone-windows.md): 了解如何使用 Docker Desktop for Windows 安装 Milvus standalone。 - [安装 Milvus Standalone 的要求](https://www.xlonglab.com/docs/getstarted/run-milvus-docker/prerequisite-docker.md): 了解安装 Milvus Standalone 前的必要准备。 - [使用 Helm Chart 运行支持 GPU 的 Milvus](https://www.xlonglab.com/docs/getstarted/run-milvus-gpu/install_cluster-helm-gpu.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 Docker Compose 运行支持 GPU 的 Milvus](https://www.xlonglab.com/docs/getstarted/run-milvus-gpu/install_standalone-docker-compose-gpu.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [安装支持 GPU 的 Milvus 的要求](https://www.xlonglab.com/docs/getstarted/run-milvus-gpu/prerequisite-gpu.md): 了解使用 GPU 安装 Milvus 前的必要准备工作。 - [使用 Helm 安装 Milvus 集群](https://www.xlonglab.com/docs/getstarted/run-milvus-k8s/install_cluster-helm.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 Milvus Operator 安装 Milvus 集群](https://www.xlonglab.com/docs/getstarted/run-milvus-k8s/install_cluster-milvusoperator.md): 了解如何使用 Milvus Operator 在 Kubernetes 上安装 Milvus 集群 - [在 Kubernetes 上运行 Milvus 的要求](https://www.xlonglab.com/docs/getstarted/run-milvus-k8s/prerequisite-helm.md): 了解使用 Helm 安装 Milvus 之前的必要准备工作。 - [搜索数据模型设计](https://www.xlonglab.com/docs/getstarted/schema-hands-on.md): 信息检索系统，又称搜索引擎，对于检索增强生成（RAG）、视觉搜索和产品推荐等各种人工智能应用至关重要。这些系统的核心是精心设计的数据模型，用于组织、索引和检索信息。 ### 生态集成 - [使用 AIMon 和 Milvus 提升 LLM 应用的检索质量](https://www.xlonglab.com/docs/integrations/AIMon_milvus_integration.md): 在本教程中，我们将帮助您构建一个能回答问题的检索增强生成（RAG）聊天机器人。 - [使用 VectorETL 将数据高效加载到 Milvus 中](https://www.xlonglab.com/docs/integrations/ETL_using_vectorETL.md): 在本教程中，我们将探讨如何使用专为向量数据库设计的轻量级 ETL 框架 \[VectorETL\](https://github.com/ContextData/VectorETL)，高效地将数据加载到 Milvus 中。… - [与 Milvus 一起使用 NLWeb](https://www.xlonglab.com/docs/integrations/NLWeb_with_milvus.md): 了解如何将 Microsoft NLWeb 与 Milvus 集成，为网站构建强大的自然语言界面。本教程演示了如何在 NLWeb 应用程序中利用 Milvus 的向量数据库功能进行高效语义搜索、嵌入存储和上下文检索。 - [使用 Milvus + PII 屏蔽器构建 RAG](https://www.xlonglab.com/docs/integrations/RAG_with_pii_and_milvus.md): 在本教程中，我们将向您展示如何使用 Milvus 和 PII Masker 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [检索增强生成：使用 Apify 抓取网站并将数据保存到 Milvus 以用于问题解答](https://www.xlonglab.com/docs/integrations/apify_milvus_rag.md): 本教程介绍如何使用 Apify 的网站内容抓取器抓取网站，并将数据保存到 Milvus 或 Zilliz Cloud 向量数据库，以便日后用于问题解答。 - [使用 Basic Memory 和 Milvus 构建语义化项目记忆](https://www.xlonglab.com/docs/integrations/basic_memory_with_milvus.md): 本教程将为应用团队构建一个小型记忆项目。我们会记录有关缓存、身份认证、部署和备份的笔记，然后通过语义搜索和 Hybrid Search 检索出合适的笔记。 - [构建 RAG Pipeline：将数据从 S3 载入 Milvus](https://www.xlonglab.com/docs/integrations/build_RAG_from_s3_with_milvus.md): 本教程将指导您使用 Milvus 和亚马逊 S3 构建一个 RAG Pipeline。您将学习如何高效地从 S3 文件桶中加载文档，将其分割成易于管理的块，并将其 Embedding 存储在 Milvus 中，以便进行快速、可扩展的检索。为了简化这一过程，我们将使用 LangChain 作为工具，从 S3 加载数据并将其存储到 Milvus 中。 - [使用 Milvus 和 Cognee 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_cognee.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Cognee 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Crawl4AI 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_crawl4ai.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Crawl4AI 构建一个 RAG Pipeline。该管道集成了用于网络数据抓取的 Crawl4AI、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [利用 Milvus 和 DeepSeek 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_deepseek.md): 在本教程中，我们将向您展示如何使用 Milvus 和 DeepSeek 构建 RAG Pipeline。 - [使用 Milvus 和 Docling 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_docling.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Docling 构建 RAG Pipeline。该管道集成了用于文档解析的 Docling、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [用 Milvus 和 EmbedAnything 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_embedAnything.md): 在本教程中，我们将演示如何使用 EmbedAnything 和 Milvus 一起构建 RAG Pipeline。EmbedAnything 使用可插拔的适配器系统，而不是与任何特定数据库紧密耦合，适配器作为封装器，定义了嵌入数据的格式化、索引和在目标向量存储中的存储方式。 - [使用 Exa 和 Milvus 构建双源 RAG Agent](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_exa.md): 本教程演示了如何构建一个 AI Agent，同时搜索公共网络（通过 Exa）和私人知识库（通过 Milvus），然后合成一个统一的答案。该 AI Agent 使用 OpenAI 的函数调用功能，根据用户的问题自动决定查询哪个来源。 - [使用 Milvus 和 Feast 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_feast.md): 在本教程中，我们将使用 Feast 和 Milvus 构建一个 RAG Pipeline。Feast 是一个开源特征库，可简化机器学习的特征管理，为训练和实时推理提供高效的结构化数据存储和检索。Milvus 是一个高性能向量数据库，专为快速相似性搜索而设计，因此非常适合在 RAG 工作流中检索相关文档。 - [利用 Milvus 和 Firecrawl 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_firecrawl.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Firecrawl 构建 RAG Pipeline。该管道集成了用于网络数据抓取的 Firecrawl、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [使用 Milvus 和 Fireworks AI 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_fireworks.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Fireworks AI 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Gemini 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_gemini.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Gemini 构建 RAG Pipeline（Retrieval-Augmented Generation）。我们将使用 Gemini 模型根据给定的查询生成文本。我们还将使用 Milvus 来存储和检索生成的文本。 - [利用 Milvus 和 Lepton AI 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_lepton.md): 在本教程中，我们将向您展示如何利用 Milvus 和 Lepton AI 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Ollama 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_ollama.md): 在本指南中，我们将向您展示如何利用 Ollama 和 Milvus 高效、安全地构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 SiliconFlow 构建 RAG](https://www.xlonglab.com/docs/integrations/build_RAG_with_milvus_and_siliconflow.md): 本教程介绍如何使用 Milvus 和 SiliconFlow 构建 RAG Pipeline。 - [在 Arm 架构上构建 RAG](https://www.xlonglab.com/docs/integrations/build_rag_on_arm.md): 在本教程中，您将学习如何在基于 Arm 的基础架构上构建检索增强生成（RAG）应用程序。在向量存储方面，我们利用了全面管理的 Milvus 向量数据库 Zilliz Cloud。Zilliz Cloud 可在 AWS、GCP 和 Azure 等主流云上使用。在本演示中，我们使用部署在 AWS 上的 Zilliz Cloud 和 Arm 机器。… - [使用 Milvus 部署 Dify](https://www.xlonglab.com/docs/integrations/dify_with_milvus.md): 本教程介绍如何将 Dify 与 Milvus 集成，使用 Milvus 作为向量数据库，为知识检索与 RAG Pipeline 提供存储和检索能力。 - [使用 DeepEval 进行评估](https://www.xlonglab.com/docs/integrations/evaluation_with_deepeval.md): 本指南演示了如何使用 DeepEval 评估基于 Milvus 的 RAG Pipeline。 - [使用 Arize Phoenix 进行评估](https://www.xlonglab.com/docs/integrations/evaluation_with_phoenix.md): 本指南演示了如何使用 Arize Phoenix 评估基于 Milvus 的 RAG Pipeline。 - [使用 EverOS 和 Milvus 构建 AI Agent 长期记忆](https://www.xlonglab.com/docs/integrations/everos_with_milvus.md): 本教程将构建一个项目助手，使其能够跨多个独立会话记住版本发布决策。我们会添加有关 Project Atlas 发布的对话，并混入其他项目的无关对话。EverOS 使用 LLM 提取记忆，Milvus 则存储 Hybrid Search 所需的 BM25 索引和向量索引。 - [使用 Milvus 和 Haystack 进行全文搜索](https://www.xlonglab.com/docs/integrations/full_text_search_with_milvus_and_haystack.md): 本教程演示如何使用 Haystack 和 Milvus 在应用程序中实现全文和混合搜索。 - [Google ADK 与 Milvus](https://www.xlonglab.com/docs/integrations/google_adk_with_milvus.md): 本教程将使用 adk-milvus，在两个常见场景中连接 ADK 与 Milvus：基于知识库的检索工具集，以及用于保存用户专属 Agent 记忆的跨 Session 记忆服务。Notebook 默认使用 Milvus Lite，因此无需单独部署 Milvus Server，即可在本地或 Google Colab 中运行。 - [将 Milvus 与 Agno 相集成](https://www.xlonglab.com/docs/integrations/integrate_with_agno.md): Milvus 向量数据库能够高效地存储和检索嵌入信息。借助 Milvus 和 Agno，您可以轻松地将知识整合到 Agent 工作流程中。本文档是如何使用 Milvus 与 Agno 集成的基本指南。 - [Airbyte：开源数据移动基础架构](https://www.xlonglab.com/docs/integrations/integrate_with_airbyte.md): Airbyte 是一种开源数据移动基础架构，用于构建提取和加载（EL）数据管道。它具有多功能性、可扩展性和易用性。Airbyte 的连接器目录 "开箱即用"，预置了 350 多个连接器。这些连接器可用于在短短几分钟内开始将数据从源复制到目标。 - [使用 Milvus 和 BentoML 的检索增强生成（RAG）](https://www.xlonglab.com/docs/integrations/integrate_with_bentoml.md): 本指南演示了如何使用 BentoCloud 上的开源 Embedding 模型和大语言模型与 Milvus 向量数据库来构建检索增强生成（RAG）应用程序。 - [使用 Milvus 与 CAMEL-AI 构建 RAG 系统](https://www.xlonglab.com/docs/integrations/integrate_with_camel.md): 本指南介绍如何结合 CAMEL-AI 与 Milvus 构建检索增强生成（RAG）系统，并使用 Milvus 检索相关文档，为大语言模型生成提供上下文。 - [使用 Milvus 和 Cohere 进行问题解答](https://www.xlonglab.com/docs/integrations/integrate_with_cohere.md): 本页说明了如何使用 Milvus 作为向量数据库和 Cohere 作为嵌入系统，创建基于 SQuAD 数据集的问题解答系统。 - [Milvus 与 DSPy 集成](https://www.xlonglab.com/docs/integrations/integrate_with_dspy.md): 本指南演示了如何使用 DSPy 的 Retriever 模块之一 MilvusRM 来优化 RAG 程序。 - [使用 Milvus 部署 FastGPT](https://www.xlonglab.com/docs/integrations/integrate_with_fastgpt.md): 本教程将指导您如何使用 Milvus 快速部署自己的专属 FastGPT 应用程序。 - [使用 Milvus 和 Haystack 的检索增强生成（RAG）](https://www.xlonglab.com/docs/integrations/integrate_with_haystack.md): 本指南演示了如何使用 Haystack 和 Milvus 建立检索增强生成（RAG）系统。 - [使用 Milvus 和 Hugging Face 进行问题解答](https://www.xlonglab.com/docs/integrations/integrate_with_hugging-face.md): 本教程展示了如何使用 Hugging Face 作为数据加载器和嵌入生成器进行数据处理，并使用 Milvus 作为向量数据库进行语义搜索，从而构建一个问题解答系统。 - [Milvus 与 Jina 集成](https://www.xlonglab.com/docs/integrations/integrate_with_jina.md): 本指南演示了如何使用 Jina 嵌入和 Milvus 进行相似性搜索和检索任务。 - [使用 Langfuse 对 RAG 查询进行 Tracing](https://www.xlonglab.com/docs/integrations/integrate_with_langfuse.md): 本教程演示如何使用 Langfuse 的 LlamaIndex 集成对 RAG 查询进行 Tracing，并使用 Milvus Lite 存储和检索文档。 - [使用 Milvus 和 LlamaIndex 的检索增强生成（RAG）](https://www.xlonglab.com/docs/integrations/integrate_with_llamaindex.md): 本指南演示了如何使用 LlamaIndex 和 Milvus 建立检索增强生成（RAG）系统。 - [与 Milvus 集成的 MemGPT](https://www.xlonglab.com/docs/integrations/integrate_with_memgpt.md): MemGPT 可轻松构建和部署有状态 LLM Agent。通过与 Milvus 集成，您可以构建可连接外部数据源（RAG）的 AI Agent。 - [使用 Milvus 和 OpenAI 进行语义搜索](https://www.xlonglab.com/docs/integrations/integrate_with_openai.md): 本页讨论向量数据库与 OpenAI 的 Embedding API 的集成。 - [使用 PyTorch 和 Milvus 进行图像搜索](https://www.xlonglab.com/docs/integrations/integrate_with_pytorch.md): 本页演示如何使用 PyTorch 和 Milvus 创建图像搜索 - [使用 Ragas 进行评估](https://www.xlonglab.com/docs/integrations/integrate_with_ragas.md): 本指南演示了如何使用 Ragas 评估基于 Milvus 的 RAG Pipeline。 - [使用 Milvus 和 Sentence Transformers 进行电影搜索](https://www.xlonglab.com/docs/integrations/integrate_with_sentencetransformers.md): 本页讨论如何使用 Milvus 进行电影搜索 - [在 Snowpark Container Services 上使用 Milvus](https://www.xlonglab.com/docs/integrations/integrate_with_snowpark.md): 本指南演示如何在 Snowpark Container Services 上启动 Milvus 演示。 - [在 AI Pipeline 中使用 Apache Spark™ 与 Milvus/Zilliz Cloud](https://www.xlonglab.com/docs/integrations/integrate_with_spark.md): Apache Spark 和 Databricks 与 Milvus 和 Zilliz Cloud 集成，将大数据处理与向量搜索相结合。了解如何利用 Spark-Milvus 连接器构建 AI 驱动的搜索与分析。 - [用 Vanna 和 Milvus 编写 SQL](https://www.xlonglab.com/docs/integrations/integrate_with_vanna.md): 本指南演示如何使用 Vanna 根据存储在数据库中的数据生成并执行 SQL 查询。 - [使用 Milvus 和 FiftyOne 进行视觉搜索](https://www.xlonglab.com/docs/integrations/integrate_with_voxel51.md): 本页讨论与 voxel51 的集成 - [使用 Milvus 和 VoyageAI 进行语义搜索](https://www.xlonglab.com/docs/integrations/integrate_with_voyageai.md): 本页讨论向量数据库与 VoyageAI 的 Embedding API 的集成。 - [Milvus 与 WhyHow 的集成](https://www.xlonglab.com/docs/integrations/integrate_with_whyhow.md): 本指南演示了如何使用 whyHow.ai 和 Milvus Lite 进行基于规则的检索。 - [将 Milvus 与 MindsDB 相集成](https://www.xlonglab.com/docs/integrations/integration_with_mindsdb.md): 本教程演示了如何将 Milvus 与 MindsDB 集成，使您能够通过类似 SQL 的操作来管理和查询 Embedding，从而利用 MindsDB 的人工智能功能和 Milvus 的向量数据库功能。 - [集成概述](https://www.xlonglab.com/docs/integrations/integrations_overview.md): 本页为您提供与 Milvus 和第三方工具互动的教程列表。 - [将 Apache Kafka® 与 Milvus/Zilliz Cloud 连接起来，实现向量数据的实时摄取](https://www.xlonglab.com/docs/integrations/kafka-connect-milvus.md): Apache Kafka 与 Milvus 和 Zilliz Cloud 集成，可实现向量数据流。了解如何使用 Kafka-Milvus 连接器为语义搜索、推荐系统和人工智能驱动的分析构建实时管道。 - [使用 Milvus 的知识表](https://www.xlonglab.com/docs/integrations/knowledge_table_with_milvus.md): 默认情况下，知识表使用 Milvus 数据库来存储和检索提取的数据。这样，用户就可以利用 Milvus 的强大功能轻松搜索、过滤和分析数据。在本教程中，我们将介绍如何开始使用 Knowledge Table 和 Milvus。 - [使用 Milvus 的 Kotaemon RAG](https://www.xlonglab.com/docs/integrations/kotaemon_with_milvus.md): 本教程将指导您如何使用 Milvus 定制 kotaemon 应用程序。 - [使用 Milvus 和 LangGraph 的 Agentic RAG](https://www.xlonglab.com/docs/integrations/langchain/agentic_rag_with_milvus_and_langgraph.md): 本指南演示了如何使用 LangGraph 和 Milvus 构建先进的检索增强生成（RAG）系统。与简单检索和生成的传统 RAG 系统不同，Agentic RAG 系统可以就何时检索信息、如何处理无关文档以及何时重写查询以获得更好的结果做出智能决策。 - [将 Milvus 用作向量存储库](https://www.xlonglab.com/docs/integrations/langchain/basic_usage_langchain.md): 本笔记本介绍如何使用与 Milvus 向量数据库相关的功能。 - [使用 LangChain 和 Milvus 进行全文搜索](https://www.xlonglab.com/docs/integrations/langchain/full_text_search_with_langchain.md): 本教程将介绍如何使用 LangChain 和 Milvus 在应用程序中实现全文搜索。 - [使用 Milvus 和 LangChain 的检索增强生成（RAG）](https://www.xlonglab.com/docs/integrations/langchain/integrate_with_langchain.md): 本指南演示了如何使用 LangChain 和 Milvus 构建检索-增强生成（RAG）系统。 - [LangChain Milvus 集成中的异步函数](https://www.xlonglab.com/docs/integrations/langchain/langchain_milvus_async.md): 本教程探讨如何利用 langchain-milvus 中的异步函数构建高性能应用程序。通过使用异步方法，您可以显著提高应用程序的吞吐量和响应速度，尤其是在处理大规模检索时。 - [Milvus 文本 Embedding Function 与 LangChain 的整合](https://www.xlonglab.com/docs/integrations/langchain/langchain_milvus_dido.md): 本指南演示了如何将 Milvus 2.6 的文本 Embedding Function（也称为数据输入数据输出）与 LangChain 结合使用。该功能可让 Milvus 服务器自动将原始文本转换为 Embedding，从而简化客户端代码并集中管理 API 密钥。 - [Milvus Hybrid Search Retriever](https://www.xlonglab.com/docs/integrations/langchain/milvus_hybrid_search_retriever.md): 本笔记本介绍如何使用与 Milvus 向量数据库相关的功能。 - [LangExtract + Milvus 集成](https://www.xlonglab.com/docs/integrations/langextract_milvus_demo.md): 本指南演示了如何使用 LangExtract 与 Milvus 一起构建智能文档处理和检索系统。 - [Multi-agent Systems with Mistral AI, Milvus and Llama-agents](https://www.xlonglab.com/docs/integrations/llama_agents_metadata.md): 在本笔记本中，我们将探讨不同的想法：将数据存储到 Milvus 中，使用 llama-index 与 Mistral 模型进行数据查询，创建自动数据搜索与读取 AI Agent，以及开发根据用户查询进行元数据过滤 AI Agent。 - [利用 Milvus 的 Llama Stack 构建 RAG](https://www.xlonglab.com/docs/integrations/llama_stack_with_milvus.md): 本教程将介绍如何使用 Milvus 搭建一个 Llama Stack 服务器，从而导入私人数据作为知识库。然后，我们将在服务器上执行查询，创建一个完整的 RAG 应用程序。 - [使用 LlamaIndex 和 Milvus Async API 构建 RAG](https://www.xlonglab.com/docs/integrations/llamaindex_milvus_async.md): 本教程介绍如何使用 LlamaIndex Async API 和 Milvus 构建异步 RAG Pipeline，并比较同步与异步实现的性能。 - [使用 LlamaIndex 和 Milvus 进行全文搜索](https://www.xlonglab.com/docs/integrations/llamaindex_milvus_full_text_search.md): 在本教程中，您将学习如何使用 LlamaIndex 和 Milvus 建立一个使用全文搜索和混合搜索的 RAG 系统。我们将首先单独实施全文搜索，然后通过整合语义搜索来增强其功能，以获得更全面的结果。 - [使用 Milvus 和 LlamaIndex 混合搜索的 RAG](https://www.xlonglab.com/docs/integrations/llamaindex_milvus_hybrid_search.md): 本笔记本演示了如何在 \[LlamaIndex\](https://www.llamaindex.ai/) RAG Pipeline 中使用 Milvus 进行混合搜索。我们将从推荐的默认混合搜索（语义 + BM25）开始，然后探索其他可供选择的稀疏嵌入方法和自定义混合 Reranker。 - [使用 LlamaIndex 和 Milvus 进行元数据过滤](https://www.xlonglab.com/docs/integrations/llamaindex_milvus_metadata_filter.md): 本笔记本说明了在 LlamaIndex 中如何使用 Milvus 向量存储，重点是元数据过滤功能。您将学习如何使用元数据索引文档、使用 LlamaIndex 的内置元数据过滤器执行向量搜索，以及如何将 Milvus 的本地过滤表达式应用到向量存储。 - [MemPalace 与 Milvus](https://www.xlonglab.com/docs/integrations/mempalace_with_milvus.md): 在本教程中，我们将使用 MemPalace CLI 从公开的 Milvus 文档中挖掘一个真实的子集，并将其存储到 Milvus 中。该语料库包含关于 Analyzer、Tokenizer 和 Token Filter 的文档。这些内容紧密相关的页面提供了足够多的干扰项，使检索示例更具实际意义。 - [MCP + Milvus：连接 AI 与 Vector Database](https://www.xlonglab.com/docs/integrations/milvus_and_mcp.md): 本教程将指导您为 Milvus 设置 MCP Server，让人工智能应用程序能够使用自然语言命令执行向量搜索、管理 Collection 和检索数据，而无需编写自定义数据库查询。 - [开始使用 Milvus 和 n8n](https://www.xlonglab.com/docs/integrations/milvus_and_n8n.md): n8n 是一个功能强大的开源工作流自动化平台，可将各种应用程序、服务和 API 连接在一起，无需编码即可创建自动化工作流。n8n 采用基于节点的可视化界面，用户只需连接代表不同服务或操作的节点，即可构建复杂的自动化流程。它可自行托管，具有高度可扩展性，并支持公平代码和企业许可。 - [开始使用 Dynamiq 和 Milvus](https://www.xlonglab.com/docs/integrations/milvus_rag_with_dynamiq.md): 在本教程中，我们将探讨如何将 Dynamiq 与 Milvus（专为 RAG 工作流打造的高性能向量数据库）无缝结合使用。Milvus 擅长 Embedding 的高效存储、索引和检索，是要求快速、精确访问上下文数据的人工智能系统不可或缺的组件。 - [用 Milvus、vLLM 和 Llama 3.1 构建 RAG](https://www.xlonglab.com/docs/integrations/milvus_rag_with_vllm.md): 本博客将向您展示如何使用 Milvus、vLLM 和 Llama 3.1 构建并运行 RAG。更具体地说，我将向你展示如何在 Milvus 中将文本信息嵌入并存储为 Embedding，并将此向量存储作为知识库来高效检索与用户问题相关的文本块。 - [使用 Mistral OCR 和 Milvus 理解文档](https://www.xlonglab.com/docs/integrations/mistral_ocr_with_milvus.md): 本教程演示如何使用 Milvus 和 Mistral OCR 构建文档理解系统。 - [Milvus 与 OpenAI Agent 的集成：分步指南](https://www.xlonglab.com/docs/integrations/openai_agents_milvus.md): 本笔记本展示了如何创建一个可以通过函数调用使用自然语言查询 Milvus 的 AI Agent。我们将把 OpenAI 的 Agents 框架与 Milvus 强大的向量搜索功能结合起来，创建一个不错的搜索体验。 - [开始使用 Mem0 和 Milvus](https://www.xlonglab.com/docs/integrations/quickstart_mem0_with_milvus.md): 在本教程中，我们将介绍基本的 Mem0 内存管理操作--添加、检索、更新、搜索、删除和跟踪内存历史记录--使用 Milvus，这是一个高性能、开源的向量数据库，可支持高效的存储和检索。本实践介绍将指导您完成基础内存操作，帮助您利用 Mem0 和 Milvus 构建个性化的人工智能交互。 - [使用 Langflow 和 Milvus 构建 RAG 系统](https://www.xlonglab.com/docs/integrations/rag_with_langflow.md): 本指南演示了如何使用 Langflow 与 Milvus 一起构建 RAG Pipeline。 - [利用 Milvus 和 Unstructured 创建 RAG](https://www.xlonglab.com/docs/integrations/rag_with_milvus_and_unstructured.md): 在本教程中，我们将使用 Unstructured 来摄取 PDF 文档，然后使用 Milvus 构建 RAG Pipeline。 - [使用 Milvus + PII Masker 构建 RAG](https://www.xlonglab.com/docs/integrations/search_with_jev.md): 向量检索可以查找与查询相关的信息。要构建实用的搜索应用，还需要做出一系列判断：哪些段落真正回答了问题、能否复用之前的答案，以及 AI Agent 是否已有足够证据停止搜索。 - [在 AnythingLLM 中使用 Milvus](https://www.xlonglab.com/docs/integrations/use_milvus_in_anythingllm.md): 本指南将指导您将 Milvus 配置为 AnythingLLM 中的向量数据库，使您能够嵌入、存储和搜索文档，实现智能检索和聊天。 - [在 DocsGPT 中使用 Milvus](https://www.xlonglab.com/docs/integrations/use_milvus_in_docsgpt.md): 在本教程中，我们将向您展示如何使用 Milvus 作为 DocsGPT 的后台向量数据库。 - [在 PrivateGPT 中使用 Milvus](https://www.xlonglab.com/docs/integrations/use_milvus_in_private_gpt.md): 在本教程中，我们将向您展示如何使用 Milvus 作为 PrivateGPT 的后台向量数据库。 - [将 Milvus 与 SambaNova 结合使用](https://www.xlonglab.com/docs/integrations/use_milvus_with_sambanova.md): 本教程利用 SambaNova 人工智能入门套件中的 Milvus 集成，构建了一个类似于 RAG（检索-增强生成）的企业知识检索系统，用于基于企业私有文档的检索和回答。 - [高级视频搜索：利用 Twelve Labs 和 Milvus 进行语义检索](https://www.xlonglab.com/docs/integrations/video_search_with_twelvelabs_and_milvus.md): 了解如何通过将 Twelve Labs 用于生成多模态嵌入的 Embedding API 与 Milvus 集成来创建语义视频搜索应用程序。它涵盖了从设置开发环境到实现混合搜索和时态视频分析等高级功能的整个过程，为构建复杂的视频内容分析和检索系统奠定了全面的基础。 ### 参考资料 - [Milvus 架构概述](https://www.xlonglab.com/docs/reference/architecture/architecture_overview.md): Milvus 提供快速、可靠、稳定的向量数据库，专为相似性搜索和人工智能而建。 - [数据处理](https://www.xlonglab.com/docs/reference/architecture/data_processing.md): 了解 Milvus 的数据处理程序。 - [存储与计算分离](https://www.xlonglab.com/docs/reference/architecture/four_layers.md): Milvus 的存储与计算分离结构。 - [主要组件](https://www.xlonglab.com/docs/reference/architecture/main_components.md): 了解 Milvus Standalone 和集群的主要组件。 - [Storage V3](https://www.xlonglab.com/docs/reference/architecture/storage-v3.md): 了解哪些 Milvus 3.0 功能需要 Storage V3、如何启用 Storage V3，以及适用的兼容性限制。 - [Streaming Service](https://www.xlonglab.com/docs/reference/architecture/streaming_service.md): Streaming Service 是 Milvus 内部流媒体系统模块的一个概念，围绕 WAL（Write-Ahead Log） 构建，支持各种与流媒体相关的功能。 - [Woodpecker 架构](https://www.xlonglab.com/docs/reference/architecture/woodpecker_architecture.md): Woodpecker 是 Milvus 2.6 中的一个云原生 WAL 系统。它采用零磁盘架构和两种部署模式，可在 Object Storage 上提供高吞吐量、低运维开销和无缝可扩展性。 - [Milvus 2.2 基准测试报告](https://www.xlonglab.com/docs/reference/benchmark.md): 了解 Milvus 的基准结果。 - [Bitset](https://www.xlonglab.com/docs/reference/bitset.md): 了解 Milvus 中的 Bitset。 - [Coordinator HA](https://www.xlonglab.com/docs/reference/coordinator_ha.md): 了解 Milvus Coordinator 积极待命的工作动机和程序。 - [On-disk Index](https://www.xlonglab.com/docs/reference/disk_index.md): Milvus 中用于磁盘优化向量搜索的磁盘索引机制。 - [术语](https://www.xlonglab.com/docs/reference/glossary.md) - [GPU 索引](https://www.xlonglab.com/docs/reference/gpu_index.md): Milvus 的 GPU 索引机制。 - [内存索引](https://www.xlonglab.com/docs/reference): Milvus 的索引机制。 - [Knowhere](https://www.xlonglab.com/docs/reference/knowhere.md): 了解 Milvus 的 Knowhere。 - [实施多租户](https://www.xlonglab.com/docs/reference/multi_tenancy.md): 在 Milvus，多租户意味着多个客户或团队（称为租户）共享同一个集群，同时保持隔离的数据环境。 - [In-Memory Replica](https://www.xlonglab.com/docs/reference/replica.md): 了解 Milvus 中的内存复制。 - [标量索引](https://www.xlonglab.com/docs/reference/scalar_index.md): Milvus 标量索引。 - [Schema 说明](https://www.xlonglab.com/docs/reference/schema.md): Schema 定义了 Collection 的数据结构。在创建 Collection 之前，你需要设计好它的 Schema。本页将帮助你理解 Collection Schema，并自行设计一个示例 Schema。 - [common 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_common.md): 了解如何为 Milvus 配置常用功能。 - [DataCoord 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_datacoord.md): 了解如何为 Milvus 配置 dataCoord。 - [DataNode 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_datanode.md): 了解如何为 Milvus 配置 dataNode。 - [etcd 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_etcd.md): 了解如何为 Milvus 配置 etcd。 - [GPU 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_gpu.md): 了解如何为 Milvus 配置 gpu。 - [gRPC 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_grpc.md): 了解如何为 Milvus 配置 grpc。 - [IndexCoord 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_indexcoord.md): 了解如何为 Milvus 配置 indexCoord。 - [IndexNode 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_indexnode.md): 了解如何为 Milvus 配置 indexNode。 - [内部 TLS 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_internaltls.md): 了解如何为 Milvus 配置 internaltls。 - [Knowhere 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_knowhere.md): 了解如何为 Milvus 配置 knowhere。 - [本地存储配置](https://www.xlonglab.com/docs/reference/sys_config/configure_localstorage.md): 了解如何为 Milvus 配置 localStorage。 - [日志配置](https://www.xlonglab.com/docs/reference/sys_config/configure_log.md): 了解如何为 Milvus 配置日志。 - [Meta storage 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_metastore.md): 了解如何为 Milvus 配置 Meta storage。 - [MinIO 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_minio.md): 了解如何为 Milvus 配置 minio。 - [MQ 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_mq.md): 了解如何为 Milvus 配置 mq。 - [消息通道配置](https://www.xlonglab.com/docs/reference/sys_config/configure_msgchannel.md): 了解如何为 Milvus 配置 msgChannel。 - [Proxy 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_proxy.md): 了解如何为 Milvus 配置 Proxy。 - [Pulsar 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_pulsar.md): 了解如何为 Milvus 配置 Pulsar。 - [QueryCoord 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_querycoord.md): 了解如何为 Milvus 配置 queryCoord。 - [QueryNode 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_querynode.md): 了解如何为 Milvus 配置 queryNode。 - [配额与限制配置](https://www.xlonglab.com/docs/reference/sys_config/configure_quotaandlimits.md): 了解如何为 Milvus 配置 quotaAndLimits。 - [RocksMQ 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_rocksmq.md): 了解如何为 Milvus 配置 rocksmq。 - [RootCoord 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_rootcoord.md): 了解如何为 Milvus 配置 rootCoord。 - [Streaming 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_streaming.md): 了解如何为 Milvus 配置流媒体。 - [Streaming Node 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_streamingnode.md): 了解如何为 Milvus 配置 streamingNode。 - [TiKV 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_tikv.md): 了解如何为 Milvus 配置 tikv。 - [TLS 配置参数](https://www.xlonglab.com/docs/reference/sys_config/configure_tls.md): 了解 Milvus Proxy 对外服务的 TLS 模式、服务器证书、私钥和 CA 证书路径配置。 - [Trace 配置](https://www.xlonglab.com/docs/reference/sys_config/configure_trace.md): 了解如何为 Milvus 配置 Trace。 - [系统配置](https://www.xlonglab.com/docs/reference/sys_config/system_configuration.md): 了解 Milvus 的系统配置。 - [时间同步](https://www.xlonglab.com/docs/reference/time_sync.md): 了解 Milvus 的时间同步系统。 - [Timestamp](https://www.xlonglab.com/docs/reference/timestamp.md): 了解时间戳的概念和 Milvus 向量数据库中与时间戳相关的四个主要参数。 - [Consistency Level](https://www.xlonglab.com/docs/reference/tune_consistency.md): Milvus 支持 Strong、Bounded、Session 和 Eventually 四种 Consistency Level，其中 Bounded 为默认值。 ### release_notes.md - [发布说明](https://www.xlonglab.com/docs/release_notes.md): Milvus 版本说明 ### Reranker - [BGE](https://www.xlonglab.com/docs/rerankers/rerankers-bge.md): Milvus 通过`BGERerankFunction`类支持 BGE Reranker 模型。通过该功能，您可以有效地对查询-文档对的相关性进行评分。 - [Reranker Cohere](https://www.xlonglab.com/docs/rerankers/rerankers-cohere.md): Milvus 通过 `CohereRerankFunction` 类支持 Cohere Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [交叉编码器](https://www.xlonglab.com/docs/rerankers/rerankers-cross-encoder.md): Milvus 通过 "CrossEncoderRerankFunction "类支持交叉编码器 Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Jina AI - Reranker](https://www.xlonglab.com/docs/rerankers/rerankers-jina.md): Milvus 通过 "JinaRerankFunction "类支持 Jina Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Reranker 概览](https://www.xlonglab.com/docs/rerankers/rerankers-overview.md): PyMilvus 模型库集成了 Reranker 函数，用于优化初始搜索返回结果的顺序。 - [Reranker Voyage](https://www.xlonglab.com/docs/rerankers/rerankers-voyage.md): Milvus 通过 "VoyageRerankFunction "类支持 Voyage Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 ### 教程 - [音频相似性搜索](https://www.xlonglab.com/docs/tutorials/audio_similarity_search.md): 利用 Milvus 建立音频相似性搜索系统。 - [用 Milvus 创建 RAG](https://www.xlonglab.com/docs/tutorials/build-rag-with-milvus.md): 使用 Milvus 构建 RAG Pipeline，完成文档检索、上下文增强和大语言模型生成。 - [使用 Milvus 进行上下文检索](https://www.xlonglab.com/docs/tutorials/contextual_retrieval_with_milvus.md): 上下文检索（Contextual Retrieval）是 Anthropic 提出的一种先进的检索方法，旨在解决当前检索增强生成（RAG）解决方案中出现的块的语义隔离问题。在当前实用的 RAG 范式中，文档被分成若干个块，使用向量数据库进行查询搜索，检索出最相关的块。然后，LLM 使用这些检索到的分块对查询做出响应。… - [DNA 序列分类](https://www.xlonglab.com/docs/tutorials/dna_sequence_classification.md): 利用 Milvus 建立 DNA 序列分类系统。 - [使用 LangChain 和 Milvus 进行全文搜索](https://www.xlonglab.com/docs/tutorials/full_text_search_with_milvus.md): 全文搜索是一种通过匹配文本中特定关键词或短语来检索文档的传统方法。它根据术语频率等因素计算出的相关性分数对结果进行排序。语义搜索更善于理解含义和上下文，而全文搜索则擅长精确的关键词匹配，因此是语义搜索的有益补充。构建 "RAG Pipeline 的常见方法包括通过语义搜索和全文搜索检索文档，然后进行 Reranking 以完善结果。 - [Funnel Search with Matryoshka Embeddings](https://www.xlonglab.com/docs/tutorials/funnel_search_with_matryoshka.md): 在本笔记本中，我们将研究如何将 Matryoshka 嵌入与 Milvus 一起用于语义搜索。我们展示了一种名为 "漏斗搜索 "的算法，它允许我们在嵌入维度的一小部分子集上执行相似性搜索，而不会导致召回率急剧下降。 - [使用大型语言模型生成 Milvus 查询过滤表达式](https://www.xlonglab.com/docs/tutorials/generating_milvus_query_filter_expressions.md): 在本教程中，我们将演示如何使用大型语言模型（LLMs）从自然语言查询自动生成 Milvus 过滤表达式。通过这种方法，用户可以用普通英语表达复杂的过滤条件，然后将其转换为适当的 Milvus 语法，从而使向量数据库查询更易于使用。 - [使用 Milvus 构建 Graph RAG](https://www.xlonglab.com/docs/tutorials/graph_rag_with_milvus.md): 使用 Milvus 构建 Graph RAG - [利用 Milvus 进行 HDBSCAN 聚类](https://www.xlonglab.com/docs/tutorials/hdbscan_clustering_with_milvus.md): 在本笔记本中，我们将使用 BGE-M3 Embedding 模型从新闻标题数据集中提取嵌入，利用 Milvus 高效计算嵌入之间的距离以帮助 HDBSCAN 进行聚类，然后使用 UMAP 方法将结果可视化以进行分析。本笔记本是 Milvus 对 Dylan Castillo 文章的改编。 - [如何提高 RAG Pipeline 的性能](https://www.xlonglab.com/docs/tutorials/how_to_enhance_your_rag.md): 随着 RAG 应用的日益普及，人们越来越关注如何提高其性能。本文介绍了优化 RAG Pipeline 的所有可能方法，并提供了相应的图解，帮助您快速了解主流的 RAG 优化策略。 - [使用 Milvus 进行混合搜索](https://www.xlonglab.com/docs/tutorials/hybrid_search_with_milvus.md): 使用 Milvus 进行混合搜索 - [重复数据删除](https://www.xlonglab.com/docs/tutorials/image_deduplication_system.md): 使用 Milvus 构建重复数据删除图像系统。 - [使用 Milvus 搜索图像](https://www.xlonglab.com/docs/tutorials/image_similarity_search.md): 使用 Milvus 进行图像搜索 - [用 Milvus 推荐电影](https://www.xlonglab.com/docs/tutorials/movie_recommendation_with_milvus.md): 在本笔记本中，我们将探索如何使用 OpenAI 生成电影描述的 Embedding，并在 Milvus 中利用这些 Embedding 描述来推荐符合您偏好的电影。为了增强搜索结果，我们将利用过滤功能来执行元数据搜索。本示例中使用的数据集来自 Hugging Face 数据集，包含 8000 多个电影条目，为电影推荐提供了丰富的选择。 - [使用 Milvus 制作多模态 RAG](https://www.xlonglab.com/docs/tutorials/multimodal_rag_with_milvus.md): 使用 Milvus 的多模态 RAG - [问答系统](https://www.xlonglab.com/docs/tutorials/question_answering_system.md): 使用 Milvus 建立问题解答系统。 - [Attu 快速入门](https://www.xlonglab.com/docs/tutorials/quickstart_with_attu.md): Attu 是 Milvus 的一体化开源管理工具。它具有直观的图形用户界面（GUI），可让您轻松与数据库交互。只需点击几下，您就可以直观地查看集群状态、管理元数据、执行数据查询等。 - [推荐系统](https://www.xlonglab.com/docs/tutorials/recommendation_system.md): 利用 Milvus 建立个性化推荐系统。 - [使用 Milvus 进行文本到图像搜索](https://www.xlonglab.com/docs/tutorials/text_image_search.md): 在本教程中，我们将探讨如何使用 OpenAI 的 CLIP（对比语言-图像预训练）模型和 Milvus 实现基于文本的图像检索。我们将使用 CLIP 生成图像嵌入，将其存储在 Milvus 中，并执行高效的相似性搜索。 - [文本搜索引擎](https://www.xlonglab.com/docs/tutorials/text_search_engine.md): 使用 Milvus 建立文本搜索引擎。 - [教程概览](https://www.xlonglab.com/docs/tutorials/tutorials-overview.md): 本页为您提供与 Milvus 互动的教程列表。 - [在 asyncio 中使用 AsyncMilvusClient](https://www.xlonglab.com/docs/tutorials/use-async-milvus-client-with-asyncio.md): AsyncMilvusClient 是一种异步 MilvusClient，它提供了基于协程（coroutine）的 API，可通过 asyncio 对 Milvus 进行非阻塞访问。本文将介绍调用 AsyncMilvusClient 提供的 API 的过程以及需要注意的方面。 - [使用 ColPali 与 Milvus 一起进行多模态检索](https://www.xlonglab.com/docs/tutorials/use_ColPali_with_milvus.md): 在本笔记本中，为了通用起见，我们将这种多向量表示法称为 "ColBERT Embedding"。不过，实际使用的模型是 ColPali 模型。我们将演示如何使用 Milvus 进行多向量检索。在此基础上，我们将介绍如何使用 ColPali 根据给定查询检索网页。 - [向量可视化](https://www.xlonglab.com/docs/tutorials/vector_visualization.md): 在本例中，我们将展示如何使用 t-SNE 可视化 Milvus 中的嵌入（向量）。 - [视频相似性搜索](https://www.xlonglab.com/docs/tutorials/video_similarity_search.md): 利用 Milvus 建立视频相似性搜索系统。 ### 用户指南 - [创建外部 Collection](https://www.xlonglab.com/docs/userGuide/collections/create-an-external-collection.md): 外部 Collection 是 Milvus 中的一种数据采集类型，可访问 AWS S3 和 Iceberg 等外部存储系统或数据库表中的数据，而无需将其复制到 Milvus 中。它充当数据湖之上的查询层，同时保持与 Milvus 查询接口的兼容性。 - [创建 Collection](https://www.xlonglab.com/docs/userGuide/collections/create-collection.md): 您可以通过定义 Schema、索引参数、度量类型以及创建时是否加载来创建一个 Collection。本页将介绍如何从头开始创建 Collection。 - [删除 Collection](https://www.xlonglab.com/docs/userGuide/collections/drop-collection.md): 如果不再需要某个 Collection，您可以将其删除。 - [加载和释放](https://www.xlonglab.com/docs/userGuide/collections/load-and-release.md): 加载 Collection 是在 Collection 中进行相似性搜索和查询的前提。本页主要介绍加载和释放 Collection 的步骤。 - [管理 Alias](https://www.xlonglab.com/docs/userGuide/collections/manage-aliases.md): Milvus 提供别名管理功能。本页演示了创建、列出、更改和删除别名的程序。 - [Collection 概述](https://www.xlonglab.com/docs/userGuide/collections/manage-collections.md): 在 Milvus 上，您可以创建多个 Collection 来管理数据，并将数据作为实体插入到 Collection 中。Collection 和实体类似于关系数据库中的表和记录。本页可帮助你了解 Collection 及相关概念。 - [管理 Partition](https://www.xlonglab.com/docs/userGuide/collections/manage-partitions.md): Partition 是一个 Collection 的子集。每个 Partition 与其父 Collection 共享相同的数据结构，但只包含 Collection 中的一个数据子集。本页将帮助你了解如何管理 Partition。 - [修改 Collection](https://www.xlonglab.com/docs/userGuide/collections/modify-collection.md): 您可以重命名一个 Collection 或更改其设置。本页主要介绍如何修改 Collection。 - [设置 Collection TTL](https://www.xlonglab.com/docs/userGuide/collections/set-collection-ttl.md): 配置 Collection 级或 Entity 级 Time-to-Live（TTL）策略，使 Milvus 自动过滤并清理过期数据。 - [查看 Collection](https://www.xlonglab.com/docs/userGuide/collections/view-collections.md): 您可以获取当前连接的数据库中所有 Collection 的名称列表，并查看特定 Collection 的详细信息。 - [导入数据](https://www.xlonglab.com/docs/userGuide/data-import/import-data.md): 本页演示导入准备好的数据的程序。 - [准备源数据](https://www.xlonglab.com/docs/userGuide/data-import/prepare-source-data.md): 本页讨论了在开始批量将数据插入 Collection 之前应该考虑的一些问题。 - [Azure OpenAI](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/azure-openai.md): 本主题介绍如何在 Milvus 中配置和使用 Azure OpenAI Embedding Function。 - [Bedrock](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/bedrock.md): 本主题介绍如何在 Milvus 中配置和使用 Amazon Bedrock Embedding Function。 - [BM25 Function](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/bm25-function.md): BM25 Function 通过将原始文本转换为稀疏向量，并根据词汇相关性对文档进行评分，从而实现全文搜索。它采用基于术语的匹配和频率感知加权，支持高效检索与查询术语密切匹配的文本文档。 - [Cohere](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/cohere.md): 本主题介绍如何在 Milvus 中配置和使用 Cohere Embedding Function。 - [DashScope](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/dashscope.md): 本主题介绍如何在 Milvus 中配置和使用 DashScope Embedding Function。 - [Embedding Function 概述](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/embedding-function-overview.md): 通过 Milvus 的 Function 模块，您可以自动调用外部 Embedding 服务提供商（如 OpenAI、AWS Bedrock、Google Vertex AI 等），将原始文本数据转换为 Embedding。… - [Google Gemini](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/google-gemini.md): 通过选择一个模型，并使用 Gemini API 密钥配置 Milvus，即可将 Google Gemini Embedding 模型与 Milvus 结合使用。 - [Hugging Face TEI](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/hugging-face-tei.md): Hugging Face 文本嵌入推理（TEI）是专为文本 Embedding 模型设计的高性能推理服务器。本指南介绍如何将 Hugging Face TEI 与 Milvus 结合使用，以高效生成文本嵌入。 - [Hugging Face](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/hugging-face.md): 本主题介绍如何在 Milvus 中使用托管的 Hugging Face Inference Providers 进行文本 Embedding。 - [MinHash Function](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/minhash-function.md): 使用 MinHash 将文本转换为二进制签名向量，以执行基于 Jaccard 相似度的搜索和近似重复检测。 - [OpenAI](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/openai.md): 通过选择一个模型并用 OpenAI API 密钥配置 Milvus，使用 Milvus 的 OpenAI Embedding 模型。 - [SiliconFlow](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/siliconflow.md): 本主题介绍如何在 Milvus 中配置和使用 SiliconFlow Embedding Function。 - [Vertex AI](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/vertex-ai.md): Google Cloud Vertex AI 是专为文本 Embedding 模型设计的高性能服务。本指南介绍如何将 Google Cloud Vertex AI 与 Milvus 结合使用，实现高效的文本嵌入生成。 - [Voyage AI](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/voyage-ai.md): 本主题介绍如何在 Milvus 中配置和使用 Voyage AI Embedding Function。 - [Yandex Cloud](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/embedding-function/yandex-cloud.md): 本文介绍如何在 Milvus 中配置和使用 Yandex Cloud embedding function。 - [Boost Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/boost-ranker.md): Boost Rankers 并不完全依赖基于向量距离计算的语义相似性，而是让您以有意义的方式影响搜索结果。它是使用元数据过滤快速调整搜索结果的理想选择。 - [Cohere Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/cohere-ranker.md): Cohere Ranker 利用 Cohere 强大的 Reranker 模型，通过语义 Reranking 增强搜索相关性。它通过强大的 API 基础设施和优化的性能为生产环境提供企业级 Reranker 功能。 - [DashScope Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/dashscope-ranker.md): 本主题介绍如何在 Milvus 中配置和使用 DashScope reranking models，例如 Qwen rerank models。 - [Decay Ranker Overview](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/decay-ranker-overview.md): 在传统的向量搜索中，搜索结果的排序完全取决于向量的相似性--向量在数学空间中的匹配程度。但在实际应用中，内容的真正相关性往往不仅仅取决于语义相似性。 - [指数衰减](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/exponential-decay.md): 指数衰减会使搜索结果在初期急剧下降，随后出现长尾。就像突发新闻周期一样，一开始相关性会迅速降低，但随着时间的推移，一些新闻的重要性会保持不变，指数衰减会对超出您理想范围的条目施加剧烈的惩罚，同时仍然保持较远的条目可以被发现。这种方法非常适合您想优先考虑近似性或相关性，但又不想完全排除较远选项的情况。 - [高斯衰减](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/gaussian-decay.md): 高斯衰减也称为正常衰减，它能对搜索结果进行最自然的调整。就像人的视力会随着距离的增加而逐渐模糊一样，高斯衰减会创建一条平滑的钟形曲线，随着项目远离您的理想点，相关性也会逐渐降低。这种方法非常适合您需要一种平衡的衰减方式，既不会对您偏好范围之外的项目造成严重影响，又能显著降低远处项目的相关性。 - [Hugging Face Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/hugging-face-ranker.md): 本主题介绍如何使用托管的 Hugging Face sentence-similarity models 对 Milvus 搜索结果进行 rerank。 - [线性衰减](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/linear-decay.md): 线性衰减会使搜索结果中的相关性直线下降，并以绝对零点为终点。就像即将发生的事件倒计时一样，相关性会逐渐减弱，直到事件过去，线性衰减会随着项目远离您的理想点而使相关性可预测地稳步降低，直到完全消失。这种方法非常适合在需要一致的衰减率和明确的分界线时使用，可确保超出一定界限的项目完全被排除在搜索结果之外。 - [Model Ranker 概述](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/model-ranker-overview.md): 传统的向量搜索纯粹通过数学相似度--向量在高维空间中的匹配程度--来对结果进行排序。这种方法虽然高效，但往往会忽略真正的语义相关性。考虑搜索 "数据库优化的最佳实践"：您可能会收到向量相似度很高的文档，这些文档经常提到这些术语，但实际上并没有提供可操作的优化策略。 - [RRF Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/rrf-ranker.md): Reciprocal Rank Fusion（RRF）Ranker 是 Milvus 混合搜索的一种 Reranking 策略，它根据多个向量搜索路径的排名位置而不是原始相似度得分来平衡搜索结果。就像体育比赛考虑的是球员的排名而不是个人统计数据一样，RRF Ranker 根据每个项目在不同搜索路径中的排名高低来组合搜索结果，从而创建一个公平、均衡的最终排名。 - [SiliconFlow Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/siliconflow-ranker.md): SiliconFlow Ranker 利用 SiliconFlow 的综合 Reranking 模型，通过语义 Reranking 增强搜索相关性。它提供灵活的文档分块功能，并支持来自不同供应商的各种专业 Reranker 模型。 - [TEI Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/tei-ranker.md): TEI Ranker 利用 Hugging Face 提供的文本嵌入推理（TEI）服务，通过语义重排来提高搜索相关性。它代表了一种超越传统向量相似性的先进搜索结果排序方法。 - [教程：在 Milvus 中实现基于时间的排名](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/tutorial-implement-a-time-based-ranking-in-milvus.md): 在许多搜索应用中，内容的新鲜度与相关性同等重要。新闻文章、产品列表、社交媒体帖子和研究论文都受益于兼顾语义相关性和新鲜度的排名系统。本教程演示了如何在 Milvus 中使用衰减 Ranker 实现基于时间的排名。 - [vLLM Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/vllm-ranker.md): vLLM Ranker 利用 vLLM 推论框架，通过语义 Reranking 来提高搜索相关性。它代表了一种超越传统向量相似性的先进搜索结果排序方法。 - [Voyage AI Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/voyage-ai-ranker.md): Voyage AI Ranker 利用 Voyage AI 的专业 Reranker，通过语义重排提高搜索相关性。它提供高性能的重排能力，针对检索增强生成（RAG）和搜索应用进行了优化。 - [Weighted Ranker](https://www.xlonglab.com/docs/userGuide/embeddings-reranking/reranking/weighted-ranker.md): WeightedRanker 通过为每个搜索路径分配不同的重要性权重，智能地组合来自多个搜索路径的结果并确定其优先级。与技艺高超的厨师平衡多种配料以制作完美菜肴的方式类似，WeightedRanker 也会平衡不同的搜索结果，以提供最相关的综合结果。这种方法非常适合在多个向量字段或模式中进行搜索，其中某些领域对最终排名的贡献应比其他领域更大。 - [BIN_FLAT](https://www.xlonglab.com/docs/userGuide/indexes/binary/bin-flat.md): BIN_FLAT 索引是 FLAT 索引的一种变体，专门为二进制嵌入而定制。在向量相似性搜索要求在相对较小的百万级数据集上达到完美精确度的应用中，它表现出色。通过采用一种穷举搜索方法--将每个目标输入与数据集中的所有向量进行比较--BIN_FLAT 可以保证得到精确的结果。… - [BIN_IVF_FLAT](https://www.xlonglab.com/docs/userGuide/indexes/binary/bin-ivf-flat.md): BIN_IVF_FLAT 索引是 IVF_FLAT 索引的变种，专门用于二元嵌入。它首先将向量数据划分为多个簇（nlist 单元），然后将目标输入向量与每个簇的中心进行比较，从而提高了查询效率。BIN_IVF_FLAT 能显著缩短查询时间，同时允许用户在准确性和速度之间进行微调。更多信息，请参阅 IVF_FLAT。 - [MINHASH_LSH](https://www.xlonglab.com/docs/userGuide/indexes/binary/minhash-lsh.md): 使用 MINHASH_LSH 索引加速大规模文本数据集的近似重复检测和 Jaccard 相似度搜索。 - [FAISS](https://www.xlonglab.com/docs/userGuide/indexes/faiss.md): 在 Milvus 3.0 中使用 FAISS Index Passthrough，传入 Faiss index-factory 字符串和特定于 factory 的搜索参数。 - [AISAQ](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/aisaq.md): AISAQ 是一种基于磁盘的向量索引，它对 DISKANN 进行了扩展，可以处理十亿规模的数据集，而不会超出内存限制。与将压缩向量保存在内存中的 DISKANN 不同，AISAQ 将所有数据保存在磁盘上，提供两种模式以平衡性能和存储成本。 - [DISKANN](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/diskann.md): 在大规模场景中，数据集可能包括数十亿甚至数万亿个向量，标准的内存索引方法（如 HNSW、IVF_FLAT）往往因内存限制而跟不上步伐。DISKANN 提供了一种基于磁盘的方法，可以在数据集大小超过可用 RAM 时保持较高的搜索精度和速度，从而应对这些挑战。 - [FLAT](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/flat.md): FLAT 索引是最简单、最直接的浮点向量索引和搜索方法之一。它依赖于一种 "蛮力 "方法，即直接将每个查询向量与数据集中的每个向量进行比较，而无需任何高级预处理或数据结构。这种方法保证了准确性，由于对每个潜在匹配都进行了评估，因此可提供 100% 的召回率。 - [HNSW_PQ](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/hnsw-pq.md): HNSW_PQ 利用分层导航小世界（HNSW）图与乘积量化（PQ），创建了一种先进的向量索引方法，提供了可控的大小与准确性权衡。与 HNSW_SQ 相比，尽管查询处理速度较低，索引构建时间较长，但在相同压缩级别下，这种索引类型的召回率更高。 - [HNSW_PRQ](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/hnsw-prq.md): HNSW_PRQ 结合 HNSW 与 Product Residual Quantization（PRQ，乘积残差量化），在索引大小、搜索精度和计算开销之间提供可调节的权衡。 - [HNSW_SQ](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/hnsw-sq.md): HNSW_SQ 将层次导航小世界（HNSW）图与标量量化（SQ）相结合，创建了一种先进的向量索引方法，提供了可控的大小与精度权衡。与标准 HNSW 相比，这种索引类型在保持较高查询处理速度的同时，索引构建时间略有增加。 - [HNSW](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/hnsw.md): HNSW 索引是一种基于图的索引算法，可以提高搜索高维浮点向量时的性能。它具有出色的搜索精度和较低的延迟，但需要较高的内存开销来维护其分层图结构。 - [IVF_FLAT](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/ivf-flat.md): IVF_FLAT 索引是一种可以提高浮点向量搜索性能的索引算法。 - [IVF_PQ](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/ivf-pq.md): IVF_PQ 索引是一种基于量化的索引算法，用于高维空间中的近似近邻搜索。虽然 IVF_PQ 的速度不如某些基于图的方法，但它所需的内存通常要少得多，因此是大型数据集的实用选择。 - [IVF_RABITQ](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/ivf-rabitq.md): IVF_RABITQ 索引是一种基于二进制量化的索引算法，可将 FP32 向量量化为二进制表示。它提供了一个高度可配置的压缩率，并可选择细化以提高召回率，因此适用于需要显著优化存储的应用。 - [IVF_SQ8](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/ivf-sq8.md): IVF_SQ8 索引是一种基于量化的索引算法，旨在解决大规模相似性搜索难题。与穷举搜索方法相比，这种索引类型能以更小的内存占用实现更快的搜索。 - [SCANN](https://www.xlonglab.com/docs/userGuide/indexes/floating-vector/scann.md): Milvus 中的 SCANN 索引由谷歌的 ScaNN 库提供支持，旨在解决向量相似性搜索的扩展难题，在速度和准确性之间取得平衡，即使在传统上会给大多数搜索算法带来挑战的大型数据集上也是如此。 - [GPU_BRUTE_FORCE](https://www.xlonglab.com/docs/userGuide/indexes/gpu/gpu-brute-force.md): GPU_BRUTE_FORCE 索引专为 GPU 环境设计，适用于对准确性要求极高的场景。它通过将每个查询与数据集中的所有向量进行详尽比较，确保不会忽略任何潜在匹配，从而保证召回率为 1。利用 GPU 加速，GPU_BRUTE_FORCE 适用于要求向量相似性搜索绝对精确的应用。 - [GPU_CAGRA](https://www.xlonglab.com/docs/userGuide/indexes/gpu/gpu-cagra.md): GPU_CAGRA 索引是为 GPU 优化的基于图形的索引。与使用昂贵的训练级 GPU 相比，使用推理级 GPU 运行 Milvus GPU 版本可以更具成本效益。 - [GPU 索引概述](https://www.xlonglab.com/docs/userGuide/indexes/gpu/gpu-index-overview.md): 在 Milvus 中建立一个支持 GPU 的索引，可以显著提高高吞吐量和高调用场景下的搜索性能。 - [GPU_IVF_FLAT](https://www.xlonglab.com/docs/userGuide/indexes/gpu/gpu-ivf-flat.md): GPU_IVF_FLAT 索引是 IVF_FLAT 索引的 GPU 加速版本，专为 GPU 环境设计。它将向量数据划分为 nlist 簇单元，并通过首先比较目标查询向量和每个簇的中心来计算相似性。通过调整 nprobe 参数，只搜索最有希望的簇，从而减少查询时间，同时保持准确性和速度之间的平衡。有关基础概念的更多信息，请参阅 IVF_FLAT。 - [GPU_IVF_PQ](https://www.xlonglab.com/docs/userGuide/indexes/gpu/gpu-ivf-pq.md): GPU_IVF_PQ 索引以 IVF_PQ 概念为基础，将倒排文件聚类与乘积量化（PQ）相结合，后者将高维向量分解为更小的子空间并对其进行量化，从而实现高效的相似性搜索。GPU_IVF_PQ 专为 GPU 环境设计，利用并行处理加速计算并有效处理大规模向量数据。有关基础概念的更多信息，请参阅 IVF_PQ。 - [索引详解](https://www.xlonglab.com/docs/userGuide/indexes/index-explained.md): 索引是在数据之上构建的附加结构，其内部组织方式取决于所采用的近似最近邻（ANN）搜索算法。索引可以加速搜索，但也会增加预处理时间、存储空间和内存开销，并通常对召回率造成轻微影响。本文介绍如何在降低索引成本的同时充分发挥其性能优势。 - [BITMAP](https://www.xlonglab.com/docs/userGuide/indexes/scalar/bitmap.md): 位图索引适用于低基数（low-cardinality）标量字段，可通过紧凑的位图表示和按位运算加速过滤查询。 - [INVERTED](https://www.xlonglab.com/docs/userGuide/indexes/scalar/inverted.md): 当你需要对数据执行频繁的过滤查询时，倒排索引可以显著提高查询性能。Milvus 不需要扫描所有文档，而是使用倒排索引来快速查找与过滤条件相匹配的准确记录。 - [NGRAM](https://www.xlonglab.com/docs/userGuide/indexes/scalar/ngram.md): Milvus 中的 NGRAM 索引用于加速 VARCHAR 字段或 JSON 字段中特定 JSON 路径上的 LIKE 查询和符合条件的 regex filter。在构建索引之前，Milvus 会将文本拆分为长度固定为 n 的短重叠子串，即 n-gram。… - [RTREE](https://www.xlonglab.com/docs/userGuide/indexes/scalar/rtree.md): RTREE 索引是一种基于树的数据结构，可加速对 Milvus 中几何字段的查询。如果您的 Collection 以已知文本 (WKT) 格式存储点、线或多边形等几何对象，并且希望加速空间过滤，那么 RTREE 是理想的选择。 - [STL_SORT](https://www.xlonglab.com/docs/userGuide/indexes/scalar/stl-sort.md): STL_SORT 索引是一种专门设计用于提高数值字段（INT8、INT16 等）、VARCHAR 字段或 TIMESTAMPTZ 字段查询性能的索引类型，其方法是按排序顺序组织数据。 - [SPARSE_INVERTED_INDEX](https://www.xlonglab.com/docs/userGuide/indexes/sparse/sparse-inverted-index.md): SPARSE_INVERTED_INDEX 索引是 Milvus 用来高效存储和搜索稀疏向量的一种索引类型。这种索引类型利用了倒排索引的原理，为稀疏数据创建了一种高效的搜索结构。 - [删除 Entity](https://www.xlonglab.com/docs/userGuide/insert-and-delete/delete-entities.md): 您可以通过过滤条件或主键删除不再需要的实体。 - [插入 Entity](https://www.xlonglab.com/docs/userGuide/insert-and-delete/insert-update-delete.md): Collection 中的实体是共享同一组字段的数据记录。每条数据记录中的字段值构成一个实体。本页介绍如何在 Collection 中插入实体。 - [Upsert Entity](https://www.xlonglab.com/docs/userGuide/insert-and-delete/upsert-entities.md): upsert 操作为插入或更新 Collection 中的实体提供了一种便捷的方法。 - [标量字段索引](https://www.xlonglab.com/docs/userGuide/manage-indexes/index-scalar-fields.md): 本指南将指导您为整数、字符串等字段创建和配置标量索引。 - [索引向量字段](https://www.xlonglab.com/docs/userGuide/manage-indexes/index-vector-fields.md): 本指南将指导您完成创建和管理 Collection 中向量字段索引的基本操作。 - [使用 GPU 建立索引](https://www.xlonglab.com/docs/userGuide/manage-indexes/index-with-gpu.md): 本指南介绍如何在 Milvus 中建立支持 GPU 的索引，以提高搜索性能。 - [Database](https://www.xlonglab.com/docs/userGuide/manage_databases.md): Milvus 在 Collection 的上方引入了一个数据库层，为管理和组织数据提供了一种更有效的方式，同时支持多租户。 - [Alter Collection Schema](https://www.xlonglab.com/docs/userGuide/schema/add-fields-to-an-existing-collection.md): 通过添加或删除用户定义字段，或添加/删除 Function 及其生成的向量字段，变更现有 Collection Schema。 - [更改 Collection 字段](https://www.xlonglab.com/docs/userGuide/schema/alter-collection-field.md): 您可以更改 Collection 字段的属性，以更改列约束或执行更严格的数据完整性规则。 - [修改 External Collection Schema](https://www.xlonglab.com/docs/userGuide/schema/alter-external-collection-schema.md): 了解如何将外部数据源中的额外字段暴露到现有 External Collection 中。 - [Analyzer 概述](https://www.xlonglab.com/docs/userGuide/schema/analyzer/analyzer-overview.md): 在文本处理中，分析器是将原始文本转换为结构化可搜索格式的关键组件。每个分析器通常由两个核心部件组成：Tokenizer（分词器）和过滤器。它们共同将输入文本转换为标记，完善这些标记，并为高效索引和检索做好准备。 - [Arabic](https://www.xlonglab.com/docs/userGuide/schema/analyzer/analyzer/arabic-analyzer.md): 内置 Arabic analyzer 通过规范化字母变体和数字、对词项进行词干提取，并移除阿拉伯语 stop words 来处理阿拉伯语文本。 - [Chinese](https://www.xlonglab.com/docs/userGuide/schema/analyzer/analyzer/chinese-analyzer.md): 中文分析器专为处理中文文本而设计，可提供有效的分段和标记化功能。 - [English](https://www.xlonglab.com/docs/userGuide/schema/analyzer/analyzer/english-analyzer.md): Milvus 中的英语分析器旨在处理英语文本，应用特定语言规则进行标记化和过滤。 - [Standard Analyzer](https://www.xlonglab.com/docs/userGuide/schema/analyzer/analyzer/standard-analyzer.md): Standard Analyzer 是 Milvus 的默认 Analyzer。如果未指定 Analyzer，Milvus 会自动将其应用于文本字段。它由 standard Tokenizer 与 lowercase Filter 组合而成。 - [Thai Analyzer](https://www.xlonglab.com/docs/userGuide/schema/analyzer/analyzer/thai-analyzer.md): 内置 Thai analyzer 会将泰语文本切分为词，规范化 Unicode 十进制数字，并移除泰语停用词。 - [根据使用场景选择 Analyzer](https://www.xlonglab.com/docs/userGuide/schema/analyzer/choose-the-right-analyzer-for-your-use-case.md): 根据语言、文本特征和搜索场景选择适合的 Analyzer，并了解 tokenizer、filter 与全文搜索的配置取舍。 - [Alphanumonly](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/alphanumonly-filter.md): alphanumonly 过滤器会删除包含非 ASCII 字符的标记，只保留字母数字术语。该过滤器适用于处理只有基本字母和数字的文本，不包括任何特殊字符或符号。 - [Arabic Normalization](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/arabic-normalization-filter.md): arabic_normalization filter 会规范化阿拉伯字母变体，并移除阿拉伯语变音符和 Tatweel。 - [ASCII Folding](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/ascii-folding-filter.md): asciifolding 过滤器可将基本拉丁统一码块（前 127 个 ASCII 字符）以外的字符转换为其 ASCII 对应字符。例如，它能将 í 等字符转换为 i，使文本处理更简单、更一致，特别是对于多语言内容。 - [Cnalphanumonly](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/cnalphanumonly-filter.md): cnalphanumonly 过滤器会删除包含除汉字、英文字母或数字以外的任何字符的标记。 - [Cncharonly](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/cncharonly-filter.md): cncharonly 过滤器会删除包含任何非中文字符的标记。当您只想关注中文文本，过滤掉任何包含其他文字、数字或符号的标记时，这个过滤器就非常有用了。 - [Decimal Digit](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/decimaldigit-filter.md): decimaldigit filter 会将 Unicode 十进制数字转换为 ASCII 数字。 - [Decompounder](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/decompounder-filter.md): 使用反编译筛选器，用内联字典或注册文件资源拆分复合词。 - [Length](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/length-filter.md): 长度过滤器可移除不符合指定长度要求的标记，从而控制文本处理过程中保留的标记长度。 - [Lowercase](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/lowercase-filter.md): 小写过滤器将 Tokenizer（分词器）生成的 Token（词元）转换为小写，使搜索不区分大小写。例如，它可以将\["高"、"性能"、"向量"、"数据库"\]转换为\["高"、"性能"、"向量"、"数据库"\]。 - [Pinyin](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/pinyin-filter.md): pinyin filter 会在文本分析期间将中文字符 token 转换为 Pinyin token，从而支持基于 Pinyin 匹配中文文本。 - [Regex](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/regex-filter.md): Regex analyzer filter 会保留匹配正则表达式的 token，并丢弃其余 token。 - [Remove Punct](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/removepunct-filter.md): removepunct 过滤器会移除包含标点符号或空白字符的 Token。对于会保留单词内部标点符号的 Tokenizer，它会移除整个受影响的 Token。 - [Stemmer](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/stemmer-filter.md): 词干过滤器将单词还原为其基本形式或词根形式（称为词干化），从而更容易匹配不同词性中含义相似的单词。词干过滤器支持多种语言，可在各种语言环境中进行有效搜索和索引。 - [Stop](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/stop-filter.md): 在分析过程中，使用停顿过滤器从标记化文本中删除配置的停用词。 - [Synonym](https://www.xlonglab.com/docs/userGuide/schema/analyzer/filter/synonym-filter.md): 在文本分析过程中，使用同义词过滤器用同义词词典重写标记。 - [管理文件资源](https://www.xlonglab.com/docs/userGuide/schema/analyzer/manage-file-resources.md): 注册和管理外部字典文件，Milvus 文本分析器可在运行时加载这些文件。 - [多语言 Analyzer](https://www.xlonglab.com/docs/userGuide/schema/analyzer/multi-language-analyzers.md): Milvus 执行文本分析时，通常会在 Collection 的整个文本字段中应用一个分析器。如果该分析器针对英语进行了优化，那么它在处理其他语言（如中文、西班牙语或法语）所需的完全不同的标记化和词干规则时就会遇到困难，从而导致召回率降低。… - [ICU](https://www.xlonglab.com/docs/userGuide/schema/analyzer/tokenizer/icu-tokenizer.md): icu tokenizer 基于 Unicode 国际化组件（ICU）开源项目开发，该项目为软件国际化提供了关键工具。通过使用 ICU 的分词算法，令牌生成器可以准确地将世界上大多数语言的文本分割成单词。 - [Jieba](https://www.xlonglab.com/docs/userGuide/schema/analyzer/tokenizer/jieba-tokenizer.md): Jieba Tokenizer（分词器）通过将中文文本分解为单词来进行处理。 - [Language Identifier](https://www.xlonglab.com/docs/userGuide/schema/analyzer/tokenizer/language-identifier.md): 语言识别器（language_identifier）是一个专门的 Tokenizer（分词器），旨在通过自动语言分析过程来增强 Milvus 的文本搜索功能。它的主要功能是检测文本字段的语言，然后动态应用最适合该语言的预配置分析器。这对于处理多种语言的应用程序来说尤为重要，因为它消除了按输入手动分配语言的需要。 - [Lindera](https://www.xlonglab.com/docs/userGuide/schema/analyzer/tokenizer/lindera-tokenizer.md): lindera tokenizer 可进行基于词典的词形分析。它专为日语和韩语设计，在这两种语言中，单词之间没有空格分隔，语法标记（微粒）直接附着在单词上。 - [Standard Tokenizer](https://www.xlonglab.com/docs/userGuide/schema/analyzer/tokenizer/standard-tokenizer.md): Milvus 中的标准 Tokenizer 将连续的 Unicode 字母和数字字符组合成 Token，并在其他字符处分割文本。 - [Thai Tokenizer](https://www.xlonglab.com/docs/userGuide/schema/analyzer/tokenizer/thai-tokenizer.md): thai tokenizer 会将 Thai 文本切分为词 token，并过滤掉空白字符和仅包含标点符号的片段。 - [Whitespace](https://www.xlonglab.com/docs/userGuide/schema/analyzer/tokenizer/whitespace-tokenizer.md): Whitespace Tokenizer 会在五种 ASCII 空白字符处分割文本：制表符、换行符、换页符、回车符和空格。 - [StructArray](https://www.xlonglab.com/docs/userGuide/schema/array-of-structs.md): 当一个 Entity 需要存储结构化元素的有序列表时，例如一篇文档包含多个 chunk、一个页面包含多个 visual patch，或一个视频包含多个 clip，可以使用 StructArray。StructArray 会将这些元素保留在父 Entity 内，同时仍支持对每个元素内部的字段进行 vector search 和 scalar filtering… - [数组字段](https://www.xlonglab.com/docs/userGuide/schema/array_data_type.md): ARRAY 字段存储同一数据类型元素的有序 Collection。下面举例说明 ARRAY 字段如何存储数据： - [Data Model Design with an Array of Structs](https://www.xlonglab.com/docs/userGuide/schema/best-practices-for-array-of-structs.md): 现代人工智能应用，尤其是物联网（IoT）和自动驾驶领域的应用，通常会对丰富的结构化事件进行推理：带有时间戳和 Embedding 的传感器读数、带有错误代码和音频片段的诊断日志，或者带有位置、速度和场景上下文的行程片段。这些都要求数据库能够原生支持嵌套数据的摄取和搜索。 - [二进制向量](https://www.xlonglab.com/docs/userGuide/schema/binary-vector.md): 二进制向量是一种特殊的数据表示形式，它将传统的高维浮点向量转换成只包含 0 和 1 的二进制向量。这种转换不仅压缩了向量的大小，还降低了存储和计算成本，同时保留了语义信息。当对非关键特征的精度要求不高时，二进制向量可以有效保持原始浮点向量的大部分完整性和实用性。 - [创建 StructArray Field](https://www.xlonglab.com/docs/userGuide/schema/create-structarray-field.md): 当一个 Entity 需要包含有序的结构化元素列表时，可以创建 StructArray Field。StructArray Field 是一种元素类型为 Struct 的 Array Field。每个 Struct 元素都遵循相同的 Schema，并且可以包含标量子字段、向量子字段，或同时包含两者。 - [默认值](https://www.xlonglab.com/docs/userGuide/schema/default-values.md): 为标量字段设置默认值，以便 Milvus 在插入实体时填补缺失值。 - [稠密向量](https://www.xlonglab.com/docs/userGuide/schema/dense-vector.md): 稠密向量是广泛应用于机器学习和数据分析的数字数据表示。它们由包含实数的数组组成，其中大部分或所有元素都不为零。与稀疏向量相比，稠密向量在同一维度上包含更多信息，因为每个维度都持有有意义的值。这种表示方法能有效捕捉复杂的模式和关系，使数据在高维空间中更容易分析和处理。稠密向量通常有固定的维数，从几十到几百甚至上千不等，具体取决于具体的应用和要求。 - [动态字段](https://www.xlonglab.com/docs/userGuide/schema/enable-dynamic-field.md): Milvus 允许你通过一个名为动态字段的特殊功能，插入结构灵活、不断发展的实体。该字段以名为 $meta 的隐藏 JSON 字段的形式实现，它会自动存储数据中任何未在 Collection Schema 中明确定义的字段。 - [Geometry 字段](https://www.xlonglab.com/docs/userGuide/schema/geometry-field.md): 在构建地理信息系统（GIS）、制图工具或基于位置的服务等应用程序时，您经常需要存储和查询几何数据。Milvus 中的 GEOMETRY 数据类型提供了一种本地方式来存储和查询灵活的几何数据，从而解决了这一难题。 - [为 StructArray 字段创建 Index](https://www.xlonglab.com/docs/userGuide/schema/index-structarray-fields.md): 在运行向量搜索或加速标量过滤之前，为 StructArray 子字段创建 Index。对于 StructArray 字段，Index 目标是子字段路径，例如 chunks\[emb_list_vector\]、chunks\[emb\] 或 chunks\[section\]。 - [向 StructArray 字段插入数据](https://www.xlonglab.com/docs/userGuide/schema/insert-data-into-structarray-fields.md): 当每个 Entity 包含一个有序的结构化元素列表时，可以向 StructArray 字段插入数据。在 insert payload 中，StructArray 字段表示为对象数组。每个对象表示一个 Struct 元素，并使用 Collection Schema 中定义的 Struct 子字段名称。 - [JSON 字段概述](https://www.xlonglab.com/docs/userGuide/schema/json-field-overview.md): 在构建产品目录、内容管理系统或用户偏好引擎等应用程序时，您往往需要在存储向量 Embedding 的同时存储灵活的元数据。产品属性因类别而异，用户偏好随时间演变，文档属性具有复杂的嵌套结构。Milvus 中的 JSON 字段可以在不影响性能的情况下存储和查询灵活的结构化数据，从而解决了这一难题。 - [JSON 索引](https://www.xlonglab.com/docs/userGuide/schema/json-indexing.md): JSON 字段为在 Milvus 中存储结构化元数据提供了一种灵活的方式。如果没有索引，对 JSON 字段的查询需要全 Collection 扫描，随着数据集增长，查询会变慢。JSON 索引会在 JSON 数据中的特定路径上创建索引，使这些路径上的等值、范围以及其他 Filter 查询能够快速执行。 - [JSON Shredding（JSON 列式拆分）](https://www.xlonglab.com/docs/userGuide/schema/json-shredding.md): JSON Shredding 将高频 JSON 键组织为面向查询优化的拆分列，并使用共享列处理低频键，从而在保留 JSON 灵活性的同时降低过滤和检索开销。 - [可空字段](https://www.xlonglab.com/docs/userGuide/schema/nullable-and-default.md): 配置可空字段和默认值，包括 Schema、插入、索引、搜索和过滤行为。 - [数字字段](https://www.xlonglab.com/docs/userGuide/schema/number.md): 数字字段是存储数值的标量字段。这些数值可以是整数（整数）或十进制数（浮点数）。它们通常用于表示数量、测量值或任何需要进行数学处理的数据。 - [主键字段与 AutoID](https://www.xlonglab.com/docs/userGuide/schema/primary-field.md): Milvus 中的每个 Collection 都必须有一个主键字段，以唯一标识每个实体。这个字段确保每个实体都能被插入、更新、查询或删除，而不会产生歧义。 - [Schema 概述](https://www.xlonglab.com/docs/userGuide/schema): Schema 定义了 Collection 的数据结构。在创建 Collection 之前，你需要设计好它的 Schema。本页将帮助你理解 Collection Schema，并自行设计一个示例 Schema。 - [稀疏向量](https://www.xlonglab.com/docs/userGuide/schema/sparse_vector.md): 稀疏向量是信息检索和自然语言处理中捕捉表层术语匹配的重要方法。虽然稠密向量在语义理解方面表现出色，但稀疏向量往往能提供更可预测的匹配结果，尤其是在搜索特殊术语或文本标识符时。 - [VarChar 字段](https://www.xlonglab.com/docs/userGuide/schema/string.md): VARCHAR 是 Milvus 中用于存储短字符串、有长度上限的字符串元数据的标量字段类型。 - [StructArray 限制](https://www.xlonglab.com/docs/userGuide/schema/structarray-limits.md): StructArray 支持覆盖 Schema 定义、Insert payload、Index、Search 模式以及 StructArray 专用 Filter。在生产环境依赖 StructArray 行为之前，请将本页作为限制参考。 - [Text Field](https://www.xlonglab.com/docs/userGuide/schema/text.md): TEXT 是 Milvus 中用于存储文档文本、段落和其他长文本内容的标量字段类型。 - [TIMESTAMPTZ 字段](https://www.xlonglab.com/docs/userGuide/schema/timestamptz-field.md): 跨区域时间跟踪应用程序，如电子商务系统、协作工具或分布式日志记录，需要精确处理带有时区的时间戳。Milvus 的 TIMESTAMPTZ 数据类型通过存储带有相关时区的时间戳，提供了这种功能。 - [JSON 字段](https://www.xlonglab.com/docs/userGuide/schema/use-json-fields.md): Milvus 允许您使用 JSON 数据类型在单个字段中存储和索引结构化数据。这样就能实现具有嵌套属性的灵活 Schema，同时还能通过 JSON 索引进行高效过滤。 - [使用 StructArray 进行基础 Vector Search](https://www.xlonglab.com/docs/userGuide/search-query-get/basic-vector-search-with-structarray.md): 本页介绍如何在 StructArray 字段中的向量子字段上执行 vector search。StructArray 支持两种基础 vector search 模式：EmbeddingList search，用于对每个 Entity 中存储的 embedding list 进行评分；… - [ARRAY Operators](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean/array-operators.md): Milvus 提供 ARRAY operators，用于过滤 ARRAY 字段并部分更新 ARRAY 字段值。 - [基本运算符](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean/basic-operators.md): Milvus 提供丰富的基本运算符，帮助您高效地过滤和查询数据。这些运算符允许您根据标量字段、数字计算、逻辑条件等来完善搜索条件。了解如何使用这些运算符对于建立精确查询和最大限度地提高搜索效率至关重要。 - [过滤说明](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean): Milvus 提供强大的过滤功能，可对数据进行精确查询。过滤表达式允许你针对特定的标量字段，用不同的条件细化搜索结果。本指南介绍如何在 Milvus 中使用过滤表达式，并以`query` 操作为例进行说明。您还可以在搜索和删除请求中应用这些过滤器。 - [过滤器模板](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean/filtering-templating.md): 在 Milvus 中，包含大量元素的复杂过滤表达式，尤其是那些涉及非 ASCII 字符（如中日韩字符）的表达式，会严重影响查询性能。为了解决这个问题，Milvus 引入了过滤表达式模板机制，旨在通过减少解析复杂表达式所花费的时间来提高效率。本页介绍了在搜索、查询和删除操作中使用过滤表达式模板的方法。 - [Geometry 运算符](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean/geometry-operators.md): Milvus 支持对几何字段进行空间过滤的一系列运算符，这些运算符对于管理和分析几何数据至关重要。通过这些运算符，可以根据对象之间的几何关系检索实体。 - [JSON 运算符](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean/json-operators.md): Milvus 支持用于查询和过滤 JSON 字段的高级运算符，使其成为管理复杂结构化数据的完美工具。这些运算符可实现对 JSON 文档的高效查询，允许您根据 JSON 字段中的特定元素、值或条件检索实体。本节将指导您在 Milvus 中使用特定于 JSON 的运算符，并提供实际示例来说明其功能。 - [Pattern Matching](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean/pattern-matching.md): Milvus 支持使用 LIKE wildcard patterns 和 RE2 regular expressions 进行字符串 Pattern Matching。你可以使用 pattern filters，在 VARCHAR 字段、JSON 字符串路径或 ARRAY 元素中匹配前缀、后缀、子串、结构化编码、邮箱域名、URL 路径以及其他字符串模式。 - [随机取样](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean/random-sampling.md): 在处理大规模数据集时，您往往不需要处理所有数据来获得洞察力或测试过滤逻辑。随机抽样提供了一种解决方案，让您可以处理具有统计代表性的数据子集，从而大大减少查询时间和资源消耗。 - [StructArray Operators](https://www.xlonglab.com/docs/userGuide/search-query-get/boolean/struct-array-operators.md): StructArray operators 通过评估 StructArray 字段内标量子字段上的谓词来过滤 Entity。本页可作为 element_filter 和 MATCH_* operator family 的语法参考。 - [选择 EmbeddingList Search Strategy](https://www.xlonglab.com/docs/userGuide/search-query-get/choose-an-embeddinglist-search-strategy.md): EmbeddingList search strategy 决定 Milvus 如何为 EmbeddingList search 构建近似候选索引。默认策略为 tokenann。当 embedding list 较大、TokenANN 成本过高，或更适合使用学习得到/压缩的行级表示时，你可以切换到 muvera 或 lemur。… - [Clustering Compaction](https://www.xlonglab.com/docs/userGuide/search-query-get/clustering-compaction.md): Clustering Compaction 根据 Clustering Key 重新组织 Segment 中的 Entity，以缩小搜索范围并提升大型 Collection 的查询性能。 - [从 Elasticsearch 查询迁移到 Milvus](https://www.xlonglab.com/docs/userGuide/search-query-get/elasticsearch-queries-to-milvus.md): 基于 Apache Lucene 构建的 Elasticsearch 是领先的开源搜索引擎。然而，它在现代人工智能应用中面临着各种挑战，包括更新成本高、实时性差、分片管理效率低、非云原生设计以及资源需求过高。作为云原生向量数据库，Milvus 通过解耦存储和计算、高效的高维数据索引以及与现代基础设施的无缝集成，克服了这些问题。… - [StructArray Filtered Search](https://www.xlonglab.com/docs/userGuide/search-query-get/filtered-search-with-structarray.md): 本页介绍如何在 StructArray 字段的 vector search 中添加 scalar filtering。StructArray filtering 包含两个层级：row-level filters 用于选择父 Entity，而 element-level filters 用于约束哪些 Struct 元素参与 element-level vect… - [过滤搜索](https://www.xlonglab.com/docs/userGuide/search-query-get/filtered-search.md): ANN 搜索能找到与指定 Embedding 最相似的 Embedding。但是，搜索结果不一定总是正确的。您可以在搜索请求中包含过滤条件，这样 Milvus 就会在进行 ANN 搜索前进行元数据过滤，将搜索范围从整个 Collection 缩小到只搜索符合指定过滤条件的实体。 - [Force Merge Compaction](https://www.xlonglab.com/docs/userGuide/search-query-get/force-merge.md): 使用 Force Merge Compaction 将小型 Segment 重组为更少、更大的 Segment，以提升查询性能和存储效率。 - [全文搜索](https://www.xlonglab.com/docs/userGuide/search-query-get/full-text-search.md): 全文搜索是一种在文本数据集中检索包含特定术语或短语的文档，然后根据相关性对结果进行排序的功能。该功能克服了语义搜索的局限性（语义搜索可能会忽略精确的术语），确保您获得最准确且与上下文最相关的结果。此外，它还能接受原始文本输入，自动将文本数据转换为稀疏嵌入，无需手动生成 Embedding，从而简化了向量搜索。 - [查询](https://www.xlonglab.com/docs/userGuide/search-query-get/get-and-scalar-query.md): 使用 Query、Get 和 QueryIterator 在 Milvus 中检索实体、过滤元数据、对查询结果排序以及聚合标量值。 - [使用 StructArray 进行 Grouping Search](https://www.xlonglab.com/docs/userGuide/search-query-get/grouping-search-with-structarray.md): 本页介绍如何按父 Entity 对 StructArray 元素级搜索结果进行分组。当多个 Struct 元素匹配查询时，元素级搜索可能会从同一个 Entity 返回多条命中结果。Grouping 会折叠这些元素命中结果，使每个父 Entity 最多只出现一次。 - [分组搜索](https://www.xlonglab.com/docs/userGuide/search-query-get/grouping-search.md): 使用分组搜索可按字段值汇总 ANN 搜索结果，减少重复实体。 - [使用 StructArray 进行 Hybrid Search](https://www.xlonglab.com/docs/userGuide/search-query-get/hybrid-search-with-structarray.md): 使用本页了解如何在一次 hybrid search 请求中，将 StructArray vector search 与其他 vector search 组合使用。StructArray hybrid search 可以生成 entity-level 结果或 element-level 结果，具体取决于你组合的 AnnSearchRequest 对象。 - [文本匹配](https://www.xlonglab.com/docs/userGuide/search-query-get/keyword-match.md): Milvus 中的文本匹配功能可根据特定术语进行精确的文档检索。该功能主要用于满足特定条件的过滤搜索，并可结合标量过滤来细化查询结果，允许在符合标量标准的向量内进行相似性搜索。 - [管理 Snapshot](https://www.xlonglab.com/docs/userGuide/search-query-get/manage-snapshots.md): 介绍如何创建、列出、查看、固定、恢复和删除 Snapshot，以及如何监控恢复任务。 - [度量类型](https://www.xlonglab.com/docs/userGuide/search-query-get/metric.md): 相似度量用于衡量向量之间的相似性。选择合适的距离度量有助于显著提高分类和聚类性能。 - [使用 mmap](https://www.xlonglab.com/docs/userGuide/search-query-get/mmap.md): 内存映射（Mmap）实现了对磁盘上大型文件的直接内存访问，使 Milvus 可以在内存和硬盘中同时存储索引和数据。这种方法有助于根据访问频率优化数据放置策略，在不明显影响搜索性能的情况下扩大 Collection 的存储容量。本页帮助你了解 Milvus 如何使用 mmap 实现快速高效的数据存储和检索。 - [多向量混合搜索](https://www.xlonglab.com/docs/userGuide/search-query-get/multi-vector-search.md): 在许多应用中，可以通过标题和描述等丰富的信息集或文本、图像和音频等多种模式来搜索对象。例如，如果文本或图片与搜索查询的语义相符，就可以搜索包含一段文本和一张图片的推文。混合搜索将这些不同领域的搜索结合在一起，从而增强了搜索体验。Milvus 允许在多个向量字段上进行搜索，同时进行多个近似近邻（ANN）搜索，从而支持这种搜索。… - [Phrase Match](https://www.xlonglab.com/docs/userGuide/search-query-get/phrase-match.md): 短语匹配可让您搜索包含精确短语查询词的文档。默认情况下，单词必须以相同的顺序出现，并且彼此直接相邻。例如，查询 "机器人机器学习 "会匹配"...典型的机器人机器学习模型... "这样的文本，其中 "机器人"、"机器 "和 "学习 "依次出现，中间没有其他词。 - [主键搜索](https://www.xlonglab.com/docs/userGuide/search-query-get/primary-key-search.md): 在进行相似性搜索时，总是会要求您提供一个或多个查询向量，即使查询向量已经存在于目标 Collection 中。为了避免在搜索前检索向量，可以使用主键来代替。 - [使用 StructArray 执行 Range Search](https://www.xlonglab.com/docs/userGuide/search-query-get/range-search-with-structarray.md): 本页介绍如何在 StructArray 的向量子字段上执行 Range Search。Range Search 会返回 score 或 distance 落在指定边界内的向量命中结果。对于 StructArray 字段，可将 Range Search 与元素级 vector search 结合使用，其中每个 Struct 元素都会被独立搜索。 - [范围搜索](https://www.xlonglab.com/docs/userGuide/search-query-get/range-search.md): 范围搜索将返回实体的距离或得分限制在特定范围内，从而提高搜索结果的相关性。本页将帮助您了解什么是范围搜索以及进行范围搜索的步骤。 - [Search Aggregation](https://www.xlonglab.com/docs/userGuide/search-query-get/search-aggregation.md): 将向量搜索结果分组到 bucket，计算每个 bucket 的指标，对 bucket 排序，并返回代表性命中结果。 - [使用 EmbeddingLists 进行检索：ColBERT 和 ColPali](https://www.xlonglab.com/docs/userGuide/search-query-get/search-with-embedding-lists.md): 本教程介绍如何在 Milvus 中，基于 StructArray 向量子字段的 EmbeddingList search 构建 ColBERT 风格和 ColPali 风格的检索系统。当你的 query 和已存储数据都表示为向量列表，并且希望使用 MAX_SIM* metrics 实现 Entity 级 late-interaction retrieval… - [基本向量搜索](https://www.xlonglab.com/docs/userGuide/search-query-get/single-vector-search.md): 在 Milvus 中使用查询向量、输出字段、过滤器、范围和迭代器运行基本的 ANN 搜索。 - [Snapshot 用例](https://www.xlonglab.com/docs/userGuide/search-query-get/snapshot-use-cases.md): 介绍 Snapshot 的常见用例，包括快速恢复、版本管理、测试和离线分析。 - [Snapshot](https://www.xlonglab.com/docs/userGuide/search-query-get/snapshots.md): Snapshot（快照）用于捕获 Collection 在特定时间点的状态，适合快速回滚、版本管理和测试。 - [文本高亮显示](https://www.xlonglab.com/docs/userGuide/search-query-get/text-highlighter.md): Milvus 中的高亮显示器通过使用可定制的标签对文本字段中的匹配术语进行注释。高亮显示有助于解释文档匹配的原因，提高结果的可读性，并支持在搜索和 RAG 应用程序中进行丰富的渲染。 - [使用 Partition Key](https://www.xlonglab.com/docs/userGuide/search-query-get/use-partition-key.md): Partition Key 是一种基于 Partition 的搜索优化解决方案。通过指定一个特定的标量字段作为 Partition Key，并在搜索过程中根据 Partition Key 指定过滤条件，可以将搜索范围缩小到几个 Partition，从而提高搜索效率。本文将介绍如何使用 Partition Key 及相关注意事项。 - [搜索迭代器](https://www.xlonglab.com/docs/userGuide/search-query-get/with-iterators.md): ANN Search 对单次查询可调用的实体数量有最大限制，因此仅使用基本 ANN Search 可能无法满足大规模检索的需求。对于 topK 超过 16,384 的 ANN Search 请求，建议考虑使用 SearchIterator。本节将介绍如何使用 SearchIterator 以及相关注意事项。 - [分层存储的最佳实践](https://www.xlonglab.com/docs/userGuide/storage-optimization/best-practices-for-tiered-storage.md): Milvus 提供分层存储，帮助您有效处理大规模数据，同时平衡查询延迟、容量和资源使用。本指南总结了典型工作负载的推荐配置，并解释了每种调整策略背后的原因。 - [驱逐](https://www.xlonglab.com/docs/userGuide/storage-optimization/eviction.md): 驱逐管理 Milvus 中每个 QueryNode 的缓存资源。启用后，一旦达到资源阈值，它就会自动删除缓存数据，确保性能稳定，防止内存或磁盘耗尽。 - [分层存储概述](https://www.xlonglab.com/docs/userGuide/storage-optimization/tiered-storage-overview.md): 在 Milvus 中，传统的满载模式要求每个 QueryNode 在初始化时加载段的所有数据字段和索引，即使是可能永远不会被访问的数据。这虽然确保了数据的即时可用性，但往往会造成资源浪费，包括内存使用率高、磁盘活动频繁以及 I/O 开销大，尤其是在处理大规模数据集时。 - [预热](https://www.xlonglab.com/docs/userGuide/storage-optimization/warm-up.md): 预热功能是对分层存储的补充，可在段可查询前将选定字段或索引预加载到缓存中。您可以在集群、 Collection 或单个字段/索引级别配置预热，从而实现对首次查询延迟和资源使用的精细控制。 - [安装 Birdwatcher](https://www.xlonglab.com/docs/userGuide/tools/birdwatcher_install_guides.md): 了解如何安装 Birdwatcher 以调试 Milvus。 - [Birdwatcher](https://www.xlonglab.com/docs/userGuide/tools/birdwatcher_overview.md): Birdwatcher 是 Milvus 2.x 的一个调试工具。它连接到 etcd，并检查 Milvus 系统的状态。 - [使用 Birdwatcher](https://www.xlonglab.com/docs/userGuide/tools/birdwatcher_usage_guides.md): 了解如何使用 Birdwatcher 调试 Milvus。 - [Milvus CLI 命令参考](https://www.xlonglab.com/docs/userGuide/tools/cli_commands.md): 使用命令与 Milvus 互动。 - [Milvus 命令行界面](https://www.xlonglab.com/docs/userGuide/tools/cli_overview.md): Milvus 命令行界面（CLI）是一种命令行工具，支持数据库连接、数据操作和数据导入导出。 - [安装 Milvus CLI](https://www.xlonglab.com/docs/userGuide/tools/install_cli.md): 了解如何安装 Milvus CLI。 - [跨存储桶在实例间迁移](https://www.xlonglab.com/docs/userGuide/tools/milvus-backup/common-cases/cross-bucket-backup-and-restore.md): 本主题详细介绍如何从一个 Milvus 实例备份 Collection，并将其恢复到同一 Object Storage 中使用不同存储桶的另一个 Milvus 实例。 - [在跨 S3 环境的实例间迁移](https://www.xlonglab.com/docs/userGuide/tools/milvus-backup/common-cases/multi-storage-backup-and-restore.md): 本主题详细介绍了从一个 Milvus 实例备份一个 Collection 并将其还原到另一个实例的过程 - [在一个存储桶中的不同实例之间迁移（不同的根路径）](https://www.xlonglab.com/docs/userGuide/tools/milvus-backup/common-cases/shared-bucket-backup-and-restore.md): 本主题将详细介绍在使用共享存储桶存储对象时，从一个 Milvus 实例备份一个 Collection 并将其恢复到另一个实例的过程 - [在一个实例中备份和还原](https://www.xlonglab.com/docs/userGuide/tools/milvus-backup/common-cases/single-instance-backup-and-restore.md): 本主题将详细介绍在同一 Milvus 实例中备份 Collection 并从备份中还原的过程 - [使用 API 备份和恢复数据](https://www.xlonglab.com/docs/userGuide/tools/milvus-backup/milvus_backup_api.md): 了解如何通过 API 使用 Milvus 备份功能 - [使用命令备份和恢复数据](https://www.xlonglab.com/docs/userGuide/tools/milvus-backup/milvus_backup_cli.md): 学习如何通过 CLI 使用 Milvus 备份 - [Milvus 备份](https://www.xlonglab.com/docs/userGuide/tools/milvus-backup/milvus_backup_overview.md): Milvus-Backup 是一款允许用户备份和恢复 Milvus 数据的工具。 - [CDC Replication 中的 Bulk Import](https://www.xlonglab.com/docs/userGuide/tools/milvus-cdc/bulk_import_in_cdc_replication.md): 了解如何对使用 CDC replication 的 Milvus 集群运行 bulk import。 - [故障转移](https://www.xlonglab.com/docs/userGuide/tools/milvus-cdc/cdc_failover.md): 了解如何在主 Milvus 集群不可用时执行故障转移。 - [切换](https://www.xlonglab.com/docs/userGuide/tools/milvus-cdc/cdc_switchover.md): 了解如何使用 Milvus CDC 在主用和备用 Milvus 集群之间执行计划切换。 - [Milvus CDC](https://www.xlonglab.com/docs/userGuide/tools/milvus-cdc/milvus_cdc_overview.md): Milvus CDC 可将数据更改从一个 Milvus 集群复制到另一个集群，以实现主备灾难恢复。 - [设置 CDC 复制](https://www.xlonglab.com/docs/userGuide/tools/milvus-cdc/set_up_cdc_replication.md): 了解如何部署两个 Milvus 集群并在它们之间配置 CDC 复制。 - [Milvus SDK Code Helper 指南](https://www.xlonglab.com/docs/userGuide/tools/milvus-sdk-helper-mcp.md): 通过 MCP 为 AI coding assistant 提供与当前 Milvus SDK 一致的 API 上下文。 - [Milvus WebUI](https://www.xlonglab.com/docs/userGuide/tools/milvus-webui.md): Milvus WebUI 是内置的 Milvus GUI 与可观测性界面，可查看组件状态、Database、Collection、Segment、Channel、任务、慢请求和运行时配置。 ### API Reference - [About Milvus GO SDK | Go API · About](https://www.xlonglab.com/docs/api-reference/go/About.md) - [AddPrivilegesToGroup() | Go API · Authentication/AddPrivilegesToGroup](https://www.xlonglab.com/docs/api-reference/go/Authentication/AddPrivilegesToGroup.md) - [AlterRole() | Go API · Authentication/AlterRole](https://www.xlonglab.com/docs/api-reference/go/Authentication/AlterRole.md) - [BackupRBAC() | Go API · Authentication/BackupRBAC](https://www.xlonglab.com/docs/api-reference/go/Authentication/BackupRBAC.md) - [CreatePrivilegeGroup() | Go API · Authentication/CreatePrivilegeGroup](https://www.xlonglab.com/docs/api-reference/go/Authentication/CreatePrivilegeGroup.md) - [CreateRole() | Go API · Authentication/CreateRole](https://www.xlonglab.com/docs/api-reference/go/Authentication/CreateRole.md) - [CreateUser() | Go API · Authentication/CreateUser](https://www.xlonglab.com/docs/api-reference/go/Authentication/CreateUser.md) - [DescribeRole() | Go API · Authentication/DescribeRole](https://www.xlonglab.com/docs/api-reference/go/Authentication/DescribeRole.md) - [DescribeUser() | Go API · Authentication/DescribeUser](https://www.xlonglab.com/docs/api-reference/go/Authentication/DescribeUser.md) - [DropPrivilegeGroup() | Go API · Authentication/DropPrivilegeGroup](https://www.xlonglab.com/docs/api-reference/go/Authentication/DropPrivilegeGroup.md) - [DropRole() | Go API · Authentication/DropRole](https://www.xlonglab.com/docs/api-reference/go/Authentication/DropRole.md) - [DropUser() | Go API · Authentication/DropUser](https://www.xlonglab.com/docs/api-reference/go/Authentication/DropUser.md) - [GrantPrivilege() | Go API · Authentication/GrantPrivilege](https://www.xlonglab.com/docs/api-reference/go/Authentication/GrantPrivilege.md) - [GrantPrivilegeV2() | Go API · Authentication/GrantPrivilegeV2](https://www.xlonglab.com/docs/api-reference/go/Authentication/GrantPrivilegeV2.md) - [GrantRole() | Go API · Authentication/GrantRole](https://www.xlonglab.com/docs/api-reference/go/Authentication/GrantRole.md) - [GrantV2() | Go API · Authentication/GrantV2](https://www.xlonglab.com/docs/api-reference/go/Authentication/GrantV2.md) - [ListPrivilegeGroups() | Go API · Authentication/ListPrivilegeGroups](https://www.xlonglab.com/docs/api-reference/go/Authentication/ListPrivilegeGroups.md) - [ListRoles() | Go API · Authentication/ListRoles](https://www.xlonglab.com/docs/api-reference/go/Authentication/ListRoles.md) - [ListUsers() | Go API · Authentication/ListUsers](https://www.xlonglab.com/docs/api-reference/go/Authentication/ListUsers.md) - [OperatePrivilegeGroup() | Go API · Authentication/OperatePrivilegeGroup](https://www.xlonglab.com/docs/api-reference/go/Authentication/OperatePrivilegeGroup.md) - [PrivilegeGroup | Go API · Authentication/PrivilegeGroup](https://www.xlonglab.com/docs/api-reference/go/Authentication/PrivilegeGroup.md) - [RBACMeta | Go API · Authentication/RBACMeta](https://www.xlonglab.com/docs/api-reference/go/Authentication/RBACMeta.md) - [RemovePrivilegesFromGroup() | Go API · Authentication/RemovePrivilegesFromGroup](https://www.xlonglab.com/docs/api-reference/go/Authentication/RemovePrivilegesFromGroup.md) - [RestoreRBAC() | Go API · Authentication/RestoreRBAC](https://www.xlonglab.com/docs/api-reference/go/Authentication/RestoreRBAC.md) - [RevokePrivilege() | Go API · Authentication/RevokePrivilege](https://www.xlonglab.com/docs/api-reference/go/Authentication/RevokePrivilege.md) - [RevokePrivilegeV2() | Go API · Authentication/RevokePrivilegeV2](https://www.xlonglab.com/docs/api-reference/go/Authentication/RevokePrivilegeV2.md) - [RevokeRole() | Go API · Authentication/RevokeRole](https://www.xlonglab.com/docs/api-reference/go/Authentication/RevokeRole.md) - [RevokeV2() | Go API · Authentication/RevokeV2](https://www.xlonglab.com/docs/api-reference/go/Authentication/RevokeV2.md) - [Role | Go API · Authentication/Role](https://www.xlonglab.com/docs/api-reference/go/Authentication/Role.md) - [UpdatePassword() | Go API · Authentication/UpdatePassword](https://www.xlonglab.com/docs/api-reference/go/Authentication/UpdatePassword.md) - [User | Go API · Authentication/User](https://www.xlonglab.com/docs/api-reference/go/Authentication/User.md) - [CreateReplicateStream() | Go API · CDC/CreateReplicateStream](https://www.xlonglab.com/docs/api-reference/go/CDC/CreateReplicateStream.md) - [DumpMessages() | Go API · CDC/DumpMessages](https://www.xlonglab.com/docs/api-reference/go/CDC/DumpMessages.md) - [GetReplicateConfiguration() | Go API · CDC/GetReplicateConfiguration](https://www.xlonglab.com/docs/api-reference/go/CDC/GetReplicateConfiguration.md) - [GetReplicateInfo() | Go API · CDC/GetReplicateInfo](https://www.xlonglab.com/docs/api-reference/go/CDC/GetReplicateInfo.md) - [UpdateReplicateConfiguration() | Go API · CDC/UpdateReplicateConfiguration](https://www.xlonglab.com/docs/api-reference/go/CDC/UpdateReplicateConfiguration.md) - [ClientConfig | Go API · Client/ClientConfig](https://www.xlonglab.com/docs/api-reference/go/Client/ClientConfig.md) - [Close() | Go API · Client/Close](https://www.xlonglab.com/docs/api-reference/go/Client/Close.md) - [GetServerVersion() | Go API · Client/GetServerVersion](https://www.xlonglab.com/docs/api-reference/go/Client/GetServerVersion.md) - [GetTelemetry() | Go API · Client/GetTelemetry](https://www.xlonglab.com/docs/api-reference/go/Client/GetTelemetry.md) - [New() | Go API · Client/New](https://www.xlonglab.com/docs/api-reference/go/Client/New.md) - [AddCollectionField() | Go API · Collection/AddCollectionField](https://www.xlonglab.com/docs/api-reference/go/Collection/AddCollectionField.md) - [AddCollectionStructField() | Go API · Collection/AddCollectionStructField](https://www.xlonglab.com/docs/api-reference/go/Collection/AddCollectionStructField.md) - [AddFunctionField() | Go API · Collection/AddFunctionField](https://www.xlonglab.com/docs/api-reference/go/Collection/AddFunctionField.md) - [Alias | Go API · Collection/Alias](https://www.xlonglab.com/docs/api-reference/go/Collection/Alias.md) - [AlterAlias() | Go API · Collection/AlterAlias](https://www.xlonglab.com/docs/api-reference/go/Collection/AlterAlias.md) - [AlterCollectionFieldProperty() | Go API · Collection/AlterCollectionFieldProperty](https://www.xlonglab.com/docs/api-reference/go/Collection/AlterCollectionFieldProperty.md) - [AlterCollectionProperties() | Go API · Collection/AlterCollectionProperties](https://www.xlonglab.com/docs/api-reference/go/Collection/AlterCollectionProperties.md) - [Collection | Go API · Collection/Collection](https://www.xlonglab.com/docs/api-reference/go/Collection/Collection.md) - [ConsistencyLevel | Go API · Collection/ConsistencyLevel](https://www.xlonglab.com/docs/api-reference/go/Collection/ConsistencyLevel.md) - [CreateAlias() | Go API · Collection/CreateAlias](https://www.xlonglab.com/docs/api-reference/go/Collection/CreateAlias.md) - [CreateCollection() | Go API · Collection/CreateCollection](https://www.xlonglab.com/docs/api-reference/go/Collection/CreateCollection.md) - [DescribeAlias() | Go API · Collection/DescribeAlias](https://www.xlonglab.com/docs/api-reference/go/Collection/DescribeAlias.md) - [DescribeCollection() | Go API · Collection/DescribeCollection](https://www.xlonglab.com/docs/api-reference/go/Collection/DescribeCollection.md) - [DropAlias() | Go API · Collection/DropAlias](https://www.xlonglab.com/docs/api-reference/go/Collection/DropAlias.md) - [DropCollection() | Go API · Collection/DropCollection](https://www.xlonglab.com/docs/api-reference/go/Collection/DropCollection.md) - [DropCollectionField() | Go API · Collection/DropCollectionField](https://www.xlonglab.com/docs/api-reference/go/Collection/DropCollectionField.md) - [DropCollectionProperties() | Go API · Collection/DropCollectionProperties](https://www.xlonglab.com/docs/api-reference/go/Collection/DropCollectionProperties.md) - [DropFunctionField() | Go API · Collection/DropFunctionField](https://www.xlonglab.com/docs/api-reference/go/Collection/DropFunctionField.md) - [Field | Go API · Collection/Field](https://www.xlonglab.com/docs/api-reference/go/Collection/Field.md) - [FieldType | Go API · Collection/FieldType](https://www.xlonglab.com/docs/api-reference/go/Collection/FieldType.md) - [Function | Go API · Collection/Function](https://www.xlonglab.com/docs/api-reference/go/Collection/Function.md) - [FunctionScore | Go API · Collection/FunctionScore](https://www.xlonglab.com/docs/api-reference/go/Collection/FunctionScore.md) - [GetCollectionStats() | Go API · Collection/GetCollectionStats](https://www.xlonglab.com/docs/api-reference/go/Collection/GetCollectionStats.md) - [GetRefreshExternalCollectionProgress() | Go API · Collection/GetRefreshExternalCollectionProgress](https://www.xlonglab.com/docs/api-reference/go/Collection/GetRefreshExternalCollectionProgress.md) - [HasCollection() | Go API · Collection/HasCollection](https://www.xlonglab.com/docs/api-reference/go/Collection/HasCollection.md) - [ListAliases() | Go API · Collection/ListAliases](https://www.xlonglab.com/docs/api-reference/go/Collection/ListAliases.md) - [ListCollections() | Go API · Collection/ListCollections](https://www.xlonglab.com/docs/api-reference/go/Collection/ListCollections.md) - [ListRefreshExternalCollectionJobs() | Go API · Collection/ListRefreshExternalCollectionJobs](https://www.xlonglab.com/docs/api-reference/go/Collection/ListRefreshExternalCollectionJobs.md) - [RefreshExternalCollection() | Go API · Collection/RefreshExternalCollection](https://www.xlonglab.com/docs/api-reference/go/Collection/RefreshExternalCollection.md) - [RefreshExternalCollectionJobInfo | Go API · Collection/RefreshExternalCollectionJobInfo](https://www.xlonglab.com/docs/api-reference/go/Collection/RefreshExternalCollectionJobInfo.md) - [RefreshExternalCollectionState | Go API · Collection/RefreshExternalCollectionState](https://www.xlonglab.com/docs/api-reference/go/Collection/RefreshExternalCollectionState.md) - [RenameCollection() | Go API · Collection/RenameCollection](https://www.xlonglab.com/docs/api-reference/go/Collection/RenameCollection.md) - [Schema | Go API · Collection/Schema](https://www.xlonglab.com/docs/api-reference/go/Collection/Schema.md) - [TruncateCollection() | Go API · Collection/TruncateCollection](https://www.xlonglab.com/docs/api-reference/go/Collection/TruncateCollection.md) - [AbortImport() | Go API · DataImport/AbortImport](https://www.xlonglab.com/docs/api-reference/go/DataImport/AbortImport.md) - [AbortImportOption | Go API · DataImport/AbortImportOption](https://www.xlonglab.com/docs/api-reference/go/DataImport/AbortImportOption.md) - [AbortImportResponse | Go API · DataImport/AbortImportResponse](https://www.xlonglab.com/docs/api-reference/go/DataImport/AbortImportResponse.md) - [BulkImport() | Go API · DataImport/BulkImport](https://www.xlonglab.com/docs/api-reference/go/DataImport/BulkImport.md) - [BulkImportOption | Go API · DataImport/BulkImportOption](https://www.xlonglab.com/docs/api-reference/go/DataImport/BulkImportOption.md) - [BulkImportResponse | Go API · DataImport/BulkImportResponse](https://www.xlonglab.com/docs/api-reference/go/DataImport/BulkImportResponse.md) - [CommitImport() | Go API · DataImport/CommitImport](https://www.xlonglab.com/docs/api-reference/go/DataImport/CommitImport.md) - [CommitImportOption | Go API · DataImport/CommitImportOption](https://www.xlonglab.com/docs/api-reference/go/DataImport/CommitImportOption.md) - [CommitImportResponse | Go API · DataImport/CommitImportResponse](https://www.xlonglab.com/docs/api-reference/go/DataImport/CommitImportResponse.md) - [GetImportProgress() | Go API · DataImport/GetImportProgress](https://www.xlonglab.com/docs/api-reference/go/DataImport/GetImportProgress.md) - [GetImportProgressOption | Go API · DataImport/GetImportProgressOption](https://www.xlonglab.com/docs/api-reference/go/DataImport/GetImportProgressOption.md) - [GetImportProgressResponse | Go API · DataImport/GetImportProgressResponse](https://www.xlonglab.com/docs/api-reference/go/DataImport/GetImportProgressResponse.md) - [ListImportJobs() | Go API · DataImport/ListImportJobs](https://www.xlonglab.com/docs/api-reference/go/DataImport/ListImportJobs.md) - [ListImportJobsOption | Go API · DataImport/ListImportJobsOption](https://www.xlonglab.com/docs/api-reference/go/DataImport/ListImportJobsOption.md) - [AlterDatabaseProperties() | Go API · Database/AlterDatabaseProperties](https://www.xlonglab.com/docs/api-reference/go/Database/AlterDatabaseProperties.md) - [CreateDatabase() | Go API · Database/CreateDatabase](https://www.xlonglab.com/docs/api-reference/go/Database/CreateDatabase.md) - [Database | Go API · Database/Database](https://www.xlonglab.com/docs/api-reference/go/Database/Database.md) - [DescribeDatabase() | Go API · Database/DescribeDatabase](https://www.xlonglab.com/docs/api-reference/go/Database/DescribeDatabase.md) - [DropDatabase() | Go API · Database/DropDatabase](https://www.xlonglab.com/docs/api-reference/go/Database/DropDatabase.md) - [DropDatabaseProperties() | Go API · Database/DropDatabaseProperties](https://www.xlonglab.com/docs/api-reference/go/Database/DropDatabaseProperties.md) - [ListDatabase() | Go API · Database/ListDatabase](https://www.xlonglab.com/docs/api-reference/go/Database/ListDatabase.md) - [UseDatabase() | Go API · Database/UseDatabase](https://www.xlonglab.com/docs/api-reference/go/Database/UseDatabase.md) - [AddFileResource() | Go API · FileResources/AddFileResource](https://www.xlonglab.com/docs/api-reference/go/FileResources/AddFileResource.md) - [FileResource | Go API · FileResources/FileResource](https://www.xlonglab.com/docs/api-reference/go/FileResources/FileResource.md) - [ListFileResources() | Go API · FileResources/ListFileResources](https://www.xlonglab.com/docs/api-reference/go/FileResources/ListFileResources.md) - [RemoveFileResource() | Go API · FileResources/RemoveFileResource](https://www.xlonglab.com/docs/api-reference/go/FileResources/RemoveFileResource.md) - [AlterIndexProperties() | Go API · Management/AlterIndexProperties](https://www.xlonglab.com/docs/api-reference/go/Management/AlterIndexProperties.md) - [Compact() | Go API · Management/Compact](https://www.xlonglab.com/docs/api-reference/go/Management/Compact.md) - [CompactionState | Go API · Management/CompactionState](https://www.xlonglab.com/docs/api-reference/go/Management/CompactionState.md) - [CreateIndex() | Go API · Management/CreateIndex](https://www.xlonglab.com/docs/api-reference/go/Management/CreateIndex.md) - [DescribeIndex() | Go API · Management/DescribeIndex](https://www.xlonglab.com/docs/api-reference/go/Management/DescribeIndex.md) - [DropIndex() | Go API · Management/DropIndex](https://www.xlonglab.com/docs/api-reference/go/Management/DropIndex.md) - [DropIndexProperties() | Go API · Management/DropIndexProperties](https://www.xlonglab.com/docs/api-reference/go/Management/DropIndexProperties.md) - [Flush() | Go API · Management/Flush](https://www.xlonglab.com/docs/api-reference/go/Management/Flush.md) - [FlushTask | Go API · Management/FlushTask](https://www.xlonglab.com/docs/api-reference/go/Management/FlushTask.md) - [GetCompactionState() | Go API · Management/GetCompactionState](https://www.xlonglab.com/docs/api-reference/go/Management/GetCompactionState.md) - [GetLoadState() | Go API · Management/GetLoadState](https://www.xlonglab.com/docs/api-reference/go/Management/GetLoadState.md) - [GetPersistentSegmentInfo() | Go API · Management/GetPersistentSegmentInfo](https://www.xlonglab.com/docs/api-reference/go/Management/GetPersistentSegmentInfo.md) - [Index | Go API · Management/Index/Index](https://www.xlonglab.com/docs/api-reference/go/Management/Index/Index.md) - [NewAISAQIndex() | Go API · Management/Index/NewAISAQIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewAISAQIndex.md) - [NewAutoIndex() | Go API · Management/Index/NewAutoIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewAutoIndex.md) - [NewBinFlatIndex() | Go API · Management/Index/NewBinFlatIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewBinFlatIndex.md) - [NewBinIvfFlatIndex() | Go API · Management/Index/NewBinIvfFlatIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewBinIvfFlatIndex.md) - [NewBitmapIndex() | Go API · Management/Index/NewBitmapIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewBitmapIndex.md) - [NewDiskANNIndex() | Go API · Management/Index/NewDiskANNIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewDiskANNIndex.md) - [NewFMIndex() | Go API · Management/Index/NewFMIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewFMIndex.md) - [NewFlatIndex() | Go API · Management/Index/NewFlatIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewFlatIndex.md) - [NewGPUBruteForceIndex() | Go API · Management/Index/NewGPUBruteForceIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewGPUBruteForceIndex.md) - [NewGPUCagraIndex() | Go API · Management/Index/NewGPUCagraIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewGPUCagraIndex.md) - [NewGPUIVPFlatIndex() | Go API · Management/Index/NewGPUIVPFlatIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewGPUIVPFlatIndex.md) - [NewGPUIVPPQIndex() | Go API · Management/Index/NewGPUIVPPQIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewGPUIVPPQIndex.md) - [NewGenericIndex() | Go API · Management/Index/NewGenericIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewGenericIndex.md) - [NewHNSWIndex() | Go API · Management/Index/NewHNSWIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewHNSWIndex.md) - [NewHNSWPQIndex() | Go API · Management/Index/NewHNSWPQIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewHNSWPQIndex.md) - [NewHNSWPRQIndex() | Go API · Management/Index/NewHNSWPRQIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewHNSWPRQIndex.md) - [NewHNSWSQIndex() | Go API · Management/Index/NewHNSWSQIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewHNSWSQIndex.md) - [NewInvertedIndex() | Go API · Management/Index/NewInvertedIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewInvertedIndex.md) - [NewIvfFlatIndex() | Go API · Management/Index/NewIvfFlatIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewIvfFlatIndex.md) - [NewIvfPQIndex() | Go API · Management/Index/NewIvfPQIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewIvfPQIndex.md) - [NewIvfRabitQIndex() | Go API · Management/Index/NewIvfRabitQIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewIvfRabitQIndex.md) - [NewIvfSQ8Index() | Go API · Management/Index/NewIvfSQ8Index](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewIvfSQ8Index.md) - [NewJSONPathIndex() | Go API · Management/Index/NewJSONPathIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewJSONPathIndex.md) - [NewMinHashLSHIndex() | Go API · Management/Index/NewMinHashLSHIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewMinHashLSHIndex.md) - [NewNgramIndex() | Go API · Management/Index/NewNgramIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewNgramIndex.md) - [NewRTreeIndex() | Go API · Management/Index/NewRTreeIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewRTreeIndex.md) - [NewRTreeIndexBuilder() | Go API · Management/Index/NewRTreeIndexBuilder](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewRTreeIndexBuilder.md) - [NewRTreeIndexWithParams() | Go API · Management/Index/NewRTreeIndexWithParams](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewRTreeIndexWithParams.md) - [NewSCANNIndex() | Go API · Management/Index/NewSCANNIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewSCANNIndex.md) - [NewSortedIndex() | Go API · Management/Index/NewSortedIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewSortedIndex.md) - [NewSparseInvertedIndex() | Go API · Management/Index/NewSparseInvertedIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewSparseInvertedIndex.md) - [NewSparseWANDIndex() | Go API · Management/Index/NewSparseWANDIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewSparseWANDIndex.md) - [NewTrieIndex() | Go API · Management/Index/NewTrieIndex](https://www.xlonglab.com/docs/api-reference/go/Management/Index/NewTrieIndex.md) - [WithExtraIndexParams() | Go API · Management/Index/WithExtraIndexParams](https://www.xlonglab.com/docs/api-reference/go/Management/Index/WithExtraIndexParams.md) - [IndexDescription | Go API · Management/IndexDescription](https://www.xlonglab.com/docs/api-reference/go/Management/IndexDescription.md) - [IndexType | Go API · Management/IndexType](https://www.xlonglab.com/docs/api-reference/go/Management/IndexType.md) - [ListIndexes() | Go API · Management/ListIndexes](https://www.xlonglab.com/docs/api-reference/go/Management/ListIndexes.md) - [LoadCollection() | Go API · Management/LoadCollection](https://www.xlonglab.com/docs/api-reference/go/Management/LoadCollection.md) - [LoadPartitions() | Go API · Management/LoadPartitions](https://www.xlonglab.com/docs/api-reference/go/Management/LoadPartitions.md) - [LoadState | Go API · Management/LoadState](https://www.xlonglab.com/docs/api-reference/go/Management/LoadState.md) - [LoadTask | Go API · Management/LoadTask](https://www.xlonglab.com/docs/api-reference/go/Management/LoadTask.md) - [MetricType | Go API · Management/MetricType](https://www.xlonglab.com/docs/api-reference/go/Management/MetricType.md) - [RefreshLoad() | Go API · Management/RefreshLoad](https://www.xlonglab.com/docs/api-reference/go/Management/RefreshLoad.md) - [ReleaseCollection() | Go API · Management/ReleaseCollection](https://www.xlonglab.com/docs/api-reference/go/Management/ReleaseCollection.md) - [Segment | Go API · Management/Segment](https://www.xlonglab.com/docs/api-reference/go/Management/Segment.md) - [CreatePartition() | Go API · Partition/CreatePartition](https://www.xlonglab.com/docs/api-reference/go/Partition/CreatePartition.md) - [DropPartition() | Go API · Partition/DropPartition](https://www.xlonglab.com/docs/api-reference/go/Partition/DropPartition.md) - [GetPartitionStats() | Go API · Partition/GetPartitionStats](https://www.xlonglab.com/docs/api-reference/go/Partition/GetPartitionStats.md) - [HasPartition() | Go API · Partition/HasPartition](https://www.xlonglab.com/docs/api-reference/go/Partition/HasPartition.md) - [ListPartitions() | Go API · Partition/ListPartitions](https://www.xlonglab.com/docs/api-reference/go/Partition/ListPartitions.md) - [ReleasePartitions() | Go API · Partition/ReleasePartitions](https://www.xlonglab.com/docs/api-reference/go/Partition/ReleasePartitions.md) - [CreateResourceGroup() | Go API · ResourceGroup/CreateResourceGroup](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/CreateResourceGroup.md) - [DescribeReplica() | Go API · ResourceGroup/DescribeReplica](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/DescribeReplica.md) - [DescribeResourceGroup() | Go API · ResourceGroup/DescribeResourceGroup](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/DescribeResourceGroup.md) - [DropResourceGroup() | Go API · ResourceGroup/DropResourceGroup](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/DropResourceGroup.md) - [ListResourceGroups() | Go API · ResourceGroup/ListResourceGroups](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/ListResourceGroups.md) - [ReplicaInfo | Go API · ResourceGroup/ReplicaInfo](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/ReplicaInfo.md) - [ResourceGroup | Go API · ResourceGroup/ResourceGroup](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/ResourceGroup.md) - [ResourceGroupConfig | Go API · ResourceGroup/ResourceGroupConfig](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/ResourceGroupConfig.md) - [TransferReplica() | Go API · ResourceGroup/TransferReplica](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/TransferReplica.md) - [UpdateResourceGroup() | Go API · ResourceGroup/UpdateResourceGroup](https://www.xlonglab.com/docs/api-reference/go/ResourceGroup/UpdateResourceGroup.md) - [CreateSnapshot() | Go API · Snapshot/CreateSnapshot](https://www.xlonglab.com/docs/api-reference/go/Snapshot/CreateSnapshot.md) - [DescribeSnapshot() | Go API · Snapshot/DescribeSnapshot](https://www.xlonglab.com/docs/api-reference/go/Snapshot/DescribeSnapshot.md) - [DropSnapshot() | Go API · Snapshot/DropSnapshot](https://www.xlonglab.com/docs/api-reference/go/Snapshot/DropSnapshot.md) - [ExportSnapshot() | Go API · Snapshot/ExportSnapshot](https://www.xlonglab.com/docs/api-reference/go/Snapshot/ExportSnapshot.md) - [GetExportSnapshotState() | Go API · Snapshot/GetExportSnapshotState](https://www.xlonglab.com/docs/api-reference/go/Snapshot/GetExportSnapshotState.md) - [GetRestoreSnapshotState() | Go API · Snapshot/GetRestoreSnapshotState](https://www.xlonglab.com/docs/api-reference/go/Snapshot/GetRestoreSnapshotState.md) - [ListRestoreSnapshotJobs() | Go API · Snapshot/ListRestoreSnapshotJobs](https://www.xlonglab.com/docs/api-reference/go/Snapshot/ListRestoreSnapshotJobs.md) - [ListSnapshots() | Go API · Snapshot/ListSnapshots](https://www.xlonglab.com/docs/api-reference/go/Snapshot/ListSnapshots.md) - [PinSnapshotData() | Go API · Snapshot/PinSnapshotData](https://www.xlonglab.com/docs/api-reference/go/Snapshot/PinSnapshotData.md) - [RestoreExternalSnapshot() | Go API · Snapshot/RestoreExternalSnapshot](https://www.xlonglab.com/docs/api-reference/go/Snapshot/RestoreExternalSnapshot.md) - [RestoreSnapshot() | Go API · Snapshot/RestoreSnapshot](https://www.xlonglab.com/docs/api-reference/go/Snapshot/RestoreSnapshot.md) - [UnpinSnapshotData() | Go API · Snapshot/UnpinSnapshotData](https://www.xlonglab.com/docs/api-reference/go/Snapshot/UnpinSnapshotData.md) - [AnalyzerResult | Go API · Vector/AnalyzerResult](https://www.xlonglab.com/docs/api-reference/go/Vector/AnalyzerResult.md) - [AnnParam | Go API · Vector/AnnParam/AnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/AnnParam.md) - [NewAISAQAnnParam() | Go API · Vector/AnnParam/NewAISAQAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewAISAQAnnParam.md) - [NewAutoAnnParam() | Go API · Vector/AnnParam/NewAutoAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewAutoAnnParam.md) - [NewCustomAnnParam() | Go API · Vector/AnnParam/NewCustomAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewCustomAnnParam.md) - [NewDiskAnnParam() | Go API · Vector/AnnParam/NewDiskAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewDiskAnnParam.md) - [NewHNSWAnnParam() | Go API · Vector/AnnParam/NewHNSWAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewHNSWAnnParam.md) - [NewHNSWPQAnnParam() | Go API · Vector/AnnParam/NewHNSWPQAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewHNSWPQAnnParam.md) - [NewHNSWPRQAnnParam() | Go API · Vector/AnnParam/NewHNSWPRQAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewHNSWPRQAnnParam.md) - [NewHNSWSQAnnParam() | Go API · Vector/AnnParam/NewHNSWSQAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewHNSWSQAnnParam.md) - [NewIvfAnnParam() | Go API · Vector/AnnParam/NewIvfAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewIvfAnnParam.md) - [NewIvfRabitQAnnParam() | Go API · Vector/AnnParam/NewIvfRabitQAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewIvfRabitQAnnParam.md) - [NewMinHashLSHAnnParam() | Go API · Vector/AnnParam/NewMinHashLSHAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewMinHashLSHAnnParam.md) - [NewSCANNAnnParam() | Go API · Vector/AnnParam/NewSCANNAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewSCANNAnnParam.md) - [NewSparseAnnParam() | Go API · Vector/AnnParam/NewSparseAnnParam](https://www.xlonglab.com/docs/api-reference/go/Vector/AnnParam/NewSparseAnnParam.md) - [Delete() | Go API · Vector/Delete](https://www.xlonglab.com/docs/api-reference/go/Vector/Delete.md) - [DeleteResult | Go API · Vector/DeleteResult](https://www.xlonglab.com/docs/api-reference/go/Vector/DeleteResult.md) - [Get() | Go API · Vector/Get](https://www.xlonglab.com/docs/api-reference/go/Vector/Get.md) - [HybridSearch() | Go API · Vector/HybridSearch](https://www.xlonglab.com/docs/api-reference/go/Vector/HybridSearch.md) - [Insert() | Go API · Vector/Insert](https://www.xlonglab.com/docs/api-reference/go/Vector/Insert.md) - [InsertResult | Go API · Vector/InsertResult](https://www.xlonglab.com/docs/api-reference/go/Vector/InsertResult.md) - [Query() | Go API · Vector/Query](https://www.xlonglab.com/docs/api-reference/go/Vector/Query.md) - [QueryIterator() | Go API · Vector/QueryIterator](https://www.xlonglab.com/docs/api-reference/go/Vector/QueryIterator.md) - [Reranker | Go API · Vector/Reranker](https://www.xlonglab.com/docs/api-reference/go/Vector/Reranker.md) - [ResultSet | Go API · Vector/ResultSet](https://www.xlonglab.com/docs/api-reference/go/Vector/ResultSet.md) - [RunAnalyzer() | Go API · Vector/RunAnalyzer](https://www.xlonglab.com/docs/api-reference/go/Vector/RunAnalyzer.md) - [Search() | Go API · Vector/Search](https://www.xlonglab.com/docs/api-reference/go/Vector/Search.md) - [SearchAggregation | Go API · Vector/SearchAggregation](https://www.xlonglab.com/docs/api-reference/go/Vector/SearchAggregation.md) - [SearchIterator() | Go API · Vector/SearchIterator](https://www.xlonglab.com/docs/api-reference/go/Vector/SearchIterator.md) - [Upsert() | Go API · Vector/Upsert](https://www.xlonglab.com/docs/api-reference/go/Vector/Upsert.md) - [UpsertResult | Go API · Vector/UpsertResult](https://www.xlonglab.com/docs/api-reference/go/Vector/UpsertResult.md) - [About Milvus Java SDK | Java API · About](https://www.xlonglab.com/docs/api-reference/java/About.md) - [alterAlias() | Java API · v1/Alias/alterAlias](https://www.xlonglab.com/docs/api-reference/java/v1/Alias/alterAlias.md) - [createAlias() | Java API · v1/Alias/createAlias](https://www.xlonglab.com/docs/api-reference/java/v1/Alias/createAlias.md) - [dropAlias() | Java API · v1/Alias/dropAlias](https://www.xlonglab.com/docs/api-reference/java/v1/Alias/dropAlias.md) - [listAliases() | Java API · v1/Alias/listAliases](https://www.xlonglab.com/docs/api-reference/java/v1/Alias/listAliases.md) - [createCredential() | Java API · v1/Authentication/createCredential](https://www.xlonglab.com/docs/api-reference/java/v1/Authentication/createCredential.md) - [deleteCredential() | Java API · v1/Authentication/deleteCredential](https://www.xlonglab.com/docs/api-reference/java/v1/Authentication/deleteCredential.md) - [listCredUsers() | Java API · v1/Authentication/listCredUsers](https://www.xlonglab.com/docs/api-reference/java/v1/Authentication/listCredUsers.md) - [updateCredential() | Java API · v1/Authentication/updateCredential](https://www.xlonglab.com/docs/api-reference/java/v1/Authentication/updateCredential.md) - [bulkInsert() | Java API · v1/BulkInsertOps/bulkInsert](https://www.xlonglab.com/docs/api-reference/java/v1/BulkInsertOps/bulkInsert.md) - [getBulkInsertState() | Java API · v1/BulkInsertOps/getBulkInsertState](https://www.xlonglab.com/docs/api-reference/java/v1/BulkInsertOps/getBulkInsertState.md) - [listBulkInsertTasks() | Java API · v1/BulkInsertOps/listBulkInsertTasks](https://www.xlonglab.com/docs/api-reference/java/v1/BulkInsertOps/listBulkInsertTasks.md) - [LocalBulkWriter | Java API · v1/BulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/api-reference/java/v1/BulkWriter/LocalBulkWriter.md) - [RemoteBulkWriter | Java API · v1/BulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/api-reference/java/v1/BulkWriter/RemoteBulkWriter.md) - [alterCollection() | Java API · v1/Collection/alterCollection](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/alterCollection.md) - [createCollection() | Java API · v1/Collection/createCollection](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/createCollection.md) - [delete() | Java API · v1/Collection/delete](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/delete.md) - [describeCollection() | Java API · v1/Collection/describeCollection](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/describeCollection.md) - [dropCollection() | Java API · v1/Collection/dropCollection](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/dropCollection.md) - [flush() | Java API · v1/Collection/flush](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/flush.md) - [getCollectionStatistics() | Java API · v1/Collection/getCollectionStatistics](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/getCollectionStatistics.md) - [getLoadState() | Java API · v1/Collection/getLoadState](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/getLoadState.md) - [getLoadingProgress() | Java API · v1/Collection/getLoadingProgress](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/getLoadingProgress.md) - [getPersistentSegmentInfo() | Java API · v1/Collection/getPersistentSegmentInfo](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/getPersistentSegmentInfo.md) - [getQuerySegmentInfo() | Java API · v1/Collection/getQuerySegmentInfo](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/getQuerySegmentInfo.md) - [getReplicas() | Java API · v1/Collection/getReplicas](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/getReplicas.md) - [insert() | Java API · v1/Collection/insert](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/insert.md) - [insertAsync() | Java API · v1/Collection/insertAsync](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/insertAsync.md) - [loadCollection() | Java API · v1/Collection/loadCollection](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/loadCollection.md) - [releaseCollection() | Java API · v1/Collection/releaseCollection](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/releaseCollection.md) - [renameCollection() | Java API · v1/Collection/renameCollection](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/renameCollection.md) - [showCollections() | Java API · v1/Collection/showCollections](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/showCollections.md) - [upsert() | Java API · v1/Collection/upsert](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/upsert.md) - [upsertAsync() | Java API · v1/Collection/upsertAsync](https://www.xlonglab.com/docs/api-reference/java/v1/Collection/upsertAsync.md) - [MilvusClient | Java API · v1/Connections/MilvusClient](https://www.xlonglab.com/docs/api-reference/java/v1/Connections/MilvusClient.md) - [MilvusClientV1Pool | Java API · v1/Connections/MilvusClientV1Pool](https://www.xlonglab.com/docs/api-reference/java/v1/Connections/MilvusClientV1Pool.md) - [alterDatabase() | Java API · v1/Database/alterDatabase](https://www.xlonglab.com/docs/api-reference/java/v1/Database/alterDatabase.md) - [createDatabase() | Java API · v1/Database/createDatabase](https://www.xlonglab.com/docs/api-reference/java/v1/Database/createDatabase.md) - [dropDatabase() | Java API · v1/Database/dropDatabase](https://www.xlonglab.com/docs/api-reference/java/v1/Database/dropDatabase.md) - [listDatabases() | Java API · v1/Database/listDatabases](https://www.xlonglab.com/docs/api-reference/java/v1/Database/listDatabases.md) - [createCollection() | Java API · v1/HighlevelAPI/createCollection](https://www.xlonglab.com/docs/api-reference/java/v1/HighlevelAPI/createCollection.md) - [delete() | Java API · v1/HighlevelAPI/delete](https://www.xlonglab.com/docs/api-reference/java/v1/HighlevelAPI/delete.md) - [get() | Java API · v1/HighlevelAPI/get](https://www.xlonglab.com/docs/api-reference/java/v1/HighlevelAPI/get.md) - [insert() | Java API · v1/HighlevelAPI/insert](https://www.xlonglab.com/docs/api-reference/java/v1/HighlevelAPI/insert.md) - [listCollections() | Java API · v1/HighlevelAPI/listCollections](https://www.xlonglab.com/docs/api-reference/java/v1/HighlevelAPI/listCollections.md) - [query() | Java API · v1/HighlevelAPI/query](https://www.xlonglab.com/docs/api-reference/java/v1/HighlevelAPI/query.md) - [search() | Java API · v1/HighlevelAPI/search](https://www.xlonglab.com/docs/api-reference/java/v1/HighlevelAPI/search.md) - [alterIndex() | Java API · v1/Index/alterIndex](https://www.xlonglab.com/docs/api-reference/java/v1/Index/alterIndex.md) - [createIndex() | Java API · v1/Index/createIndex](https://www.xlonglab.com/docs/api-reference/java/v1/Index/createIndex.md) - [describeIndex() | Java API · v1/Index/describeIndex](https://www.xlonglab.com/docs/api-reference/java/v1/Index/describeIndex.md) - [dropIndex() | Java API · v1/Index/dropIndex](https://www.xlonglab.com/docs/api-reference/java/v1/Index/dropIndex.md) - [getIndexBuildProgress() | Java API · v1/Index/getIndexBuildingProgress](https://www.xlonglab.com/docs/api-reference/java/v1/Index/getIndexBuildingProgress.md) - 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[grantPrivilegeV2() | Node.js API · Authentication/grantPrivilegeV2](https://www.xlonglab.com/docs/api-reference/node/Authentication/grantPrivilegeV2.md) - [grantRole() | Node.js API · Authentication/grantRole](https://www.xlonglab.com/docs/api-reference/node/Authentication/grantRole.md) - [hasRole() | Node.js API · Authentication/hasRole](https://www.xlonglab.com/docs/api-reference/node/Authentication/hasRole.md) - [listGrant() | Node.js API · Authentication/listGrant](https://www.xlonglab.com/docs/api-reference/node/Authentication/listGrant.md) - [listGrants() | Node.js API · Authentication/listGrants](https://www.xlonglab.com/docs/api-reference/node/Authentication/listGrants.md) - [listPrivilegeGroups() | Node.js API · Authentication/listPrivilegeGroups](https://www.xlonglab.com/docs/api-reference/node/Authentication/listPrivilegeGroups.md) - [listRoles() | Node.js API · Authentication/listRoles](https://www.xlonglab.com/docs/api-reference/node/Authentication/listRoles.md) - [listUsers() | Node.js API · Authentication/listUsers](https://www.xlonglab.com/docs/api-reference/node/Authentication/listUsers.md) - [removePrivilegesFromGroup() | Node.js API · Authentication/removePrivilegesFromGroup](https://www.xlonglab.com/docs/api-reference/node/Authentication/removePrivilegesFromGroup.md) - [removeUserFromRole() | Node.js API · Authentication/removeUserFromRole](https://www.xlonglab.com/docs/api-reference/node/Authentication/removeUserFromRole.md) - [restoreRBAC() | Node.js API · Authentication/restoreRBAC](https://www.xlonglab.com/docs/api-reference/node/Authentication/restoreRBAC.md) - [revokePrivilege() | Node.js API · Authentication/revokePrivilege](https://www.xlonglab.com/docs/api-reference/node/Authentication/revokePrivilege.md) - [revokePrivilegeV2() | Node.js API · Authentication/revokePrivilegeV2](https://www.xlonglab.com/docs/api-reference/node/Authentication/revokePrivilegeV2.md) - [revokeRole() | Node.js API · Authentication/revokeRole](https://www.xlonglab.com/docs/api-reference/node/Authentication/revokeRole.md) - [updateUser() | Node.js API · Authentication/updateUser](https://www.xlonglab.com/docs/api-reference/node/Authentication/updateUser.md) - [MilvusClient | Node.js API · Client/MilvusClient](https://www.xlonglab.com/docs/api-reference/node/Client/MilvusClient.md) - [checkCompatibility() | Node.js API · Client/checkCompatibility](https://www.xlonglab.com/docs/api-reference/node/Client/checkCompatibility.md) - [checkHealth() | Node.js API · Client/checkHealth](https://www.xlonglab.com/docs/api-reference/node/Client/checkHealth.md) - [close() | Node.js API · Client/close](https://www.xlonglab.com/docs/api-reference/node/Client/close.md) - [closeConnection() | Node.js API · Client/closeConnection](https://www.xlonglab.com/docs/api-reference/node/Client/closeConnection.md) - [connect() | Node.js API · Client/connect](https://www.xlonglab.com/docs/api-reference/node/Client/connect.md) - [getVersion() | Node.js API · Client/getVersion](https://www.xlonglab.com/docs/api-reference/node/Client/getVersion.md) - [reconnectToPrimary() | Node.js API · Client/reconnectToPrimary](https://www.xlonglab.com/docs/api-reference/node/Client/reconnectToPrimary.md) - [session() | Node.js API · Client/session](https://www.xlonglab.com/docs/api-reference/node/Client/session.md) - [use() | Node.js API · Client/use](https://www.xlonglab.com/docs/api-reference/node/Client/use.md) - [DataType | Node.js API · Collections/DataType](https://www.xlonglab.com/docs/api-reference/node/Collections/DataType.md) - [FunctionType | Node.js API · Collections/FunctionType](https://www.xlonglab.com/docs/api-reference/node/Collections/FunctionType.md) - [addCollectionField() | Node.js API · Collections/addCollectionField](https://www.xlonglab.com/docs/api-reference/node/Collections/addCollectionField.md) - [addCollectionFields() | Node.js API · Collections/addCollectionFields](https://www.xlonglab.com/docs/api-reference/node/Collections/addCollectionFields.md) - [addCollectionFunction() | Node.js API · Collections/addCollectionFunction](https://www.xlonglab.com/docs/api-reference/node/Collections/addCollectionFunction.md) - [addFunctionField() | Node.js API · Collections/addFunctionField](https://www.xlonglab.com/docs/api-reference/node/Collections/addFunctionField.md) - [alterAlias() | Node.js API · Collections/alterAlias](https://www.xlonglab.com/docs/api-reference/node/Collections/alterAlias.md) - [alterCollectionFieldProperties() | Node.js API · Collections/alterCollectionFieldProperties](https://www.xlonglab.com/docs/api-reference/node/Collections/alterCollectionFieldProperties.md) - [alterCollectionFunction() | Node.js API · Collections/alterCollectionFunction](https://www.xlonglab.com/docs/api-reference/node/Collections/alterCollectionFunction.md) - [alterCollectionProperties() | Node.js API · Collections/alterCollectionProperties](https://www.xlonglab.com/docs/api-reference/node/Collections/alterCollectionProperties.md) - [alterCollectionSchema() | Node.js API · Collections/alterCollectionSchema](https://www.xlonglab.com/docs/api-reference/node/Collections/alterCollectionSchema.md) - [batchDescribeCollections() | Node.js API · Collections/batchDescribeCollections](https://www.xlonglab.com/docs/api-reference/node/Collections/batchDescribeCollections.md) - [createAlias() | Node.js API · Collections/createAlias](https://www.xlonglab.com/docs/api-reference/node/Collections/createAlias.md) - [createCollection() | Node.js API · Collections/createCollection](https://www.xlonglab.com/docs/api-reference/node/Collections/createCollection.md) - [createSnapshot() | Node.js API · Collections/createSnapshot](https://www.xlonglab.com/docs/api-reference/node/Collections/createSnapshot.md) - [describeAlias() | Node.js API · Collections/describeAlias](https://www.xlonglab.com/docs/api-reference/node/Collections/describeAlias.md) - [describeCollection() | Node.js API · Collections/describeCollection](https://www.xlonglab.com/docs/api-reference/node/Collections/describeCollection.md) - [describeSnapshot() | Node.js API · Collections/describeSnapshot](https://www.xlonglab.com/docs/api-reference/node/Collections/describeSnapshot.md) - [dropAlias() | Node.js API · Collections/dropAlias](https://www.xlonglab.com/docs/api-reference/node/Collections/dropAlias.md) - [dropCollection() | Node.js API · Collections/dropCollection](https://www.xlonglab.com/docs/api-reference/node/Collections/dropCollection.md) - [dropCollectionField() | Node.js API · Collections/dropCollectionField](https://www.xlonglab.com/docs/api-reference/node/Collections/dropCollectionField.md) - [dropCollectionFunction() | Node.js API · Collections/dropCollectionFunction](https://www.xlonglab.com/docs/api-reference/node/Collections/dropCollectionFunction.md) - [dropCollectionProperties() | Node.js API · Collections/dropCollectionProperties](https://www.xlonglab.com/docs/api-reference/node/Collections/dropCollectionProperties.md) - [dropFunctionField() | Node.js API · Collections/dropFunctionField](https://www.xlonglab.com/docs/api-reference/node/Collections/dropFunctionField.md) - [dropSnapshot() | Node.js API · Collections/dropSnapshot](https://www.xlonglab.com/docs/api-reference/node/Collections/dropSnapshot.md) - [getCollectionStatistics() | Node.js API · Collections/getCollectionStatistics](https://www.xlonglab.com/docs/api-reference/node/Collections/getCollectionStatistics.md) - [getPkField() | Node.js API · Collections/getPkField](https://www.xlonglab.com/docs/api-reference/node/Collections/getPkField.md) - [getPkFieldName() | Node.js API · Collections/getPkFieldName](https://www.xlonglab.com/docs/api-reference/node/Collections/getPkFieldName.md) - [getPkFieldType() | Node.js API · Collections/getPkFieldType](https://www.xlonglab.com/docs/api-reference/node/Collections/getPkFieldType.md) - [getRefreshExternalCollectionProgress() | Node.js API · Collections/getRefreshExternalCollectionProgress](https://www.xlonglab.com/docs/api-reference/node/Collections/getRefreshExternalCollectionProgress.md) - [getRestoreSnapshotState() | Node.js API · Collections/getRestoreSnapshotState](https://www.xlonglab.com/docs/api-reference/node/Collections/getRestoreSnapshotState.md) - [hasCollection() | Node.js API · Collections/hasCollection](https://www.xlonglab.com/docs/api-reference/node/Collections/hasCollection.md) - [listAliases() | Node.js API · Collections/listAliases](https://www.xlonglab.com/docs/api-reference/node/Collections/listAliases.md) - [listCollections() | Node.js API · Collections/listCollections](https://www.xlonglab.com/docs/api-reference/node/Collections/listCollections.md) - [listRefreshExternalCollectionJobs() | Node.js API · Collections/listRefreshExternalCollectionJobs](https://www.xlonglab.com/docs/api-reference/node/Collections/listRefreshExternalCollectionJobs.md) - [listRestoreSnapshotJobs() | Node.js API · Collections/listRestoreSnapshotJobs](https://www.xlonglab.com/docs/api-reference/node/Collections/listRestoreSnapshotJobs.md) - [listSnapshots() | Node.js API · Collections/listSnapshots](https://www.xlonglab.com/docs/api-reference/node/Collections/listSnapshots.md) - [pinSnapshotData() | Node.js API · Collections/pinSnapshotData](https://www.xlonglab.com/docs/api-reference/node/Collections/pinSnapshotData.md) - [refreshExternalCollection() | Node.js API · Collections/refreshExternalCollection](https://www.xlonglab.com/docs/api-reference/node/Collections/refreshExternalCollection.md) - [renameCollection() | Node.js API · Collections/renameCollection](https://www.xlonglab.com/docs/api-reference/node/Collections/renameCollection.md) - [restoreSnapshot() | Node.js API · Collections/restoreSnapshot](https://www.xlonglab.com/docs/api-reference/node/Collections/restoreSnapshot.md) - [runAnalyzer() | Node.js API · Collections/runAnalyzer](https://www.xlonglab.com/docs/api-reference/node/Collections/runAnalyzer.md) - [truncateCollection() | Node.js API · Collections/truncateCollection](https://www.xlonglab.com/docs/api-reference/node/Collections/truncateCollection.md) - [unpinSnapshotData() | Node.js API · Collections/unpinSnapshotData](https://www.xlonglab.com/docs/api-reference/node/Collections/unpinSnapshotData.md) - [BulkWriter | Node.js API · DataImport/BulkWriter](https://www.xlonglab.com/docs/api-reference/node/DataImport/BulkWriter.md) - [BulkWriterOptions | Node.js API · DataImport/BulkWriterOptions](https://www.xlonglab.com/docs/api-reference/node/DataImport/BulkWriterOptions.md) - [BulkWriterSchema | Node.js API · DataImport/BulkWriterSchema](https://www.xlonglab.com/docs/api-reference/node/DataImport/BulkWriterSchema.md) - [FlushEvent | Node.js API · DataImport/FlushEvent](https://www.xlonglab.com/docs/api-reference/node/DataImport/FlushEvent.md) - [Formatter | Node.js API · DataImport/Formatter](https://www.xlonglab.com/docs/api-reference/node/DataImport/Formatter.md) - [HttpImportCreateReq | Node.js API · DataImport/HttpImportCreateReq](https://www.xlonglab.com/docs/api-reference/node/DataImport/HttpImportCreateReq.md) - [HttpImportCreateResponse | Node.js API · DataImport/HttpImportCreateResponse](https://www.xlonglab.com/docs/api-reference/node/DataImport/HttpImportCreateResponse.md) - [HttpImportListResponse | Node.js API · DataImport/HttpImportListResponse](https://www.xlonglab.com/docs/api-reference/node/DataImport/HttpImportListResponse.md) - [HttpImportProgressReq | Node.js API · DataImport/HttpImportProgressReq](https://www.xlonglab.com/docs/api-reference/node/DataImport/HttpImportProgressReq.md) - [HttpImportProgressResponse | Node.js API · DataImport/HttpImportProgressResponse](https://www.xlonglab.com/docs/api-reference/node/DataImport/HttpImportProgressResponse.md) - [Storage | Node.js API · DataImport/Storage](https://www.xlonglab.com/docs/api-reference/node/DataImport/Storage.md) - [bulkInsert() | Node.js API · DataImport/bulkInsert](https://www.xlonglab.com/docs/api-reference/node/DataImport/bulkInsert.md) - [createImportJobs() | Node.js API · DataImport/createImportJobs](https://www.xlonglab.com/docs/api-reference/node/DataImport/createImportJobs.md) - [getImportJobProgress() | Node.js API · DataImport/getImportJobProgress](https://www.xlonglab.com/docs/api-reference/node/DataImport/getImportJobProgress.md) - [getImportState() | Node.js API · DataImport/getImportState](https://www.xlonglab.com/docs/api-reference/node/DataImport/getImportState.md) - [listImportJobs() | Node.js API · DataImport/listImportJobs](https://www.xlonglab.com/docs/api-reference/node/DataImport/listImportJobs.md) - [listImportTasks() | Node.js API · DataImport/listImportTasks](https://www.xlonglab.com/docs/api-reference/node/DataImport/listImportTasks.md) - [alterDatabase() | Node.js API · Database/alterDatabase](https://www.xlonglab.com/docs/api-reference/node/Database/alterDatabase.md) - [alterDatabaseProperties() | Node.js API · Database/alterDatabaseProperties](https://www.xlonglab.com/docs/api-reference/node/Database/alterDatabaseProperties.md) - [createDatabase() | Node.js API · Database/createDatabase](https://www.xlonglab.com/docs/api-reference/node/Database/createDatabase.md) - [describeDatabase() | Node.js API · Database/describeDatabase](https://www.xlonglab.com/docs/api-reference/node/Database/describeDatabase.md) - [dropDatabase() | Node.js API · Database/dropDatabase](https://www.xlonglab.com/docs/api-reference/node/Database/dropDatabase.md) - [dropDatabaseProperties() | Node.js API · Database/dropDatabaseProperties](https://www.xlonglab.com/docs/api-reference/node/Database/dropDatabaseProperties.md) - [listDatabases() | Node.js API · Database/listDatabases](https://www.xlonglab.com/docs/api-reference/node/Database/listDatabases.md) - [useDatabase() | Node.js API · Database/useDatabase](https://www.xlonglab.com/docs/api-reference/node/Database/useDatabase.md) - [addFileResource() | Node.js API · FileResources/addFileResource](https://www.xlonglab.com/docs/api-reference/node/FileResources/addFileResource.md) - [listFileResources() | Node.js API · FileResources/listFileResources](https://www.xlonglab.com/docs/api-reference/node/FileResources/listFileResources.md) - [removeFileResource() | Node.js API · FileResources/removeFileResource](https://www.xlonglab.com/docs/api-reference/node/FileResources/removeFileResource.md) - [IndexType | Node.js API · Management/IndexType](https://www.xlonglab.com/docs/api-reference/node/Management/IndexType.md) - [MetricType | Node.js API · Management/MetricType](https://www.xlonglab.com/docs/api-reference/node/Management/MetricType.md) - [alterIndexProperties() | Node.js API · Management/alterIndexProperties](https://www.xlonglab.com/docs/api-reference/node/Management/alterIndexProperties.md) - [compact() | Node.js API · Management/compact](https://www.xlonglab.com/docs/api-reference/node/Management/compact.md) - [createIndex() | Node.js API · Management/createIndex](https://www.xlonglab.com/docs/api-reference/node/Management/createIndex.md) - [describeIndex() | Node.js API · Management/describeIndex](https://www.xlonglab.com/docs/api-reference/node/Management/describeIndex.md) - [dropIndex() | Node.js API · Management/dropIndex](https://www.xlonglab.com/docs/api-reference/node/Management/dropIndex.md) - [dropIndexProperties() | Node.js API · Management/dropIndexProperties](https://www.xlonglab.com/docs/api-reference/node/Management/dropIndexProperties.md) - [flush() | Node.js API · Management/flush](https://www.xlonglab.com/docs/api-reference/node/Management/flush.md) - [flushAll() | Node.js API · Management/flushAll](https://www.xlonglab.com/docs/api-reference/node/Management/flushAll.md) - [flushAllSync() | Node.js API · Management/flushAllSync](https://www.xlonglab.com/docs/api-reference/node/Management/flushAllSync.md) - [flushSync() | Node.js API · Management/flushSync](https://www.xlonglab.com/docs/api-reference/node/Management/flushSync.md) - [getCompactionState() | Node.js API · Management/getCompactionState](https://www.xlonglab.com/docs/api-reference/node/Management/getCompactionState.md) - [getCompactionStateWithPlans() | Node.js API · Management/getCompactionStateWithPlans](https://www.xlonglab.com/docs/api-reference/node/Management/getCompactionStateWithPlans.md) - [getFlushAllState() | Node.js API · Management/getFlushAllState](https://www.xlonglab.com/docs/api-reference/node/Management/getFlushAllState.md) - [getFlushState() | Node.js API · Management/getFlushState](https://www.xlonglab.com/docs/api-reference/node/Management/getFlushState.md) - [getIndexBuildProgress() | Node.js API · Management/getIndexBuildProgress](https://www.xlonglab.com/docs/api-reference/node/Management/getIndexBuildProgress.md) - [getIndexState() | Node.js API · Management/getIndexState](https://www.xlonglab.com/docs/api-reference/node/Management/getIndexState.md) - [getIndexStatistics() | Node.js API · Management/getIndexStatistics](https://www.xlonglab.com/docs/api-reference/node/Management/getIndexStatistics.md) - [getLoadState() | Node.js API · Management/getLoadState](https://www.xlonglab.com/docs/api-reference/node/Management/getLoadState.md) - [getLoadingProgress() | Node.js API · Management/getLoadingProgress](https://www.xlonglab.com/docs/api-reference/node/Management/getLoadingProgress.md) - [getPersistentSegmentInfo() | Node.js API · Management/getPersistentSegmentInfo](https://www.xlonglab.com/docs/api-reference/node/Management/getPersistentSegmentInfo.md) - [getQuerySegmentInfo() | Node.js API · Management/getQuerySegmentInfo](https://www.xlonglab.com/docs/api-reference/node/Management/getQuerySegmentInfo.md) - [getReplicas() | Node.js API · Management/getReplicas](https://www.xlonglab.com/docs/api-reference/node/Management/getReplicas.md) - [listIndexes() | Node.js API · Management/listIndexes](https://www.xlonglab.com/docs/api-reference/node/Management/listIndexes.md) - [loadBalance() | Node.js API · Management/loadBalance](https://www.xlonglab.com/docs/api-reference/node/Management/loadBalance.md) - [loadCollection() | Node.js API · Management/loadCollection](https://www.xlonglab.com/docs/api-reference/node/Management/loadCollection.md) - [loadCollectionAsync() | Node.js API · Management/loadCollectionAsync](https://www.xlonglab.com/docs/api-reference/node/Management/loadCollectionAsync.md) - [loadCollectionSync() | Node.js API · Management/loadCollectionSync](https://www.xlonglab.com/docs/api-reference/node/Management/loadCollectionSync.md) - [refreshLoad() | Node.js API · Management/refreshLoad](https://www.xlonglab.com/docs/api-reference/node/Management/refreshLoad.md) - [releaseCollection() | Node.js API · Management/releaseCollection](https://www.xlonglab.com/docs/api-reference/node/Management/releaseCollection.md) - [createPartition() | Node.js API · Partitions/createPartition](https://www.xlonglab.com/docs/api-reference/node/Partitions/createPartition.md) - [dropPartition() | Node.js API · Partitions/dropPartition](https://www.xlonglab.com/docs/api-reference/node/Partitions/dropPartition.md) - [getPartitionStatistics() | Node.js API · Partitions/getPartitionStatistics](https://www.xlonglab.com/docs/api-reference/node/Partitions/getPartitionStatistics.md) - [hasPartition() | Node.js API · Partitions/hasPartition](https://www.xlonglab.com/docs/api-reference/node/Partitions/hasPartition.md) - [listPartitions() | Node.js API · Partitions/listPartitions](https://www.xlonglab.com/docs/api-reference/node/Partitions/listPartitions.md) - [loadPartitions() | Node.js API · Partitions/loadPartitions](https://www.xlonglab.com/docs/api-reference/node/Partitions/loadPartitions.md) - [loadPartitionsSync() | Node.js API · Partitions/loadPartitionsSync](https://www.xlonglab.com/docs/api-reference/node/Partitions/loadPartitionsSync.md) - [releasePartitions() | Node.js API · Partitions/releasePartitions](https://www.xlonglab.com/docs/api-reference/node/Partitions/releasePartitions.md) - [ResourceGroupConfig | Node.js API · ResourceGroup/ResourceGroupConfig](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/ResourceGroupConfig.md) - [createResourceGroup() | Node.js API · ResourceGroup/createResourceGroup](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/createResourceGroup.md) - [describeResourceGroup() | Node.js API · ResourceGroup/describeResourceGroup](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/describeResourceGroup.md) - [dropAllResourceGroups() | Node.js API · ResourceGroup/dropAllResourceGroups](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/dropAllResourceGroups.md) - [dropResourceGroup() | Node.js API · ResourceGroup/dropResourceGroup](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/dropResourceGroup.md) - [getReplicateConfiguration() | Node.js API · ResourceGroup/getReplicateConfiguration](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/getReplicateConfiguration.md) - [listResourceGroups() | Node.js API · ResourceGroup/listResourceGroups](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/listResourceGroups.md) - [transferNode() | Node.js API · ResourceGroup/transferNode](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/transferNode.md) - [transferReplica() | Node.js API · ResourceGroup/transferReplica](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/transferReplica.md) - [updateReplicateConfiguration() | Node.js API · ResourceGroup/updateReplicateConfiguration](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/updateReplicateConfiguration.md) - [updateResourceGroups() | Node.js API · ResourceGroup/updateResourceGroups](https://www.xlonglab.com/docs/api-reference/node/ResourceGroup/updateResourceGroups.md) - [createSnapshot() | Node.js API · Snapshot/createSnapshot](https://www.xlonglab.com/docs/api-reference/node/Snapshot/createSnapshot.md) - [describeSnapshot() | Node.js API · Snapshot/describeSnapshot](https://www.xlonglab.com/docs/api-reference/node/Snapshot/describeSnapshot.md) - [dropSnapshot() | Node.js API · Snapshot/dropSnapshot](https://www.xlonglab.com/docs/api-reference/node/Snapshot/dropSnapshot.md) - [getRestoreSnapshotState() | Node.js API · Snapshot/getRestoreSnapshotState](https://www.xlonglab.com/docs/api-reference/node/Snapshot/getRestoreSnapshotState.md) - [listRestoreSnapshotJobs() | Node.js API · Snapshot/listRestoreSnapshotJobs](https://www.xlonglab.com/docs/api-reference/node/Snapshot/listRestoreSnapshotJobs.md) - [listSnapshots() | Node.js API · Snapshot/listSnapshots](https://www.xlonglab.com/docs/api-reference/node/Snapshot/listSnapshots.md) - [pinSnapshotData() | Node.js API · Snapshot/pinSnapshotData](https://www.xlonglab.com/docs/api-reference/node/Snapshot/pinSnapshotData.md) - [restoreSnapshot() | Node.js API · Snapshot/restoreSnapshot](https://www.xlonglab.com/docs/api-reference/node/Snapshot/restoreSnapshot.md) - [unpinSnapshotData() | Node.js API · Snapshot/unpinSnapshotData](https://www.xlonglab.com/docs/api-reference/node/Snapshot/unpinSnapshotData.md) - [count() | Node.js API · Vector/count](https://www.xlonglab.com/docs/api-reference/node/Vector/count.md) - [delete() | Node.js API · Vector/delete](https://www.xlonglab.com/docs/api-reference/node/Vector/delete.md) - [deleteEntities() | Node.js API · Vector/deleteEntities](https://www.xlonglab.com/docs/api-reference/node/Vector/deleteEntities.md) - [get() | Node.js API · Vector/get](https://www.xlonglab.com/docs/api-reference/node/Vector/get.md) - [getMetric() | Node.js API · Vector/getMetric](https://www.xlonglab.com/docs/api-reference/node/Vector/getMetric.md) - [hybridSearch() | Node.js API · Vector/hybridSearch](https://www.xlonglab.com/docs/api-reference/node/Vector/hybridSearch.md) - [insert() | Node.js API · Vector/insert](https://www.xlonglab.com/docs/api-reference/node/Vector/insert.md) - [query() | Node.js API · Vector/query](https://www.xlonglab.com/docs/api-reference/node/Vector/query.md) - [queryIterator() | Node.js API · Vector/queryIterator](https://www.xlonglab.com/docs/api-reference/node/Vector/queryIterator.md) - [search() | Node.js API · Vector/search](https://www.xlonglab.com/docs/api-reference/node/Vector/search.md) - [searchIterator() | Node.js API · Vector/searchIterator](https://www.xlonglab.com/docs/api-reference/node/Vector/searchIterator.md) - [upsert() | Node.js API · Vector/upsert](https://www.xlonglab.com/docs/api-reference/node/Vector/upsert.md) - [About PyMilvus | Python API · About](https://www.xlonglab.com/docs/api-reference/pymilvus/About.md) - [BulkFileType | Python API · DataImport/BulkFileType](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/BulkFileType.md) - [bulk_import() | Python API · DataImport/BulkImport/bulk_import](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/BulkImport/bulk_import.md) - [get_import_progress() | Python API · DataImport/BulkImport/get_import_progress](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/BulkImport/get_import_progress.md) - [list_import_jobs() | Python API · DataImport/BulkImport/list_import_jobs](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/BulkImport/list_import_jobs.md) - [LocalBulkWriter | Python API · DataImport/LocalBulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/LocalBulkWriter/LocalBulkWriter.md) - [append_row() | Python API · DataImport/LocalBulkWriter/append_row](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/LocalBulkWriter/append_row.md) - [commit() | Python API · DataImport/LocalBulkWriter/commit](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/LocalBulkWriter/commit.md) - [AzureConnectParam | Python API · DataImport/RemoteBulkWriter/AzureConnectParam](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/RemoteBulkWriter/AzureConnectParam.md) - [RemoteBulkWriter | Python API · DataImport/RemoteBulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/RemoteBulkWriter/RemoteBulkWriter.md) - [S3ConnectParam | Python API · DataImport/RemoteBulkWriter/S3ConnectParam](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/RemoteBulkWriter/S3ConnectParam.md) - [append_row() | Python API · DataImport/RemoteBulkWriter/append_row](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/RemoteBulkWriter/append_row.md) - [commit() | Python API · DataImport/RemoteBulkWriter/commit](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/RemoteBulkWriter/commit.md) - [VolumeBulkWriter | Python API · DataImport/VolumeBulkWriter/VolumeBulkWriter](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/VolumeBulkWriter/VolumeBulkWriter.md) - [append_row() | Python API · DataImport/VolumeBulkWriter/append_row](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/VolumeBulkWriter/append_row.md) - [commit() | Python API · DataImport/VolumeBulkWriter/commit](https://www.xlonglab.com/docs/api-reference/pymilvus/DataImport/VolumeBulkWriter/commit.md) - [BGEM3EmbeddingFunction | Python API · EmbeddingModels/BGEM3EmbeddingFunction/BGEM3EmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/BGEM3EmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/encode_queries.md) - [BM25EmbeddingFunction | Python API · EmbeddingModels/BM25EmbeddingFunction/BM25EmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/BM25EmbeddingFunction.md) - [encode_documents() | Python API · EmbeddingModels/BM25EmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/BM25EmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/encode_queries.md) - [fit() | Python API · EmbeddingModels/BM25EmbeddingFunction/fit](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/fit.md) - [load() | Python API · EmbeddingModels/BM25EmbeddingFunction/load](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/load.md) - [save() | Python API · EmbeddingModels/BM25EmbeddingFunction/save](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/save.md) - [CohereEmbeddingFunction | Python API · EmbeddingModels/CohereEmbeddingFunction/CohereEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/CohereEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/CohereEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/CohereEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/CohereEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/encode_queries.md) - [GeminiEmbeddingFunction | Python API · EmbeddingModels/GeminiEmbeddingFunction/GeminiEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/GeminiEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/GeminiEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/GeminiEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/GeminiEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/encode_queries.md) - [InstructorEmbeddingFunction | Python API · EmbeddingModels/InstructorEmbeddingFunction/InstructorEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/InstructorEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/InstructorEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/InstructorEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/InstructorEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/encode_queries.md) - [JinaEmbeddingFunction | Python API · EmbeddingModels/JinaEmbeddingFunction/JinaEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/JinaEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/JinaEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/JinaEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/JinaEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/encode_queries.md) - [MGTEEmbeddingFunction | Python API · EmbeddingModels/MGTEEmbeddingFunction/MGTEEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/MGTEEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/MGTEEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/MGTEEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/MGTEEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/encode_queries.md) - [MistralAIEmbeddingFunction | Python API · EmbeddingModels/MistralAIEmbeddingFunction/MistralAIEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/MistralAIEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/encode_queries.md) - [Model2VecEmbeddingFunction | Python API · EmbeddingModels/Model2VecEmbeddingFunction/Model2VecEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/Model2VecEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/Model2VecEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/Model2VecEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/Model2VecEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/encode_queries.md) - [NomicEmbeddingFunction | Python API · EmbeddingModels/NomicEmbeddingFunction/NomicEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/NomicEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/NomicEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/NomicEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/NomicEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/encode_queries.md) - [OnnxEmbeddingFunction | Python API · EmbeddingModels/OnnxEmbeddingFunction/OnnxEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/OnnxEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/OnnxEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/OnnxEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/OnnxEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/encode_queries.md) - [OpenAIEmbeddingFunction | Python API · EmbeddingModels/OpenAIEmbeddingFunction/OpenAIEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/OpenAIEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/encode_queries.md) - [SentenceTransformerEmbeddingFunction | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/SentenceTransformerEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/SentenceTransformerEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_queries.md) - [SpladeEmbeddingFunction | Python API · EmbeddingModels/SpladeEmbeddingFunction/SpladeEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/SpladeEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/SpladeEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/SpladeEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/SpladeEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/encode_queries.md) - [VoyageEmbeddingFunction | Python API · EmbeddingModels/VoyageEmbeddingFunction/VoyageEmbeddingFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/VoyageEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/VoyageEmbeddingFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/VoyageEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/VoyageEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/encode_queries.md) - [add_file_resource() | Python API · FileResource/add_file_resource](https://www.xlonglab.com/docs/api-reference/pymilvus/FileResource/add_file_resource.md) - [list_file_resources() | Python API · FileResource/list_file_resources](https://www.xlonglab.com/docs/api-reference/pymilvus/FileResource/list_file_resources.md) - [remove_file_resource() | Python API · FileResource/remove_file_resource](https://www.xlonglab.com/docs/api-reference/pymilvus/FileResource/remove_file_resource.md) - [add_privileges_to_group() | Python API · MilvusClient/Authentication/add_privileges_to_group](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/add_privileges_to_group.md) - [alter_role() | Python API · MilvusClient/Authentication/alter_role](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/alter_role.md) - [create_privilege_group() | Python API · MilvusClient/Authentication/create_privilege_group](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/create_privilege_group.md) - [create_role() | Python API · MilvusClient/Authentication/create_role](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/create_role.md) - [create_user() | Python API · MilvusClient/Authentication/create_user](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/create_user.md) - [describe_role() | Python API · MilvusClient/Authentication/describe_role](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/describe_role.md) - [describe_user() | Python API · MilvusClient/Authentication/describe_user](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/describe_user.md) - [drop_privilege_group() | Python API · MilvusClient/Authentication/drop_privilege_group](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/drop_privilege_group.md) - [drop_role() | Python API · MilvusClient/Authentication/drop_role](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/drop_role.md) - [drop_user() | Python API · MilvusClient/Authentication/drop_user](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/drop_user.md) - [grant_privilege() | Python API · MilvusClient/Authentication/grant_privilege](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/grant_privilege.md) - [grant_privilege_v2() | Python API · MilvusClient/Authentication/grant_privilege_v2](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/grant_privilege_v2.md) - [grant_role() | Python API · MilvusClient/Authentication/grant_role](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/grant_role.md) - [list_privilege_groups() | Python API · MilvusClient/Authentication/list_privilege_groups](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/list_privilege_groups.md) - [list_roles() | Python API · MilvusClient/Authentication/list_roles](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/list_roles.md) - [list_users() | Python API · MilvusClient/Authentication/list_users](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/list_users.md) - [remove_privileges_from_group() | Python API · MilvusClient/Authentication/remove_privileges_from_group](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/remove_privileges_from_group.md) - [revoke_privilege() | Python API · MilvusClient/Authentication/revoke_privilege](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/revoke_privilege.md) - [revoke_privilege_v2() | Python API · MilvusClient/Authentication/revoke_privilege_v2](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/revoke_privilege_v2.md) - [revoke_privileges() | Python API · MilvusClient/Authentication/revoke_privileges](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/revoke_privileges.md) - [revoke_role() | Python API · MilvusClient/Authentication/revoke_role](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/revoke_role.md) - [update_password() | Python API · MilvusClient/Authentication/update_password](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/update_password.md) - [update_user() | Python API · MilvusClient/Authentication/update_user](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Authentication/update_user.md) - [AsyncMilvusClient | Python API · MilvusClient/Client/AsyncMilvusClient](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Client/AsyncMilvusClient.md) - [MilvusClient | Python API · MilvusClient/Client/MilvusClient](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Client/MilvusClient.md) - [close() | Python API · MilvusClient/Client/close](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Client/close.md) - [session() | Python API · MilvusClient/Client/session](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Client/session.md) - [CollectionSchema | Python API · MilvusClient/Collections/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/CollectionSchema/CollectionSchema.md) - [add_field() | Python API · MilvusClient/Collections/CollectionSchema/add_field](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/CollectionSchema/add_field.md) - [construct_from_dict() | Python API · MilvusClient/Collections/CollectionSchema/construct_from_dict](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/CollectionSchema/construct_from_dict.md) - [run_analyzer() | Python API · MilvusClient/Collections/CollectionSchema/run_analyzer](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/CollectionSchema/run_analyzer.md) - [to_dict() | Python API · MilvusClient/Collections/CollectionSchema/to_dict](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/CollectionSchema/to_dict.md) - [verify() | Python API · MilvusClient/Collections/CollectionSchema/verify](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/CollectionSchema/verify.md) - [CompactionTaskState | Python API · MilvusClient/Collections/CompactionTaskState](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/CompactionTaskState.md) - [CompactionType | Python API · MilvusClient/Collections/CompactionType](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/CompactionType.md) - [DataType | Python API · MilvusClient/Collections/DataType](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/DataType.md) - [FieldSchema | Python API · MilvusClient/Collections/FieldSchema/FieldSchema](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/FieldSchema/FieldSchema.md) - [construct_from_dict() | Python API · MilvusClient/Collections/FieldSchema/construct_from_dict](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/FieldSchema/construct_from_dict.md) - [to_dict() | Python API · MilvusClient/Collections/FieldSchema/to_dict](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/FieldSchema/to_dict.md) - [Function | Python API · MilvusClient/Collections/Function/Function](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/Function/Function.md) - [add_function() | Python API · MilvusClient/Collections/Function/add_function](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/Function/add_function.md) - [construct_from_dict() | Python API · MilvusClient/Collections/Function/construct_from_dict](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/Function/construct_from_dict.md) - [to_dict() | Python API · MilvusClient/Collections/Function/to_dict](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/Function/to_dict.md) - [verify() | Python API · MilvusClient/Collections/Function/verify](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/Function/verify.md) - [FunctionType | Python API · MilvusClient/Collections/FunctionType](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/FunctionType.md) - [IndexType | Python API · MilvusClient/Collections/IndexType](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/IndexType.md) - [SegmentState | Python API · MilvusClient/Collections/SegmentState](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/SegmentState.md) - [StructFieldSchema | Python API · MilvusClient/Collections/StructFieldSchema/StructFieldSchema](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/StructFieldSchema/StructFieldSchema.md) - [add_field() | Python API · MilvusClient/Collections/StructFieldSchema/add_field](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/StructFieldSchema/add_field.md) - [construct_from_dict() | Python API · MilvusClient/Collections/StructFieldSchema/construct_from_dict](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/StructFieldSchema/construct_from_dict.md) - [to_dict() | Python API · MilvusClient/Collections/StructFieldSchema/to_dict](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/StructFieldSchema/to_dict.md) - [add_collection_field() | Python API · MilvusClient/Collections/add_collection_field](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/add_collection_field.md) - [add_collection_function() | Python API · MilvusClient/Collections/add_collection_function](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/add_collection_function.md) - [add_collection_struct_field() | Python API · MilvusClient/Collections/add_collection_struct_field](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/add_collection_struct_field.md) - [add_function_field() | Python API · MilvusClient/Collections/add_function_field](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/add_function_field.md) - [alter_alias() | Python API · MilvusClient/Collections/alter_alias](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/alter_alias.md) - [alter_collection_field() | Python API · MilvusClient/Collections/alter_collection_field](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/alter_collection_field.md) - [alter_collection_function() | Python API · MilvusClient/Collections/alter_collection_function](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/alter_collection_function.md) - [alter_collection_properties() | Python API · MilvusClient/Collections/alter_collection_properties](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/alter_collection_properties.md) - [create_alias() | Python API · MilvusClient/Collections/create_alias](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/create_alias.md) - [create_collection() | Python API · MilvusClient/Collections/create_collection](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/create_collection.md) - [create_schema() | Python API · MilvusClient/Collections/create_schema](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/create_schema.md) - [describe_alias() | Python API · MilvusClient/Collections/describe_alias](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/describe_alias.md) - [describe_collection() | Python API · MilvusClient/Collections/describe_collection](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/describe_collection.md) - [drop_alias() | Python API · MilvusClient/Collections/drop_alias](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/drop_alias.md) - [drop_collection() | Python API · MilvusClient/Collections/drop_collection](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/drop_collection.md) - [drop_collection_field() | Python API · MilvusClient/Collections/drop_collection_field](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/drop_collection_field.md) - [drop_collection_function() | Python API · MilvusClient/Collections/drop_collection_function](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/drop_collection_function.md) - [drop_collection_properties() | Python API · MilvusClient/Collections/drop_collection_properties](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/drop_collection_properties.md) - [drop_function_field() | Python API · MilvusClient/Collections/drop_function_field](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/drop_function_field.md) - [get_collection_stats() | Python API · MilvusClient/Collections/get_collection_stats](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/get_collection_stats.md) - [get_refresh_external_collection_progress() | Python API · MilvusClient/Collections/get_refresh_external_collection_progress](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/get_refresh_external_collection_progress.md) - [has_collection() | Python API · MilvusClient/Collections/has_collection](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/has_collection.md) - [list_aliases() | Python API · MilvusClient/Collections/list_aliases](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/list_aliases.md) - [list_collections() | Python API · MilvusClient/Collections/list_collections](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/list_collections.md) - [list_refresh_external_collection_jobs() | Python API · MilvusClient/Collections/list_refresh_external_collection_jobs](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/list_refresh_external_collection_jobs.md) - [refresh_external_collection() | Python API · MilvusClient/Collections/refresh_external_collection](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/refresh_external_collection.md) - [rename_collection() | Python API · MilvusClient/Collections/rename_collection](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Collections/rename_collection.md) - 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[list_databases() | Python API · MilvusClient/Database/list_databases](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Database/list_databases.md) - [use_database() | Python API · MilvusClient/Database/use_database](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Database/use_database.md) - [using_database() | Python API · MilvusClient/Database/using_database](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Database/using_database.md) - [add_index() | Python API · MilvusClient/Management/add_index](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/add_index.md) - [alter_index_properties() | Python API · MilvusClient/Management/alter_index_properties](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/alter_index_properties.md) - [compact() | Python API · MilvusClient/Management/compact](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/compact.md) - [create_index() | Python API · MilvusClient/Management/create_index](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/create_index.md) - 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[get_compaction_plans() | Python API · MilvusClient/Management/get_compaction_plans](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/get_compaction_plans.md) - [get_compaction_state() | Python API · MilvusClient/Management/get_compaction_state](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/get_compaction_state.md) - [get_flush_all_state() | Python API · MilvusClient/Management/get_flush_all_state](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/get_flush_all_state.md) - [get_load_state() | Python API · MilvusClient/Management/get_load_state](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/get_load_state.md) - [get_replicate_info() | Python API · MilvusClient/Management/get_replicate_info](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/get_replicate_info.md) - [get_server_version() | Python API · MilvusClient/Management/get_server_version](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/get_server_version.md) - [list_compaction_tasks() | Python API · MilvusClient/Management/list_compaction_tasks](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/list_compaction_tasks.md) - [list_indexes() | Python API · MilvusClient/Management/list_indexes](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/list_indexes.md) - [list_loaded_segments() | Python API · MilvusClient/Management/list_loaded_segments](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/list_loaded_segments.md) - [list_persistent_segments() | Python API · MilvusClient/Management/list_persistent_segments](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/list_persistent_segments.md) - [list_segments() | Python API · MilvusClient/Management/list_segments](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/list_segments.md) - [load_collection() | Python API · MilvusClient/Management/load_collection](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Management/load_collection.md) - 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[get_partition_stats() | Python API · MilvusClient/Partitions/get_partition_stats](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Partitions/get_partition_stats.md) - [has_partition() | Python API · MilvusClient/Partitions/has_partition](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Partitions/has_partition.md) - [list_partitions() | Python API · MilvusClient/Partitions/list_partitions](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Partitions/list_partitions.md) - [load_partitions() | Python API · MilvusClient/Partitions/load_partitions](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Partitions/load_partitions.md) - [release_partitions() | Python API · MilvusClient/Partitions/release_partitions](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Partitions/release_partitions.md) - [ResourceGroupConfig | Python API · MilvusClient/ResourceGroup/ResourceGroupConfig](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/ResourceGroupConfig.md) - [create_resource_group() | Python API · MilvusClient/ResourceGroup/create_resource_group](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/create_resource_group.md) - [describe_replica() | Python API · MilvusClient/ResourceGroup/describe_replica](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/describe_replica.md) - [describe_resource_group() | Python API · MilvusClient/ResourceGroup/describe_resource_group](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/describe_resource_group.md) - [drop_resource_group() | Python API · MilvusClient/ResourceGroup/drop_resource_group](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/drop_resource_group.md) - [get_replicate_configuration() | Python API · MilvusClient/ResourceGroup/get_replicate_configuration](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/get_replicate_configuration.md) - [list_resource_groups() | Python API · MilvusClient/ResourceGroup/list_resource_groups](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/list_resource_groups.md) - [transfer_replica() | Python API · MilvusClient/ResourceGroup/transfer_replica](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/transfer_replica.md) - [update_replicate_configuration() | Python API · MilvusClient/ResourceGroup/update_replicate_configuration](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/update_replicate_configuration.md) - [update_resource_groups() | Python API · MilvusClient/ResourceGroup/update_resource_groups](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/ResourceGroup/update_resource_groups.md) - [create_snapshot() | Python API · MilvusClient/Snapshot/create_snapshot](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/create_snapshot.md) - [describe_snapshot() | Python API · MilvusClient/Snapshot/describe_snapshot](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/describe_snapshot.md) - [drop_snapshot() | Python API · MilvusClient/Snapshot/drop_snapshot](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/drop_snapshot.md) - [get_restore_snapshot_state() | Python API · MilvusClient/Snapshot/get_restore_snapshot_state](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/get_restore_snapshot_state.md) - [list_restore_snapshot_jobs() | Python API · MilvusClient/Snapshot/list_restore_snapshot_jobs](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/list_restore_snapshot_jobs.md) - [list_snapshots() | Python API · MilvusClient/Snapshot/list_snapshots](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/list_snapshots.md) - [pin_snapshot_data() | Python API · MilvusClient/Snapshot/pin_snapshot_data](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/pin_snapshot_data.md) - [restore_snapshot() | Python API · MilvusClient/Snapshot/restore_snapshot](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/restore_snapshot.md) - [unpin_snapshot_data() | Python API · MilvusClient/Snapshot/unpin_snapshot_data](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Snapshot/unpin_snapshot_data.md) - [AggregationBucket | Python API · MilvusClient/Vector/AggregationBucket](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/AggregationBucket.md) - [AggregationHit | Python API · MilvusClient/Vector/AggregationHit](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/AggregationHit.md) - [EmbeddingList | Python API · MilvusClient/Vector/EmbeddingList/EmbeddingList](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/EmbeddingList/EmbeddingList.md) - [add() | Python API · MilvusClient/Vector/EmbeddingList/add](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/EmbeddingList/add.md) - [add_batch() | Python API · MilvusClient/Vector/EmbeddingList/add_batch](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/EmbeddingList/add_batch.md) - [clear() | Python API · MilvusClient/Vector/EmbeddingList/clear](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/EmbeddingList/clear.md) - 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[TopHits | Python API · MilvusClient/Vector/TopHits](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/TopHits.md) - [delete() | Python API · MilvusClient/Vector/delete](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/delete.md) - [get() | Python API · MilvusClient/Vector/get](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/get.md) - [hybrid_search() | Python API · MilvusClient/Vector/hybrid_search](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/hybrid_search.md) - [insert() | Python API · MilvusClient/Vector/insert](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/insert.md) - [query() | Python API · MilvusClient/Vector/query](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/query.md) - [query_iterator() | Python API · MilvusClient/Vector/query_iterator](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/query_iterator.md) - [search() | Python API · MilvusClient/Vector/search](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/search.md) - [search_iterator() | Python API · MilvusClient/Vector/search_iterator](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/search_iterator.md) - [upsert() | Python API · MilvusClient/Vector/upsert](https://www.xlonglab.com/docs/api-reference/pymilvus/MilvusClient/Vector/upsert.md) - [Collection | Python API · ORM/Collection/Collection](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/Collection.md) - [compact() | Python API · ORM/Collection/compact](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/compact.md) - [construct_from_dataframe() | Python API · ORM/Collection/construct_from_dataframe](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/construct_from_dataframe.md) - [create_index() | Python API · ORM/Collection/create_index](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/create_index.md) - [create_partition() | Python API · ORM/Collection/create_partition](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/create_partition.md) - [delete() | Python API · ORM/Collection/delete](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/delete.md) - [describe() | Python API · ORM/Collection/describe](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/describe.md) - [drop() | Python API · ORM/Collection/drop](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/drop.md) - [drop_index() | Python API · ORM/Collection/drop_index](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/drop_index.md) - [drop_partition() | Python API · ORM/Collection/drop_partition](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/drop_partition.md) - [flush() | Python API · ORM/Collection/flush](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/flush.md) - [get_compaction_plans() | Python API · ORM/Collection/get_compaction_plans](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/get_compaction_plans.md) - 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[load() | Python API · ORM/Collection/load](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/load.md) - [partition() | Python API · ORM/Collection/partition](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/partition.md) - [query() | Python API · ORM/Collection/query](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/query.md) - [query_iterator() | Python API · ORM/Collection/query_iterator](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/query_iterator.md) - [release() | Python API · ORM/Collection/release](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/release.md) - [search() | Python API · ORM/Collection/search](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/search.md) - [search_iterator() | Python API · ORM/Collection/search_iterator](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/search_iterator.md) - [set_properties() | Python API · ORM/Collection/set_properties](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Collection/set_properties.md) - 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[Connections | Python API · ORM/Connections/Connections](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Connections/Connections.md) - [add_connection() | Python API · ORM/Connections/add_connection](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Connections/add_connection.md) - [connect() | Python API · ORM/Connections/connect](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Connections/connect.md) - [disconnect() | Python API · ORM/Connections/disconnect](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Connections/disconnect.md) - [get_connection_addr() | Python API · ORM/Connections/get_connection_addr](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Connections/get_connection_addr.md) - [has_connection() | Python API · ORM/Connections/has_connection](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Connections/has_connection.md) - [list_connections() | Python API · ORM/Connections/list_connections](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Connections/list_connections.md) - [remove_connection() | Python API · ORM/Connections/remove_connection](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Connections/remove_connection.md) - [Partition | Python API · ORM/Partition/Partition](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/Partition.md) - [delete() | Python API · ORM/Partition/delete](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/delete.md) - [drop() | Python API · ORM/Partition/drop](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/drop.md) - [flush() | Python API · ORM/Partition/flush](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/flush.md) - [get_replicas() | Python API · ORM/Partition/get_replicas](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/get_replicas.md) - [insert() | Python API · ORM/Partition/insert](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/insert.md) - [load() | Python API · ORM/Partition/load](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/load.md) - [query() | Python API · ORM/Partition/query](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/query.md) - [release() | Python API · ORM/Partition/release](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/release.md) - [search() | Python API · ORM/Partition/search](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/search.md) - [upsert() | Python API · ORM/Partition/upsert](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Partition/upsert.md) - [Role | Python API · ORM/Role/Role](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Role/Role.md) - [add_user() | Python API · ORM/Role/add_user](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Role/add_user.md) - [create() | Python API · ORM/Role/create](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Role/create.md) - [drop() | Python API · ORM/Role/drop](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Role/drop.md) - [get_users() | Python API · ORM/Role/get_users](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/Role/get_users.md) - 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[list_roles() | Python API · ORM/utility/list_roles](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/list_roles.md) - [list_user() | Python API · ORM/utility/list_user](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/list_user.md) - [list_usernames() | Python API · ORM/utility/list_usernames](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/list_usernames.md) - [list_users() | Python API · ORM/utility/list_users](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/list_users.md) - [load_balance() | Python API · ORM/utility/load_balance](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/load_balance.md) - [load_state() | Python API · ORM/utility/load_state](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/load_state.md) - [loading_progress() | Python API · ORM/utility/loading_progress](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/loading_progress.md) - [mkts_from_datetime() | Python API · ORM/utility/mkts_from_datetime](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/mkts_from_datetime.md) - 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[wait_for_index_building_complete() | Python API · ORM/utility/wait_for_index_building_complete](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/wait_for_index_building_complete.md) - [wait_for_loading_complete() | Python API · ORM/utility/wait_for_loading_complete](https://www.xlonglab.com/docs/api-reference/pymilvus/ORM/utility/wait_for_loading_complete.md) - [BGERerankFunction | Python API · Rerankers/BGERerankFunction/BGERerankFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/BGERerankFunction/BGERerankFunction.md) - [__call__() | Python API · Rerankers/BGERerankFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/BGERerankFunction/__call__.md) - [CohereRerankFunction | Python API · Rerankers/CohereRerankFunction/CohereRerankFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/CohereRerankFunction/CohereRerankFunction.md) - [__call__() | Python API · Rerankers/CohereRerankFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/CohereRerankFunction/__call__.md) - [CrossEncoderRerankFunction | Python API · Rerankers/CrossEncoderRerankFunction/CrossEncoderRerankFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/CrossEncoderRerankFunction/CrossEncoderRerankFunction.md) - [__call__() | Python API · Rerankers/CrossEncoderRerankFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/CrossEncoderRerankFunction/__call__.md) - [JinaRerankFunction | Python API · Rerankers/JinaRerankFunction/JinaRerankFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/JinaRerankFunction/JinaRerankFunction.md) - [__call__() | Python API · Rerankers/JinaRerankFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/JinaRerankFunction/__call__.md) - [VoyageRerankFunction | Python API · Rerankers/VoyageRerankFunction/VoyageRerankFunction](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/VoyageRerankFunction/VoyageRerankFunction.md) - [__call__() | Python API · Rerankers/VoyageRerankFunction/__call__](https://www.xlonglab.com/docs/api-reference/pymilvus/Rerankers/VoyageRerankFunction/__call__.md) ## v2.6.x ### 概览与基础概念 - [比较](https://www.xlonglab.com/docs/v2.6.x/about/comparison.md): 本文对 Milvus 与其他向量搜索解决方案进行了比较。 - [Milvus 限制](https://www.xlonglab.com/docs/v2.6.x/about/limitations.md): 了解使用 Milvus 时的限制。 - [Milvus 用户](https://www.xlonglab.com/docs/v2.6.x/about/milvus_adopters.md): 了解哪些公司在生产环境中采用了 Milvus。 - [Milvus 是什么？](https://www.xlonglab.com/docs/v2.6.x/about/overview.md): Milvus 是一个高性能、高度可扩展的向量数据库，可在从笔记本电脑到大型分布式系统等各种环境中高效运行。它既可以开源软件的形式提供，也可以云服务的形式提供。 - [Milvus 路线图](https://www.xlonglab.com/docs/v2.6.x/about/roadmap.md): Milvus 是一个开源向量数据库，旨在为人工智能应用提供动力。以下是我们的发展路线图。 ### 管理与运维 - [为 Kubernetes 上的 Milvus 分配资源](https://www.xlonglab.com/docs/v2.6.x/adminGuide/allocate.md): 了解如何在 Kubernetes 上为 Milvus 分配资源。 - [Milvus 用户认证](https://www.xlonglab.com/docs/v2.6.x/adminGuide/authenticate.md): 配置 Milvus 用户认证：启用 Authentication，使用默认 root/Milvus 凭据通过 Python、Java、Node.js、Go 和 cURL 连接，并管理用户密码。 - [配置块缓存](https://www.xlonglab.com/docs/v2.6.x/adminGuide/chunk_cache.md) - [在 EC2 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/aws): 了解如何在 AWS EC2 上部署 Milvus 集群。 - [在 AWS 上为 Milvus 配置 Layer-7 Load Balancer](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/aws/aws_layer7.md): 了解如何在 AWS 上的 Layer-7 负载均衡器后面部署 Milvus 集群。 - [在 EKS 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/aws/eks.md): 了解如何在 EKS 上部署 Milvus 集群 - [按 IAM 角色配置 S3 访问权限](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/aws/s3.md): 了解如何使用 IAM 角色配置 s3。 - [通过工作负载身份配置 Blob 存储访问](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/azure/abs.md): 了解如何使用 Workload Identity 配置 Blob Storage。 - [使用 Kubernetes 在 Microsoft Azure 上部署 Milvus](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/azure): 了解如何在 Azure 上部署 Milvus 集群。 - [为 Milvus 配置 ingress-nginx](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/azure/ingress.md): 了解如何使用 Milvus 配置 ingress nginx。 - [在 GKE 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/gcp): 了解如何在 GKE 上部署 Milvus 集群。 - [在 GCP 上为 Milvus 配置 Layer-7 Load Balancer](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/gcp/gcp_layer7.md): 了解如何在 GCP 上的 Layer-7 负载均衡器后面部署 Milvus 集群。 - [使用 Workload Identity 配置 GCS 访问](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/gcp/gcs.md): 了解如何使用工作负载身份配置 gcs。 - [在 OpenShift 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/adminGuide/clouds/openshift): 了解如何在 OpenShift 上部署 Milvus 集群。 - [配置 Jaeger Tracing](https://www.xlonglab.com/docs/v2.6.x/adminGuide/config_jaeger_tracing.md): 本指南介绍如何使用 Jaeger 收集 Milvus 的 Trace 数据。 - [使用 Docker Compose 配置 Milvus](https://www.xlonglab.com/docs/v2.6.x/adminGuide/configure-docker.md): 使用 Docker Compose 配置 Milvus。 - [使用 Helm Chart 配置 Milvus](https://www.xlonglab.com/docs/v2.6.x/adminGuide/configure-helm.md): 使用 Helm Chart 配置 Milvus。 - [使用本地磁盘配置 Milvus QueryNode](https://www.xlonglab.com/docs/v2.6.x/adminGuide/configure-querynode-localdisk.md): 了解如何配置 Milvus QueryNode 以使用本地磁盘。 - [配置访问日志](https://www.xlonglab.com/docs/v2.6.x/adminGuide/configure_access_logs.md) - [使用 Milvus Operator 配置 Milvus](https://www.xlonglab.com/docs/v2.6.x/adminGuide/configure_operator.md): 了解如何使用 Milvus Operator 配置 Milvus。 - [使用 SASL/SSL 连接到 Kafka](https://www.xlonglab.com/docs/v2.6.x/adminGuide/connect_kafka_ssl.md): 本指南列出了几种连接 Milvus 和 Kafka 的方法，从不带 SASL/SSL 的最简单方法到带 SASL/SSL 的完全安全方法。 - [使用 Docker Compose 或 Helm 配置 Meta storage](https://www.xlonglab.com/docs/v2.6.x/adminGuide/deploy_etcd.md): 了解如何使用 Docker Compose/Helm 为 Milvus 配置 Meta storage。 - [使用 Docker Compose 或 Helm 配置消息存储](https://www.xlonglab.com/docs/v2.6.x/adminGuide/deploy_pulsar.md): 了解如何使用 Docker Compose 或 Helm 配置消息存储。 - [使用 Docker Compose 或 Helm 配置 Object Storage](https://www.xlonglab.com/docs/v2.6.x/adminGuide/deploy_s3.md): 了解如何使用 Docker Compose 或 Helm 为 Milvus 设置 S3 存储。 - [删除 User 与 Role](https://www.xlonglab.com/docs/v2.6.x/adminGuide/drop_users_roles.md): 为确保数据安全，建议删除不再使用的用户和角色。本指南介绍如何删除用户和角色。 - [动态配置 Milvus](https://www.xlonglab.com/docs/v2.6.x/adminGuide/dynamic_config.md): 了解 Milvus 的动态配置。 - [为 Role 授予 Privilege 或 Privilege Group](https://www.xlonglab.com/docs/v2.6.x/adminGuide/grant_privileges.md): 创建角色后，就可以向角色授予权限。本指南将介绍如何向角色授予权限或权限组。 - [向 User 授予 Role](https://www.xlonglab.com/docs/v2.6.x/adminGuide/grant_roles.md): 创建角色并授予角色权限后，就可以将角色授予用户，这样用户就可以访问资源并执行角色所定义的操作。可以向一个用户授予多个角色，也可以向多个用户授予一个角色。本指南将介绍如何向用户授予角色。 - [为 Milvus 配置水平 Pod 自动扩展 (HPA)](https://www.xlonglab.com/docs/v2.6.x/adminGuide/hpa.md): 了解如何配置水平 Pod 自动扩展（HPA），以动态扩展 Milvus 集群。 - [设置 Collection 数量限制](https://www.xlonglab.com/docs/v2.6.x/adminGuide/limit_collection_counts.md) - [使用 Milvus Operator 配置消息存储](https://www.xlonglab.com/docs/v2.6.x/adminGuide/message_storage_operator.md): 了解如何使用 Milvus Operator 配置消息存储。 - [使用 Milvus Operator 配置 Meta storage](https://www.xlonglab.com/docs/v2.6.x/adminGuide/meta_storage_operator.md): 了解如何使用 Milvus Operator 配置 Meta storage。 - [创建警报](https://www.xlonglab.com/docs/v2.6.x/adminGuide/monitor/alert.md): 了解如何在 Grafana 中为 Milvus 服务创建警报。 - [配置 Grafana Loki](https://www.xlonglab.com/docs/v2.6.x/adminGuide/monitor/configure_grafana_loki.md): 本主题介绍如何使用 Loki 收集日志，并使用 Grafana 查询 Milvus 集群的日志。 - [Milvus 指标控制面板](https://www.xlonglab.com/docs/v2.6.x/adminGuide/monitor/metrics_dashboard.md): 本主题介绍 Milvus 仪表板中显示的监控指标。 - [部署监控服务](https://www.xlonglab.com/docs/v2.6.x/adminGuide/monitor): 了解如何使用 Prometheus 为 Milvus 集群部署监控服务。 - [监控概述](https://www.xlonglab.com/docs/v2.6.x/adminGuide/monitor/monitor_overview.md): 了解如何在 Milvus 中使用 Prometheus 和 Grafana 进行监控和警报服务。 - [可视化指标](https://www.xlonglab.com/docs/v2.6.x/adminGuide/monitor/visualize.md): 了解如何在 Grafana 中可视化 Milvus 指标。 - [使用 Milvus Operator 配置 Object Storage](https://www.xlonglab.com/docs/v2.6.x/adminGuide/object_storage_operator.md): 了解如何使用 Milvus Operator 配置 Object Storage。 - [创建 Privilege Group](https://www.xlonglab.com/docs/v2.6.x/adminGuide/privilege_group.md): 为简化授予权限的流程，建议将多个权限合并为一个权限组。 - [Milvus RBAC](https://www.xlonglab.com/docs/v2.6.x/adminGuide/rbac.md): RBAC（基于角色的访问控制）是一种基于角色的访问控制方法。通过 RBAC，可以精细控制用户在 Collection、数据库和实例级别上可以执行的操作，增强数据的安全性。 - [管理 Resource Group](https://www.xlonglab.com/docs/v2.6.x/adminGuide/resource_group.md): 了解如何管理资源组。 - [扩展 Milvus 依赖项](https://www.xlonglab.com/docs/v2.6.x/adminGuide/scale-dependencies.md) - [扩展 Milvus Standalone](https://www.xlonglab.com/docs/v2.6.x/adminGuide/scale-standalone.md): Milvus Standalone 是一种单机服务器部署方式。Milvus Standalone 的所有组件都打包到一个 Docker 镜像中，部署起来非常方便。本主题介绍如何扩展在此模式下运行的 Milvus 实例。 - [扩展 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/adminGuide/scaleout.md): 了解如何在 Milvus 集群中手动或自动缩放和扩展。 - [切换 Milvus 集群的 MQ 类型](https://www.xlonglab.com/docs/v2.6.x/adminGuide/switch_milvus_cluster_mq_type-operator.md): 了解如何切换 Milvus 集群的消息队列类型。 - [为 Milvus Standalone 切换 MQ 类型](https://www.xlonglab.com/docs/v2.6.x/adminGuide/switch_milvus_standalone_mq_type-operator.md): 了解如何切换 Milvus Standalone 的信息队列类型。 - [传输中的加密](https://www.xlonglab.com/docs/v2.6.x/adminGuide/tls.md): 了解如何在 Milvus 启用 TLS Proxy。 - [将 Milvus 的 Pulsar 从 V2 升级到 V3](https://www.xlonglab.com/docs/v2.6.x/adminGuide/upgrade-pulsar-v3.md): 了解如何在 Milvus 中将 Pulsar 从 V2 升级到 V3，以便使用最新版本的 Milvus v2.5.x。 - [使用 Docker Compose 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/adminGuide/upgrade_milvus_cluster-docker.md): 了解如何使用 Docker Compose 升级 Milvus 集群。 - [使用 Helm Chart 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/adminGuide/upgrade_milvus_cluster-helm.md): 了解如何使用 Helm Chart 升级 Milvus 集群。 - [使用 Milvus Operator 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/adminGuide/upgrade_milvus_cluster-operator.md): 了解如何使用 Milvus Operator 升级 Milvus 集群。 - [使用 Docker Compose 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.6.x/adminGuide/upgrade_milvus_standalone-docker.md): 了解如何使用 Docker Compose 升级 Milvus Standalone。 - [使用 Helm Chart 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.6.x/adminGuide/upgrade_milvus_standalone-helm.md): 了解如何使用 Helm Chart 升级 Milvus Standalone。 - [使用 Milvus Operator 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.6.x/adminGuide/upgrade_milvus_standalone-operator.md): 了解如何使用 Milvus Operator 升级 Milvus Standalone。 - [在 Milvus v2.5.x 中使用 Pulsar v2](https://www.xlonglab.com/docs/v2.6.x/adminGuide/use-pulsar-v2.md): Milvus 建议您将 Pulsar 升级到适用于 Milvus v2.5.x 的 v3 版本。不过，如果您更喜欢使用 Pulsar v2，本文将指导您完成在 Milvus v2.5.x 中继续使用 Pulsar v2 的步骤。 - [使用 Woodpecker](https://www.xlonglab.com/docs/v2.6.x/adminGuide/use-woodpecker.md): 了解如何启用 Woodpecker 作为 Milvus 中的 WAL。 - [创建 User 与 Role](https://www.xlonglab.com/docs/v2.6.x/adminGuide/users_and_roles.md): Milvus 通过 RBAC 实现细粒度访问控制。您可以从创建用户和角色开始，然后为角色分配权限或权限组，最后通过向用户授予角色来管理访问控制。这种方法可确保访问管理的效率和安全性。本页将介绍如何在 Milvus 中创建用户和角色。 ### AI 工具 - [AGENTS.md for Milvus](https://www.xlonglab.com/docs/v2.6.x/aiTools/agents_overview.md): 使用 PyMilvus 生成、审查或调试 Milvus 向量数据库代码的 AI coding agent 的规则和模式。 - [Prompt：Milvus 索引选择](https://www.xlonglab.com/docs/v2.6.x/aiTools/index_selection.md): AI coding assistant 选择和配置 Milvus 索引的规则。 - [面向 AI Agent 的 Milvus](https://www.xlonglab.com/docs/v2.6.x/aiTools/milvus_for_agents.md): 了解 AI Agent 如何将 Milvus 作为向量数据库，用于 RAG、语义搜索和长期记忆。 - [Prompt：Milvus Python SDK](https://www.xlonglab.com/docs/v2.6.x/aiTools/python_sdk.md): AI coding assistant 使用 MilvusClient 编写正确的 Milvus Python 代码的规则。 - [RAG Pipeline](https://www.xlonglab.com/docs/v2.6.x/aiTools/rag_pipeline.md): AI coding assistant 使用 Milvus 构建 RAG Pipeline 的规则。 - [Prompt：Milvus Schema 设计](https://www.xlonglab.com/docs/v2.6.x/aiTools/schema_design.md): AI coding assistant 设计正确的 Milvus Collection Schema 的规则。 - [Prompt：Milvus 搜索模式](https://www.xlonglab.com/docs/v2.6.x/aiTools/search_patterns.md): AI coding assistant 在 Milvus 中实现搜索、混合搜索和全文搜索的规则。 ### Embedding 模型 - [BGE M3](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-bgm-m3.md): BGE-M3 因其在多语言、多功能和多地域性方面的能力而得名。 - [Cohere Embedding](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-cohere.md): 本文介绍如何使用 CohereEmbeddingFunction 使用 Cohere Embedding 模型对文档和查询进行编码。 - [Gemini](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-gemini.md): Milvus 通过 GeminiEmbeddingFunction 类与 OpenAI 的模型集成。 - [Instructor](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-instructor.md): 本文介绍如何使用 InstructorEmbeddingFunction 使用 Instructor Embedding 模型对文档和查询进行编码。 - [Jina AI - Embedding](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-jina.md): 本文介绍如何使用 JinaEmbeddingFunction 使用 Jina AI Embedding 模型对文档和查询进行编码。 - [mGTE](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-mgte.md): 本文介绍如何使用 MGTEEmbeddingFunction 使用 mGTE Embedding 模型对文档和查询进行编码。 - [Mistral AI](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-mistral-ai.md): 本文介绍如何使用 MistralAIEmbeddingFunction 使用 Mistral AI Embedding 模型对文档和查询进行编码。 - [Model2Vec](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-model2vec.md): Milvus 通过 Model2VecEmbeddingFunction 类与 Model2Vec 的模型集成。 - [Nomic](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-nomic.md): 本文介绍如何使用 NomicEmbeddingFunction 使用 Nomic Embedding 模型对文档和查询进行编码。 - [OpenAI](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-openai.md): Milvus 通过 OpenAIEmbeddingFunction 类与 OpenAI 的模型集成。 - [Sentence Transformers](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-sentence-transform.md): 本文演示了如何使用 Milvus 中的 Sentence Transformers 将文档和查询编码为稠密向量。 - [SPLADE](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-splade.md): 本文介绍如何使用 SpladeEmbeddingFunction 使用 SPLADE 模型对文档和查询进行编码。 - [Voyage](https://www.xlonglab.com/docs/v2.6.x/embeddings/embed-with-voyage.md): 本文介绍如何使用 VoyageEmbeddingFunction 使用 Voyage 模型对文档和查询进行编码。 - [Embedding Overview](https://www.xlonglab.com/docs/v2.6.x/embeddings): 了解如何为数据生成 Embedding。 ### 常见问题 - [操作常见问题](https://www.xlonglab.com/docs/v2.6.x/faq/operational_faq.md): 查找有关 Milvus Operator 常见问题的答案。 - [性能常见问题](https://www.xlonglab.com/docs/v2.6.x/faq/performance_faq.md): 查找有关搜索性能、性能增强和其他性能相关问题的常见问题答案。 - [产品常见问题](https://www.xlonglab.com/docs/v2.6.x/faq/product_faq.md): 查找有关世界上最先进的向量数据库常见问题的答案。 - [故障排除](https://www.xlonglab.com/docs/v2.6.x/faq/troubleshooting.md): 了解使用 Milvus 时可能遇到的常见问题，以及如何克服这些问题。 ### 安装与入门 - [连接 Milvus 服务器](https://www.xlonglab.com/docs/v2.6.x/getstarted/connect-to-milvus-server.md): 本主题介绍如何建立客户端与 Milvus 服务器的连接，并配置常用的连接选项。 - [Milvus 部署选项概述](https://www.xlonglab.com/docs/v2.6.x/getstarted/install-overview.md): Milvus 是一个高性能、可扩展的向量数据库。它支持各种规模的用例，从在 Jupyter Notebooks 中本地运行的演示到处理数百亿向量的大规模 Kubernetes 集群。目前，有三种 Milvus 部署选项：Milvus Lite、Milvus Standalone 和 Milvus Distributed。 - [安装 Milvus Go SDK](https://www.xlonglab.com/docs/v2.6.x/getstarted/install_SDKs/install-go.md): 了解如何安装 Milvus 的 GO SDK。 - [安装 Milvus Java SDK](https://www.xlonglab.com/docs/v2.6.x/getstarted/install_SDKs/install-java.md): 了解如何安装 Milvus 的 Java SDK。 - [安装 Milvus Node.js SDK](https://www.xlonglab.com/docs/v2.6.x/getstarted/install_SDKs/install-node.md): 了解如何安装 Milvus 的 Node.js SDK。 - [安装 Milvus Python SDK](https://www.xlonglab.com/docs/v2.6.x/getstarted/install_SDKs/install-pymilvus.md): 了解如何安装 Milvus 的 Python SDK。 - [本地运行 Milvus Lite](https://www.xlonglab.com/docs/v2.6.x/getstarted/milvus_lite.md): 开始使用 Milvus Lite。 - [快速入门](https://www.xlonglab.com/docs/v2.6.x/getstarted/quickstart.md): 开始使用 Milvus。 - [使用 RPM/DEB 软件包安装 Milvus Standalone](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-docker/install_standalone-binary.md): 了解如何使用预编译的 RPM/DEB 软件包安装 Milvus Standalone。 - [使用 Docker Compose 运行 Milvus（Linux）](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-docker/install_standalone-docker-compose.md): 了解如何使用 Docker Compose 安装 Milvus Standalone。 - [在 Docker 中运行 Milvus（Linux）](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-docker/install_standalone-docker.md): 了解如何使用 Docker 安装 Milvus Standalone。 - [在 Docker 中运行 Milvus（Windows）](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-docker/install_standalone-windows.md): 了解如何使用 Docker Desktop for Windows 安装 Milvus Standalone。 - [安装 Milvus Standalone 的要求](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-docker/prerequisite-docker.md): 了解安装 Milvus Standalone 前的必要准备。 - [使用 Helm Chart 运行支持 GPU 的 Milvus](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-gpu/install_cluster-helm-gpu.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 Docker Compose 运行支持 GPU 的 Milvus](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-gpu/install_standalone-docker-compose-gpu.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [安装支持 GPU 的 Milvus 的要求](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-gpu/prerequisite-gpu.md): 了解使用 GPU 安装 Milvus 前的必要准备工作。 - [使用 Helm 安装 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-k8s/install_cluster-helm.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 Milvus Operator 安装 Milvus 集群](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-k8s/install_cluster-milvusoperator.md): 了解如何使用 Milvus Operator 在 Kubernetes 上安装 Milvus 集群 - [在 Kubernetes 上运行 Milvus 的要求](https://www.xlonglab.com/docs/v2.6.x/getstarted/run-milvus-k8s/prerequisite-helm.md): 了解使用 Helm 安装 Milvus 之前的必要准备工作。 - [搜索数据模型设计](https://www.xlonglab.com/docs/v2.6.x/getstarted/schema-hands-on.md): 信息检索系统，又称搜索引擎，对于检索增强生成（RAG）、视觉搜索和产品推荐等各种人工智能应用至关重要。这些系统的核心是精心设计的数据模型，用于组织、索引和检索信息。 ### 生态集成 - [使用 AIMon 和 Milvus 提升 LLM 应用的检索质量](https://www.xlonglab.com/docs/v2.6.x/integrations/AIMon_milvus_integration.md): 在本教程中，我们将帮助您构建一个能回答问题的检索增强生成（RAG）聊天机器人。 - [使用 VectorETL 将数据高效加载到 Milvus 中](https://www.xlonglab.com/docs/v2.6.x/integrations/ETL_using_vectorETL.md): 在本教程中，我们将探讨如何使用专为向量数据库设计的轻量级 ETL 框架 \[VectorETL\](https://github.com/ContextData/VectorETL)，高效地将数据加载到 Milvus 中。… - [与 Milvus 一起使用 NLWeb](https://www.xlonglab.com/docs/v2.6.x/integrations/NLWeb_with_milvus.md): 了解如何将 Microsoft NLWeb 与 Milvus 集成，为网站构建强大的自然语言界面。本教程演示了如何在 NLWeb 应用程序中利用 Milvus 的向量数据库功能进行高效语义搜索、嵌入存储和上下文检索。 - [使用 Milvus + PII 屏蔽器构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/RAG_with_pii_and_milvus.md): 在本教程中，我们将向您展示如何使用 Milvus 和 PII Masker 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [检索增强生成：使用 Apify 抓取网站并将数据保存到 Milvus 以用于问题解答](https://www.xlonglab.com/docs/v2.6.x/integrations/apify_milvus_rag.md): 本教程介绍如何使用 Apify 的网站内容抓取器抓取网站，并将数据保存到 Milvus 或 Zilliz Cloud 向量数据库，以便日后用于问题解答。 - [构建 RAG Pipeline：将数据从 S3 载入 Milvus](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_from_s3_with_milvus.md): 本教程将指导您使用 Milvus 和亚马逊 S3 构建一个 RAG Pipeline。您将学习如何高效地从 S3 文件桶中加载文档，将其分割成易于管理的块，并将其 Embedding 存储在 Milvus 中，以便进行快速、可扩展的检索。为了简化这一过程，我们将使用 LangChain 作为工具，从 S3 加载数据并将其存储到 Milvus 中。 - [使用 Milvus 和 Cognee 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_cognee.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Cognee 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Crawl4AI 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_crawl4ai.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Crawl4AI 构建一个 RAG Pipeline。该管道集成了用于网络数据抓取的 Crawl4AI、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [利用 Milvus 和 DeepSeek 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_deepseek.md): 在本教程中，我们将向您展示如何使用 Milvus 和 DeepSeek 构建 RAG Pipeline。 - [使用 Milvus 和 Docling 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_docling.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Docling 构建 RAG Pipeline。该管道集成了用于文档解析的 Docling、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [用 Milvus 和 EmbedAnything 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_embedAnything.md): 在本教程中，我们将演示如何使用 EmbedAnything 和 Milvus 一起构建 RAG Pipeline。EmbedAnything 使用可插拔的适配器系统，而不是与任何特定数据库紧密耦合，适配器作为封装器，定义了嵌入数据的格式化、索引和在目标向量存储中的存储方式。 - [使用 Exa 和 Milvus 构建双源 RAG Agent](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_exa.md): 本教程演示了如何构建一个 AI Agent，同时搜索公共网络（通过 Exa）和私人知识库（通过 Milvus），然后合成一个统一的答案。该 AI Agent 使用 OpenAI 的函数调用功能，根据用户的问题自动决定查询哪个来源。 - [使用 Milvus 和 Feast 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_feast.md): 在本教程中，我们将使用 Feast 和 Milvus 构建一个 RAG Pipeline。Feast 是一个开源特征库，可简化机器学习的特征管理，为训练和实时推理提供高效的结构化数据存储和检索。Milvus 是一个高性能向量数据库，专为快速相似性搜索而设计，因此非常适合在 RAG 工作流中检索相关文档。 - [利用 Milvus 和 Firecrawl 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_firecrawl.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Firecrawl 构建 RAG Pipeline。该管道集成了用于网络数据抓取的 Firecrawl、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [使用 Milvus 和 Fireworks AI 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_fireworks.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Fireworks AI 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Gemini 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_gemini.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Gemini 构建 RAG Pipeline（Retrieval-Augmented Generation）。我们将使用 Gemini 模型根据给定的查询生成文本。我们还将使用 Milvus 来存储和检索生成的文本。 - [利用 Milvus 和 Lepton AI 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_lepton.md): 在本教程中，我们将向您展示如何利用 Milvus 和 Lepton AI 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Ollama 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_ollama.md): 在本指南中，我们将向您展示如何利用 Ollama 和 Milvus 高效、安全地构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 SiliconFlow 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_RAG_with_milvus_and_siliconflow.md): 本教程介绍如何使用 Milvus 和 SiliconFlow 构建 RAG Pipeline。 - [在 Arm 架构上构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/build_rag_on_arm.md): 在本教程中，您将学习如何在基于 Arm 的基础架构上构建检索增强生成（RAG）应用程序。在向量存储方面，我们利用了全面管理的 Milvus 向量数据库 Zilliz Cloud。Zilliz Cloud 可在 AWS、GCP 和 Azure 等主流云上使用。在本演示中，我们使用部署在 AWS 上的 Zilliz Cloud 和 Arm 机器。… - [使用 Milvus 部署 Dify](https://www.xlonglab.com/docs/v2.6.x/integrations/dify_with_milvus.md): 本教程介绍如何将 Dify 与 Milvus 集成，使用 Milvus 作为向量数据库，为知识检索与 RAG Pipeline 提供存储和检索能力。 - [使用 DeepEval 进行评估](https://www.xlonglab.com/docs/v2.6.x/integrations/evaluation_with_deepeval.md): 本指南演示了如何使用 DeepEval 评估基于 Milvus 的 RAG Pipeline。 - [使用 Arize Phoenix 进行评估](https://www.xlonglab.com/docs/v2.6.x/integrations/evaluation_with_phoenix.md): 本指南演示了如何使用 Arize Phoenix 评估基于 Milvus 的 RAG Pipeline。 - [使用 Milvus 和 Haystack 进行全文搜索](https://www.xlonglab.com/docs/v2.6.x/integrations/full_text_search_with_milvus_and_haystack.md): 本教程演示如何使用 Haystack 和 Milvus 在应用程序中实现全文和混合搜索。 - [将 Milvus 与 Agno 相集成](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_agno.md): Milvus 向量数据库能够高效地存储和检索嵌入信息。借助 Milvus 和 Agno，您可以轻松地将知识整合到 Agent 工作流程中。本文档是如何使用 Milvus 与 Agno 集成的基本指南。 - [Airbyte：开源数据移动基础架构](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_airbyte.md): Airbyte 是一种开源数据移动基础架构，用于构建提取和加载（EL）数据管道。它具有多功能性、可扩展性和易用性。Airbyte 的连接器目录 "开箱即用"，预置了 350 多个连接器。使用这些连接器，只需几分钟即可开始从源数据复制到目标数据。 - [使用 Milvus 和 BentoML 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_bentoml.md): 本指南演示了如何使用 BentoCloud 上的开源 Embedding 模型和大语言模型与 Milvus 向量数据库来构建检索增强生成（RAG）应用程序。 - [使用 Milvus 与 CAMEL-AI 构建 RAG 系统](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_camel.md): 本指南介绍如何结合 CAMEL-AI 与 Milvus 构建检索增强生成（RAG）系统，并使用 Milvus 检索相关文档，为大语言模型生成提供上下文。 - [使用 Milvus 和 Cohere 进行问题解答](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_cohere.md): 本页说明了如何使用 Milvus 作为向量数据库和 Cohere 作为嵌入系统，创建基于 SQuAD 数据集的问题解答系统。 - [Milvus 与 DSPy 集成](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_dspy.md): 本指南演示了如何使用 DSPy 的 Retriever 模块之一 MilvusRM 来优化 RAG 程序。 - [使用 Milvus 部署 FastGPT](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_fastgpt.md): 本教程将指导您如何使用 Milvus 快速部署自己的专属 FastGPT 应用程序。 - [使用 Milvus 和 Haystack 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_haystack.md): 本指南演示了如何使用 Haystack 和 Milvus 建立检索增强生成（RAG）系统。 - [使用 Milvus 和 Hugging Face 进行问题解答](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_hugging-face.md): 本教程展示了如何使用 Hugging Face 作为数据加载器和嵌入生成器进行数据处理，并使用 Milvus 作为向量数据库进行语义搜索，从而构建一个问题解答系统。 - [Milvus 与 Jina 集成](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_jina.md): 本指南演示了如何使用 Jina 嵌入和 Milvus 进行相似性搜索和检索任务。 - [使用 Langfuse 对 RAG 查询进行 Tracing](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_langfuse.md): 本教程演示如何使用 Langfuse 的 LlamaIndex 集成对 RAG 查询进行 Tracing，并使用 Milvus Lite 存储和检索文档。 - [使用 Milvus 和 LlamaIndex 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_llamaindex.md): 本指南演示了如何使用 LlamaIndex 和 Milvus 建立检索增强生成（RAG）系统。 - [与 Milvus 集成的 MemGPT](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_memgpt.md): MemGPT 可轻松构建和部署有状态 LLM Agent。通过 Milvus 集成，您可以构建可连接外部数据源（RAG）的 AI Agent。 - [使用 Milvus 和 OpenAI 进行语义搜索](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_openai.md): 本页讨论向量数据库与 OpenAI 的 Embedding API 的集成。 - [使用 PyTorch 和 Milvus 进行图像搜索](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_pytorch.md): 本页演示如何使用 PyTorch 和 Milvus 创建图像搜索 - [使用 Ragas 进行评估](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_ragas.md): 本指南演示了如何使用 Ragas 评估基于 Milvus 的 RAG Pipeline。 - [使用 Milvus 和 Sentence Transformers 进行电影搜索](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_sentencetransformers.md): 本页讨论如何使用 Milvus 进行电影搜索 - [在 Snowpark Container Services 上使用 Milvus](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_snowpark.md): 本指南演示如何在 Snowpark Container Services 上启动 Milvus 演示。 - [在 AI Pipeline 中使用 Apache Spark™ 与 Milvus/Zilliz Cloud](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_spark.md): Apache Spark 和 Databricks 与 Milvus 和 Zilliz Cloud 集成，将大数据处理与向量搜索相结合。了解如何利用 Spark-Milvus 连接器构建 AI 驱动的搜索与分析。 - [用 Vanna 和 Milvus 编写 SQL](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_vanna.md): 本指南演示如何使用 Vanna 根据存储在数据库中的数据生成并执行 SQL 查询。 - [使用 Milvus 和 FiftyOne 进行视觉搜索](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_voxel51.md): 本页讨论与 voxel51 的集成 - [使用 Milvus 和 VoyageAI 进行语义搜索](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_voyageai.md): 本页讨论向量数据库与 VoyageAI 的 Embedding API 的集成。 - [Milvus 与 WhyHow 的集成](https://www.xlonglab.com/docs/v2.6.x/integrations/integrate_with_whyhow.md): 本指南演示了如何使用 whyHow.ai 和 Milvus Lite 进行基于规则的检索。 - [将 Milvus 与 MindsDB 相集成](https://www.xlonglab.com/docs/v2.6.x/integrations/integration_with_mindsdb.md): 本教程演示了如何将 Milvus 与 MindsDB 集成，使您能够通过类似 SQL 的操作来管理和查询 Embedding，从而利用 MindsDB 的人工智能功能和 Milvus 的向量数据库功能。 - [集成概述](https://www.xlonglab.com/docs/v2.6.x/integrations/integrations_overview.md): 本页为您提供与 Milvus 和第三方工具互动的教程列表。 - [将 Apache Kafka® 与 Milvus/Zilliz Cloud 连接起来，实现向量数据的实时摄取](https://www.xlonglab.com/docs/v2.6.x/integrations/kafka-connect-milvus.md): Apache Kafka 与 Milvus 和 Zilliz Cloud 集成，可实现向量数据流。了解如何使用 Kafka-Milvus 连接器为语义搜索、推荐系统和人工智能驱动的分析构建实时管道。 - [使用 Milvus 的知识表](https://www.xlonglab.com/docs/v2.6.x/integrations/knowledge_table_with_milvus.md): 默认情况下，知识表使用 Milvus 数据库来存储和检索提取的数据。这样，用户就可以利用 Milvus 的强大功能轻松搜索、过滤和分析数据。在本教程中，我们将介绍如何开始使用 Knowledge Table 和 Milvus。 - [使用 Milvus 的 Kotaemon RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/kotaemon_with_milvus.md): 本教程将指导您如何使用 Milvus 定制 kotaemon 应用程序。 - [使用 Milvus 和 LangGraph 的 Agentic RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/langchain/agentic_rag_with_milvus_and_langgraph.md): 本指南演示了如何使用 LangGraph 和 Milvus 构建先进的检索增强生成（RAG）系统。与简单检索和生成的传统 RAG 系统不同，Agentic RAG 系统可以就何时检索信息、如何处理无关文档以及何时重写查询以获得更好的结果做出智能决策。 - [将 Milvus 用作向量存储库](https://www.xlonglab.com/docs/v2.6.x/integrations/langchain/basic_usage_langchain.md): 本笔记本介绍如何使用与 Milvus 向量数据库相关的功能。 - [使用 LangChain 和 Milvus 进行全文搜索](https://www.xlonglab.com/docs/v2.6.x/integrations/langchain/full_text_search_with_langchain.md): 本教程将介绍如何使用 LangChain 和 Milvus 在应用程序中实现全文搜索。 - [使用 Milvus 和 LangChain 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.6.x/integrations/langchain/integrate_with_langchain.md): 本指南演示了如何使用 LangChain 和 Milvus 构建检索-增强生成（RAG）系统。 - [LangChain Milvus 集成中的异步函数](https://www.xlonglab.com/docs/v2.6.x/integrations/langchain/langchain_milvus_async.md): 本教程探讨如何利用 langchain-milvus 中的异步函数构建高性能应用程序。通过使用异步方法，您可以显著提高应用程序的吞吐量和响应速度，尤其是在处理大规模检索时。 - [Milvus 文本 Embedding Function 与 LangChain 的整合](https://www.xlonglab.com/docs/v2.6.x/integrations/langchain/langchain_milvus_dido.md): 本指南演示了如何将 Milvus 2.6 的文本 Embedding Function（也称为数据输入数据输出）与 LangChain 结合使用。该功能可让 Milvus 服务器自动将原始文本转换为 Embedding，从而简化客户端代码并集中管理 API 密钥。 - [Milvus Hybrid Search Retriever](https://www.xlonglab.com/docs/v2.6.x/integrations/langchain/milvus_hybrid_search_retriever.md): 本笔记本介绍如何使用与 Milvus 向量数据库相关的功能。 - [LangExtract + Milvus 集成](https://www.xlonglab.com/docs/v2.6.x/integrations/langextract_milvus_demo.md): 本指南演示了如何使用 LangExtract 与 Milvus 一起构建智能文档处理和检索系统。 - [Multi-agent Systems with Mistral AI, Milvus and Llama-agents](https://www.xlonglab.com/docs/v2.6.x/integrations/llama_agents_metadata.md): 在本笔记本中，我们将探讨不同的想法：将数据存储到 Milvus 中，使用 llama-index 与 Mistral 模型进行数据查询，创建自动数据搜索与读取 AI Agent，以及开发根据用户查询进行元数据过滤 AI Agent。 - [利用 Milvus 的 Llama Stack 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/llama_stack_with_milvus.md): 本教程将介绍如何使用 Milvus 搭建一个 Llama Stack 服务器，从而导入私人数据作为知识库。然后，我们将在服务器上执行查询，创建一个完整的 RAG 应用程序。 - [使用 LlamaIndex 和 Milvus Async API 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/llamaindex_milvus_async.md): 本教程介绍如何使用 LlamaIndex Async API 和 Milvus 构建异步 RAG Pipeline，并比较同步与异步实现的性能。 - [使用 LlamaIndex 和 Milvus 进行全文搜索](https://www.xlonglab.com/docs/v2.6.x/integrations/llamaindex_milvus_full_text_search.md): 在本教程中，您将学习如何使用 LlamaIndex 和 Milvus 建立一个使用全文搜索和混合搜索的 RAG 系统。我们将首先单独实施全文搜索，然后通过整合语义搜索来增强其功能，以获得更全面的结果。 - [使用 Milvus 和 LlamaIndex 混合搜索的 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/llamaindex_milvus_hybrid_search.md): 本笔记本演示了如何在 \[LlamaIndex\](https://www.llamaindex.ai/) RAG Pipeline 中使用 Milvus 进行混合搜索。我们将从推荐的默认混合搜索（语义 + BM25）开始，然后探索其他可供选择的稀疏嵌入方法和自定义混合 Reranker。 - [使用 LlamaIndex 和 Milvus 进行元数据过滤](https://www.xlonglab.com/docs/v2.6.x/integrations/llamaindex_milvus_metadata_filter.md): 本笔记本说明了在 LlamaIndex 中如何使用 Milvus 向量存储，重点是元数据过滤功能。您将学习如何使用元数据索引文档、使用 LlamaIndex 的内置元数据过滤器执行向量搜索，以及如何将 Milvus 的本地过滤表达式应用到向量存储。 - [MCP + Milvus：连接 AI 与 Vector Database](https://www.xlonglab.com/docs/v2.6.x/integrations/milvus_and_mcp.md): 本教程将指导您为 Milvus 设置 MCP Server，让人工智能应用程序能够使用自然语言命令执行向量搜索、管理 Collection 和检索数据，而无需编写自定义数据库查询。 - [开始使用 Milvus 和 n8n](https://www.xlonglab.com/docs/v2.6.x/integrations/milvus_and_n8n.md): n8n 是一个功能强大的开源工作流自动化平台，可将各种应用程序、服务和 API 连接在一起，无需编码即可创建自动化工作流。n8n 采用基于节点的可视化界面，用户只需连接代表不同服务或操作的节点，即可构建复杂的自动化流程。它可自行托管，具有高度可扩展性，并支持公平代码和企业许可。 - [开始使用 Dynamiq 和 Milvus](https://www.xlonglab.com/docs/v2.6.x/integrations/milvus_rag_with_dynamiq.md): 在本教程中，我们将探讨如何将 Dynamiq 与 Milvus（专为 RAG 工作流打造的高性能向量数据库）无缝结合使用。Milvus 擅长 Embedding 的高效存储、索引和检索，是要求快速、精确访问上下文数据的人工智能系统不可或缺的组件。 - [用 Milvus、vLLM 和 Llama 3.1 构建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/milvus_rag_with_vllm.md): 本博客将向您展示如何使用 Milvus、vLLM 和 Llama 3.1 构建并运行 RAG。更具体地说，我将向你展示如何在 Milvus 中将文本信息嵌入并存储为 Embedding，并将此向量存储作为知识库来高效检索与用户问题相关的文本块。 - [使用 Mistral OCR 和 Milvus 理解文档](https://www.xlonglab.com/docs/v2.6.x/integrations/mistral_ocr_with_milvus.md): 本教程演示如何使用 Milvus 和 Mistral OCR 构建文档理解系统。 - [Milvus 与 OpenAI Agent 的集成：分步指南](https://www.xlonglab.com/docs/v2.6.x/integrations/openai_agents_milvus.md): 本笔记本展示了如何创建一个可以通过函数调用使用自然语言查询 Milvus 的 AI Agent。我们将把 OpenAI 的 Agents 框架与 Milvus 强大的向量搜索功能结合起来，创建一个不错的搜索体验。 - [开始使用 Mem0 和 Milvus](https://www.xlonglab.com/docs/v2.6.x/integrations/quickstart_mem0_with_milvus.md): 在本教程中，我们将介绍基本的 Mem0 内存管理操作--添加、检索、更新、搜索、删除和跟踪内存历史记录--使用 Milvus，这是一个高性能、开源的向量数据库，可支持高效的存储和检索。本实践介绍将指导您完成基础内存操作，帮助您利用 Mem0 和 Milvus 构建个性化的人工智能交互。 - [使用 Langflow 和 Milvus 构建 RAG 系统](https://www.xlonglab.com/docs/v2.6.x/integrations/rag_with_langflow.md): 本指南演示了如何使用 Langflow 与 Milvus 一起构建 RAG Pipeline。 - [利用 Milvus 和 Unstructured 创建 RAG](https://www.xlonglab.com/docs/v2.6.x/integrations/rag_with_milvus_and_unstructured.md): 在本教程中，我们将使用 Unstructured 来摄取 PDF 文档，然后使用 Milvus 构建 RAG Pipeline。 - [在 AnythingLLM 中使用 Milvus](https://www.xlonglab.com/docs/v2.6.x/integrations/use_milvus_in_anythingllm.md): 本指南将指导您将 Milvus 配置为 AnythingLLM 中的向量数据库，使您能够嵌入、存储和搜索文档，实现智能检索和聊天。 - [在 DocsGPT 中使用 Milvus](https://www.xlonglab.com/docs/v2.6.x/integrations/use_milvus_in_docsgpt.md): 在本教程中，我们将向您展示如何使用 Milvus 作为 DocsGPT 的后台向量数据库。 - [在 PrivateGPT 中使用 Milvus](https://www.xlonglab.com/docs/v2.6.x/integrations/use_milvus_in_private_gpt.md): 在本教程中，我们将向您展示如何使用 Milvus 作为 PrivateGPT 的后台向量数据库。 - [将 Milvus 与 SambaNova 结合使用](https://www.xlonglab.com/docs/v2.6.x/integrations/use_milvus_with_sambanova.md): 本教程利用 SambaNova 人工智能入门套件中的 Milvus 集成，构建了一个类似于 RAG（检索-增强生成）的企业知识检索系统，用于基于企业私有文档的检索和回答。 - [高级视频搜索：利用 Twelve Labs 和 Milvus 进行语义检索](https://www.xlonglab.com/docs/v2.6.x/integrations/video_search_with_twelvelabs_and_milvus.md): 了解如何通过将 Twelve Labs 用于生成多模态嵌入的 Embedding API 与 Milvus 集成来创建语义视频搜索应用程序。它涵盖了从设置开发环境到实现混合搜索和时态视频分析等高级功能的整个过程，为构建复杂的视频内容分析和检索系统奠定了全面的基础。 ### 参考资料 - [Milvus 架构概述](https://www.xlonglab.com/docs/v2.6.x/reference/architecture/architecture_overview.md): Milvus 提供快速、可靠、稳定的向量数据库，专为相似性搜索和人工智能而建。 - [数据处理](https://www.xlonglab.com/docs/v2.6.x/reference/architecture/data_processing.md): 了解 Milvus 的数据处理程序。 - [存储与计算分离](https://www.xlonglab.com/docs/v2.6.x/reference/architecture/four_layers.md): Milvus 的存储与计算分离结构。 - [主要组件](https://www.xlonglab.com/docs/v2.6.x/reference/architecture/main_components.md): 了解 Milvus Standalone 和集群的主要组件。 - [Streaming Service](https://www.xlonglab.com/docs/v2.6.x/reference/architecture/streaming_service.md): Streaming Service 是 Milvus 内部流媒体系统模块的一个概念，围绕 WAL（Write-Ahead Log） 构建，支持各种与流媒体相关的功能。 - [Woodpecker](https://www.xlonglab.com/docs/v2.6.x/reference/architecture/woodpecker_architecture.md): Woodpecker 是 Milvus 2.6 中的一个云原生 WAL 系统。它采用零磁盘架构和两种部署模式，可在 Object Storage 上提供高吞吐量、低运维开销和无缝可扩展性。 - [Milvus 2.2 基准测试报告](https://www.xlonglab.com/docs/v2.6.x/reference/benchmark.md): 了解 Milvus 的基准结果。 - [Bitset](https://www.xlonglab.com/docs/v2.6.x/reference/bitset.md): 了解 Milvus 中的 Bitset。 - [Coordinator HA](https://www.xlonglab.com/docs/v2.6.x/reference/coordinator_ha.md): 了解 Milvus Coordinator 积极待命的工作动机和程序。 - [On-disk Index](https://www.xlonglab.com/docs/v2.6.x/reference/disk_index.md): Milvus 中用于磁盘优化向量搜索的磁盘索引机制。 - [术语](https://www.xlonglab.com/docs/v2.6.x/reference/glossary.md) - [GPU 索引](https://www.xlonglab.com/docs/v2.6.x/reference/gpu_index.md): Milvus 的 GPU 索引机制。 - [内存索引](https://www.xlonglab.com/docs/v2.6.x/reference): Milvus 的索引机制。 - [Knowhere](https://www.xlonglab.com/docs/v2.6.x/reference/knowhere.md): 了解 Milvus 的 Knowhere。 - [实施多租户](https://www.xlonglab.com/docs/v2.6.x/reference/multi_tenancy.md): 在 Milvus，多租户意味着多个客户或团队（称为租户）共享同一个集群，同时保持隔离的数据环境。 - [In-Memory Replica](https://www.xlonglab.com/docs/v2.6.x/reference/replica.md): 了解 Milvus 中的内存复制。 - [标量索引](https://www.xlonglab.com/docs/v2.6.x/reference/scalar_index.md): Milvus 标量索引。 - [Schema 说明](https://www.xlonglab.com/docs/v2.6.x/reference/schema.md): Schema 定义了 Collection 的数据结构。在创建 Collection 之前，你需要设计好它的 Schema。本页将帮助你理解 Collection Schema，并自行设计一个示例 Schema。 - [common 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_common.md): 了解如何为 Milvus 配置常用功能。 - [DataCoord 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_datacoord.md): 了解如何为 Milvus 配置 dataCoord。 - [DataNode 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_datanode.md): 了解如何为 Milvus 配置 dataNode。 - [etcd 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_etcd.md): 了解如何为 Milvus 配置 etcd。 - [GPU 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_gpu.md): 了解如何为 Milvus 配置 gpu。 - [gRPC 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_grpc.md): 了解如何为 Milvus 配置 grpc。 - [IndexCoord 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_indexcoord.md): 了解如何为 Milvus 配置 indexCoord。 - [IndexNode 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_indexnode.md): 了解如何为 Milvus 配置 indexNode。 - [内部 TLS 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_internaltls.md): 了解如何为 Milvus 配置 internaltls。 - [Knowhere 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_knowhere.md): 了解如何为 Milvus 配置 knowhere。 - [本地存储配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_localstorage.md): 了解如何为 Milvus 配置 localStorage。 - [日志配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_log.md): 了解如何为 Milvus 配置日志。 - [Meta storage 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_metastore.md): 了解如何为 Milvus 配置 Meta storage。 - [MinIO 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_minio.md): 了解如何为 Milvus 配置 minio。 - [MQ 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_mq.md): 了解如何为 Milvus 配置 mq。 - [消息通道配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_msgchannel.md): 了解如何为 Milvus 配置 msgChannel。 - [Proxy 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_proxy.md): 了解如何为 Milvus 配置 Proxy。 - [Pulsar 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_pulsar.md): 了解如何为 Milvus 配置 Pulsar。 - [QueryCoord 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_querycoord.md): 了解如何为 Milvus 配置 queryCoord。 - [QueryNode 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_querynode.md): 了解如何为 Milvus 配置 queryNode。 - [配额与限制配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_quotaandlimits.md): 了解如何为 Milvus 配置 quotaAndLimits。 - [RocksMQ 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_rocksmq.md): 了解如何为 Milvus 配置 rocksmq。 - [RootCoord 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_rootcoord.md): 了解如何为 Milvus 配置 rootCoord。 - [Streaming 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_streaming.md): 了解如何为 Milvus 配置流媒体。 - [Streaming Node 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_streamingnode.md): 了解如何为 Milvus 配置 streamingNode。 - [TiKV 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_tikv.md): 了解如何为 Milvus 配置 tikv。 - [TLS 配置参数](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_tls.md): 了解 Milvus Proxy 对外服务的 TLS 模式、服务器证书、私钥和 CA 证书路径配置。 - [Trace 配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/configure_trace.md): 了解如何为 Milvus 配置 Trace。 - [系统配置](https://www.xlonglab.com/docs/v2.6.x/reference/sys_config/system_configuration.md): 了解 Milvus 的系统配置。 - [时间同步](https://www.xlonglab.com/docs/v2.6.x/reference/time_sync.md): 了解 Milvus 的时间同步系统。 - [Timestamp](https://www.xlonglab.com/docs/v2.6.x/reference/timestamp.md): 了解时间戳的概念和 Milvus 向量数据库中与时间戳相关的四个主要参数。 - [Consistency Level](https://www.xlonglab.com/docs/v2.6.x/reference/tune_consistency.md): Milvus 支持 Strong、Bounded、Session 和 Eventually 四种 Consistency Level，其中 Bounded 为默认值。 ### release_notes.md - [发布说明](https://www.xlonglab.com/docs/v2.6.x/release_notes.md): Milvus 版本说明 ### Reranker - [BGE](https://www.xlonglab.com/docs/v2.6.x/rerankers/rerankers-bge.md): Milvus 通过`BGERerankFunction`类支持 BGE reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Reranker Cohere](https://www.xlonglab.com/docs/v2.6.x/rerankers/rerankers-cohere.md): Milvus 通过 `CohereRerankFunction` 类支持 Cohere Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [交叉编码器](https://www.xlonglab.com/docs/v2.6.x/rerankers/rerankers-cross-encoder.md): Milvus 通过 "CrossEncoderRerankFunction "类支持交叉编码器 Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Jina AI - Reranker](https://www.xlonglab.com/docs/v2.6.x/rerankers/rerankers-jina.md): Milvus 通过 "JinaRerankFunction "类支持 Jina Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Reranker 概览](https://www.xlonglab.com/docs/v2.6.x/rerankers/rerankers-overview.md): PyMilvus 模型库集成了 Reranker 函数，用于优化初始搜索返回结果的顺序。 - [Reranker Voyage](https://www.xlonglab.com/docs/v2.6.x/rerankers/rerankers-voyage.md): Milvus 通过 "VoyageRerankFunction "类支持 Voyage Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 ### 教程 - [音频相似性搜索](https://www.xlonglab.com/docs/v2.6.x/tutorials/audio_similarity_search.md): 利用 Milvus 建立音频相似性搜索系统。 - [用 Milvus 创建 RAG](https://www.xlonglab.com/docs/v2.6.x/tutorials/build-rag-with-milvus.md): 使用 Milvus 构建 RAG Pipeline，完成文档检索、上下文增强和大语言模型生成。 - [使用 Milvus 进行上下文检索](https://www.xlonglab.com/docs/v2.6.x/tutorials/contextual_retrieval_with_milvus.md): 上下文检索（Contextual Retrieval）是 Anthropic 提出的一种先进的检索方法，旨在解决当前检索增强生成（RAG）解决方案中出现的块的语义隔离问题。在当前实用的 RAG 范式中，文档被分成若干个块，使用向量数据库进行查询搜索，检索出最相关的块。然后，LLM 使用这些检索到的分块对查询做出响应。… - [DNA 序列分类](https://www.xlonglab.com/docs/v2.6.x/tutorials/dna_sequence_classification.md): 利用 Milvus 建立 DNA 序列分类系统。 - [使用 LangChain 和 Milvus 进行全文搜索](https://www.xlonglab.com/docs/v2.6.x/tutorials/full_text_search_with_milvus.md): 全文搜索是一种通过匹配文本中特定关键词或短语来检索文档的传统方法。它根据术语频率等因素计算出的相关性分数对结果进行排序。语义搜索更善于理解含义和上下文，而全文搜索则擅长精确的关键词匹配，因此是语义搜索的有益补充。构建 "RAG Pipeline 的常见方法包括通过语义搜索和全文搜索检索文档，然后进行 Reranking 以完善结果。 - [Funnel Search with Matryoshka Embeddings](https://www.xlonglab.com/docs/v2.6.x/tutorials/funnel_search_with_matryoshka.md): 在本笔记本中，我们将研究如何将 Matryoshka 嵌入与 Milvus 一起用于语义搜索。我们展示了一种名为 "漏斗搜索 "的算法，它允许我们在嵌入维度的一小部分子集上执行相似性搜索，而不会导致召回率急剧下降。 - [使用大型语言模型生成 Milvus 查询过滤表达式](https://www.xlonglab.com/docs/v2.6.x/tutorials/generating_milvus_query_filter_expressions.md): 在本教程中，我们将演示如何使用大型语言模型（LLMs）从自然语言查询自动生成 Milvus 过滤表达式。通过这种方法，用户可以用普通英语表达复杂的过滤条件，然后将其转换为适当的 Milvus 语法，从而使向量数据库查询更易于使用。 - [使用 Milvus 构建 Graph RAG](https://www.xlonglab.com/docs/v2.6.x/tutorials/graph_rag_with_milvus.md): 使用 Milvus 构建 Graph RAG - [利用 Milvus 进行 HDBSCAN 聚类](https://www.xlonglab.com/docs/v2.6.x/tutorials/hdbscan_clustering_with_milvus.md): 在本笔记本中，我们将使用 BGE-M3 Embedding 模型从新闻标题数据集中提取嵌入，利用 Milvus 高效计算嵌入之间的距离以帮助 HDBSCAN 进行聚类，然后使用 UMAP 方法将结果可视化以进行分析。本笔记本是 Milvus 对 Dylan Castillo 文章的改编。 - [如何提高 RAG Pipeline 的性能](https://www.xlonglab.com/docs/v2.6.x/tutorials/how_to_enhance_your_rag.md): 随着 RAG 应用的日益普及，人们越来越关注如何提高其性能。本文介绍了优化 RAG Pipeline 的所有可能方法，并提供了相应的图解，帮助您快速了解主流的 RAG 优化策略。 - [使用 Milvus 进行混合搜索](https://www.xlonglab.com/docs/v2.6.x/tutorials/hybrid_search_with_milvus.md): 使用 Milvus 进行混合搜索 - [重复数据删除](https://www.xlonglab.com/docs/v2.6.x/tutorials/image_deduplication_system.md): 使用 Milvus 构建重复数据删除图像系统。 - [使用 Milvus 搜索图像](https://www.xlonglab.com/docs/v2.6.x/tutorials/image_similarity_search.md): 使用 Milvus 进行图像搜索 - [用 Milvus 推荐电影](https://www.xlonglab.com/docs/v2.6.x/tutorials/movie_recommendation_with_milvus.md): 在本笔记本中，我们将探索如何使用 OpenAI 生成电影描述的 Embedding，并在 Milvus 中利用这些 Embedding 描述来推荐符合您偏好的电影。为了增强搜索结果，我们将利用过滤功能来执行元数据搜索。本示例中使用的数据集来自 Hugging Face 数据集，包含 8000 多个电影条目，为电影推荐提供了丰富的选择。 - [使用 Milvus 制作多模态 RAG](https://www.xlonglab.com/docs/v2.6.x/tutorials/multimodal_rag_with_milvus.md): 使用 Milvus 的多模态 RAG - [问答系统](https://www.xlonglab.com/docs/v2.6.x/tutorials/question_answering_system.md): 使用 Milvus 建立问题解答系统。 - [Attu 快速入门](https://www.xlonglab.com/docs/v2.6.x/tutorials/quickstart_with_attu.md): Attu 是 Milvus 的一体化开源管理工具。它具有直观的图形用户界面（GUI），可让您轻松与数据库交互。只需点击几下，您就可以直观地查看集群状态、管理元数据、执行数据查询等。 - [推荐系统](https://www.xlonglab.com/docs/v2.6.x/tutorials/recommendation_system.md): 利用 Milvus 建立个性化推荐系统。 - [使用 Milvus 进行文本到图像搜索](https://www.xlonglab.com/docs/v2.6.x/tutorials/text_image_search.md): 在本教程中，我们将探讨如何使用 OpenAI 的 CLIP（对比语言-图像预训练）模型和 Milvus 实现基于文本的图像检索。我们将使用 CLIP 生成图像嵌入，将其存储在 Milvus 中，并执行高效的相似性搜索。 - [文本搜索引擎](https://www.xlonglab.com/docs/v2.6.x/tutorials/text_search_engine.md): 使用 Milvus 建立文本搜索引擎。 - [教程概览](https://www.xlonglab.com/docs/v2.6.x/tutorials/tutorials-overview.md): 本页为您提供与 Milvus 互动的教程列表。 - [在 asyncio 中使用 AsyncMilvusClient](https://www.xlonglab.com/docs/v2.6.x/tutorials/use-async-milvus-client-with-asyncio.md): AsyncMilvusClient 是一种异步 MilvusClient，它提供了一种基于协程（coroutine）的 API，可通过 asyncio 对 Milvus 进行非阻塞访问。本文将介绍调用 AsyncMilvusClient 提供的 API 的过程以及需要注意的方面。 - [使用 ColPali 与 Milvus 一起进行多模态检索](https://www.xlonglab.com/docs/v2.6.x/tutorials/use_ColPali_with_milvus.md): 在本笔记本中，我们将这种多向量表示法称为 "ColBERT Embedding"，以求通俗易懂。不过，实际使用的模型是 ColPali 模型。我们将演示如何使用 Milvus 进行多向量检索。在此基础上，我们将介绍如何使用 ColPali 根据给定查询检索网页。 - [向量可视化](https://www.xlonglab.com/docs/v2.6.x/tutorials/vector_visualization.md): 在本例中，我们将展示如何使用 t-SNE 可视化 Milvus 中的嵌入（向量）。 - [视频相似性搜索](https://www.xlonglab.com/docs/v2.6.x/tutorials/video_similarity_search.md): 利用 Milvus 建立视频相似性搜索系统。 ### 用户指南 - [创建 Collection](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/create-collection.md): 您可以通过定义 Schema、索引参数、度量类型以及创建时是否加载来创建一个 Collection。本页将介绍如何从头开始创建 Collection。 - [删除 Collection](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/drop-collection.md): 如果不再需要某个 Collection，您可以将其删除。 - [加载和释放](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/load-and-release.md): 加载 Collection 是在 Collection 中进行相似性搜索和查询的前提。本页主要介绍加载和释放 Collection 的步骤。 - [管理 Alias](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/manage-aliases.md): Milvus 提供别名管理功能。本页演示了创建、列出、更改和删除别名的程序。 - [Collection 概述](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/manage-collections.md): 在 Milvus 上，您可以创建多个 Collection 来管理数据，并将数据作为实体插入到 Collection 中。Collection 和实体类似于关系数据库中的表和记录。本页可帮助你了解 Collection 及相关概念。 - [管理 Partition](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/manage-partitions.md): Partition 是一个 Collection 的子集。每个 Partition 与其父 Collection 共享相同的数据结构，但只包含 Collection 中的一个数据子集。本页将帮助你了解如何管理 Partition。 - [修改 Collection](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/modify-collection.md): 您可以重命名一个 Collection 或更改其设置。本页主要介绍如何修改 Collection。 - [设置 Collection TTL](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/set-collection-ttl.md): 配置 Collection 的 Time-to-Live（TTL）属性，使 Milvus 自动清理超过保留期限的数据。 - [查看 Collection](https://www.xlonglab.com/docs/v2.6.x/userGuide/collections/view-collections.md): 您可以获取当前连接的数据库中所有 Collection 的名称列表，并查看特定 Collection 的详细信息。 - [导入数据](https://www.xlonglab.com/docs/v2.6.x/userGuide/data-import/import-data.md): 本页演示导入准备好的数据的程序。 - [准备源数据](https://www.xlonglab.com/docs/v2.6.x/userGuide/data-import/prepare-source-data.md): 本页讨论了在开始批量将数据插入 Collection 之前应该考虑的一些问题。 - [Azure OpenAI](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/azure-openai.md): 本主题介绍如何在 Milvus 中配置和使用 Azure OpenAI Embedding Function。 - [Bedrock](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/bedrock.md): 本主题介绍如何在 Milvus 中配置和使用 Amazon Bedrock Embedding Function。 - [Cohere](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/cohere.md): 本主题介绍如何在 Milvus 中配置和使用 Cohere Embedding Function。 - [DashScope](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/dashscope.md): 本主题介绍如何在 Milvus 中配置和使用 DashScope Embedding Function。 - [Embedding Function 概述](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/embedding-function-overview.md): 通过 Milvus 的 Function 模块，您可以自动调用外部 Embedding 服务提供商（如 OpenAI、AWS Bedrock、Google Vertex AI 等），将原始文本数据转换为 Embedding。… - [Google Gemini](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/google-gemini.md): 通过选择一个模型，并使用 Gemini API 密钥配置 Milvus，即可将 Google Gemini Embedding 模型与 Milvus 结合使用。 - [Hugging Face TEI](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/hugging-face-tei.md): Hugging Face 文本嵌入推理（TEI）是专为文本 Embedding 模型设计的高性能推理服务器。本指南介绍如何将 Hugging Face TEI 与 Milvus 结合使用，以高效生成文本嵌入。 - [Hugging Face](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/hugging-face.md): 本主题介绍如何在 Milvus 中使用托管的 Hugging Face Inference Providers 生成文本 Embedding。 - [OpenAI](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/openai.md): 通过选择一个模型并用 OpenAI API 密钥配置 Milvus，使用 Milvus 的 OpenAI Embedding 模型。 - [SiliconFlow](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/siliconflow.md): 本主题介绍如何在 Milvus 中配置和使用 SiliconFlow Embedding Function。 - [Vertex AI](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/vertex-ai.md): Google Cloud Vertex AI 是专为文本 Embedding 模型设计的高性能服务。本指南介绍如何将 Google Cloud Vertex AI 与 Milvus 结合使用，实现高效的文本嵌入生成。 - [Voyage AI](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/embedding-function/voyage-ai.md): 本主题介绍如何在 Milvus 中配置和使用 Voyage AI Embedding Function。 - [Boost Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/boost-ranker.md): Boost Rankers 并不完全依赖基于向量距离计算的语义相似性，而是让您以有意义的方式影响搜索结果。它是使用元数据过滤快速调整搜索结果的理想选择。 - [Cohere Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/cohere-ranker.md): Cohere Ranker 利用 Cohere 强大的 Reranker 模型，通过语义 Reranking 增强搜索相关性。它通过强大的 API 基础设施和优化的性能为生产环境提供企业级 Reranker 功能。 - [Decay Ranker Overview](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/decay-ranker-overview.md): 在传统的向量搜索中，搜索结果的排序完全取决于向量的相似性--向量在数学空间中的匹配程度。但在实际应用中，内容的真正相关性往往不仅仅取决于语义相似性。 - [指数衰减](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/exponential-decay.md): 指数衰减会使搜索结果在初期急剧下降，随后出现长尾。就像突发新闻周期一样，一开始相关性会迅速降低，但随着时间的推移，一些新闻的重要性会保持不变，指数衰减会对超出您理想范围的条目施加剧烈的惩罚，同时仍然保持较远的条目可以被发现。这种方法非常适合您想优先考虑近似性或相关性，但又不想完全排除较远选项的情况。 - [高斯衰减](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/gaussian-decay.md): 高斯衰减也称为正常衰减，它能对搜索结果进行最自然的调整。就像人的视力会随着距离的增加而逐渐模糊一样，高斯衰减会创建一条平滑的钟形曲线，随着项目远离您的理想点，相关性也会逐渐降低。这种方法非常适合您需要一种平衡的衰减方式，既不会对您偏好范围之外的项目造成严重影响，又能显著降低远处项目的相关性。 - [Hugging Face Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/hugging-face-ranker.md): 本主题介绍如何使用托管的 Hugging Face sentence-similarity models 对 Milvus 搜索结果进行 rerank。 - [线性衰减](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/linear-decay.md): 线性衰减会使搜索结果中的相关性直线下降，并以绝对零点为终点。就像即将发生的事件倒计时一样，相关性会逐渐减弱，直到事件过去，线性衰减会随着项目远离您的理想点而使相关性可预测地稳步降低，直到完全消失。这种方法非常适合在需要一致的衰减率和明确的分界线时使用，可确保超出一定界限的项目完全被排除在搜索结果之外。 - [Model Ranker 概述](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/model-ranker-overview.md): 传统的向量搜索纯粹通过数学相似度--向量在高维空间中的匹配程度--来对结果进行排序。这种方法虽然高效，但往往会忽略真正的语义相关性。考虑搜索 "数据库优化的最佳实践"：您可能会收到向量相似度很高的文档，这些文档经常提到这些术语，但实际上并没有提供可操作的优化策略。 - [RRF Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/rrf-ranker.md): Reciprocal Rank Fusion（RRF）Ranker 是 Milvus 混合搜索的一种 Reranking 策略，它根据多个向量搜索路径的排名位置而不是原始相似度得分来平衡搜索结果。就像体育比赛考虑的是球员的排名而不是个人统计数据一样，RRF Ranker 根据每个项目在不同搜索路径中的排名高低来组合搜索结果，从而创建一个公平、均衡的最终排名。 - [SiliconFlow Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/siliconflow-ranker.md): SiliconFlow Ranker 利用 SiliconFlow 的综合 Reranking 模型，通过语义 Reranking 增强搜索相关性。它提供灵活的文档分块功能，并支持来自不同供应商的各种专业 Reranker 模型。 - [TEI Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/tei-ranker.md): TEI Ranker 利用 Hugging Face 提供的文本嵌入推理（TEI）服务，通过语义重排来提高搜索相关性。它代表了一种超越传统向量相似性的先进搜索结果排序方法。 - [教程：在 Milvus 中实现基于时间的排名](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/tutorial-implement-a-time-based-ranking-in-milvus.md): 在许多搜索应用中，内容的新鲜度与相关性同等重要。新闻文章、产品列表、社交媒体帖子和研究论文都受益于兼顾语义相关性和新鲜度的排名系统。本教程演示了如何在 Milvus 中使用衰减 Ranker 实现基于时间的排名。 - [vLLM Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/vllm-ranker.md): vLLM Ranker 利用 vLLM 推论框架，通过语义 Reranking 来提高搜索相关性。它代表了一种超越传统向量相似性的先进搜索结果排序方法。 - [Voyage AI Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/voyage-ai-ranker.md): Voyage AI Ranker 利用 Voyage AI 的专业 Reranker，通过语义重排提高搜索相关性。它提供高性能的重排能力，针对检索增强生成（RAG）和搜索应用进行了优化。 - [Weighted Ranker](https://www.xlonglab.com/docs/v2.6.x/userGuide/embeddings-reranking/reranking/weighted-ranker.md): WeightedRanker 通过为每个搜索路径分配不同的重要性权重，智能地组合来自多个搜索路径的结果并确定优先级。与技艺高超的厨师平衡多种配料以制作完美菜肴的方式类似，WeightedRanker 也会平衡不同的搜索结果，以提供最相关的综合结果。这种方法非常适合在多个向量字段或模式中进行搜索，其中某些领域对最终排名的贡献应比其他领域更大。 - [BIN_FLAT](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/binary/bin-flat.md): BIN_FLAT 索引是 FLAT 索引的一种变体，专门为二进制嵌入而定制。在向量相似性搜索要求在相对较小的百万级数据集上达到完美精确度的应用中，它表现出色。通过采用一种穷举搜索方法--将每个目标输入与数据集中的所有向量进行比较--BIN_FLAT 可以保证得到精确的结果。… - [BIN_IVF_FLAT](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/binary/bin-ivf-flat.md): BIN_IVF_FLAT 索引是 IVF_FLAT 索引的变种，专门用于二元嵌入。它首先将向量数据划分为多个簇（nlist 单元），然后将目标输入向量与每个簇的中心进行比较，从而提高了查询效率。BIN_IVF_FLAT 能显著缩短查询时间，同时允许用户在准确性和速度之间进行微调。更多信息，请参阅 IVF_FLAT。 - [MINHASH_LSH](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/binary/minhash-lsh.md): 使用 MINHASH_LSH 索引加速大规模文本数据集的近似重复检测和 Jaccard 相似度搜索。 - [AISAQ](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/aisaq.md): AISAQ 是一种基于磁盘的向量索引，它对 DISKANN 进行了扩展，可以处理十亿规模的数据集，而不会超出内存限制。与将压缩向量保存在内存中的 DISKANN 不同，AISAQ 将所有数据保存在磁盘上，提供两种模式以平衡性能和存储成本。 - [DISKANN](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/diskann.md): 在大规模场景中，数据集可能包括数十亿甚至数万亿个向量，标准的内存索引方法（如 HNSW、IVF_FLAT）往往因内存限制而跟不上步伐。DISKANN 提供了一种基于磁盘的方法，可以在数据集大小超过可用 RAM 时保持较高的搜索精度和速度，从而应对这些挑战。 - [FLAT](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/flat.md): FLAT 索引是最简单、最直接的浮点向量索引和搜索方法之一。它依赖于一种 "蛮力 "方法，即直接将每个查询向量与数据集中的每个向量进行比较，而无需任何高级预处理或数据结构。这种方法保证了准确性，由于对每个潜在匹配都进行了评估，因此可提供 100% 的召回率。 - [HNSW_PQ](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/hnsw-pq.md): HNSW_PQ 利用分层导航小世界（HNSW）图与乘积量化（PQ），创建了一种先进的向量索引方法，提供了可控的大小与准确性权衡。与 HNSW_SQ 相比，尽管查询处理速度较低，索引构建时间较长，但在相同压缩级别下，这种索引类型的召回率更高。 - [HNSW_PRQ](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/hnsw-prq.md): HNSW_PRQ 结合 HNSW 与 Product Residual Quantization（PRQ，乘积残差量化），在索引大小、搜索精度和计算开销之间提供可调节的权衡。 - [HNSW_SQ](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/hnsw-sq.md): HNSW_SQ 将层次导航小世界（HNSW）图与标量量化（SQ）相结合，创建了一种先进的向量索引方法，提供了可控的大小与精度权衡。与标准 HNSW 相比，这种索引类型在保持较高查询处理速度的同时，索引构建时间略有增加。 - [HNSW](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/hnsw.md): HNSW 索引是一种基于图的索引算法，可以提高搜索高维浮点向量时的性能。它具有出色的搜索精度和较低的延迟，但需要较高的内存开销来维护其分层图结构。 - [IVF_FLAT](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/ivf-flat.md): IVF_FLAT 索引是一种可以提高浮点向量搜索性能的索引算法。 - [IVF_PQ](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/ivf-pq.md): IVF_PQ 索引是一种基于量化的索引算法，用于高维空间中的近似近邻搜索。虽然 IVF_PQ 的速度不如某些基于图的方法，但它所需的内存通常要少得多，因此是大型数据集的实用选择。 - [IVF_RABITQ](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/ivf-rabitq.md): IVF_RABITQ 索引是一种基于二进制量化的索引算法，可将 FP32 向量量化为二进制表示。它提供了一个高度可配置的压缩率，并可选择细化以提高召回率，因此适用于需要显著优化存储的应用。 - [IVF_SQ8](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/ivf-sq8.md): IVF_SQ8 索引是一种基于量化的索引算法，旨在解决大规模相似性搜索难题。与穷举搜索方法相比，这种索引类型能以更小的内存占用实现更快的搜索。 - [SCANN](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/floating-vector/scann.md): Milvus 中的 SCANN 索引由谷歌的 ScaNN 库提供支持，旨在解决向量相似性搜索的扩展难题，在速度和准确性之间取得平衡，即使在传统上会给大多数搜索算法带来挑战的大型数据集上也是如此。 - [GPU_BRUTE_FORCE](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/gpu/gpu-brute-force.md): GPU_BRUTE_FORCE 索引专为 GPU 环境设计，适用于对准确性要求极高的场景。它通过将每个查询与数据集中的所有向量进行详尽比较，确保不会忽略任何潜在匹配，从而保证召回率为 1。利用 GPU 加速，GPU_BRUTE_FORCE 适用于要求向量相似性搜索绝对精确的应用。 - [GPU_CAGRA](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/gpu/gpu-cagra.md): GPU_CAGRA 索引是为 GPU 优化的基于图形的索引。与使用昂贵的训练级 GPU 相比，使用推理级 GPU 运行 Milvus GPU 版本可以更具成本效益。 - [GPU 索引概述](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/gpu/gpu-index-overview.md): 在 Milvus 中建立一个支持 GPU 的索引，可以显著提高高吞吐量和高调用场景下的搜索性能。 - [GPU_IVF_FLAT](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/gpu/gpu-ivf-flat.md): GPU_IVF_FLAT 索引是 IVF_FLAT 索引的 GPU 加速版本，专为 GPU 环境设计。它将向量数据划分为 nlist 簇单元，并通过首先比较目标查询向量和每个簇的中心来计算相似性。通过调整 nprobe 参数，只搜索最有希望的簇，从而减少查询时间，同时保持准确性和速度之间的平衡。有关基础概念的更多信息，请参阅 IVF_FLAT。 - [GPU_IVF_PQ](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/gpu/gpu-ivf-pq.md): GPU_IVF_PQ 索引以 IVF_PQ 概念为基础，将倒排文件聚类与乘积量化（PQ）相结合，后者将高维向量分解为更小的子空间并对其进行量化，从而实现高效的相似性搜索。GPU_IVF_PQ 专为 GPU 环境设计，利用并行处理加速计算并有效处理大规模向量数据。有关基础概念的更多信息，请参阅 IVF_PQ。 - [索引解释](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/index-explained.md): 索引是建立在数据之上的附加结构。其内部结构取决于所使用的近似近邻搜索算法。索引可以加快搜索速度，但在搜索过程中会产生额外的预处理时间、空间和 RAM。此外，使用索引通常会降低召回率（虽然影响可以忽略不计，但仍然很重要）。因此，本文将解释如何尽量减少使用索引的成本，同时最大限度地提高索引的效益。 - [BITMAP](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/scalar/bitmap.md): 位图索引适用于低基数（low-cardinality）标量字段，可通过紧凑的位图表示和按位运算加速过滤查询。 - [INVERTED](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/scalar/inverted.md): 当你需要对数据执行频繁的过滤查询时，倒排索引可以显著提高查询性能。Milvus 不需要扫描所有文档，而是使用倒排索引来快速查找与过滤条件相匹配的准确记录。 - [NGRAM](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/scalar/ngram.md): Milvus 中的 NGRAM 索引是为了加速对 VARCHAR 字段或 JSON 字段中特定 JSON 路径的 LIKE 查询而构建的。在建立索引之前，Milvus 会将文本分割成固定长度为 n 的重叠短子串，称为 n-gram。例如，当 n = 3 时，单词 "Milvus "会被拆分成 3 个词组："Mil"、"ilv"、"lvu "和 "vus"。… - [RTREE](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/scalar/rtree.md): RTREE 索引是一种基于树的数据结构，可加速对 Milvus 中几何字段的查询。如果您的 Collection 以已知文本 (WKT) 格式存储点、线或多边形等几何对象，并且希望加速空间过滤，那么 RTREE 是理想的选择。 - [STL_SORT](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/scalar/stl-sort.md): STL_SORT 索引是一种专门设计用于提高数值字段（INT8、INT16 等）、VARCHAR 字段或 TIMESTAMPTZ 字段查询性能的索引类型，其方法是按排序顺序组织数据。 - [SPARSE_INVERTED_INDEX](https://www.xlonglab.com/docs/v2.6.x/userGuide/indexes/sparse/sparse-inverted-index.md): SPARSE_INVERTED_INDEX 索引是 Milvus 用来高效存储和搜索稀疏向量的一种索引类型。这种索引类型利用了倒排索引的原理，为稀疏数据创建了一种高效的搜索结构。 - [删除 Entity](https://www.xlonglab.com/docs/v2.6.x/userGuide/insert-and-delete/delete-entities.md): 您可以通过过滤条件或主键删除不再需要的实体。 - [插入 Entity](https://www.xlonglab.com/docs/v2.6.x/userGuide/insert-and-delete/insert-update-delete.md): Collection 中的实体是共享同一组字段的数据记录。每条数据记录中的字段值构成一个实体。本页介绍如何在 Collection 中插入实体。 - [Upsert Entity](https://www.xlonglab.com/docs/v2.6.x/userGuide/insert-and-delete/upsert-entities.md): upsert 操作为插入或更新 Collection 中的实体提供了一种便捷的方法。 - [标量字段索引](https://www.xlonglab.com/docs/v2.6.x/userGuide/manage-indexes/index-scalar-fields.md): 本指南将指导您为整数、字符串等字段创建和配置标量索引。 - [索引向量字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/manage-indexes/index-vector-fields.md): 本指南将指导您完成创建和管理 Collection 中向量字段索引的基本操作。 - [使用 GPU 建立索引](https://www.xlonglab.com/docs/v2.6.x/userGuide/manage-indexes/index-with-gpu.md): 本指南介绍如何在 Milvus 中建立支持 GPU 的索引，以提高搜索性能。 - [Database](https://www.xlonglab.com/docs/v2.6.x/userGuide/manage_databases.md): Milvus 在 Collection 的上方引入了一个数据库层，为管理和组织数据提供了一种更有效的方式，同时支持多租户。 - [向现有 Collection 添加字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/add-fields-to-an-existing-collection.md): Milvus 允许您在现有的 Collection 中动态添加新字段，使您可以根据应用需求的变化，轻松发展数据 Schema。本指南通过实际示例向你展示如何在不同情况下添加字段。 - [更改 Collection 字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/alter-collection-field.md): 您可以更改 Collection 字段的属性，以更改列约束或执行更严格的数据完整性规则。 - [Analyzer 概述](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/analyzer-overview.md): 在文本处理中，分析器是将原始文本转换为结构化可搜索格式的关键组件。每个分析器通常由两个核心部件组成：Tokenizer（分词器）和过滤器。它们共同将输入文本转换为标记，完善这些标记，并为高效索引和检索做好准备。 - [Chinese](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/analyzer/chinese-analyzer.md): 中文分析器专为处理中文文本而设计，可提供有效的分段和标记化功能。 - [English](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/analyzer/english-analyzer.md): Milvus 中的英语分析器旨在处理英语文本，应用特定语言规则进行标记化和过滤。 - [Standard Analyzer](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/analyzer/standard-analyzer.md): 标准分析器是 Milvus 的默认分析器，如果没有指定分析器，它将自动应用于文本字段。它使用基于语法的标记化，因此对大多数语言都很有效。 - [根据使用场景选择 Analyzer](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/choose-the-right-analyzer-for-your-use-case.md): 根据语言、文本特征和搜索场景选择适合的 Analyzer，并了解 tokenizer、filter 与全文搜索的配置取舍。 - [Alphanumonly](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/alphanumonly-filter.md): alphanumonly 过滤器会删除包含非 ASCII 字符的标记，只保留字母数字术语。该过滤器适用于处理只有基本字母和数字的文本，不包括任何特殊字符或符号。 - [ASCII Folding](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/ascii-folding-filter.md): asciifolding 过滤器可将基本拉丁统一码块（前 127 个 ASCII 字符）以外的字符转换为其 ASCII 对应字符。例如，它能将 í 等字符转换为 i，使文本处理更简单、更一致，特别是对于多语言内容。 - [Cnalphanumonly](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/cnalphanumonly-filter.md): cnalphanumonly 过滤器会删除包含除汉字、英文字母或数字以外的任何字符的标记。 - [Cncharonly](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/cncharonly-filter.md): cncharonly 过滤器会删除包含任何非中文字符的标记。当您只想关注中文文本，过滤掉任何包含其他文字、数字或符号的标记时，这个过滤器就非常有用了。 - [Decompounder](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/decompounder-filter.md): 复合词拆分过滤器可根据指定词典将复合词拆分成单个成分，从而更方便地搜索复合词的部分内容。该过滤器对德语等经常使用复合词的语言特别有用。 - [Length](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/length-filter.md): 长度过滤器可移除不符合指定长度要求的标记，从而控制文本处理过程中保留的标记长度。 - [Lowercase](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/lowercase-filter.md): 小写过滤器将 Tokenizer（分词器）生成的 Token（词元）转换为小写，使搜索不区分大小写。例如，它可以将\["高"、"性能"、"向量"、"数据库"\]转换为\["高"、"性能"、"向量"、"数据库"\]。 - [Regex](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/regex-filter.md): regex 过滤器是一种正则表达式过滤器：令牌生成器生成的任何令牌只有在与您提供的表达式相匹配时才会被保留，否则都会被丢弃。 - [Remove Punct](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/removepunct-filter.md): removepunct 过滤器会去掉一些 Tokenizer（分词器）（如 jieba、Lindera 和 icu）通常会保留的标点符号、空格和换行符。当你想要一个更干净的标记流，只包含有意义的文本标记，不含逗号、句号和其他标点符号时，就可以使用它。 - [Stemmer](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/stemmer-filter.md): 词干过滤器将单词还原为其基本形式或词根形式（称为词干化），从而更容易匹配不同词性中含义相似的单词。词干过滤器支持多种语言，可在各种语言环境中进行有效搜索和索引。 - [Stop](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/filter/stop-filter.md): 停用过滤器会从标记化文本中移除指定的停用词，帮助剔除常见的、意义不大的词。您可以使用 stop_words 参数配置停用词列表。 - [多语言 Analyzer](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/multi-language-analyzers.md): Milvus 在进行文本分析时，通常会在 Collection 的整个文本字段中应用一个分析器。如果该分析器针对英语进行了优化，那么它在处理其他语言（如中文、西班牙语或法语）所需的完全不同的标记化和词干规则时就会遇到困难，从而导致召回率降低。… - [ICU](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/tokenizer/icu-tokenizer.md): icu tokenizer 基于 Unicode 国际化组件（ICU）开源项目开发，该项目为软件国际化提供了关键工具。通过使用 ICU 的分词算法，令牌生成器可以准确地将世界上大多数语言的文本分割成单词。 - [Jieba](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/tokenizer/jieba-tokenizer.md): Jieba Tokenizer（分词器）通过将中文文本分解为单词来进行处理。 - [Language Identifier](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/tokenizer/language-identifier.md): 语言识别器（language_identifier）是一个专门的 Tokenizer（分词器），旨在通过自动语言分析过程来增强 Milvus 的文本搜索功能。它的主要功能是检测文本字段的语言，然后动态应用最适合该语言的预配置分析器。这对于处理多种语言的应用程序来说尤为重要，因为它消除了按输入手动分配语言的需要。 - [Lindera](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/tokenizer/lindera-tokenizer.md): lindera tokenizer 可进行基于词典的词形分析。它专为日语和韩语设计，在这两种语言中，单词之间没有空格分隔，语法标记（微粒）直接附着在单词上。 - [Standard Tokenizer](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/tokenizer/standard-tokenizer.md): Milvus 中的标准 Tokenizer 根据空格和标点符号分割文本，因此适用于大多数语言。 - [Whitespace](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/analyzer/tokenizer/whitespace-tokenizer.md): 只要单词之间有空格，空白 Tokenizer（分词器）就会将文本划分为术语。 - [StructArray](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/array-of-structs.md): 使用 StructArray 字段，以向量和标量字段的共享模式存储有序的 Struct 元素。 - [数组字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/array_data_type.md): ARRAY 字段存储同一数据类型元素的有序 Collection。下面举例说明 ARRAY 字段如何存储数据： - [Data Model Design with an Array of Structs](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/best-practices-for-array-of-structs.md): 现代人工智能应用，尤其是物联网（IoT）和自动驾驶领域的应用，通常会对丰富的结构化事件进行推理：带有时间戳和 Embedding 的传感器读数、带有错误代码和音频片段的诊断日志，或者带有位置、速度和场景上下文的行程片段。这些都要求数据库能够原生支持嵌套数据的摄取和搜索。 - [二进制向量](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/binary-vector.md): 二进制向量是一种特殊的数据表示形式，它将传统的高维浮点向量转换成只包含 0 和 1 的二进制向量。这种转换不仅压缩了向量的大小，还降低了存储和计算成本，同时保留了语义信息。当对非关键特征的精度要求不高时，二进制向量可以有效保持原始浮点向量的大部分完整性和实用性。 - [默认值](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/default-values.md): 为标量字段设置默认值，以便 Milvus 在插入实体时填补缺失值。 - [稠密向量](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/dense-vector.md): 稠密向量是广泛应用于机器学习和数据分析的数字数据表示。它们由包含实数的数组组成，其中大部分或所有元素都不为零。与稀疏向量相比，稠密向量在同一维度上包含更多信息，因为每个维度都持有有意义的值。这种表示方法能有效捕捉复杂的模式和关系，使数据在高维空间中更容易分析和处理。稠密向量通常有固定的维数，从几十到几百甚至上千不等，具体取决于具体的应用和要求。 - [动态字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/enable-dynamic-field.md): Milvus 允许你通过一个名为动态字段的特殊功能，插入结构灵活、不断发展的实体。该字段以名为 $meta 的隐藏 JSON 字段的形式实现，它会自动存储数据中任何未在 Collection Schema 中明确定义的字段。 - [Geometry 字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/geometry-field.md): 在构建地理信息系统（GIS）、制图工具或基于位置的服务等应用程序时，您经常需要存储和查询几何数据。Milvus 中的 GEOMETRY 数据类型提供了一种本地方式来存储和查询灵活的几何数据，从而解决了这一难题。 - [JSON 字段概述](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/json-field-overview.md): 在构建产品目录、内容管理系统或用户偏好引擎等应用程序时，您往往需要在存储向量 Embedding 的同时存储灵活的元数据。产品属性因类别而异，用户偏好随时间演变，文档属性具有复杂的嵌套结构。Milvus 中的 JSON 字段可以在不影响性能的情况下存储和查询灵活的结构化数据，从而解决了这一难题。 - [JSON 索引](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/json-indexing.md): JSON 字段为在 Milvus 中存储结构化元数据提供了一种灵活的方式。如果没有索引，对 JSON 字段的查询需要全 Collection 扫描，随着数据集的增长，扫描速度也会变慢。JSON 索引通过在 JSON 数据中创建索引来实现快速查询。 - [JSON Shredding（JSON 列式拆分）](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/json-shredding.md): JSON Shredding 将高频 JSON 键组织为面向查询优化的拆分列，并使用共享列处理低频键，从而在保留 JSON 灵活性的同时降低过滤和检索开销。 - [可空字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/nullable-and-default.md): 配置可空字段和默认值，包括 Schema、插入、索引、搜索和过滤行为。 - [数字字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/number.md): 数字字段是存储数值的标量字段。这些数值可以是整数（整数）或十进制数（浮点数）。它们通常用于表示数量、测量值或任何需要进行数学处理的数据。 - [主键字段与 AutoID](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/primary-field.md): Milvus 中的每个 Collection 都必须有一个主键字段，以唯一标识每个实体。这个字段确保每个实体都能被插入、更新、查询或删除，而不会产生歧义。 - [Schema 概述](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema): Schema 定义了 Collection 的数据结构。在创建 Collection 之前，你需要设计好它的 Schema。本页将帮助你理解 Collection Schema，并自行设计一个示例 Schema。 - [稀疏向量](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/sparse_vector.md): 稀疏向量是信息检索和自然语言处理中捕捉表层术语匹配的重要方法。虽然稠密向量在语义理解方面表现出色，但稀疏向量往往能提供更可预测的匹配结果，尤其是在搜索特殊术语或文本标识符时。 - [字符串字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/string.md): 在 Milvus 中，VARCHAR 是用于存储字符串数据的数据类型。 - [TIMESTAMPTZ 字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/timestamptz-field.md): 跨区域时间跟踪应用程序，如电子商务系统、协作工具或分布式日志记录，需要精确处理带有时区的时间戳。Milvus 的 TIMESTAMPTZ 数据类型通过存储带有相关时区的时间戳，提供了这种功能。 - [JSON 字段](https://www.xlonglab.com/docs/v2.6.x/userGuide/schema/use-json-fields.md): Milvus 允许您使用 JSON 数据类型在单个字段中存储和索引结构化数据。这样就能实现具有嵌套属性的灵活 Schema，同时还能通过 JSON 索引进行高效过滤。 - [ARRAY Operators](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/boolean/array-operators.md): Milvus 提供 ARRAY Operators，用于过滤 ARRAY 字段并部分更新 ARRAY 字段值。 - [基本运算符](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/boolean/basic-operators.md): Milvus 提供丰富的基本运算符，帮助您高效地过滤和查询数据。这些运算符允许您根据标量字段、数字计算、逻辑条件等来完善搜索条件。了解如何使用这些运算符对于建立精确查询和最大限度地提高搜索效率至关重要。 - [过滤说明](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/boolean): Milvus 提供强大的过滤功能，可对数据进行精确查询。过滤表达式允许你针对特定的标量字段，用不同的条件细化搜索结果。本指南介绍如何在 Milvus 中使用过滤表达式，并以`query` 操作为例进行说明。您还可以在搜索和删除请求中应用这些过滤器。 - [过滤器模板](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/boolean/filtering-templating.md): 在 Milvus 中，包含大量元素的复杂过滤表达式，尤其是那些涉及非 ASCII 字符（如中日韩字符）的表达式，会严重影响查询性能。为了解决这个问题，Milvus 引入了过滤表达式模板机制，旨在通过减少解析复杂表达式所花费的时间来提高效率。本页介绍了在搜索、查询和删除操作中使用过滤表达式模板的方法。 - [Geometry 运算符](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/boolean/geometry-operators.md): Milvus 支持对几何字段进行空间过滤的一系列运算符，这些运算符对于管理和分析几何数据至关重要。通过这些运算符，可以根据对象之间的几何关系检索实体。 - [JSON 运算符](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/boolean/json-operators.md): Milvus 支持用于查询和过滤 JSON 字段的高级运算符，使其成为管理复杂结构化数据的完美工具。这些运算符可实现对 JSON 文档的高效查询，允许您根据 JSON 字段中的特定元素、值或条件检索实体。本节将指导您在 Milvus 中使用特定于 JSON 的运算符，并提供实际示例来说明其功能。 - [随机抽样](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/boolean/random-sampling.md): 在处理大规模数据集时，您往往不需要处理所有数据来获得洞察力或测试过滤逻辑。随机抽样提供了一种解决方案，让您可以处理具有统计代表性的数据子集，从而大大减少查询时间和资源消耗。 - [StructArray Operators](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/boolean/struct-array-operators.md): 使用元素过滤器和匹配族运算符过滤 StructArray 字段内的标量子字段。 - [Clustering Compaction](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/clustering-compaction.md): Clustering Compaction 根据 Clustering Key 重新组织 Segment 中的 Entity，以缩小搜索范围并提升大型 Collection 的查询性能。 - [从 Elasticsearch 查询迁移到 Milvus](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/elasticsearch-queries-to-milvus.md): 基于 Apache Lucene 构建的 Elasticsearch 是领先的开源搜索引擎。然而，它在现代人工智能应用中面临着各种挑战，包括更新成本高、实时性差、分片管理效率低、非云原生设计以及资源需求过高。作为云原生向量数据库，Milvus 通过解耦存储和计算、高效的高维数据索引以及与现代基础设施的无缝集成，克服了这些问题。… - [过滤搜索](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/filtered-search.md): ANN 搜索能找到与指定 Embedding 最相似的 Embedding。但是，搜索结果不一定总是正确的。您可以在搜索请求中包含过滤条件，这样 Milvus 就会在进行 ANN 搜索前进行元数据过滤，将搜索范围从整个 Collection 缩小到只搜索符合指定过滤条件的实体。 - [全文搜索](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/full-text-search.md): 全文搜索是一种在文本数据集中检索包含特定术语或短语的文档，然后根据相关性对结果进行排序的功能。该功能克服了语义搜索的局限性（语义搜索可能会忽略精确的术语），确保您获得最准确且与上下文最相关的结果。此外，它还能接受原始文本输入，自动将文本数据转换为稀疏嵌入，无需手动生成 Embedding，从而简化了向量搜索。 - [查询](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/get-and-scalar-query.md): 除 ANN 搜索外，Milvus 还支持通过查询进行元数据过滤。本页将介绍如何使用查询、获取和查询迭代器来执行元数据过滤。 - [分组搜索](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/grouping-search.md): 分组搜索允许 Milvus 按指定字段中的值对搜索结果进行分组，以便在更高层次上汇总数据。例如，你可以使用基本的 ANN 搜索来查找与手头这本书类似的书籍，但你也可以使用分组搜索来查找可能涉及该书所讨论主题的书籍类别。本主题将介绍如何使用分组搜索以及主要注意事项。 - [文本匹配](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/keyword-match.md): Milvus 中的文本匹配功能可根据特定术语进行精确的文档检索。该功能主要用于满足特定条件的过滤搜索，并可结合标量过滤来细化查询结果，允许在符合标量标准的向量内进行相似性搜索。 - [度量类型](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/metric.md): 相似度量用于衡量向量之间的相似性。选择合适的距离度量有助于显著提高分类和聚类性能。 - [使用 mmap](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/mmap.md): 内存映射（Mmap）实现了对磁盘上大型文件的直接内存访问，使 Milvus 可以在内存和硬盘中同时存储索引和数据。这种方法有助于根据访问频率优化数据放置策略，在不明显影响搜索性能的情况下扩大 Collection 的存储容量。本页帮助你了解 Milvus 如何使用 mmap 实现快速高效的数据存储和检索。 - [多向量混合搜索](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/multi-vector-search.md): 在许多应用中，可以通过标题和描述等丰富的信息集或文本、图像和音频等多种模式来搜索对象。例如，如果文本或图像符合搜索查询的语义，就可以搜索包含一段文本和一幅图像的推文。混合搜索将这些不同领域的搜索结合在一起，从而增强了搜索体验。Milvus 允许在多个向量字段上进行搜索，同时进行多个近似近邻（ANN）搜索，从而支持这种搜索。… - [Phrase Match](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/phrase-match.md): 短语匹配可让您搜索包含精确短语查询词的文档。默认情况下，单词必须以相同的顺序出现，并且彼此直接相邻。例如，查询 "机器人机器学习 "会匹配"...典型的机器人机器学习模型... "这样的文本，其中 "机器人"、"机器 "和 "学习 "依次出现，中间没有其他词。 - [主键搜索](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/primary-key-search.md): 在进行相似性搜索时，总是会要求您提供一个或多个查询向量，即使查询向量已经存在于目标 Collection 中。为了避免在搜索前检索向量，可以使用主键来代替。 - [范围搜索](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/range-search.md): 范围搜索将返回实体的距离或得分限制在特定范围内，从而提高搜索结果的相关性。本页将帮助您了解什么是范围搜索以及进行范围搜索的步骤。 - [使用 Embedding 列表进行检索](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/search-with-embedding-lists.md): 本页介绍如何使用 Milvus 中的结构体数组设置 ColBERT 文本检索系统和 ColPali 文本检索系统，通过结构体数组，可以将文档连同其向量块一起存储在嵌入列表中。 - [基本向量搜索](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/single-vector-search.md): 近似近邻（ANN）搜索以记录 Embedding 排序顺序的索引文件为基础，根据接收到的搜索请求中携带的查询向量定位 Embedding 子集，将查询向量与子群中的向量进行比较，并返回最相似的结果。通过 ANN 搜索，Milvus 提供了高效的搜索体验。本页将帮助您了解如何进行基本的 ANN 搜索。 - [文本高亮显示](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/text-highlighter.md): Milvus 中的高亮显示器通过使用可定制的标签对文本字段中的匹配术语进行注释。高亮显示有助于解释文档匹配的原因，提高结果的可读性，并支持在搜索和 RAG 应用程序中进行丰富的渲染。 - [使用 Partition Key](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/use-partition-key.md): Partition Key 是一种基于 Partition 的搜索优化解决方案。通过指定一个特定的标量字段作为 Partition Key，并在搜索过程中根据 Partition Key 指定过滤条件，可以将搜索范围缩小到几个 Partition，从而提高搜索效率。本文将介绍如何使用 Partition Key 及相关注意事项。 - [搜索迭代器](https://www.xlonglab.com/docs/v2.6.x/userGuide/search-query-get/with-iterators.md): ANN Search 对单次查询可调用的实体数量有最大限制，因此仅使用基本 ANN Search 可能无法满足大规模检索的需求。对于 topK 超过 16,384 的 ANN Search 请求，建议考虑使用 SearchIterator。本节将介绍如何使用 SearchIterator 以及相关注意事项。 - [分层存储的最佳实践](https://www.xlonglab.com/docs/v2.6.x/userGuide/storage-optimization/best-practices-for-tiered-storage.md): Milvus 提供分层存储，帮助您有效处理大规模数据，同时平衡查询延迟、容量和资源使用。本指南总结了典型工作负载的推荐配置，并解释了每种调整策略背后的原因。 - [驱逐](https://www.xlonglab.com/docs/v2.6.x/userGuide/storage-optimization/eviction.md): 驱逐管理 Milvus 中每个 QueryNode 的缓存资源。启用后，一旦达到资源阈值，它就会自动删除缓存数据，确保性能稳定，防止内存或磁盘耗尽。 - [分层存储概述](https://www.xlonglab.com/docs/v2.6.x/userGuide/storage-optimization/tiered-storage-overview.md): 在 Milvus 中，传统的满载模式要求每个 QueryNode 在初始化时加载段的所有数据字段和索引，即使是可能永远不会被访问的数据。这虽然确保了数据的即时可用性，但往往会造成资源浪费，包括内存使用率高、磁盘活动频繁以及 I/O 开销大，尤其是在处理大规模数据集时。 - [预热](https://www.xlonglab.com/docs/v2.6.x/userGuide/storage-optimization/warm-up.md): 预热功能是对分层存储的补充，可在段可查询前将选定字段或索引预加载到缓存中。您可以在集群、 Collection 或单个字段/索引级别配置预热，从而实现对首次查询延迟和资源使用的精细控制。 - [安装 Birdwatcher](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/birdwatcher_install_guides.md): 了解如何安装 Birdwatcher 以调试 Milvus。 - [Birdwatcher](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/birdwatcher_overview.md): Birdwatcher 是 Milvus 2.x 的一个调试工具。它连接到 etcd，并检查 Milvus 系统的状态。 - [使用 Birdwatcher](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/birdwatcher_usage_guides.md): 了解如何使用 Birdwatcher 调试 Milvus。 - [Milvus CLI 命令参考](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/cli_commands.md): 使用命令与 Milvus 互动。 - [Milvus 命令行界面](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/cli_overview.md): Milvus 命令行界面（CLI）是一种命令行工具，支持数据库连接、数据操作和数据导入导出。 - [安装 Milvus CLI](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/install_cli.md): 了解如何安装 Milvus CLI。 - [跨存储桶在实例间迁移](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-backup/common-cases/cross-bucket-backup-and-restore.md): 本主题详细介绍如何从一个 Milvus 实例备份 Collection，并将其恢复到同一 Object Storage 中使用不同存储桶的另一个 Milvus 实例。 - [在跨 S3 环境的实例间迁移](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-backup/common-cases/multi-storage-backup-and-restore.md): 本主题详细介绍了从一个 Milvus 实例备份一个 Collection 并将其还原到另一个实例的过程 - [在一个存储桶中的不同实例之间迁移（不同的根路径）](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-backup/common-cases/shared-bucket-backup-and-restore.md): 本主题将详细介绍在使用共享存储桶存储对象时，从一个 Milvus 实例备份一个 Collection 并将其恢复到另一个实例的过程 - [在一个实例中备份和还原](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-backup/common-cases/single-instance-backup-and-restore.md): 本主题将详细介绍在同一 Milvus 实例中备份 Collection 并从备份中还原的过程 - [使用 API 备份和恢复数据](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-backup/milvus_backup_api.md): 了解如何通过 API 使用 Milvus 备份功能 - [使用命令备份和恢复数据](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-backup/milvus_backup_cli.md): 学习如何通过 CLI 使用 Milvus 备份 - [Milvus 备份](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-backup/milvus_backup_overview.md): Milvus-Backup 是一款允许用户备份和恢复 Milvus 数据的工具。 - [故障转移](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-cdc/cdc_failover.md): 了解如何在主 Milvus 集群不可用时执行故障转移。 - [切换](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-cdc/cdc_switchover.md): 了解如何使用 Milvus CDC 在主用和备用 Milvus 集群之间执行计划切换。 - [Milvus CDC](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-cdc/milvus_cdc_overview.md): Milvus CDC 可将数据更改从一个 Milvus 集群复制到另一个集群，以实现主备灾难恢复。 - [设置 CDC 复制](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-cdc/set_up_cdc_replication.md): 了解如何部署两个 Milvus 集群并在它们之间配置 CDC 复制。 - [Milvus SDK Code Helper 指南](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-sdk-helper-mcp.md): 通过 MCP 为 AI coding assistant 提供与当前 Milvus SDK 一致的 API 上下文。 - [Milvus WebUI](https://www.xlonglab.com/docs/v2.6.x/userGuide/tools/milvus-webui.md): Milvus Web UI 是 Milvus 的图形化管理工具。它以简单直观的界面提高了系统的可观察性。你可以 ### API Reference - [About Milvus GO SDK | Go API · About](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/About.md) - [AddPrivilegesToGroup() | Go API · Authentication/AddPrivilegesToGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/AddPrivilegesToGroup.md) - [BackupRBAC() | Go API · Authentication/BackupRBAC](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/BackupRBAC.md) - [CreatePrivilegeGroup() | Go API · Authentication/CreatePrivilegeGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/CreatePrivilegeGroup.md) - [CreateRole() | Go API · Authentication/CreateRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/CreateRole.md) - [CreateUser() | Go API · Authentication/CreateUser](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/CreateUser.md) - [DescribeRole() | Go API · Authentication/DescribeRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/DescribeRole.md) - [DescribeUser() | Go API · Authentication/DescribeUser](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/DescribeUser.md) - [DropPrivilegeGroup() | Go API · Authentication/DropPrivilegeGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/DropPrivilegeGroup.md) - [DropRole() | Go API · Authentication/DropRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/DropRole.md) - [DropUser() | Go API · Authentication/DropUser](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/DropUser.md) - [GrantPrivilege() | Go API · Authentication/GrantPrivilege](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/GrantPrivilege.md) - [GrantPrivilegeV2() | Go API · Authentication/GrantPrivilegeV2](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/GrantPrivilegeV2.md) - [GrantRole() | Go API · Authentication/GrantRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/GrantRole.md) - [ListPrivilegeGroups() | Go API · Authentication/ListPrivilegeGroups](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/ListPrivilegeGroups.md) - [ListRoles() | Go API · Authentication/ListRoles](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/ListRoles.md) - [ListUsers() | Go API · Authentication/ListUsers](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/ListUsers.md) - [PrivilegeGroup | Go API · Authentication/PrivilegeGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/PrivilegeGroup.md) - [RBACMeta | Go API · Authentication/RBACMeta](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/RBACMeta.md) - [RemovePrivilegesFromGroup() | Go API · Authentication/RemovePrivilegesFromGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/RemovePrivilegesFromGroup.md) - [RestoreRBAC() | Go API · Authentication/RestoreRBAC](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/RestoreRBAC.md) - [RevokePrivilege() | Go API · Authentication/RevokePrivilege](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/RevokePrivilege.md) - [RevokePrivilegeV2() | Go API · Authentication/RevokePrivilegeV2](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/RevokePrivilegeV2.md) - [RevokeRole() | Go API · Authentication/RevokeRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/RevokeRole.md) - [Role | Go API · Authentication/Role](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/Role.md) - [UpdatePassword() | Go API · Authentication/UpdatePassword](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/UpdatePassword.md) - [User | Go API · Authentication/User](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Authentication/User.md) - [CreateReplicateStream() | Go API · CDC/CreateReplicateStream](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/CDC/CreateReplicateStream.md) - [GetReplicateConfiguration() | Go API · CDC/GetReplicateConfiguration](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/CDC/GetReplicateConfiguration.md) - [GetReplicateInfo() | Go API · CDC/GetReplicateInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/CDC/GetReplicateInfo.md) - [UpdateReplicateConfiguration() | Go API · CDC/UpdateReplicateConfiguration](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/CDC/UpdateReplicateConfiguration.md) - [ClientConfig | Go API · Client/ClientConfig](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Client/ClientConfig.md) - [Close() | Go API · Client/Close](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Client/Close.md) - [GetServerVersion() | Go API · Client/GetServerVersion](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Client/GetServerVersion.md) - [New() | Go API · Client/New](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Client/New.md) - [AddCollectionField() | Go API · Collection/AddCollectionField](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/AddCollectionField.md) - [Alias | Go API · Collection/Alias](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/Alias.md) - [AlterAlias() | Go API · Collection/AlterAlias](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/AlterAlias.md) - [AlterCollectionFieldProperty() | Go API · Collection/AlterCollectionFieldProperty](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/AlterCollectionFieldProperty.md) - [AlterCollectionProperties() | Go API · Collection/AlterCollectionProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/AlterCollectionProperties.md) - [ConsistencyLevel | Go API · Collection/ConsistencyLevel](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/ConsistencyLevel.md) - [CreateAlias() | Go API · Collection/CreateAlias](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/CreateAlias.md) - [CreateCollection() | Go API · Collection/CreateCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/CreateCollection.md) - [DescribeAlias() | Go API · Collection/DescribeAlias](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/DescribeAlias.md) - [DescribeCollection() | Go API · Collection/DescribeCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/DescribeCollection.md) - [DropAlias() | Go API · Collection/DropAlias](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/DropAlias.md) - [DropCollection() | Go API · Collection/DropCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/DropCollection.md) - [DropCollectionProperties() | Go API · Collection/DropCollectionProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/DropCollectionProperties.md) - [Field | Go API · Collection/Field](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/Field.md) - [FieldType | Go API · Collection/FieldType](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/FieldType.md) - [Function | Go API · Collection/Function](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/Function.md) - [GetCollectionStats() | Go API · Collection/GetCollectionStats](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/GetCollectionStats.md) - [HasCollection() | Go API · Collection/HasCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/HasCollection.md) - [ListAliases() | Go API · Collection/ListAliases](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/ListAliases.md) - [ListCollections() | Go API · Collection/ListCollections](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/ListCollections.md) - [RenameCollection() | Go API · Collection/RenameCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/RenameCollection.md) - [Schema | Go API · Collection/Schema](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/Schema.md) - [TruncateCollection() | Go API · Collection/TruncateCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Collection/TruncateCollection.md) - [BulkImport() | Go API · DataImport/BulkImport](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/BulkImport.md) - [BulkImportOption | Go API · DataImport/BulkImportOption](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/BulkImportOption.md) - [BulkImportResponse | Go API · DataImport/BulkImportResponse](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/BulkImportResponse.md) - [GetImportProgress() | Go API · DataImport/GetImportProgress](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/GetImportProgress.md) - [GetImportProgressOption | Go API · DataImport/GetImportProgressOption](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/GetImportProgressOption.md) - [GetImportProgressResponse | Go API · DataImport/GetImportProgressResponse](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/GetImportProgressResponse.md) - [ListImportJobs() | Go API · DataImport/ListImportJobs](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/ListImportJobs.md) - [ListImportJobsOption | Go API · DataImport/ListImportJobsOption](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/ListImportJobsOption.md) - [ListImportJobsResponse | Go API · DataImport/ListImportJobsResponse](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/DataImport/ListImportJobsResponse.md) - [AlterDatabaseProperties() | Go API · Database/AlterDatabaseProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Database/AlterDatabaseProperties.md) - [CreateDatabase() | Go API · Database/CreateDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Database/CreateDatabase.md) - [DescribeDatabase() | Go API · Database/DescribeDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Database/DescribeDatabase.md) - [DropDatabase() | Go API · Database/DropDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Database/DropDatabase.md) - [DropDatabaseProperties() | Go API · Database/DropDatabaseProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Database/DropDatabaseProperties.md) - [ListDatabase() | Go API · Database/ListDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Database/ListDatabase.md) - [UseDatabase() | Go API · Database/UseDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Database/UseDatabase.md) - [AlterIndexProperties() | Go API · Management/AlterIndexProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/AlterIndexProperties.md) - [Compact() | Go API · Management/Compact](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Compact.md) - [CompactionState | Go API · Management/CompactionState](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/CompactionState.md) - [CreateIndex() | Go API · Management/CreateIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/CreateIndex.md) - [CreateIndexTask | Go API · Management/CreateIndexTask](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/CreateIndexTask.md) - [DescribeIndex() | Go API · Management/DescribeIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/DescribeIndex.md) - [DropIndex() | Go API · Management/DropIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/DropIndex.md) - [DropIndexProperties() | Go API · Management/DropIndexProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/DropIndexProperties.md) - [Flush() | Go API · Management/Flush](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Flush.md) - [FlushTask | Go API · Management/FlushTask](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/FlushTask.md) - [GetCompactionState() | Go API · Management/GetCompactionState](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/GetCompactionState.md) - [GetLoadState() | Go API · Management/GetLoadState](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/GetLoadState.md) - [GetPersistentSegmentInfo() | Go API · Management/GetPersistentSegmentInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/GetPersistentSegmentInfo.md) - [Index | Go API · Management/Index/Index](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/Index.md) - [NewAutoIndex() | Go API · Management/Index/NewAutoIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewAutoIndex.md) - [NewBinFlatIndex() | Go API · Management/Index/NewBinFlatIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewBinFlatIndex.md) - [NewBinIvfFlatIndex() | Go API · Management/Index/NewBinIvfFlatIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewBinIvfFlatIndex.md) - [NewBitmapIndex() | Go API · Management/Index/NewBitmapIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewBitmapIndex.md) - [NewDiskANNIndex() | Go API · Management/Index/NewDiskANNIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewDiskANNIndex.md) - [NewFlatIndex() | Go API · Management/Index/NewFlatIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewFlatIndex.md) - [NewGPUBruteForceIndex() | Go API · Management/Index/NewGPUBruteForceIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewGPUBruteForceIndex.md) - [NewGPUCagraIndex() | Go API · Management/Index/NewGPUCagraIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewGPUCagraIndex.md) - [NewGPUIVPFlatIndex() | Go API · Management/Index/NewGPUIVPFlatIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewGPUIVPFlatIndex.md) - [NewGPUIVPPQIndex() | Go API · Management/Index/NewGPUIVPPQIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewGPUIVPPQIndex.md) - [NewGenericIndex() | Go API · Management/Index/NewGenericIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewGenericIndex.md) - [NewHNSWIndex() | Go API · Management/Index/NewHNSWIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewHNSWIndex.md) - [NewInvertedIndex() | Go API · Management/Index/NewInvertedIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewInvertedIndex.md) - [NewIvfFlatIndex() | Go API · Management/Index/NewIvfFlatIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewIvfFlatIndex.md) - [NewIvfPQIndex() | Go API · Management/Index/NewIvfPQIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewIvfPQIndex.md) - [NewIvfRabitQIndex() | Go API · Management/Index/NewIvfRabitQIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewIvfRabitQIndex.md) - [NewIvfSQ8Index() | Go API · Management/Index/NewIvfSQ8Index](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewIvfSQ8Index.md) - [NewJSONPathIndex() | Go API · Management/Index/NewJSONPathIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewJSONPathIndex.md) - [NewMinHashLSHIndex() | Go API · Management/Index/NewMinHashLSHIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewMinHashLSHIndex.md) - [NewRTreeIndex() | Go API · Management/Index/NewRTreeIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewRTreeIndex.md) - [NewSCANNIndex() | Go API · Management/Index/NewSCANNIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewSCANNIndex.md) - [NewSortedIndex() | Go API · Management/Index/NewSortedIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewSortedIndex.md) - [NewSparseInvertedIndex() | Go API · Management/Index/NewSparseInvertedIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewSparseInvertedIndex.md) - [NewSparseWANDIndex() | Go API · Management/Index/NewSparseWANDIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewSparseWANDIndex.md) - [NewTrieIndex() | Go API · Management/Index/NewTrieIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Index/NewTrieIndex.md) - [IndexDescription | Go API · Management/IndexDescription](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/IndexDescription.md) - [IndexType | Go API · Management/IndexType](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/IndexType.md) - [ListIndexes() | Go API · Management/ListIndexes](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/ListIndexes.md) - [LoadCollection() | Go API · Management/LoadCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/LoadCollection.md) - [LoadPartitions() | Go API · Management/LoadPartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/LoadPartitions.md) - [LoadState | Go API · Management/LoadState](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/LoadState.md) - [LoadTask | Go API · Management/LoadTask](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/LoadTask.md) - [MetricType | Go API · Management/MetricType](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/MetricType.md) - [RefreshLoad() | Go API · Management/RefreshLoad](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/RefreshLoad.md) - [ReleaseCollection() | Go API · Management/ReleaseCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/ReleaseCollection.md) - [ReleasePartitions() | Go API · Management/ReleasePartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/ReleasePartitions.md) - [Segment | Go API · Management/Segment](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Management/Segment.md) - [CreatePartition() | Go API · Partition/CreatePartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Partition/CreatePartition.md) - [DropPartition() | Go API · Partition/DropPartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Partition/DropPartition.md) - [GetPartitionStats() | Go API · Partition/GetPartitionStats](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Partition/GetPartitionStats.md) - [HasPartition() | Go API · Partition/HasPartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Partition/HasPartition.md) - [ListPartitions() | Go API · Partition/ListPartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Partition/ListPartitions.md) - [CreateResourceGroup() | Go API · ResourceGroup/CreateResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/ResourceGroup/CreateResourceGroup.md) - [DescribeReplica() | Go API · ResourceGroup/DescribeReplica](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/ResourceGroup/DescribeReplica.md) - [DescribeResourceGroup() | Go API · ResourceGroup/DescribeResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/ResourceGroup/DescribeResourceGroup.md) - [DropResourceGroup() | Go API · ResourceGroup/DropResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/ResourceGroup/DropResourceGroup.md) - [ListResourceGroups() | Go API · ResourceGroup/ListResourceGroups](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/ResourceGroup/ListResourceGroups.md) - [ResourceGroupConfig | Go API · ResourceGroup/ResourceGroupConfig](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/ResourceGroup/ResourceGroupConfig.md) - [TransferReplica() | Go API · ResourceGroup/TransferReplica](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/ResourceGroup/TransferReplica.md) - [UpdateResourceGroup() | Go API · ResourceGroup/UpdateResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/ResourceGroup/UpdateResourceGroup.md) - [AnnParam | Go API · Vector/AnnParam/AnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/AnnParam.md) - [NewAutoAnnParam() | Go API · Vector/AnnParam/NewAutoAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewAutoAnnParam.md) - [NewCustomAnnParam() | Go API · Vector/AnnParam/NewCustomAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewCustomAnnParam.md) - [NewDiskAnnParam() | Go API · Vector/AnnParam/NewDiskAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewDiskAnnParam.md) - [NewHNSWAnnParam() | Go API · Vector/AnnParam/NewHNSWAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewHNSWAnnParam.md) - [NewIvfAnnParam() | Go API · Vector/AnnParam/NewIvfAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewIvfAnnParam.md) - [NewIvfRabitQAnnParam() | Go API · Vector/AnnParam/NewIvfRabitQAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewIvfRabitQAnnParam.md) - [NewMinHashLSHAnnParam() | Go API · Vector/AnnParam/NewMinHashLSHAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewMinHashLSHAnnParam.md) - [NewSCANNAnnParam() | Go API · Vector/AnnParam/NewSCANNAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewSCANNAnnParam.md) - [NewSparseAnnParam() | Go API · Vector/AnnParam/NewSparseAnnParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/AnnParam/NewSparseAnnParam.md) - [Delete() | Go API · Vector/Delete](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/Delete.md) - [DeleteResult | Go API · Vector/DeleteResult](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/DeleteResult.md) - [Get() | Go API · Vector/Get](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/Get.md) - [HybridSearch() | Go API · Vector/HybridSearch](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/HybridSearch.md) - [Insert() | Go API · Vector/Insert](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/Insert.md) - [InsertResult | Go API · Vector/InsertResult](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/InsertResult.md) - [Query() | Go API · Vector/Query](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/Query.md) - [QueryIterator() | Go API · Vector/QueryIterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/QueryIterator.md) - [ResultSet | Go API · Vector/ResultSet](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/ResultSet.md) - [RunAnalyzer() | Go API · Vector/RunAnalyzer](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/RunAnalyzer.md) - [Search() | Go API · Vector/Search](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/Search.md) - [SearchIterator() | Go API · Vector/SearchIterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/SearchIterator.md) - [Upsert() | Go API · Vector/Upsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/Upsert.md) - [UpsertResult | Go API · Vector/UpsertResult](https://www.xlonglab.com/docs/v2.6.x/api-reference/go/Vector/UpsertResult.md) - [About Milvus Java SDK | Java API · About](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/About.md) - [alterAlias() | Java API · v1/Alias/alterAlias](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Alias/alterAlias.md) - [createAlias() | Java API · v1/Alias/createAlias](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Alias/createAlias.md) - [dropAlias() | Java API · v1/Alias/dropAlias](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Alias/dropAlias.md) - [listAliases() | Java API · v1/Alias/listAliases](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Alias/listAliases.md) - [createCredential() | Java API · v1/Authentication/createCredential](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Authentication/createCredential.md) - [deleteCredential() | Java API · v1/Authentication/deleteCredential](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Authentication/deleteCredential.md) - [listCredUsers() | Java API · v1/Authentication/listCredUsers](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Authentication/listCredUsers.md) - [updateCredential() | Java API · v1/Authentication/updateCredential](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Authentication/updateCredential.md) - [bulkInsert() | Java API · v1/BulkInsertOps/bulkInsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/BulkInsertOps/bulkInsert.md) - [getBulkInsertState() | Java API · v1/BulkInsertOps/getBulkInsertState](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/BulkInsertOps/getBulkInsertState.md) - [listBulkInsertTasks() | Java API · v1/BulkInsertOps/listBulkInsertTasks](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/BulkInsertOps/listBulkInsertTasks.md) - [LocalBulkWriter | Java API · v1/BulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/BulkWriter/LocalBulkWriter.md) - [RemoteBulkWriter | Java API · v1/BulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/BulkWriter/RemoteBulkWriter.md) - [alterCollection() | Java API · v1/Collection/alterCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/alterCollection.md) - [createCollection() | Java API · v1/Collection/createCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/createCollection.md) - [delete() | Java API · v1/Collection/delete](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/delete.md) - [describeCollection() | Java API · v1/Collection/describeCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/describeCollection.md) - [dropCollection() | Java API · v1/Collection/dropCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/dropCollection.md) - [flush() | Java API · v1/Collection/flush](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/flush.md) - [getCollectionStatistics() | Java API · v1/Collection/getCollectionStatistics](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/getCollectionStatistics.md) - [getLoadState() | Java API · v1/Collection/getLoadState](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/getLoadState.md) - [getLoadingProgress() | Java API · v1/Collection/getLoadingProgress](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/getLoadingProgress.md) - [getPersistentSegmentInfo() | Java API · v1/Collection/getPersistentSegmentInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/getPersistentSegmentInfo.md) - [getQuerySegmentInfo() | Java API · v1/Collection/getQuerySegmentInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/getQuerySegmentInfo.md) - [getReplicas() | Java API · v1/Collection/getReplicas](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/getReplicas.md) - [insert() | Java API · v1/Collection/insert](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/insert.md) - [insertAsync() | Java API · v1/Collection/insertAsync](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/insertAsync.md) - [loadCollection() | Java API · v1/Collection/loadCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/loadCollection.md) - [releaseCollection() | Java API · v1/Collection/releaseCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/releaseCollection.md) - [renameCollection() | Java API · v1/Collection/renameCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/renameCollection.md) - [showCollections() | Java API · v1/Collection/showCollections](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/showCollections.md) - [upsert() | Java API · v1/Collection/upsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/upsert.md) - [upsertAsync() | Java API · v1/Collection/upsertAsync](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Collection/upsertAsync.md) - [MilvusClient | Java API · v1/Connections/MilvusClient](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Connections/MilvusClient.md) - [MilvusClientV1Pool | Java API · v1/Connections/MilvusClientV1Pool](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Connections/MilvusClientV1Pool.md) - [alterDatabase() | Java API · v1/Database/alterDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Database/alterDatabase.md) - [createDatabase() | Java API · v1/Database/createDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Database/createDatabase.md) - [dropDatabase() | Java API · v1/Database/dropDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Database/dropDatabase.md) - [listDatabases() | Java API · v1/Database/listDatabases](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Database/listDatabases.md) - [createCollection() | Java API · v1/HighlevelAPI/createCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/HighlevelAPI/createCollection.md) - [delete() | Java API · v1/HighlevelAPI/delete](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/HighlevelAPI/delete.md) - [get() | Java API · v1/HighlevelAPI/get](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/HighlevelAPI/get.md) - [insert() | Java API · v1/HighlevelAPI/insert](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/HighlevelAPI/insert.md) - [listCollections() | Java API · v1/HighlevelAPI/listCollections](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/HighlevelAPI/listCollections.md) - [query() | Java API · v1/HighlevelAPI/query](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/HighlevelAPI/query.md) - [search() | Java API · v1/HighlevelAPI/search](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/HighlevelAPI/search.md) - [alterIndex() | Java API · v1/Index/alterIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Index/alterIndex.md) - [createIndex() | Java API · v1/Index/createIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Index/createIndex.md) - [describeIndex() | Java API · v1/Index/describeIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Index/describeIndex.md) - [dropIndex() | Java API · v1/Index/dropIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Index/dropIndex.md) - [getIndexBuildProgress() | Java API · v1/Index/getIndexBuildingProgress](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Index/getIndexBuildingProgress.md) - [getIndexState() | Java API · v1/Index/getIndexState](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Index/getIndexState.md) - [getCompactionState() | Java API · v1/Management/getCompactionState](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Management/getCompactionState.md) - [getCompactionStateWithPlans() | Java API · v1/Management/getCompactionStateWithPlans](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Management/getCompactionStateWithPlans.md) - [getFlushAllState() | Java API · v1/Management/getFlushAllState](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Management/getFlushAllState.md) - [getFlushState() | Java API · v1/Management/getFlushState](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Management/getFlushState.md) - [getMetrics() | Java API · v1/Management/getMetrics](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Management/getMetrics.md) - [loadBalance() | Java API · v1/Management/loadBalance](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Management/loadBalance.md) - [manualCompact() | Java API · v1/Management/manualCompact](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Management/manualCompact.md) - [ConsistencyLevelEnum | Java API · v1/Misc/ConsistencyLevelEnum](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Misc/ConsistencyLevelEnum.md) - [Constant | Java API · v1/Misc/Constant](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Misc/Constant.md) - [DataType | Java API · v1/Misc/DataType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Misc/DataType.md) - [IndexType | Java API · v1/Misc/IndexType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Misc/IndexType.md) - [LogLevel | Java API · v1/Misc/LogLevel](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Misc/LogLevel.md) - [MetricType | Java API · v1/Misc/MetricType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Misc/MetricType.md) - [R | Java API · v1/Misc/RT](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Misc/RT.md) - [createPartition() | Java API · v1/Partition/createPartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Partition/createPartition.md) - [dropPartition() | Java API · v1/Partition/dropPartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Partition/dropPartition.md) - [getPartitionStatistics() | Java API · v1/Partition/getPartitionStatistics](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Partition/getPartitionStatistics.md) - [hasPartition() | Java API · v1/Partition/hasPartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Partition/hasPartition.md) - [loadPartitions() | Java API · v1/Partition/loadPartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Partition/loadPartitions.md) - [releasePartitions() | Java API · v1/Partition/releasePartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Partition/releasePartitions.md) - [showPartitions() | Java API · v1/Partition/showPartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/Partition/showPartitions.md) - [hybridSearch() | Java API · v1/QuerySearch/hybridSearch](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/QuerySearch/hybridSearch.md) - [query() | Java API · v1/QuerySearch/query](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/QuerySearch/query.md) - [queryAsync() | Java API · v1/QuerySearch/queryAsync](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/QuerySearch/queryAsync.md) - [queryIterator() | Java API · v1/QuerySearch/queryIterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/QuerySearch/queryIterator.md) - [search() | Java API · v1/QuerySearch/search](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/QuerySearch/search.md) - [searchAsync() | Java API · v1/QuerySearch/searchAsync](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/QuerySearch/searchAsync.md) - [searchIterator() | Java API · v1/QuerySearch/searchIterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/QuerySearch/searchIterator.md) - [addUserToRole() | Java API · v1/RBAC/addUserToRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/addUserToRole.md) - [createRole() | Java API · v1/RBAC/createRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/createRole.md) - [dropRole() | Java API · v1/RBAC/dropRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/dropRole.md) - [grantRolePrivilege() | Java API · v1/RBAC/grantRolePrivilege](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/grantRolePrivilege.md) - [removeUserFromRole() | Java API · v1/RBAC/removeUserFromRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/removeUserFromRole.md) - [revokeRolePrivilege() | Java API · v1/RBAC/revokeRolePrivilege](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/revokeRolePrivilege.md) - [selectGrantForRole() | Java API · v1/RBAC/selectGrantForRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/selectGrantForRole.md) - [selectGrantForRoleAndObject() | Java API · v1/RBAC/selectGrantForRoleAndObject](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/selectGrantForRoleAndObject.md) - [selectRole() | Java API · v1/RBAC/selectRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/selectRole.md) - [selectUser() | Java API · v1/RBAC/selectUser](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/RBAC/selectUser.md) - [createResourceGroup() | Java API · v1/ResourceGroup/createResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/ResourceGroup/createResourceGroup.md) - [describeResourceGroup() | Java API · v1/ResourceGroup/describeResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/ResourceGroup/describeResourceGroup.md) - [dropResourceGroup() | Java API · v1/ResourceGroup/dropResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/ResourceGroup/dropResourceGroup.md) - [listResourceGroups() | Java API · v1/ResourceGroup/listResourceGroups](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/ResourceGroup/listResourceGroups.md) - [transferNode() | Java API · v1/ResourceGroup/transferNode](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/ResourceGroup/transferNode.md) - [transferReplica() | Java API · v1/ResourceGroup/transferReplica](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/ResourceGroup/transferReplica.md) - [checkHealth() | Java API · v1/System/checkHealth](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/System/checkHealth.md) - [getVersion() | Java API · v1/System/getVersion](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v1/System/getVersion.md) - [addPrivilegesToGroup() | Java API · v2/Authentication/addPrivilegesToGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/addPrivilegesToGroup.md) - [alterRole() | Java API · v2/Authentication/alterRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/alterRole.md) - [createPrivilegeGroup() | Java API · v2/Authentication/createPrivilegeGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/createPrivilegeGroup.md) - [createRole() | Java API · v2/Authentication/createRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/createRole.md) - [createUser() | Java API · v2/Authentication/createUser](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/createUser.md) - [describeRole() | Java API · v2/Authentication/describeRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/describeRole.md) - [describeUser() | Java API · v2/Authentication/describeUser](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/describeUser.md) - 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[listRoles() | Java API · v2/Authentication/listRoles](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/listRoles.md) - [listUsers() | Java API · v2/Authentication/listUsers](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/listUsers.md) - [removePrivilegesFromGroup() | Java API · v2/Authentication/removePrivilegesFromGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/removePrivilegesFromGroup.md) - [revokePrivilege() | Java API · v2/Authentication/revokePrivilege](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/revokePrivilege.md) - [revokePrivilegeV2() | Java API · v2/Authentication/revokePrivilegeV2](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/revokePrivilegeV2.md) - [revokeRole() | Java API · v2/Authentication/revokeRole](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/revokeRole.md) - [updatePassword() | Java API · v2/Authentication/updatePassword](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/updatePassword.md) - [updateUser() | Java API · v2/Authentication/updateUser](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Authentication/updateUser.md) - [ConnectConfig | Java API · v2/Client/ConnectConfig](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Client/ConnectConfig.md) - [MilvusClientV2 | Java API · v2/Client/MilvusClientV2](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Client/MilvusClientV2.md) - [MilvusClientV2Pool | Java API · v2/Client/MilvusClientV2Pool](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Client/MilvusClientV2Pool.md) - [clientIsReady() | Java API · v2/Client/clientIsReady](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Client/clientIsReady.md) - [getServerVersionV2() | Java API · v2/Client/getServerVersionV2](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Client/getServerVersionV2.md) - [CollectionSchema | Java API · v2/Collections/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/CollectionSchema/CollectionSchema.md) - 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[getStructFields() | Java API · v2/Collections/CollectionSchema/getStructFields](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/CollectionSchema/getStructFields.md) - [isEnableDynamicField() | Java API · v2/Collections/CollectionSchema/isEnableDynamicField](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/CollectionSchema/isEnableDynamicField.md) - [ConsistencyLevel | Java API · v2/Collections/ConsistencyLevel](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/ConsistencyLevel.md) - [CreateSchema() | Java API · v2/Collections/CreateSchema](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/CreateSchema.md) - [DataType | Java API · v2/Collections/DataType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/DataType.md) - [EmbeddingList | Java API · v2/Collections/EmbeddingList/EmbeddingList](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/EmbeddingList/EmbeddingList.md) - 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[FunctionType | Java API · v2/Collections/Function/FunctionType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/Function/FunctionType.md) - [ModelRanker | Java API · v2/Collections/Function/ModelRanker](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/Function/ModelRanker.md) - [RRFRanker | Java API · v2/Collections/Function/RRFRanker](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/Function/RRFRanker.md) - [WeightedRanker | Java API · v2/Collections/Function/WeightedRanker](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/Function/WeightedRanker.md) - [ListCollectionsV2() | Java API · v2/Collections/ListCollectionsV2](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/ListCollectionsV2.md) - [StructFieldSchema | Java API · v2/Collections/StructFieldSchema/StructFieldSchema](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/StructFieldSchema/StructFieldSchema.md) - [addField() | Java API · v2/Collections/StructFieldSchema/addField](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/StructFieldSchema/addField.md) - [getDataType() | Java API · v2/Collections/StructFieldSchema/getDataType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/StructFieldSchema/getDataType.md) - [getDescription() | Java API · v2/Collections/StructFieldSchema/getDescription](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/StructFieldSchema/getDescription.md) - [getElementType() | Java API · v2/Collections/StructFieldSchema/getElementType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/StructFieldSchema/getElementType.md) - [getFields() | Java API · v2/Collections/StructFieldSchema/getFields](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/StructFieldSchema/getFields.md) - [getMaxCapacity() | Java API · v2/Collections/StructFieldSchema/getMaxCapacity](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/StructFieldSchema/getMaxCapacity.md) - [getName() | Java API · v2/Collections/StructFieldSchema/getName](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/StructFieldSchema/getName.md) - [WaitForDropCollection() | Java API · v2/Collections/WaitForDropCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/WaitForDropCollection.md) - [addCollectionField() | Java API · v2/Collections/addCollectionField](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/addCollectionField.md) - [addCollectionFunction() | Java API · v2/Collections/addCollectionFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/addCollectionFunction.md) - [alterAlias() | Java API · v2/Collections/alterAlias](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/alterAlias.md) - [alterCollectionField() | Java API · v2/Collections/alterCollectionField](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/alterCollectionField.md) - [alterCollectionFunction() | Java API · v2/Collections/alterCollectionFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/alterCollectionFunction.md) - 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[getLoadStateV2() | Java API · v2/Collections/getLoadStateV2](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/getLoadStateV2.md) - [hasCollection() | Java API · v2/Collections/hasCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/hasCollection.md) - [listAliases() | Java API · v2/Collections/listAliases](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/listAliases.md) - [listCollections() | Java API · v2/Collections/listCollections](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/listCollections.md) - [renameCollection() | Java API · v2/Collections/renameCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/renameCollection.md) - [truncateCollection() | Java API · v2/Collections/truncateCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Collections/truncateCollection.md) - [BulkFileType | Java API · v2/DataImport/BulkFileType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/BulkFileType.md) - [bulkImport() | Java API · v2/DataImport/BulkImport/bulkImport-2](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/BulkImport/bulkImport-2.md) - [getImportProgress() | Java API · v2/DataImport/BulkImport/getImportProgress](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/BulkImport/getImportProgress.md) - [listImportJobs() | Java API · v2/DataImport/BulkImport/listImportJobs](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/BulkImport/listImportJobs.md) - [LocalBulkWriter | Java API · v2/DataImport/LocalBulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/LocalBulkWriter/LocalBulkWriter.md) - [appendRow() | Java API · v2/DataImport/LocalBulkWriter/appendRow](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/LocalBulkWriter/appendRow.md) - [close() | Java API · v2/DataImport/LocalBulkWriter/close](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/LocalBulkWriter/close.md) - [commit() | Java API · v2/DataImport/LocalBulkWriter/commit](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/LocalBulkWriter/commit.md) - [getBatchFiles() | Java API · v2/DataImport/LocalBulkWriter/getBatchFiles](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/LocalBulkWriter/getBatchFiles.md) - [getTotalRowCount() | Java API · v2/DataImport/LocalBulkWriter/getTotalRowCount](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/LocalBulkWriter/getTotalRowCount.md) - [RemoteBulkWriter | Java API · v2/DataImport/RemoteBulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/RemoteBulkWriter/RemoteBulkWriter.md) - [appendRow() | Java API · v2/DataImport/RemoteBulkWriter/appendRow](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/RemoteBulkWriter/appendRow.md) - [close() | Java API · v2/DataImport/RemoteBulkWriter/close](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/RemoteBulkWriter/close.md) - 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[commit() | Java API · v2/DataImport/VolumeBulkWriter/commit](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/VolumeBulkWriter/commit.md) - [getBatchFiles() | Java API · v2/DataImport/VolumeBulkWriter/getBatchFiles](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/VolumeBulkWriter/getBatchFiles.md) - [getTotalRowCount() | Java API · v2/DataImport/VolumeBulkWriter/getTotalRowCount](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/VolumeBulkWriter/getTotalRowCount.md) - [getVolumeUploadResult() | Java API · v2/DataImport/VolumeBulkWriter/getVolumeUploadResult](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/DataImport/VolumeBulkWriter/getVolumeUploadResult.md) - [alterDatabaseProperties() | Java API · v2/Database/alterDatabaseProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Database/alterDatabaseProperties.md) - [createDatabase() | Java API · v2/Database/createDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Database/createDatabase.md) - 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[IndexType | Java API · v2/Management/IndexType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/IndexType.md) - [MetricType | Java API · v2/Management/MetricType](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/MetricType.md) - [alterIndexProperties() | Java API · v2/Management/alterIndexProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/alterIndexProperties.md) - [checkHealth() | Java API · v2/Management/checkHealth](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/checkHealth.md) - [compact() | Java API · v2/Management/compact](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/compact.md) - [createIndex() | Java API · v2/Management/createIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/createIndex.md) - [describeIndex() | Java API · v2/Management/describeIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/describeIndex.md) - 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[getFlushAllState() | Java API · v2/Management/getFlushAllState](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/getFlushAllState.md) - [getLoadState() | Java API · v2/Management/getLoadState](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/getLoadState.md) - [getPersistentSegmentInfo() | Java API · v2/Management/getPersistentSegmentInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/getPersistentSegmentInfo.md) - [getQuerySegmentInfo() | Java API · v2/Management/getQuerySegmentInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/getQuerySegmentInfo.md) - [getReplicateConfiguration() | Java API · v2/Management/getReplicateConfiguration](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/getReplicateConfiguration.md) - [getReplicateInfo() | Java API · v2/Management/getReplicateInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/getReplicateInfo.md) - [getServerVersion() | Java API · v2/Management/getServerVersion](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Management/getServerVersion.md) - 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[LexicalHighlighter | Java API · v2/Vector/Highlighter/LexicalHighlighter](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Vector/Highlighter/LexicalHighlighter.md) - [SemanticHighlighter | Java API · v2/Vector/Highlighter/SemanticHighlighter](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Vector/Highlighter/SemanticHighlighter.md) - [SearchIteratorV2() | Java API · v2/Vector/SearchIteratorV2](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Vector/SearchIteratorV2.md) - [delete() | Java API · v2/Vector/delete](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Vector/delete.md) - [get() | Java API · v2/Vector/get](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Vector/get.md) - [getAsync() | Java API · v2/Vector/getAsync](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Vector/getAsync.md) - [hybridSearch() | Java API · v2/Vector/hybridSearch](https://www.xlonglab.com/docs/v2.6.x/api-reference/java/v2/Vector/hybridSearch.md) - 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[dropCollectionFunction() | Node.js API · Collections/dropCollectionFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/dropCollectionFunction.md) - [dropCollectionProperties() | Node.js API · Collections/dropCollectionProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/dropCollectionProperties.md) - [getCollectionStatistics() | Node.js API · Collections/getCollectionStatistics](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/getCollectionStatistics.md) - [getPkField() | Node.js API · Collections/getPkField](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/getPkField.md) - [getPkFieldName() | Node.js API · Collections/getPkFieldName](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/getPkFieldName.md) - [getPkFieldType() | Node.js API · Collections/getPkFieldType](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/getPkFieldType.md) - [hasCollection() | Node.js API · Collections/hasCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/hasCollection.md) - [listAliases() | Node.js API · Collections/listAliases](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/listAliases.md) - [listCollections() | Node.js API · Collections/listCollections](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/listCollections.md) - [renameCollection() | Node.js API · Collections/renameCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/renameCollection.md) - [runAnalyzer() | Node.js API · Collections/runAnalyzer](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/runAnalyzer.md) - [truncateCollection() | Node.js API · Collections/truncateCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Collections/truncateCollection.md) - [BulkWriter | Node.js API · DataImport/BulkWriter](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/BulkWriter.md) - [BulkWriterOptions | Node.js API · DataImport/BulkWriterOptions](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/BulkWriterOptions.md) - [BulkWriterSchema | Node.js API · DataImport/BulkWriterSchema](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/BulkWriterSchema.md) - [FlushEvent | Node.js API · DataImport/FlushEvent](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/FlushEvent.md) - [Formatter | Node.js API · DataImport/Formatter](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/Formatter.md) - [HttpImportCreateReq | Node.js API · DataImport/HttpImportCreateReq](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/HttpImportCreateReq.md) - [HttpImportCreateResponse | Node.js API · DataImport/HttpImportCreateResponse](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/HttpImportCreateResponse.md) - [HttpImportListResponse | Node.js API · DataImport/HttpImportListResponse](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/HttpImportListResponse.md) - [HttpImportProgressReq | Node.js API · DataImport/HttpImportProgressReq](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/HttpImportProgressReq.md) - [HttpImportProgressResponse | Node.js API · DataImport/HttpImportProgressResponse](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/HttpImportProgressResponse.md) - [Storage | Node.js API · DataImport/Storage](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/Storage.md) - [bulkInsert() | Node.js API · DataImport/bulkInsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/bulkInsert.md) - [createImportJobs() | Node.js API · DataImport/createImportJobs](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/createImportJobs.md) - [getImportJobProgress() | Node.js API · DataImport/getImportJobProgress](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/getImportJobProgress.md) - [getImportState() | Node.js API · DataImport/getImportState](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/getImportState.md) - [listImportJobs() | Node.js API · DataImport/listImportJobs](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/listImportJobs.md) - [listImportTasks() | Node.js API · DataImport/listImportTasks](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/DataImport/listImportTasks.md) - [alterDatabase() | Node.js API · Database/alterDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Database/alterDatabase.md) - [alterDatabaseProperties() | Node.js API · Database/alterDatabaseProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Database/alterDatabaseProperties.md) - [createDatabase() | Node.js API · Database/createDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Database/createDatabase.md) - [describeDatabase() | Node.js API · Database/describeDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Database/describeDatabase.md) - [dropDatabase() | Node.js API · Database/dropDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Database/dropDatabase.md) - [dropDatabaseProperties() | Node.js API · Database/dropDatabaseProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Database/dropDatabaseProperties.md) - [listDatabases() | Node.js API · Database/listDatabases](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Database/listDatabases.md) - [useDatabase() | Node.js API · Database/useDatabase](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Database/useDatabase.md) - [IndexType | Node.js API · Management/IndexType](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/IndexType.md) - [MetricType | Node.js API · Management/MetricType](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/MetricType.md) - [alterIndexProperties() | Node.js API · Management/alterIndexProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/alterIndexProperties.md) - [compact() | Node.js API · Management/compact](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/compact.md) - [createIndex() | Node.js API · Management/createIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/createIndex.md) - [describeIndex() | Node.js API · Management/describeIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/describeIndex.md) - [dropIndex() | Node.js API · Management/dropIndex](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/dropIndex.md) - [dropIndexProperties() | Node.js API · Management/dropIndexProperties](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/dropIndexProperties.md) - [flush() | Node.js API · Management/flush](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/flush.md) - [flushAll() | Node.js API · Management/flushAll](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/flushAll.md) - [flushAllSync() | Node.js API · Management/flushAllSync](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/flushAllSync.md) - [flushSync() | Node.js API · Management/flushSync](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/flushSync.md) - [getCompactionState() | Node.js API · Management/getCompactionState](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getCompactionState.md) - [getCompactionStateWithPlans() | Node.js API · Management/getCompactionStateWithPlans](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getCompactionStateWithPlans.md) - [getFlushAllState() | Node.js API · Management/getFlushAllState](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getFlushAllState.md) - [getFlushState() | Node.js API · Management/getFlushState](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getFlushState.md) - [getIndexBuildProgress() | Node.js API · Management/getIndexBuildProgress](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getIndexBuildProgress.md) - [getIndexState() | Node.js API · Management/getIndexState](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getIndexState.md) - [getIndexStatistics() | Node.js API · Management/getIndexStatistics](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getIndexStatistics.md) - [getLoadState() | Node.js API · Management/getLoadState](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getLoadState.md) - [getLoadingProgress() | Node.js API · Management/getLoadingProgress](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getLoadingProgress.md) - [getPersistentSegmentInfo() | Node.js API · Management/getPersistentSegmentInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getPersistentSegmentInfo.md) - [getQuerySegmentInfo() | Node.js API · Management/getQuerySegmentInfo](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getQuerySegmentInfo.md) - [getReplicas() | Node.js API · Management/getReplicas](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/getReplicas.md) - [listIndexes() | Node.js API · Management/listIndexes](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/listIndexes.md) - [loadBalance() | Node.js API · Management/loadBalance](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/loadBalance.md) - [loadCollection() | Node.js API · Management/loadCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/loadCollection.md) - [loadCollectionAsync() | Node.js API · Management/loadCollectionAsync](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/loadCollectionAsync.md) - [loadCollectionSync() | Node.js API · Management/loadCollectionSync](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/loadCollectionSync.md) - [refreshLoad() | Node.js API · Management/refreshLoad](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/refreshLoad.md) - [releaseCollection() | Node.js API · Management/releaseCollection](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Management/releaseCollection.md) - [createPartition() | Node.js API · Partitions/createPartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Partitions/createPartition.md) - [dropPartition() | Node.js API · Partitions/dropPartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Partitions/dropPartition.md) - [getPartitionStatistics() | Node.js API · Partitions/getPartitionStatistics](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Partitions/getPartitionStatistics.md) - [hasPartition() | Node.js API · Partitions/hasPartition](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Partitions/hasPartition.md) - [listPartitions() | Node.js API · Partitions/listPartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Partitions/listPartitions.md) - [loadPartitions() | Node.js API · Partitions/loadPartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Partitions/loadPartitions.md) - [loadPartitionsSync() | Node.js API · Partitions/loadPartitionsSync](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Partitions/loadPartitionsSync.md) - [releasePartitions() | Node.js API · Partitions/releasePartitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Partitions/releasePartitions.md) - [ResourceGroupConfig | Node.js API · ResourceGroup/ResourceGroupConfig](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/ResourceGroupConfig.md) - [createResourceGroup() | Node.js API · ResourceGroup/createResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/createResourceGroup.md) - [describeResourceGroup() | Node.js API · ResourceGroup/describeResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/describeResourceGroup.md) - [dropAllResourceGroups() | Node.js API · ResourceGroup/dropAllResourceGroups](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/dropAllResourceGroups.md) - [dropResourceGroup() | Node.js API · ResourceGroup/dropResourceGroup](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/dropResourceGroup.md) - [listResourceGroups() | Node.js API · ResourceGroup/listResourceGroups](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/listResourceGroups.md) - [transferNode() | Node.js API · ResourceGroup/transferNode](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/transferNode.md) - [transferReplica() | Node.js API · ResourceGroup/transferReplica](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/transferReplica.md) - [updateResourceGroups() | Node.js API · ResourceGroup/updateResourceGroups](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/ResourceGroup/updateResourceGroups.md) - [bulkInsert() | Node.js API · Vector/bulkInsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/bulkInsert.md) - [count() | Node.js API · Vector/count](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/count.md) - [delete() | Node.js API · Vector/delete](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/delete.md) - [deleteEntities() | Node.js API · Vector/deleteEntities](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/deleteEntities.md) - [get() | Node.js API · Vector/get](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/get.md) - [getMetric() | Node.js API · Vector/getMetric](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/getMetric.md) - [hybridSearch() | Node.js API · Vector/hybridSearch](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/hybridSearch.md) - [insert() | Node.js API · Vector/insert](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/insert.md) - [listImportTasks() | Node.js API · Vector/listImportTasks](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/listImportTasks.md) - [query() | Node.js API · Vector/query](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/query.md) - [queryIterator() | Node.js API · Vector/queryIterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/queryIterator.md) - [search() | Node.js API · Vector/search](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/search.md) - [searchIterator() | Node.js API · Vector/searchIterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/searchIterator.md) - [upsert() | Node.js API · Vector/upsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/node/Vector/upsert.md) - [About PyMilvus | Python API · About](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/About.md) - [BulkFileType | Python API · DataImport/BulkFileType](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/BulkFileType.md) - [bulk_import() | Python API · DataImport/BulkImport/bulk_import](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/BulkImport/bulk_import.md) - [get_import_progress() | Python API · DataImport/BulkImport/get_import_progress](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/BulkImport/get_import_progress.md) - [list_import_jobs() | Python API · DataImport/BulkImport/list_import_jobs](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/BulkImport/list_import_jobs.md) - [LocalBulkWriter | Python API · DataImport/LocalBulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/LocalBulkWriter/LocalBulkWriter.md) - [append_row() | Python API · DataImport/LocalBulkWriter/append_row](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/LocalBulkWriter/append_row.md) - [commit() | Python API · DataImport/LocalBulkWriter/commit](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/LocalBulkWriter/commit.md) - [AzureConnectParam | Python API · DataImport/RemoteBulkWriter/AzureConnectParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/AzureConnectParam.md) - [RemoteBulkWriter | Python API · DataImport/RemoteBulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/RemoteBulkWriter.md) - [S3ConnectParam | Python API · DataImport/RemoteBulkWriter/S3ConnectParam](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/S3ConnectParam.md) - [append_row() | Python API · DataImport/RemoteBulkWriter/append_row](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/append_row.md) - [commit() | Python API · DataImport/RemoteBulkWriter/commit](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/commit.md) - [VolumeBulkWriter | Python API · DataImport/VolumeBulkWriter/VolumeBulkWriter](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/VolumeBulkWriter/VolumeBulkWriter.md) - [append_row() | Python API · DataImport/VolumeBulkWriter/append_row](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/VolumeBulkWriter/append_row.md) - [commit() | Python API · DataImport/VolumeBulkWriter/commit](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/DataImport/VolumeBulkWriter/commit.md) - [BGEM3EmbeddingFunction | Python API · EmbeddingModels/BGEM3EmbeddingFunction/BGEM3EmbeddingFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/BGEM3EmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/encode_queries.md) - [BM25EmbeddingFunction | Python API · EmbeddingModels/BM25EmbeddingFunction/BM25EmbeddingFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/BM25EmbeddingFunction.md) - [encode_documents() | Python API · EmbeddingModels/BM25EmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/BM25EmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/encode_queries.md) - [fit() | Python API · EmbeddingModels/BM25EmbeddingFunction/fit](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/fit.md) - [load() | Python API · EmbeddingModels/BM25EmbeddingFunction/load](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/load.md) - [save() | Python API · EmbeddingModels/BM25EmbeddingFunction/save](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/BM25EmbeddingFunction/save.md) - [CohereEmbeddingFunction | Python API · EmbeddingModels/CohereEmbeddingFunction/CohereEmbeddingFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/CohereEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/CohereEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/CohereEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/CohereEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/encode_queries.md) - [GeminiEmbeddingFunction | Python API · EmbeddingModels/GeminiEmbeddingFunction/GeminiEmbeddingFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/GeminiEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/GeminiEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/GeminiEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/GeminiEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/encode_queries.md) - [InstructorEmbeddingFunction | Python API · EmbeddingModels/InstructorEmbeddingFunction/InstructorEmbeddingFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/InstructorEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/InstructorEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/InstructorEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/InstructorEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/encode_queries.md) - [JinaEmbeddingFunction | Python API · EmbeddingModels/JinaEmbeddingFunction/JinaEmbeddingFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/JinaEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/JinaEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/JinaEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/encode_documents.md) - 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[MistralAIEmbeddingFunction | Python API · EmbeddingModels/MistralAIEmbeddingFunction/MistralAIEmbeddingFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/MistralAIEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/encode_queries.md) - [Model2VecEmbeddingFunction | Python API · EmbeddingModels/Model2VecEmbeddingFunction/Model2VecEmbeddingFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/Model2VecEmbeddingFunction.md) - 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[clear() | Python API · MilvusClient/EmbeddingList/clear](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/EmbeddingList/clear.md) - [to_flat_array() | Python API · MilvusClient/EmbeddingList/to_flat_array](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/EmbeddingList/to_flat_array.md) - [to_numpy() | Python API · MilvusClient/EmbeddingList/to_numpy](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/EmbeddingList/to_numpy.md) - [FieldSchema | Python API · MilvusClient/FieldSchema/FieldSchema](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/FieldSchema/FieldSchema.md) - [construct_from_dict() | Python API · MilvusClient/FieldSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/FieldSchema/construct_from_dict.md) - [to_dict() | Python API · MilvusClient/FieldSchema/to_dict](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/FieldSchema/to_dict.md) - [Function | Python API · MilvusClient/Function/Function](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Function/Function.md) - [add_function() | Python API · MilvusClient/Function/add_function](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Function/add_function.md) - [construct_from_dict() | Python API · MilvusClient/Function/construct_from_dict](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Function/construct_from_dict.md) - [to_dict() | Python API · MilvusClient/Function/to_dict](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Function/to_dict.md) - [verify() | Python API · MilvusClient/Function/verify](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Function/verify.md) - [FunctionScore | Python API · MilvusClient/FunctionScore](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/FunctionScore.md) - [LexicalHighlighter | Python API · MilvusClient/Highlighter/LexicalHighlighter](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Highlighter/LexicalHighlighter.md) - [SemanticHighlighter | Python API · MilvusClient/Highlighter/SemanticHighlighter](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Highlighter/SemanticHighlighter.md) - [with_query() | Python API · MilvusClient/Highlighter/with_query](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Highlighter/with_query.md) - [add_index() | Python API · MilvusClient/Management/add_index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/add_index.md) - [alter_index_properties() | Python API · MilvusClient/Management/alter_index_properties](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/alter_index_properties.md) - [compact() | Python API · MilvusClient/Management/compact](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/compact.md) - [create_index() | Python API · MilvusClient/Management/create_index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/create_index.md) - [describe_index() | Python API · MilvusClient/Management/describe_index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/describe_index.md) - [drop_index() | Python API · MilvusClient/Management/drop_index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/drop_index.md) - [drop_index_properties() | Python API · MilvusClient/Management/drop_index_properties](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/drop_index_properties.md) - [dump_messages() | Python API · MilvusClient/Management/dump_messages](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/dump_messages.md) - [flush() | Python API · MilvusClient/Management/flush](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/flush.md) - [flush_all() | Python API · MilvusClient/Management/flush_all](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/flush_all.md) - [get_compact_state() | Python API · MilvusClient/Management/get_compact_state](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_compact_state.md) - [get_compaction_plans() | Python API · MilvusClient/Management/get_compaction_plans](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_compaction_plans.md) - [get_compaction_state() | Python API · MilvusClient/Management/get_compaction_state](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_compaction_state.md) - [get_flush_all_state() | Python API · MilvusClient/Management/get_flush_all_state](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_flush_all_state.md) - [get_load_state() | Python API · MilvusClient/Management/get_load_state](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_load_state.md) - [get_replicate_configuration() | Python API · MilvusClient/Management/get_replicate_configuration](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_replicate_configuration.md) - [get_replicate_info() | Python API · MilvusClient/Management/get_replicate_info](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_replicate_info.md) - [get_server_type() | Python API · MilvusClient/Management/get_server_type](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_server_type.md) - [get_server_version() | Python API · MilvusClient/Management/get_server_version](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/get_server_version.md) - [list_indexes() | Python API · MilvusClient/Management/list_indexes](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/list_indexes.md) - [list_loaded_segments() | Python API · MilvusClient/Management/list_loaded_segments](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/list_loaded_segments.md) - [list_persistent_segments() | Python API · MilvusClient/Management/list_persistent_segments](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/list_persistent_segments.md) - [load_collection() | Python API · MilvusClient/Management/load_collection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/load_collection.md) - [optimize() | Python API · MilvusClient/Management/optimize](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/optimize.md) - [prepare_index_params() | Python API · MilvusClient/Management/prepare_index_params](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/prepare_index_params.md) - [refresh_load() | Python API · MilvusClient/Management/refresh_load](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/refresh_load.md) - [release_collection() | Python API · MilvusClient/Management/release_collection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Management/release_collection.md) - [create_partition() | Python API · MilvusClient/Partitions/create_partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Partitions/create_partition.md) - [drop_partition() | Python API · MilvusClient/Partitions/drop_partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Partitions/drop_partition.md) - [get_partition_stats() | Python API · MilvusClient/Partitions/get_partition_stats](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Partitions/get_partition_stats.md) - [has_partition() | Python API · MilvusClient/Partitions/has_partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Partitions/has_partition.md) - [list_partitions() | Python API · MilvusClient/Partitions/list_partitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Partitions/list_partitions.md) - [load_partitions() | Python API · MilvusClient/Partitions/load_partitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Partitions/load_partitions.md) - [release_partitions() | Python API · MilvusClient/Partitions/release_partitions](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Partitions/release_partitions.md) - [ResourceGroupConfig | Python API · MilvusClient/ResourceGroup/ResourceGroupConfig](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/ResourceGroupConfig.md) - [create_resource_group() | Python API · MilvusClient/ResourceGroup/create_resource_group](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/create_resource_group.md) - [describe_replica() | Python API · MilvusClient/ResourceGroup/describe_replica](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/describe_replica.md) - [describe_resource_group() | Python API · MilvusClient/ResourceGroup/describe_resource_group](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/describe_resource_group.md) - [drop_resource_group() | Python API · MilvusClient/ResourceGroup/drop_resource_group](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/drop_resource_group.md) - [list_resource_groups() | Python API · MilvusClient/ResourceGroup/list_resource_groups](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/list_resource_groups.md) - [transfer_replica() | Python API · MilvusClient/ResourceGroup/transfer_replica](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/transfer_replica.md) - [update_replicate_configuration() | Python API · MilvusClient/ResourceGroup/update_replicate_configuration](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/update_replicate_configuration.md) - [update_resource_groups() | Python API · MilvusClient/ResourceGroup/update_resource_groups](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/ResourceGroup/update_resource_groups.md) - [StructFieldSchema | Python API · MilvusClient/StructFieldSchema/StructFieldSchema](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/StructFieldSchema/StructFieldSchema.md) - [add_field() | Python API · MilvusClient/StructFieldSchema/add_field](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/StructFieldSchema/add_field.md) - [construct_from_dict() | Python API · MilvusClient/StructFieldSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/StructFieldSchema/construct_from_dict.md) - [to_dict() | Python API · MilvusClient/StructFieldSchema/to_dict](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/StructFieldSchema/to_dict.md) - [FieldOp | Python API · MilvusClient/Vector/FieldOp/FieldOp](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/FieldOp/FieldOp.md) - [FieldOpType | Python API · MilvusClient/Vector/FieldOp/FieldOpType](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/FieldOp/FieldOpType.md) - [array_append() | Python API · MilvusClient/Vector/FieldOp/array_append](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/FieldOp/array_append.md) - [array_remove() | Python API · MilvusClient/Vector/FieldOp/array_remove](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/FieldOp/array_remove.md) - [replace() | Python API · MilvusClient/Vector/FieldOp/replace](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/FieldOp/replace.md) - [delete() | Python API · MilvusClient/Vector/delete](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/delete.md) - [get() | Python API · MilvusClient/Vector/get](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/get.md) - [hybrid_search() | Python API · MilvusClient/Vector/hybrid_search](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/hybrid_search.md) - [insert() | Python API · MilvusClient/Vector/insert](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/insert.md) - [query() | Python API · MilvusClient/Vector/query](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/query.md) - [query_iterator() | Python API · MilvusClient/Vector/query_iterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/query_iterator.md) - [search() | Python API · MilvusClient/Vector/search](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/search.md) - [search_iterator() | Python API · MilvusClient/Vector/search_iterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/search_iterator.md) - [upsert() | Python API · MilvusClient/Vector/upsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/MilvusClient/Vector/upsert.md) - [Collection | Python API · ORM/Collection/Collection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/Collection.md) - [alter_index() | Python API · ORM/Collection/alter_index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/alter_index.md) - [compact() | Python API · ORM/Collection/compact](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/compact.md) - [construct_from_dataframe() | Python API · ORM/Collection/construct_from_dataframe](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/construct_from_dataframe.md) - [create_index() | Python API · ORM/Collection/create_index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/create_index.md) - [create_partition() | Python API · ORM/Collection/create_partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/create_partition.md) - [delete() | Python API · ORM/Collection/delete](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/delete.md) - [describe() | Python API · ORM/Collection/describe](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/describe.md) - [drop() | Python API · ORM/Collection/drop](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/drop.md) - [drop_index() | Python API · ORM/Collection/drop_index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/drop_index.md) - [drop_partition() | Python API · ORM/Collection/drop_partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/drop_partition.md) - [flush() | Python API · ORM/Collection/flush](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/flush.md) - [get_compaction_plans() | Python API · ORM/Collection/get_compaction_plans](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/get_compaction_plans.md) - [get_compaction_state() | Python API · ORM/Collection/get_compaction_state](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/get_compaction_state.md) - [get_replicas() | Python API · ORM/Collection/get_replicas](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/get_replicas.md) - [has_index() | Python API · ORM/Collection/has_index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/has_index.md) - [has_partition() | Python API · ORM/Collection/has_partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/has_partition.md) - [hybrid_search() | Python API · ORM/Collection/hybrid_search](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/hybrid_search.md) - [index() | Python API · ORM/Collection/index](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/index.md) - [insert() | Python API · ORM/Collection/insert](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/insert.md) - [load() | Python API · ORM/Collection/load](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/load.md) - [partition() | Python API · ORM/Collection/partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/partition.md) - [query() | Python API · ORM/Collection/query](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/query.md) - [query_iterator() | Python API · ORM/Collection/query_iterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/query_iterator.md) - [release() | Python API · ORM/Collection/release](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/release.md) - [search() | Python API · ORM/Collection/search](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/search.md) - [search_iterator() | Python API · ORM/Collection/search_iterator](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/search_iterator.md) - [set_properties() | Python API · ORM/Collection/set_properties](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/set_properties.md) - [truncate() | Python API · ORM/Collection/truncate](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/truncate.md) - [upsert() | Python API · ORM/Collection/upsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/upsert.md) - [wait_for_compaction_completed() | Python API · ORM/Collection/wait_for_compaction_completed](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Collection/wait_for_compaction_completed.md) - [CollectionSchema | Python API · ORM/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/CollectionSchema/CollectionSchema.md) - [add_field() | Python API · ORM/CollectionSchema/add_field](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/CollectionSchema/add_field.md) - [construct_from_dict() | Python API · ORM/CollectionSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/CollectionSchema/construct_from_dict.md) - [to_dict() | Python API · ORM/CollectionSchema/to_dict](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/CollectionSchema/to_dict.md) - [verify() | Python API · ORM/CollectionSchema/verify](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/CollectionSchema/verify.md) - [Connections | Python API · ORM/Connections/Connections](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Connections/Connections.md) - [add_connection() | Python API · ORM/Connections/add_connection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Connections/add_connection.md) - [connect() | Python API · ORM/Connections/connect](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Connections/connect.md) - [disconnect() | Python API · ORM/Connections/disconnect](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Connections/disconnect.md) - [get_connection_addr() | Python API · ORM/Connections/get_connection_addr](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Connections/get_connection_addr.md) - [has_connection() | Python API · ORM/Connections/has_connection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Connections/has_connection.md) - [list_connections() | Python API · ORM/Connections/list_connections](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Connections/list_connections.md) - [remove_connection() | Python API · ORM/Connections/remove_connection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Connections/remove_connection.md) - [Partition | Python API · ORM/Partition/Partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/Partition.md) - [delete() | Python API · ORM/Partition/delete](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/delete.md) - [drop() | Python API · ORM/Partition/drop](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/drop.md) - [flush() | Python API · ORM/Partition/flush](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/flush.md) - [get_replicas() | Python API · ORM/Partition/get_replicas](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/get_replicas.md) - [insert() | Python API · ORM/Partition/insert](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/insert.md) - [load() | Python API · ORM/Partition/load](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/load.md) - [query() | Python API · ORM/Partition/query](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/query.md) - [release() | Python API · ORM/Partition/release](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/release.md) - [search() | Python API · ORM/Partition/search](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/search.md) - [upsert() | Python API · ORM/Partition/upsert](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Partition/upsert.md) - [Role | Python API · ORM/Role/Role](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/Role.md) - [add_user() | Python API · ORM/Role/add_user](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/add_user.md) - [create() | Python API · ORM/Role/create](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/create.md) - [drop() | Python API · ORM/Role/drop](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/drop.md) - [get_users() | Python API · ORM/Role/get_users](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/get_users.md) - [grant() | Python API · ORM/Role/grant](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/grant.md) - [is_exist() | Python API · ORM/Role/is_exist](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/is_exist.md) - [list_grant() | Python API · ORM/Role/list_grant](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/list_grant.md) - [list_grants() | Python API · ORM/Role/list_grants](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/list_grants.md) - [remove_user() | Python API · ORM/Role/remove_user](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/remove_user.md) - [revoke() | Python API · ORM/Role/revoke](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/Role/revoke.md) - [create_database() | Python API · ORM/db/create_database](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/db/create_database.md) - [drop_database() | Python API · ORM/db/drop_database](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/db/drop_database.md) - [list_database() | Python API · ORM/db/list_database](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/db/list_database.md) - [using_database() | Python API · ORM/db/using_database](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/db/using_database.md) - [BulkInsertState | Python API · ORM/utility/BulkInsertState](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/BulkInsertState.md) - [alter_alias() | Python API · ORM/utility/alter_alias](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/alter_alias.md) - [create_alias() | Python API · ORM/utility/create_alias](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/create_alias.md) - [create_resource_group() | Python API · ORM/utility/create_resource_group](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/create_resource_group.md) - [create_user() | Python API · ORM/utility/create_user](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/create_user.md) - [delete_user() | Python API · ORM/utility/delete_user](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/delete_user.md) - [describe_resource_group() | Python API · ORM/utility/describe_resource_group](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/describe_resource_group.md) - [do_bulk_insert() | Python API · ORM/utility/do_bulk_insert](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/do_bulk_insert.md) - [drop_alias() | Python API · ORM/utility/drop_alias](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/drop_alias.md) - [drop_collection() | Python API · ORM/utility/drop_collection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/drop_collection.md) - [drop_resource_group() | Python API · ORM/utility/drop_resource_group](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/drop_resource_group.md) - [flush_all() | Python API · ORM/utility/flush_all](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/flush_all.md) - [get_bulk_insert_state() | Python API · ORM/utility/get_bulk_insert_state](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/get_bulk_insert_state.md) - [get_query_segment_info() | Python API · ORM/utility/get_query_segment_info](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/get_query_segment_info.md) - [get_server_type() | Python API · ORM/utility/get_server_type](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/get_server_type.md) - [get_server_version() | Python API · ORM/utility/get_server_version](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/get_server_version.md) - [has_collection() | Python API · ORM/utility/has_collection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/has_collection.md) - [has_partition() | Python API · ORM/utility/has_partition](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/has_partition.md) - [hybridts_to_datetime() | Python API · ORM/utility/hybridts_to_datetime](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/hybridts_to_datetime.md) - [hybridts_to_unixtime() | Python API · ORM/utility/hybridts_to_unixtime](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/hybridts_to_unixtime.md) - [index_building_progress() | Python API · ORM/utility/index_building_progress](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/index_building_progress.md) - [list_aliases() | Python API · ORM/utility/list_aliases](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_aliases.md) - [list_bulk_insert_tasks() | Python API · ORM/utility/list_bulk_insert_tasks](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_bulk_insert_tasks.md) - [list_collections() | Python API · ORM/utility/list_collections](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_collections.md) - [list_indexes() | Python API · ORM/utility/list_indexes](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_indexes.md) - [list_resource_groups() | Python API · ORM/utility/list_resource_groups](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_resource_groups.md) - [list_roles() | Python API · ORM/utility/list_roles](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_roles.md) - [list_user() | Python API · ORM/utility/list_user](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_user.md) - [list_usernames() | Python API · ORM/utility/list_usernames](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_usernames.md) - [list_users() | Python API · ORM/utility/list_users](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/list_users.md) - [load_balance() | Python API · ORM/utility/load_balance](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/load_balance.md) - [load_state() | Python API · ORM/utility/load_state](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/load_state.md) - [loading_progress() | Python API · ORM/utility/loading_progress](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/loading_progress.md) - [mkts_from_datetime() | Python API · ORM/utility/mkts_from_datetime](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/mkts_from_datetime.md) - [mkts_from_hybridts() | Python API · ORM/utility/mkts_from_hybridts](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/mkts_from_hybridts.md) - [mkts_from_unixtime() | Python API · ORM/utility/mkts_from_unixtime](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/mkts_from_unixtime.md) - [rename_collection() | Python API · ORM/utility/rename_collection](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/rename_collection.md) - [reset_password() | Python API · ORM/utility/reset_password](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/reset_password.md) - [transfer_node() | Python API · ORM/utility/transfer_node](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/transfer_node.md) - [transfer_replica() | Python API · ORM/utility/transfer_replica](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/transfer_replica.md) - [update_password() | Python API · ORM/utility/update_password](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/update_password.md) - [wait_for_index_building_complete() | Python API · ORM/utility/wait_for_index_building_complete](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/wait_for_index_building_complete.md) - [wait_for_loading_complete() | Python API · ORM/utility/wait_for_loading_complete](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/ORM/utility/wait_for_loading_complete.md) - [BGERerankFunction | Python API · Rerankers/BGERerankFunction/BGERerankFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/BGERerankFunction/BGERerankFunction.md) - [__call__() | Python API · Rerankers/BGERerankFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/BGERerankFunction/__call__.md) - [CohereRerankFunction | Python API · Rerankers/CohereRerankFunction/CohereRerankFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/CohereRerankFunction/CohereRerankFunction.md) - [__call__() | Python API · Rerankers/CohereRerankFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/CohereRerankFunction/__call__.md) - [CrossEncoderRerankFunction | Python API · Rerankers/CrossEncoderRerankFunction/CrossEncoderRerankFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/CrossEncoderRerankFunction/CrossEncoderRerankFunction.md) - [__call__() | Python API · Rerankers/CrossEncoderRerankFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/CrossEncoderRerankFunction/__call__.md) - [JinaRerankFunction | Python API · Rerankers/JinaRerankFunction/JinaRerankFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/JinaRerankFunction/JinaRerankFunction.md) - [__call__() | Python API · Rerankers/JinaRerankFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/JinaRerankFunction/__call__.md) - [VoyageRerankFunction | Python API · Rerankers/VoyageRerankFunction/VoyageRerankFunction](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/VoyageRerankFunction/VoyageRerankFunction.md) - [__call__() | Python API · Rerankers/VoyageRerankFunction/__call__](https://www.xlonglab.com/docs/v2.6.x/api-reference/pymilvus/Rerankers/VoyageRerankFunction/__call__.md) ## v2.5.x ### 概览与基础概念 - [比较](https://www.xlonglab.com/docs/v2.5.x/about/comparison.md): 本文对 Milvus 与其他向量搜索解决方案进行了比较。 - [Milvus 限制](https://www.xlonglab.com/docs/v2.5.x/about/limitations.md): 了解使用 Milvus 时的限制。 - [Milvus 采用者](https://www.xlonglab.com/docs/v2.5.x/about/milvus_adopters.md): 了解已在生产应用中采用 Milvus 的公司。 - [Milvus 是什么？](https://www.xlonglab.com/docs/v2.5.x/about/overview.md): Milvus 是一个高性能、高度可扩展的向量数据库，可在从笔记本电脑到大型分布式系统等各种环境中高效运行。它既可以开源软件的形式提供，也可以云服务的形式提供。 - [Milvus 路线图](https://www.xlonglab.com/docs/v2.5.x/about/roadmap.md): Milvus 是一个开源向量数据库，旨在为人工智能应用提供动力。以下是我们的发展路线图。 ### 管理与运维 - [为 Kubernetes 上的 Milvus 分配资源](https://www.xlonglab.com/docs/v2.5.x/adminGuide/allocate.md): 了解如何在 Kubernetes 上为 Milvus 分配资源。 - [Milvus 用户认证](https://www.xlonglab.com/docs/v2.5.x/adminGuide/authenticate.md): 了解如何在 Milvus 中管理用户身份验证。 - [配置块缓存](https://www.xlonglab.com/docs/v2.5.x/adminGuide/chunk_cache.md) - [在 EC2 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/aws): 了解如何在 AWS EC2 上部署 Milvus 集群。 - [在 AWS 上为 Milvus 配置 Layer-7 Load Balancer](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/aws/aws_layer7.md): 了解如何在 AWS 上的 Layer-7 负载均衡器后面部署 Milvus 集群。 - [在 EKS 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/aws/eks.md): 了解如何在 EKS 上部署 Milvus 集群 - [按 IAM 角色配置 S3 访问权限](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/aws/s3.md): 了解如何使用 IAM 角色配置 s3。 - [通过工作负载身份配置 Blob 存储访问](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/azure/abs.md): 了解如何使用 Workload Identity 配置 Blob Storage。 - [使用 Kubernetes 在 Microsoft Azure 上部署 Milvus](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/azure): 了解如何在 Azure 上部署 Milvus 集群。 - [为 Milvus 配置 ingress-nginx](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/azure/ingress.md): 了解如何使用 Milvus 配置 ingress nginx。 - [在 GKE 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/gcp): 了解如何在 GKE 上部署 Milvus 集群。 - [在 GCP 上为 Milvus 配置 Layer-7 Load Balancer](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/gcp/gcp_layer7.md): 了解如何在 GCP 上的 Layer-7 负载均衡器后面部署 Milvus 集群。 - [使用 Workload Identity 配置 GCS 访问](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/gcp/gcs.md): 了解如何使用工作负载身份配置 gcs。 - [在 OpenShift 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/adminGuide/clouds/openshift): 了解如何在 OpenShift 上部署 Milvus 集群。 - [配置 Jaeger Tracing](https://www.xlonglab.com/docs/v2.5.x/adminGuide/config_jaeger_tracing.md): 本指南介绍如何使用 Jaeger 收集 Milvus 的 Trace 数据。 - [使用 Docker Compose 配置 Milvus](https://www.xlonglab.com/docs/v2.5.x/adminGuide/configure-docker.md): 使用 Docker Compose 配置 Milvus。 - [使用 Helm Chart 配置 Milvus](https://www.xlonglab.com/docs/v2.5.x/adminGuide/configure-helm.md): 使用 Helm Chart 配置 Milvus。 - [使用本地磁盘配置 Milvus QueryNode](https://www.xlonglab.com/docs/v2.5.x/adminGuide/configure-querynode-localdisk.md): 了解如何配置 Milvus QueryNode 以使用本地磁盘。 - [配置访问日志](https://www.xlonglab.com/docs/v2.5.x/adminGuide/configure_access_logs.md) - [使用 Milvus Operator 配置 Milvus](https://www.xlonglab.com/docs/v2.5.x/adminGuide/configure_operator.md): 了解如何使用 Milvus Operator 配置 Milvus。 - [使用 SASL/SSL 连接到 Kafka](https://www.xlonglab.com/docs/v2.5.x/adminGuide/connect_kafka_ssl.md): 本指南列出了几种连接 Milvus 和 Kafka 的方法，从不带 SASL/SSL 的最简单方法到带 SASL/SSL 的完全安全方法。 - [使用 Docker Compose 或 Helm 配置 Meta storage](https://www.xlonglab.com/docs/v2.5.x/adminGuide/deploy_etcd.md): 了解如何使用 Docker Compose/Helm 为 Milvus 配置 Meta storage。 - [使用 Docker Compose 或 Helm 配置消息存储](https://www.xlonglab.com/docs/v2.5.x/adminGuide/deploy_pulsar.md): 了解如何使用 Docker Compose 或 Helm 配置消息存储。 - [使用 Docker Compose 或 Helm 配置 Object Storage](https://www.xlonglab.com/docs/v2.5.x/adminGuide/deploy_s3.md): 了解如何使用 Docker Compose 或 Helm 为 Milvus 设置 S3 存储。 - [删除 User 与 Role](https://www.xlonglab.com/docs/v2.5.x/adminGuide/drop_users_roles.md): 为确保数据安全，建议删除不再使用的用户和角色。本指南介绍如何删除用户和角色。 - [动态配置 Milvus](https://www.xlonglab.com/docs/v2.5.x/adminGuide/dynamic_config.md): 了解 Milvus 的动态配置。 - [为 Role 授予 Privilege 或 Privilege Group](https://www.xlonglab.com/docs/v2.5.x/adminGuide/grant_privileges.md): 创建角色后，您可以向该角色授予权限或权限组。本指南介绍如何为角色授予权限和权限组。 - [向 User 授予 Role](https://www.xlonglab.com/docs/v2.5.x/adminGuide/grant_roles.md): 创建角色并授予角色权限后，就可以将角色授予用户，这样用户就可以访问资源并执行角色所定义的操作。可以向一个用户授予多个角色，也可以向多个用户授予一个角色。本指南将介绍如何向用户授予角色。 - [为 Milvus 配置水平 Pod 自动扩展 (HPA)](https://www.xlonglab.com/docs/v2.5.x/adminGuide/hpa.md): 了解如何配置水平 Pod 自动扩展（HPA），以动态扩展 Milvus 集群。 - [设置 Collection 数量限制](https://www.xlonglab.com/docs/v2.5.x/adminGuide/limit_collection_counts.md) - [使用 Milvus Operator 配置消息存储](https://www.xlonglab.com/docs/v2.5.x/adminGuide/message_storage_operator.md): 了解如何使用 Milvus Operator 配置消息存储。 - [使用 Milvus Operator 配置 Meta storage](https://www.xlonglab.com/docs/v2.5.x/adminGuide/meta_storage_operator.md): 了解如何使用 Milvus Operator 配置 Meta storage。 - [创建警报](https://www.xlonglab.com/docs/v2.5.x/adminGuide/monitor/alert.md): 了解如何在 Grafana 中为 Milvus 服务创建警报。 - [配置 Grafana Loki](https://www.xlonglab.com/docs/v2.5.x/adminGuide/monitor/configure_grafana_loki.md): 本主题介绍如何使用 Loki 收集日志，并使用 Grafana 查询 Milvus 集群的日志。 - [Milvus 指标控制面板](https://www.xlonglab.com/docs/v2.5.x/adminGuide/monitor/metrics_dashboard.md): 本主题介绍 Milvus 仪表板中显示的监控指标。 - [部署监控服务](https://www.xlonglab.com/docs/v2.5.x/adminGuide/monitor): 了解如何使用 Prometheus 为 Milvus 集群部署监控服务。 - [监控概述](https://www.xlonglab.com/docs/v2.5.x/adminGuide/monitor/monitor_overview.md): 了解如何在 Milvus 中使用 Prometheus 和 Grafana 进行监控和警报服务。 - [可视化指标](https://www.xlonglab.com/docs/v2.5.x/adminGuide/monitor/visualize.md): 了解如何在 Grafana 中可视化 Milvus 指标。 - [使用 Milvus Operator 配置 Object Storage](https://www.xlonglab.com/docs/v2.5.x/adminGuide/object_storage_operator.md): 了解如何使用 Milvus Operator 配置 Object Storage。 - [创建 Privilege Group](https://www.xlonglab.com/docs/v2.5.x/adminGuide/privilege_group.md): 为简化授予权限的流程，建议将多个权限合并为一个权限组。 - [Milvus RBAC](https://www.xlonglab.com/docs/v2.5.x/adminGuide/rbac.md): RBAC（基于角色的访问控制）是一种基于角色的访问控制方法。通过 RBAC，可以精细控制用户在 Collection、数据库和实例级别上可以执行的操作，增强数据的安全性。 - [管理 Resource Group](https://www.xlonglab.com/docs/v2.5.x/adminGuide/resource_group.md): 了解如何管理资源组。 - [扩展 Milvus 依赖项](https://www.xlonglab.com/docs/v2.5.x/adminGuide/scale-dependencies.md) - [扩展 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/adminGuide/scaleout.md): 了解如何在 Milvus 集群中手动或自动缩放和扩展。 - [传输中的加密](https://www.xlonglab.com/docs/v2.5.x/adminGuide/tls.md): 了解如何在 Milvus 启用 TLS Proxy。 - [将 Milvus 的 Pulsar 从 V2 升级到 V3](https://www.xlonglab.com/docs/v2.5.x/adminGuide/upgrade-pulsar-v3.md): 了解如何在 Milvus 中将 Pulsar 从 V2 升级到 V3，以便使用最新版本的 Milvus v2.5.x。 - [使用 Docker Compose 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/adminGuide/upgrade_milvus_cluster-docker.md): 了解如何使用 Docker Compose 升级 Milvus 集群。 - [使用 Helm Chart 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/adminGuide/upgrade_milvus_cluster-helm.md): 了解如何使用 Helm Chart 升级 Milvus 集群。 - [使用 Milvus Operator 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/adminGuide/upgrade_milvus_cluster-operator.md): 了解如何使用 Milvus Operator 升级 Milvus 集群。 - [使用 Docker Compose 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.5.x/adminGuide/upgrade_milvus_standalone-docker.md): 了解如何使用 Docker Compose 升级 Milvus Standalone。 - [使用 Helm Chart 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.5.x/adminGuide/upgrade_milvus_standalone-helm.md): 了解如何使用 Helm Chart 升级 Milvus Standalone 单机版。 - [使用 Milvus Operator 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.5.x/adminGuide/upgrade_milvus_standalone-operator.md): 了解如何使用 Milvus Operator 升级 Milvus Standalone。 - [在 Milvus v2.5.x 中使用 Pulsar v2](https://www.xlonglab.com/docs/v2.5.x/adminGuide/use-pulsar-v2.md): Milvus 建议您将 Pulsar 升级到适用于 Milvus v2.5.x 的 v3 版本。不过，如果您更喜欢使用 Pulsar v2，本文将指导您完成在 Milvus v2.5.x 中继续使用 Pulsar v2 的步骤。 - [创建 User 与 Role](https://www.xlonglab.com/docs/v2.5.x/adminGuide/users_and_roles.md): Milvus 通过 RBAC 实现细粒度访问控制。您可以从创建用户和角色开始，然后为角色分配权限或权限组，最后通过向用户授予角色来管理访问控制。这种方法可确保访问管理的效率和安全性。本页将介绍如何在 Milvus 中创建用户和角色。 ### Embedding 模型 - [BGE M3](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-bgm-m3.md): BGE-M3 因其在多语言、多功能和多地域性方面的能力而得名。 - [Cohere Embedding](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-cohere.md): 本文介绍如何使用 CohereEmbeddingFunction 使用 Cohere Embedding 模型对文档和查询进行编码。 - [Gemini](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-gemini.md): Milvus 通过 GeminiEmbeddingFunction 类与 OpenAI 的模型集成。 - [Instructor](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-instructor.md): 本文介绍如何使用 InstructorEmbeddingFunction 使用 Instructor Embedding 模型对文档和查询进行编码。 - [Jina AI - Embedding](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-jina.md): 本文介绍如何使用 JinaEmbeddingFunction 使用 Jina AI Embedding 模型对文档和查询进行编码。 - [mGTE](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-mgte.md): 本文介绍如何使用 MGTEEmbeddingFunction 使用 mGTE Embedding 模型对文档和查询进行编码。 - [Mistral AI](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-mistral-ai.md): 本文介绍如何使用 MistralAIEmbeddingFunction 使用 Mistral AI Embedding 模型对文档和查询进行编码。 - [Model2Vec](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-model2vec.md): Milvus 通过 Model2VecEmbeddingFunction 类与 Model2Vec 的模型集成。 - [Nomic](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-nomic.md): 本文介绍如何使用 NomicEmbeddingFunction 使用 Nomic Embedding 模型对文档和查询进行编码。 - [OpenAI](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-openai.md): Milvus 通过 OpenAIEmbeddingFunction 类与 OpenAI 的模型集成。 - [Sentence Transformers](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-sentence-transform.md): 本文演示了如何使用 Milvus 中的 Sentence Transformers 将文档和查询编码为稠密向量。 - [SPLADE](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-splade.md): 本文介绍如何使用 SpladeEmbeddingFunction 使用 SPLADE 模型对文档和查询进行编码。 - [Voyage](https://www.xlonglab.com/docs/v2.5.x/embeddings/embed-with-voyage.md): 本文介绍如何使用 VoyageEmbeddingFunction 使用 Voyage 模型对文档和查询进行编码。 - [Embedding Overview](https://www.xlonglab.com/docs/v2.5.x/embeddings): 了解如何为数据生成 Embedding。 ### 常见问题 - [操作常见问题](https://www.xlonglab.com/docs/v2.5.x/faq/operational_faq.md): 查找有关 Milvus Operator 常见问题的答案。 - [性能常见问题](https://www.xlonglab.com/docs/v2.5.x/faq/performance_faq.md): 查找有关搜索性能、性能增强和其他性能相关问题的常见问题答案。 - [产品常见问题](https://www.xlonglab.com/docs/v2.5.x/faq/product_faq.md): 查找有关世界上最先进的向量数据库常见问题的答案。 - [故障排除](https://www.xlonglab.com/docs/v2.5.x/faq/troubleshooting.md): 了解使用 Milvus 时可能遇到的常见问题，以及如何克服这些问题。 ### 安装与入门 - [Milvus 部署选项概述](https://www.xlonglab.com/docs/v2.5.x/getstarted/install-overview.md): Milvus 是一个高性能、可扩展的向量数据库。它支持各种规模的用例，从在 Jupyter Notebooks 中本地运行的演示到处理数百亿向量的大规模 Kubernetes 集群。目前，有三种 Milvus 部署选项：Milvus Lite、Milvus Standalone 和 Milvus Distributed。 - [安装 Milvus Go SDK](https://www.xlonglab.com/docs/v2.5.x/getstarted/install_SDKs/install-go.md): 了解如何安装 Milvus 的 GO SDK。 - [安装 Milvus Java SDK](https://www.xlonglab.com/docs/v2.5.x/getstarted/install_SDKs/install-java.md): 了解如何安装 Milvus 的 Java SDK。 - [安装 Milvus Nodejs SDK](https://www.xlonglab.com/docs/v2.5.x/getstarted/install_SDKs/install-node.md): 了解如何安装 Milvus 的 Node.js SDK。 - [安装 Milvus Python SDK](https://www.xlonglab.com/docs/v2.5.x/getstarted/install_SDKs/install-pymilvus.md): 了解如何安装 Milvus 的 Python SDK。 - [本地运行 Milvus Lite](https://www.xlonglab.com/docs/v2.5.x/getstarted/milvus_lite.md): 开始使用 Milvus Lite。 - [快速入门](https://www.xlonglab.com/docs/v2.5.x/getstarted/quickstart.md): 开始使用 Milvus。 - [使用 Docker Compose 运行 Milvus (Linux)](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-docker/install_standalone-docker-compose.md): 了解如何使用 Docker Compose 独立安装 Milvus。 - [在 Docker 中运行 Milvus（Linux）](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-docker/install_standalone-docker.md): 了解如何使用 Docker 独立安装 Milvus。 - [在 Docker 中运行 Milvus（Windows）](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-docker/install_standalone-windows.md): 了解如何使用 Windows 版 Docker Desktop 独立安装 Milvus。 - [使用 Docker Compose 安装 Milvus 的要求](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-docker/prerequisite-docker.md): 了解使用 Docker Compose 安装 Milvus 之前的必要准备工作。 - [使用 Helm Chart 运行支持 GPU 的 Milvus](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-gpu/install_cluster-helm-gpu.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 Docker Compose 运行支持 GPU 的 Milvus](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-gpu/install_standalone-docker-compose-gpu.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [安装支持 GPU 的 Milvus 的要求](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-gpu/prerequisite-gpu.md): 了解使用 GPU 安装 Milvus 前的必要准备工作。 - [使用 Helm 安装 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-k8s/install_cluster-helm.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 Milvus Operator 安装 Milvus 集群](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-k8s/install_cluster-milvusoperator.md): 了解如何使用 Milvus Operator 在 Kubernetes 上安装 Milvus 集群 - [在 Kubernetes 上运行 Milvus 的要求](https://www.xlonglab.com/docs/v2.5.x/getstarted/run-milvus-k8s/prerequisite-helm.md): 了解使用 Helm 安装 Milvus 之前的必要准备工作。 - [搜索数据模型设计](https://www.xlonglab.com/docs/v2.5.x/getstarted/schema-hands-on.md): 信息检索系统，又称搜索引擎，对于检索增强生成（RAG）、视觉搜索和产品推荐等各种人工智能应用至关重要。这些系统的核心是精心设计的数据模型，用于组织、索引和检索信息。 ### 生态集成 - [使用 AIMon 和 Milvus 提升 LLM 应用的检索质量](https://www.xlonglab.com/docs/v2.5.x/integrations/AIMon_milvus_integration.md): 在本教程中，我们将帮助您构建一个能回答问题的检索增强生成（RAG）聊天机器人。 - [使用 VectorETL 将数据高效加载到 Milvus 中](https://www.xlonglab.com/docs/v2.5.x/integrations/ETL_using_vectorETL.md): 在本教程中，我们将探讨如何使用专为向量数据库设计的轻量级 ETL 框架 \[VectorETL\](https://github.com/ContextData/VectorETL)，高效地将数据加载到 Milvus 中。… - [与 Milvus 一起使用 NLWeb](https://www.xlonglab.com/docs/v2.5.x/integrations/NLWeb_with_milvus.md): 了解如何将 Microsoft NLWeb 与 Milvus 集成，为网站构建强大的自然语言界面。本教程演示了如何在 NLWeb 应用程序中利用 Milvus 的向量数据库功能进行高效语义搜索、嵌入存储和上下文检索。 - [使用 Milvus + PII 屏蔽器构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/RAG_with_pii_and_milvus.md): 在本教程中，我们将向您展示如何使用 Milvus 和 PII Masker 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [检索增强生成：使用 Apify 抓取网站并将数据保存到 Milvus 以用于问题解答](https://www.xlonglab.com/docs/v2.5.x/integrations/apify_milvus_rag.md): 本教程介绍如何使用 Apify 的网站内容抓取器抓取网站，并将数据保存到 Milvus 或 Zilliz Cloud 向量数据库，以便日后用于问题解答。 - [构建 RAG Pipeline：将数据从 S3 载入 Milvus](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_from_s3_with_milvus.md): 本教程将指导您使用 Milvus 和亚马逊 S3 构建一个 RAG Pipeline。您将学习如何高效地从 S3 文件桶中加载文档，将其分割成易于管理的块，并将其 Embedding 存储在 Milvus 中，以便进行快速、可扩展的检索。为了简化这一过程，我们将使用 LangChain 作为工具，从 S3 加载数据并将其存储到 Milvus 中。 - [使用 Milvus 和 Cognee 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_cognee.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Cognee 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Crawl4AI 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_crawl4ai.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Crawl4AI 构建一个 RAG Pipeline。该管道集成了用于网络数据抓取的 Crawl4AI、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [利用 Milvus 和 DeepSeek 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_deepseek.md): 在本教程中，我们将向您展示如何使用 Milvus 和 DeepSeek 构建 RAG Pipeline。 - [使用 Milvus 和 Docling 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_docling.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Docling 构建一个 RAG Pipeline。该管道集成了用于文档解析的 Docling、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [用 Milvus 和 EmbedAnything 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_embedAnything.md): 在本教程中，我们将演示如何使用 EmbedAnything 和 Milvus 一起构建 RAG Pipeline。EmbedAnything 使用可插拔的适配器系统，而不是与任何特定数据库紧密耦合，适配器作为封装器，定义了嵌入数据的格式化、索引和在目标向量存储中的存储方式。 - [使用 Milvus 和 Feast 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_feast.md): 在本教程中，我们将使用 Feast 和 Milvus 构建一个 RAG Pipeline。Feast 是一个开源特征库，可简化机器学习的特征管理，为训练和实时推理提供高效的结构化数据存储和检索。Milvus 是一个高性能向量数据库，专为快速相似性搜索而设计，因此非常适合在 RAG 工作流中检索相关文档。 - [利用 Milvus 和 Firecrawl 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_firecrawl.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Firecrawl 构建 RAG Pipeline。该管道集成了用于网络数据抓取的 Firecrawl、用于向量存储的 Milvus 和用于生成有洞察力的上下文感知响应的 OpenAI。 - [使用 Milvus 和 Fireworks AI 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_fireworks.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Fireworks AI 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Gemini 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_gemini.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Gemini 构建 RAG Pipeline（Retrieval-Augmented Generation）。我们将使用 Gemini 模型根据给定的查询生成文本。我们还将使用 Milvus 来存储和检索生成的文本。 - [利用 Milvus 和 Lepton AI 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_lepton.md): 在本教程中，我们将向您展示如何利用 Milvus 和 Lepton AI 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Ollama 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_ollama.md): 在本指南中，我们将向您展示如何利用 Ollama 和 Milvus 高效、安全地构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 SiliconFlow 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_RAG_with_milvus_and_siliconflow.md): 本教程介绍如何使用 Milvus 和 SiliconFlow 构建 RAG Pipeline。 - [在 Arm 架构上构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/build_rag_on_arm.md): 在本教程中，您将学习如何在基于 Arm 的基础架构上构建检索增强生成（RAG）应用程序。在向量存储方面，我们利用了全面管理的 Milvus 向量数据库 Zilliz Cloud。Zilliz Cloud 可在 AWS、GCP 和 Azure 等主流云上使用。在本演示中，我们使用部署在 AWS 上的 Zilliz Cloud 和 Arm 机器。… - [使用 Milvus 部署 Dify](https://www.xlonglab.com/docs/v2.5.x/integrations/dify_with_milvus.md): 本教程介绍如何将 Dify 与 Milvus 集成，使用 Milvus 作为向量数据库，为知识检索与 RAG Pipeline 提供存储和检索能力。 - [使用 DeepEval 进行评估](https://www.xlonglab.com/docs/v2.5.x/integrations/evaluation_with_deepeval.md): 本指南演示了如何使用 DeepEval 评估基于 Milvus 的 RAG Pipeline。 - [使用 Arize Phoenix 进行评估](https://www.xlonglab.com/docs/v2.5.x/integrations/evaluation_with_phoenix.md): 本指南演示了如何使用 Arize Phoenix 评估基于 Milvus 的 RAG Pipeline。 - [使用 Milvus 和 Haystack 进行全文搜索](https://www.xlonglab.com/docs/v2.5.x/integrations/full_text_search_with_milvus_and_haystack.md): 本教程演示了如何使用 Haystack 和 Milvus 在应用程序中实现全文和混合搜索。 - [将 Milvus 与 Agno 相集成](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_agno.md): Milvus 向量数据库能够高效地存储和检索嵌入信息。借助 Milvus 和 Agno，您可以轻松地将知识整合到 Agent 工作流程中。本文档是如何使用 Milvus 与 Agno 集成的基本指南。 - [Airbyte：开源数据移动基础架构](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_airbyte.md): Airbyte 是一种开源数据移动基础架构，用于构建提取和加载（EL）数据管道。它具有多功能性、可扩展性和易用性。Airbyte 的连接器目录 "开箱即用"，预置了 350 多个连接器。这些连接器可用于在短短几分钟内开始将数据从源复制到目标。 - [使用 Milvus 和 BentoML 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_bentoml.md): 本指南演示了如何使用 BentoCloud 上的开源 Embedding 模型和大语言模型与 Milvus 向量数据库来构建检索增强生成（RAG）应用程序。 - [使用 Milvus 与 CAMEL-AI 构建 RAG 系统](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_camel.md): 本指南介绍如何结合 CAMEL-AI 与 Milvus 构建检索增强生成（RAG）系统，并使用 Milvus 检索相关文档，为大语言模型生成提供上下文。 - [使用 Milvus 和 Cohere 进行问题解答](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_cohere.md): 本页说明了如何使用 Milvus 作为向量数据库和 Cohere 作为嵌入系统，创建基于 SQuAD 数据集的问题解答系统。 - [Milvus 与 DSPy 集成](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_dspy.md): 本指南演示了如何使用 DSPy 的 Retriever 模块之一 MilvusRM 来优化 RAG 程序。 - [使用 Milvus 部署 FastGPT](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_fastgpt.md): 本教程将指导您如何使用 Milvus 快速部署自己的专属 FastGPT 应用程序。 - [使用 Milvus 和 Haystack 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_haystack.md): 本指南演示了如何使用 Haystack 和 Milvus 建立检索增强生成（RAG）系统。 - [使用 Milvus 和 Hugging Face 进行问题解答](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_hugging-face.md): 本教程展示了如何使用 Hugging Face 作为数据加载器和嵌入生成器进行数据处理，并使用 Milvus 作为向量数据库进行语义搜索，从而构建一个问题解答系统。 - [Milvus 与 Jina 集成](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_jina.md): 本指南演示了如何使用 Jina 嵌入和 Milvus 进行相似性搜索和检索任务。 - [使用 Langfuse 对 RAG 查询进行 Tracing](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_langfuse.md): 本教程演示如何使用 Langfuse 的 LlamaIndex 集成对 RAG 查询进行 Tracing，并使用 Milvus Lite 存储和检索文档。 - [使用 Milvus 和 LlamaIndex 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_llamaindex.md): 本指南演示了如何使用 LlamaIndex 和 Milvus 建立检索增强生成（RAG）系统。 - [与 Milvus 集成的 MemGPT](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_memgpt.md): MemGPT 可轻松构建和部署有状态 LLM Agent。通过 Milvus 集成，您可以构建可连接外部数据源（RAG）的 AI Agent。 - [使用 Milvus 和 OpenAI 进行语义搜索](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_openai.md): 本页讨论向量数据库与 OpenAI 的 Embedding API 的集成。 - [使用 PyTorch 和 Milvus 进行图像搜索](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_pytorch.md): 本页演示如何使用 PyTorch 和 Milvus 创建图像搜索 - [使用 Ragas 进行评估](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_ragas.md): 本指南演示了如何使用 Ragas 评估基于 Milvus 的 RAG Pipeline。 - [使用 Milvus 和 Sentence Transformers 进行电影搜索](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_sentencetransformers.md): 本页讨论如何使用 Milvus 进行电影搜索 - [在 Snowpark Container Services 上使用 Milvus](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_snowpark.md): 本指南演示如何在 Snowpark Container Services 上启动 Milvus 演示。 - [在 AI Pipeline 中使用 Apache Spark™ 与 Milvus/Zilliz Cloud](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_spark.md): Apache Spark 和 Databricks 与 Milvus 和 Zilliz Cloud 集成，将大数据处理与向量搜索相结合。了解如何利用 Spark-Milvus 连接器构建 AI 驱动的搜索与分析。 - [用 Vanna 和 Milvus 编写 SQL](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_vanna.md): 本指南演示如何使用 Vanna 根据存储在数据库中的数据生成并执行 SQL 查询。 - [使用 Milvus 和 FiftyOne 进行视觉搜索](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_voxel51.md): 本页讨论与 voxel51 的集成 - [使用 Milvus 和 VoyageAI 进行语义搜索](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_voyageai.md): 本页讨论向量数据库与 VoyageAI 的 Embedding API 的集成。 - [Milvus 与 WhyHow 的集成](https://www.xlonglab.com/docs/v2.5.x/integrations/integrate_with_whyhow.md): 本指南演示了如何使用 whyHow.ai 和 Milvus Lite 进行基于规则的检索。 - [将 Milvus 与 MindsDB 相集成](https://www.xlonglab.com/docs/v2.5.x/integrations/integration_with_mindsdb.md): 本教程演示了如何将 Milvus 与 MindsDB 集成，使您能够通过类似 SQL 的操作来管理和查询 Embedding，从而利用 MindsDB 的人工智能功能和 Milvus 的向量数据库功能。 - [集成概述](https://www.xlonglab.com/docs/v2.5.x/integrations/integrations_overview.md): 本页为您提供与 Milvus 和第三方工具互动的教程列表。 - [将 Apache Kafka® 与 Milvus/Zilliz Cloud 连接起来，实现向量数据的实时摄取](https://www.xlonglab.com/docs/v2.5.x/integrations/kafka-connect-milvus.md): Apache Kafka 与 Milvus 和 Zilliz Cloud 集成，可实现向量数据流。了解如何使用 Kafka-Milvus 连接器为语义搜索、推荐系统和人工智能驱动的分析构建实时管道。 - [使用 Milvus 的知识表](https://www.xlonglab.com/docs/v2.5.x/integrations/knowledge_table_with_milvus.md): 默认情况下，知识表使用 Milvus 数据库来存储和检索提取的数据。这样，用户就可以利用 Milvus 的强大功能轻松搜索、过滤和分析数据。在本教程中，我们将介绍如何开始使用 Knowledge Table 和 Milvus。 - [使用 Milvus 的 Kotaemon RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/kotaemon_with_milvus.md): 本教程将指导您如何使用 Milvus 定制 kotaemon 应用程序。 - [将 Milvus 用作向量存储库](https://www.xlonglab.com/docs/v2.5.x/integrations/langchain/basic_usage_langchain.md): 本笔记本介绍如何使用与 Milvus 向量数据库相关的功能。 - [使用 LangChain 和 Milvus 进行全文搜索](https://www.xlonglab.com/docs/v2.5.x/integrations/langchain/full_text_search_with_langchain.md): 本教程将介绍如何使用 LangChain 和 Milvus 在应用程序中实现全文搜索。 - [使用 Milvus 和 LangChain 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.5.x/integrations/langchain/integrate_with_langchain.md): 本指南演示了如何使用 LangChain 和 Milvus 构建检索-增强生成（RAG）系统。 - [Milvus Hybrid Search Retriever](https://www.xlonglab.com/docs/v2.5.x/integrations/langchain/milvus_hybrid_search_retriever.md): 本笔记本介绍如何使用与 Milvus 向量数据库相关的功能。 - [Multi-agent Systems with Mistral AI, Milvus and Llama-agents](https://www.xlonglab.com/docs/v2.5.x/integrations/llama_agents_metadata.md): 在本笔记本中，我们将探讨不同的想法：将数据存储到 Milvus 中，使用 llama-index 与 Mistral 模型进行数据查询，创建自动数据搜索与读取 AI Agent，以及开发根据用户查询进行元数据过滤 AI Agent。 - [利用 Milvus 的 Llama Stack 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/llama_stack_with_milvus.md): 本教程将介绍如何使用 Milvus 搭建一个 Llama Stack 服务器，从而导入私人数据作为知识库。然后，我们将在服务器上执行查询，创建一个完整的 RAG 应用程序。 - [使用 LlamaIndex 和 Milvus Async API 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/llamaindex_milvus_async.md): 本教程介绍如何使用 LlamaIndex Async API 和 Milvus 构建异步 RAG Pipeline，并比较同步与异步实现的性能。 - [使用 LlamaIndex 和 Milvus 进行全文搜索](https://www.xlonglab.com/docs/v2.5.x/integrations/llamaindex_milvus_full_text_search.md): 在本教程中，您将学习如何使用 LlamaIndex 和 Milvus 建立一个使用全文搜索和混合搜索的 RAG 系统。我们将首先单独实施全文搜索，然后通过整合语义搜索来增强其功能，以获得更全面的结果。 - [使用 Milvus 和 LlamaIndex 混合搜索的 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/llamaindex_milvus_hybrid_search.md): 本笔记本演示了如何在 \[LlamaIndex\](https://www.llamaindex.ai/) RAG Pipeline 中使用 Milvus 进行混合搜索。我们将从推荐的默认混合搜索（语义 + BM25）开始，然后探索其他可供选择的稀疏嵌入方法和自定义混合 Reranker。 - [使用 LlamaIndex 和 Milvus 进行元数据过滤](https://www.xlonglab.com/docs/v2.5.x/integrations/llamaindex_milvus_metadata_filter.md): 本笔记本说明了在 LlamaIndex 中如何使用 Milvus 向量存储，重点是元数据过滤功能。您将学习如何使用元数据索引文档、使用 LlamaIndex 的内置元数据过滤器执行向量搜索，以及如何将 Milvus 的本地过滤表达式应用到向量存储。 - [MCP + Milvus：连接 AI 与 Vector Database](https://www.xlonglab.com/docs/v2.5.x/integrations/milvus_and_mcp.md): 本教程将指导您为 Milvus 设置 MCP Server，让人工智能应用程序能够使用自然语言命令执行向量搜索、管理 Collection 和检索数据，而无需编写自定义数据库查询。 - [开始使用 Milvus 和 n8n](https://www.xlonglab.com/docs/v2.5.x/integrations/milvus_and_n8n.md): n8n 是一个功能强大的开源工作流自动化平台，可将各种应用程序、服务和 API 连接在一起，无需编码即可创建自动化工作流。n8n 采用基于节点的可视化界面，用户只需连接代表不同服务或操作的节点，即可构建复杂的自动化流程。它可自行托管，具有高度可扩展性，并支持公平代码和企业许可。 - [开始使用 Dynamiq 和 Milvus](https://www.xlonglab.com/docs/v2.5.x/integrations/milvus_rag_with_dynamiq.md): 在本教程中，我们将探讨如何将 Dynamiq 与 Milvus（专为 RAG 工作流打造的高性能向量数据库）无缝结合使用。Milvus 擅长 Embedding 的高效存储、索引和检索，是要求快速、精确访问上下文数据的人工智能系统不可或缺的组件。 - [用 Milvus、vLLM 和 Llama 3.1 构建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/milvus_rag_with_vllm.md): 本博客将向您展示如何使用 Milvus、vLLM 和 Llama 3.1 构建并运行 RAG。更具体地说，我将向你展示如何在 Milvus 中将文本信息嵌入并存储为 Embedding，并将此向量存储作为知识库来高效检索与用户问题相关的文本块。 - [使用 Mistral OCR 和 Milvus 理解文档](https://www.xlonglab.com/docs/v2.5.x/integrations/mistral_ocr_with_milvus.md): 本教程演示如何使用 Milvus 和 Mistral OCR 构建文档理解系统。 - [Milvus 与 OpenAI Agent 的集成：分步指南](https://www.xlonglab.com/docs/v2.5.x/integrations/openai_agents_milvus.md): 本笔记本展示了如何创建一个可以通过函数调用使用自然语言查询 Milvus 的 AI Agent。我们将把 OpenAI 的 Agents 框架与 Milvus 强大的向量搜索功能结合起来，创建一个不错的搜索体验。 - [开始使用 Mem0 和 Milvus](https://www.xlonglab.com/docs/v2.5.x/integrations/quickstart_mem0_with_milvus.md): 在本教程中，我们将介绍基本的 Mem0 内存管理操作--添加、检索、更新、搜索、删除和跟踪内存历史记录--使用 Milvus，这是一个高性能、开源的向量数据库，可支持高效的存储和检索。本实践介绍将指导您完成基础内存操作，帮助您利用 Mem0 和 Milvus 构建个性化的人工智能交互。 - [使用 Langflow 和 Milvus 构建 RAG 系统](https://www.xlonglab.com/docs/v2.5.x/integrations/rag_with_langflow.md): 本指南演示了如何使用 Langflow 与 Milvus 一起构建 RAG Pipeline。 - [利用 Milvus 和 Unstructured 创建 RAG](https://www.xlonglab.com/docs/v2.5.x/integrations/rag_with_milvus_and_unstructured.md): 在本教程中，我们将使用 Unstructured 来摄取 PDF 文档，然后使用 Milvus 构建 RAG Pipeline。 - [在 AnythingLLM 中使用 Milvus](https://www.xlonglab.com/docs/v2.5.x/integrations/use_milvus_in_anythingllm.md): 本指南将指导您将 Milvus 配置为 AnythingLLM 中的向量数据库，使您能够嵌入、存储和搜索文档，实现智能检索和聊天。 - [在 DocsGPT 中使用 Milvus](https://www.xlonglab.com/docs/v2.5.x/integrations/use_milvus_in_docsgpt.md): 在本教程中，我们将向您展示如何使用 Milvus 作为 DocsGPT 的后台向量数据库。 - [在 PrivateGPT 中使用 Milvus](https://www.xlonglab.com/docs/v2.5.x/integrations/use_milvus_in_private_gpt.md): 在本教程中，我们将向您展示如何使用 Milvus 作为 PrivateGPT 的后台向量数据库。 - [将 Milvus 与 SambaNova 结合使用](https://www.xlonglab.com/docs/v2.5.x/integrations/use_milvus_with_sambanova.md): 本教程利用 SambaNova 人工智能入门套件中的 Milvus 集成，构建了一个类似于 RAG（检索-增强生成）的企业知识检索系统，用于基于企业私有文档的检索和回答。 - [高级视频搜索：利用 Twelve Labs 和 Milvus 进行语义检索](https://www.xlonglab.com/docs/v2.5.x/integrations/video_search_with_twelvelabs_and_milvus.md): 了解如何通过将 Twelve Labs 用于生成多模态嵌入的 Embedding API 与 Milvus 集成来创建语义视频搜索应用程序。它涵盖了从设置开发环境到实现混合搜索和时态视频分析等高级功能的整个过程，为构建复杂的视频内容分析和检索系统奠定了全面的基础。 ### 参考资料 - [Milvus 架构概述](https://www.xlonglab.com/docs/v2.5.x/reference/architecture/architecture_overview.md): Milvus 提供快速、可靠、稳定的向量数据库，专为相似性搜索和人工智能而建。 - [数据处理](https://www.xlonglab.com/docs/v2.5.x/reference/architecture/data_processing.md): 了解 Milvus 的数据处理程序。 - [存储与计算分离](https://www.xlonglab.com/docs/v2.5.x/reference/architecture/four_layers.md): Milvus 的存储与计算分离结构。 - [主要组件](https://www.xlonglab.com/docs/v2.5.x/reference/architecture/main_components.md): 了解 Milvus Standalone 和集群的主要组件。 - [Milvus 2.2 基准测试报告](https://www.xlonglab.com/docs/v2.5.x/reference/benchmark.md): 了解 Milvus 的基准结果。 - [Bitset](https://www.xlonglab.com/docs/v2.5.x/reference/bitset.md): 了解 Milvus 中的 Bitset。 - [Coordinator HA](https://www.xlonglab.com/docs/v2.5.x/reference/coordinator_ha.md): 了解 Milvus Coordinator 积极待命的工作动机和程序。 - [On-disk Index](https://www.xlonglab.com/docs/v2.5.x/reference/disk_index.md): Milvus 中用于磁盘优化向量搜索的磁盘索引机制。 - [术语](https://www.xlonglab.com/docs/v2.5.x/reference/glossary.md) - [GPU 索引](https://www.xlonglab.com/docs/v2.5.x/reference/gpu_index.md): Milvus 的 GPU 索引机制。 - [内存索引](https://www.xlonglab.com/docs/v2.5.x/reference): Milvus 的索引机制。 - [Knowhere](https://www.xlonglab.com/docs/v2.5.x/reference/knowhere.md): 了解 Milvus 的 Knowhere。 - [实施多租户](https://www.xlonglab.com/docs/v2.5.x/reference/multi_tenancy.md): 在 Milvus，多租户意味着多个客户或团队（称为租户）共享同一个集群，同时保持隔离的数据环境。 - [In-Memory Replica](https://www.xlonglab.com/docs/v2.5.x/reference/replica.md): 了解 Milvus 中的内存复制。 - [标量索引](https://www.xlonglab.com/docs/v2.5.x/reference/scalar_index.md): Milvus 标量索引。 - [Schema 说明](https://www.xlonglab.com/docs/v2.5.x/reference/schema.md): Schema 定义了 Collection 的数据结构。在创建 Collection 之前，你需要设计好它的 Schema。本页将帮助你理解 Collection Schema，并自行设计一个示例 Schema。 - [common 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_common.md): 了解如何为 Milvus 配置常用功能。 - [DataCoord 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_datacoord.md): 了解如何为 Milvus 配置 dataCoord。 - [DataNode 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_datanode.md): 了解如何为 Milvus 配置 dataNode。 - [etcd 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_etcd.md): 了解如何为 Milvus 配置 etcd。 - [GPU 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_gpu.md): 了解如何为 Milvus 配置 gpu。 - [gRPC 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_grpc.md): 了解如何为 Milvus 配置 grpc。 - [IndexCoord 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_indexcoord.md): 了解如何为 Milvus 配置 indexCoord。 - [IndexNode 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_indexnode.md): 了解如何为 Milvus 配置 indexNode。 - [内部 TLS 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_internaltls.md): 了解如何为 Milvus 配置 internaltls。 - [Knowhere 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_knowhere.md): 了解如何为 Milvus 配置 knowhere。 - [本地存储配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_localstorage.md): 了解如何为 Milvus 配置 localStorage。 - [日志配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_log.md): 了解如何为 Milvus 配置日志。 - [Meta storage 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_metastore.md): 了解如何为 Milvus 配置 Meta storage。 - [MinIO 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_minio.md): 了解如何为 Milvus 配置 minio。 - [MQ 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_mq.md): 了解如何为 Milvus 配置 mq。 - [消息通道配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_msgchannel.md): 了解如何为 Milvus 配置 msgChannel。 - [NATS 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_natsmq.md): 了解如何为 Milvus 配置 natsmq。 - [Proxy 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_proxy.md): 了解如何为 Milvus 配置 Proxy。 - [Pulsar 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_pulsar.md): 了解如何为 Milvus 配置 Pulsar。 - [QueryCoord 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_querycoord.md): 了解如何为 Milvus 配置 queryCoord。 - [QueryNode 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_querynode.md): 了解如何为 Milvus 配置 queryNode。 - [配额与限制配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_quotaandlimits.md): 了解如何为 Milvus 配置 quotaAndLimits。 - [RocksMQ 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_rocksmq.md): 了解如何为 Milvus 配置 rocksmq。 - [RootCoord 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_rootcoord.md): 了解如何为 Milvus 配置 rootCoord。 - [Streaming 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_streaming.md): 了解如何为 Milvus 配置流媒体。 - [Streaming Node 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_streamingnode.md): 了解如何为 Milvus 配置 streamingNode。 - [TiKV 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_tikv.md): 了解如何为 Milvus 配置 tikv。 - [TLS 配置参数](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_tls.md): 了解 Milvus Proxy 对外服务的 TLS 模式、服务器证书、私钥和 CA 证书路径配置。 - [Trace 配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/configure_trace.md): 了解如何为 Milvus 配置 Trace。 - [系统配置](https://www.xlonglab.com/docs/v2.5.x/reference/sys_config/system_configuration.md): 了解 Milvus 的系统配置。 - [时间同步](https://www.xlonglab.com/docs/v2.5.x/reference/time_sync.md): 了解 Milvus 的时间同步系统。 - [Timestamp](https://www.xlonglab.com/docs/v2.5.x/reference/timestamp.md): 了解时间戳的概念和 Milvus 向量数据库中与时间戳相关的四个主要参数。 - [Consistency Level](https://www.xlonglab.com/docs/v2.5.x/reference/tune_consistency.md): Milvus 支持 Strong、Bounded、Session 和 Eventually 四种 Consistency Level，其中 Bounded 为默认值。 ### release_notes.md - [发布说明](https://www.xlonglab.com/docs/v2.5.x/release_notes.md): Milvus 发行说明 ### Reranker - [BGE](https://www.xlonglab.com/docs/v2.5.x/rerankers/rerankers-bge.md): Milvus 通过`BGERerankFunction`类支持 BGE Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Reranker Cohere](https://www.xlonglab.com/docs/v2.5.x/rerankers/rerankers-cohere.md): Milvus 通过 `CohereRerankFunction` 类支持 Cohere Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [交叉编码器](https://www.xlonglab.com/docs/v2.5.x/rerankers/rerankers-cross-encoder.md): Milvus 通过 "CrossEncoderRerankFunction "类支持交叉编码器 Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Jina AI - Reranker](https://www.xlonglab.com/docs/v2.5.x/rerankers/rerankers-jina.md): Milvus 通过 "JinaRerankFunction "类支持 Jina Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Reranker 概览](https://www.xlonglab.com/docs/v2.5.x/rerankers/rerankers-overview.md): PyMilvus 模型库集成了 Reranker 函数，用于优化初始搜索返回结果的顺序。 - [Reranker Voyage](https://www.xlonglab.com/docs/v2.5.x/rerankers/rerankers-voyage.md): Milvus 通过 "VoyageRerankFunction "类支持 Voyage Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 ### 教程 - [音频相似性搜索](https://www.xlonglab.com/docs/v2.5.x/tutorials/audio_similarity_search.md): 利用 Milvus 建立音频相似性搜索系统。 - [用 Milvus 创建 RAG](https://www.xlonglab.com/docs/v2.5.x/tutorials/build-rag-with-milvus.md): 使用 Milvus 构建 RAG Pipeline，完成文档检索、上下文增强和大语言模型生成。 - [使用 Milvus 进行上下文检索](https://www.xlonglab.com/docs/v2.5.x/tutorials/contextual_retrieval_with_milvus.md): 上下文检索（Contextual Retrieval）是 Anthropic 提出的一种先进的检索方法，旨在解决当前检索增强生成（RAG）解决方案中出现的块的语义隔离问题。在当前实用的 RAG 范式中，文档被分成若干个块，使用向量数据库进行查询搜索，检索出最相关的块。然后，LLM 使用这些检索到的分块对查询做出响应。… - [DNA 序列分类](https://www.xlonglab.com/docs/v2.5.x/tutorials/dna_sequence_classification.md): 利用 Milvus 建立 DNA 序列分类系统。 - [使用 Milvus 进行全文搜索](https://www.xlonglab.com/docs/v2.5.x/tutorials/full_text_search_with_milvus.md): 全文搜索是一种通过匹配文本中特定关键词或短语来检索文档的传统方法。它根据术语频率等因素计算出的相关性分数对结果进行排序。语义搜索更善于理解含义和上下文，而全文搜索则擅长精确的关键词匹配，因此是语义搜索的有益补充。构建 "RAG Pipeline 的常见方法包括通过语义搜索和全文搜索检索文档，然后进行 Reranking 以完善结果。 - [Funnel Search with Matryoshka Embeddings](https://www.xlonglab.com/docs/v2.5.x/tutorials/funnel_search_with_matryoshka.md): 在本笔记本中，我们将研究如何将 Matryoshka 嵌入与 Milvus 一起用于语义搜索。我们展示了一种名为 "漏斗搜索 "的算法，它允许我们在嵌入维度的一小部分子集上执行相似性搜索，而不会导致召回率急剧下降。 - [使用 Milvus 构建 Graph RAG](https://www.xlonglab.com/docs/v2.5.x/tutorials/graph_rag_with_milvus.md): 使用 Milvus 构建 Graph RAG - [利用 Milvus 进行 HDBSCAN 聚类](https://www.xlonglab.com/docs/v2.5.x/tutorials/hdbscan_clustering_with_milvus.md): 在本笔记本中，我们将使用 BGE-M3 Embedding 模型从新闻标题数据集中提取嵌入，利用 Milvus 高效计算嵌入之间的距离以帮助 HDBSCAN 进行聚类，然后使用 UMAP 方法将结果可视化以进行分析。本笔记本是 Milvus 对 Dylan Castillo 文章的改编。 - [如何提高 RAG Pipeline 的性能](https://www.xlonglab.com/docs/v2.5.x/tutorials/how_to_enhance_your_rag.md): 随着 RAG 应用的日益普及，人们越来越关注如何提高其性能。本文介绍了优化 RAG Pipeline 的所有可能方法，并提供了相应的图解，帮助您快速了解主流的 RAG 优化策略。 - [使用 Milvus 进行混合搜索](https://www.xlonglab.com/docs/v2.5.x/tutorials/hybrid_search_with_milvus.md): 使用 Milvus 进行混合搜索 - [重复数据删除](https://www.xlonglab.com/docs/v2.5.x/tutorials/image_deduplication_system.md): 使用 Milvus 构建重复数据删除图像系统。 - [使用 Milvus 搜索图像](https://www.xlonglab.com/docs/v2.5.x/tutorials/image_similarity_search.md): 使用 Milvus 进行图像搜索 - [用 Milvus 推荐电影](https://www.xlonglab.com/docs/v2.5.x/tutorials/movie_recommendation_with_milvus.md): 在本笔记本中，我们将探索如何使用 OpenAI 生成电影描述的 Embedding，并在 Milvus 中利用这些 Embedding 描述来推荐符合您偏好的电影。为了增强搜索结果，我们将利用过滤功能来执行元数据搜索。本示例中使用的数据集来自 Hugging Face 数据集，包含 8000 多个电影条目，为电影推荐提供了丰富的选择。 - [用 Milvus 制作多模态 RAG](https://www.xlonglab.com/docs/v2.5.x/tutorials/multimodal_rag_with_milvus.md): 使用 Milvus 的多模态 RAG - [问答系统](https://www.xlonglab.com/docs/v2.5.x/tutorials/question_answering_system.md): 使用 Milvus 建立问题解答系统。 - [Attu 快速入门](https://www.xlonglab.com/docs/v2.5.x/tutorials/quickstart_with_attu.md): Attu 是 Milvus 的一体化开源管理工具。它具有直观的图形用户界面（GUI），可让您轻松与数据库交互。只需点击几下，您就可以直观地查看集群状态、管理元数据、执行数据查询等。 - [推荐系统](https://www.xlonglab.com/docs/v2.5.x/tutorials/recommendation_system.md): 利用 Milvus 建立个性化推荐系统。 - [使用 Milvus 进行文本到图像搜索](https://www.xlonglab.com/docs/v2.5.x/tutorials/text_image_search.md): 在本教程中，我们将探讨如何使用 OpenAI 的 CLIP（对比语言-图像预训练）模型和 Milvus 实现基于文本的图像检索。我们将使用 CLIP 生成图像嵌入，将其存储在 Milvus 中，并执行高效的相似性搜索。 - [文本搜索引擎](https://www.xlonglab.com/docs/v2.5.x/tutorials/text_search_engine.md): 使用 Milvus 建立文本搜索引擎。 - [教程概览](https://www.xlonglab.com/docs/v2.5.x/tutorials/tutorials-overview.md): 本页为您提供与 Milvus 互动的教程列表。 - [在 asyncio 中使用 AsyncMilvusClient](https://www.xlonglab.com/docs/v2.5.x/tutorials/use-async-milvus-client-with-asyncio.md): AsyncMilvusClient 是一种异步 MilvusClient，它提供了基于协程（coroutine）的 API，可通过 asyncio 对 Milvus 进行非阻塞访问。本文将介绍调用 AsyncMilvusClient 提供的 API 的过程以及需要注意的方面。 - [使用 ColPali 与 Milvus 一起进行多模态检索](https://www.xlonglab.com/docs/v2.5.x/tutorials/use_ColPali_with_milvus.md): 在本笔记本中，为了通用起见，我们将这种多向量表示法称为 "ColBERT Embedding"。不过，实际使用的模型是 ColPali 模型。我们将演示如何使用 Milvus 进行多向量检索。在此基础上，我们将介绍如何使用 ColPali 根据给定查询检索网页。 - [向量可视化](https://www.xlonglab.com/docs/v2.5.x/tutorials/vector_visualization.md): 在本例中，我们将展示如何使用 t-SNE 可视化 Milvus 中的嵌入（向量）。 - [视频相似性搜索](https://www.xlonglab.com/docs/v2.5.x/tutorials/video_similarity_search.md): 利用 Milvus 建立视频相似性搜索系统。 ### 用户指南 - [创建 Collection](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/create-collection.md): 您可以通过定义 Schema、索引参数、度量类型以及创建时是否加载来创建一个 Collection。本页将介绍如何从头开始创建 Collection。 - [删除 Collection](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/drop-collection.md): 如果不再需要某个 Collection，您可以将其删除。 - [加载和释放](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/load-and-release.md): 加载 Collection 是在 Collection 中进行相似性搜索和查询的前提。本页主要介绍加载和释放 Collection 的步骤。 - [管理 Alias](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/manage-aliases.md): Milvus 提供别名管理功能。本页演示了创建、列出、更改和删除别名的程序。 - [Collection 概述](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/manage-collections.md): 在 Milvus 上，您可以创建多个 Collection 来管理数据，并将数据作为实体插入到 Collection 中。Collection 和实体类似于关系数据库中的表和记录。本页可帮助你了解 Collection 及相关概念。 - [管理 Partition](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/manage-partitions.md): Partition 是一个 Collection 的子集。每个 Partition 与其父 Collection 共享相同的数据结构，但只包含 Collection 中的一个数据子集。本页将帮助你了解如何管理 Partition。 - [修改 Collection](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/modify-collection.md): 您可以重命名一个 Collection 或更改其设置。本页主要介绍如何修改 Collection。 - [设置 Collection TTL](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/set-collection-ttl.md): 配置 Collection 的 Time-to-Live（TTL）属性，使 Milvus 自动清理超过保留期限的数据。 - [查看 Collection](https://www.xlonglab.com/docs/v2.5.x/userGuide/collections/view-collections.md): 您可以获取当前连接的数据库中所有 Collection 的名称列表，并查看特定 Collection 的详细信息。 - [导入数据](https://www.xlonglab.com/docs/v2.5.x/userGuide/data-import/import-data.md): 本页演示导入准备好的数据的程序。 - [准备源数据](https://www.xlonglab.com/docs/v2.5.x/userGuide/data-import/prepare-source-data.md): 本页讨论了在开始批量将数据插入 Collection 之前应该考虑的一些问题。 - [删除 Entity](https://www.xlonglab.com/docs/v2.5.x/userGuide/insert-and-delete/delete-entities.md): 您可以通过过滤条件或主键删除不再需要的实体。 - [插入 Entity](https://www.xlonglab.com/docs/v2.5.x/userGuide/insert-and-delete/insert-update-delete.md): Collection 中的实体是共享同一组字段的数据记录。每条数据记录中的字段值构成一个实体。本页介绍如何在 Collection 中插入实体。 - [Upsert Entity](https://www.xlonglab.com/docs/v2.5.x/userGuide/insert-and-delete/upsert-entities.md): Upsert 操作结合了更新和插入数据的操作。Milvus 通过检查主键是否存在来决定执行更新还是插入操作。本节将介绍如何 Upsert 实体，以及在不同情况下 Upsert 操作的具体行为。 - [BITMAP](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/bitmap.md): 位图索引适用于低基数（low-cardinality）标量字段，可通过紧凑的位图表示和按位运算加速过滤查询。 - [FLAT](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/floating-vector/flat.md): FLAT 索引是最简单、最直接的浮点向量索引和搜索方法之一。它依赖于一种 "蛮力 "方法，即直接将每个查询向量与数据集中的每个向量进行比较，而无需任何高级预处理或数据结构。这种方法保证了准确性，由于对每个潜在匹配都进行了评估，因此可提供 100% 的召回率。 - [HNSW](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/floating-vector/hnsw.md): HNSW 索引是一种基于图的索引算法，可以提高搜索高维浮点向量时的性能。它具有出色的搜索精度和较低的延迟，但需要较高的内存开销来维护其分层图结构。 - [IVF_FLAT](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/floating-vector/ivf-flat.md): IVF_FLAT 索引是一种可以提高浮点向量搜索性能的索引算法。 - [IVF_PQ](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/floating-vector/ivf-pq.md): IVF_PQ 索引是一种基于量化的索引算法，用于高维空间中的近似近邻搜索。虽然 IVF_PQ 的速度不如某些基于图的方法，但它所需的内存通常要少得多，因此是大型数据集的实用选择。 - [索引解释](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/index-explained.md): 索引是建立在数据之上的附加结构。其内部结构取决于所使用的近似近邻搜索算法。索引可以加快搜索速度，但在搜索过程中会产生额外的预处理时间、空间和 RAM。此外，使用索引通常会降低召回率（虽然影响可以忽略不计，但仍然很重要）。因此，本文将解释如何尽量减少使用索引的成本，同时最大限度地提高索引的效益。 - [标量字段索引](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/index-scalar-fields.md): 本指南将指导您为整数、字符串等字段创建和配置标量索引。 - [索引向量字段](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/index-vector-fields.md): 本指南将指导您完成创建和管理 Collection 中向量字段索引的基本操作。 - [使用 GPU 建立索引](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage-indexes/index-with-gpu.md): 本指南介绍如何在 Milvus 中建立支持 GPU 的索引，以提高搜索性能。 - [Database](https://www.xlonglab.com/docs/v2.5.x/userGuide/manage_databases.md): Milvus 在 Collection 的上方引入了一个数据库层，为管理和组织数据提供了一种更有效的方式，同时支持多租户。 - [更改 Collection 字段](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/alter-collection-field.md): 您可以更改 Collection 字段的属性，以更改列约束或执行更严格的数据完整性规则。 - [Analyzer 概述](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/analyzer-overview.md): 在文本处理中，分析器是将原始文本转换为结构化可搜索格式的关键组件。每个分析器通常由两个核心部件组成：Tokenizer（分词器）和过滤器。它们共同将输入文本转换为标记，完善这些标记，并为高效索引和检索做好准备。 - [Chinese](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/analyzer/chinese-analyzer.md): 中文分析器专为处理中文文本而设计，可提供有效的分段和标记化功能。 - [English](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/analyzer/english-analyzer.md): Milvus 中的英语分析器旨在处理英语文本，应用特定语言规则进行标记化和过滤。 - [Standard Analyzer](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/analyzer/standard-analyzer.md): 标准分析器是 Milvus 的默认分析器，如果没有指定分析器，它将自动应用于文本字段。它使用基于语法的标记化，因此对大多数语言都很有效。 - [Alphanumonly](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/alphanumonly-filter.md): alphanumonly 过滤器会删除包含非 ASCII 字符的标记，只保留字母数字术语。该过滤器适用于处理只有基本字母和数字的文本，不包括任何特殊字符或符号。 - [ASCII Folding](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/ascii-folding-filter.md): asciifolding 过滤器可将基本拉丁统一码块（前 127 个 ASCII 字符）以外的字符转换为其 ASCII 对应字符。例如，它能将 í 等字符转换为 i，使文本处理更简单、更一致，特别是对于多语言内容。 - [Cnalphanumonly](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/cnalphanumonly-filter.md): cnalphanumonly 过滤器会删除包含除汉字、英文字母或数字以外的任何字符的标记。 - [Cncharonly](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/cncharonly-filter.md): cncharonly 过滤器会删除包含任何非中文字符的标记。当您只想关注中文文本，过滤掉任何包含其他文字、数字或符号的标记时，这个过滤器就非常有用了。 - [Decompounder](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/decompounder-filter.md): 复合词拆分过滤器可根据指定词典将复合词拆分成单个成分，从而更方便地搜索复合词的部分内容。该过滤器对德语等经常使用复合词的语言特别有用。 - [Length](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/length-filter.md): 长度过滤器可移除不符合指定长度要求的标记，从而控制文本处理过程中保留的标记长度。 - [Lowercase](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/lowercase-filter.md): 小写过滤器将 Tokenizer（分词器）生成的 Token（词元）转换为小写，使搜索不区分大小写。例如，它可以将\["高"、"性能"、"向量"、"数据库"\]转换为\["高"、"性能"、"向量"、"数据库"\]。 - [Regex](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/regex-filter.md): regex 过滤器是一种正则表达式过滤器：令牌生成器生成的任何令牌只有在与您提供的表达式相匹配时才会被保留，否则都会被丢弃。 - [Remove Punct](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/removepunct-filter.md): removepunct 过滤器会去掉一些 Tokenizer（分词器）（如 jieba、Lindera 和 icu）通常会保留的标点符号、空格和换行符。当你想要一个更干净的标记流，只包含有意义的文本标记，不含逗号、句号和其他标点符号时，就可以使用它。 - [Stemmer](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/stemmer-filter.md): 词干过滤器将单词还原为其基本形式或词根形式（称为词干化），从而更容易匹配不同词性中含义相似的单词。词干过滤器支持多种语言，可在各种语言环境中进行有效搜索和索引。 - [Stop](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/filter/stop-filter.md): 停用过滤器会从标记化文本中移除指定的停用词，帮助剔除常见的、意义不大的词。您可以使用 stop_words 参数配置停用词列表。 - [多语言 Analyzer](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/multi-language-analyzers.md): Milvus 在进行文本分析时，通常会在 Collection 的整个文本字段中应用一个分析器。如果该分析器针对英语进行了优化，那么它在处理其他语言（如中文、西班牙语或法语）所需的完全不同的标记化和词干规则时就会遇到困难，从而导致召回率降低。… - [ICU](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/tokenizer/icu-tokenizer.md): icu tokenizer 基于 Unicode 国际化组件（ICU）开源项目开发，该项目为软件国际化提供了关键工具。通过使用 ICU 的分词算法，令牌生成器可以准确地将世界上大多数语言的文本分割成单词。 - [Jieba](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/tokenizer/jieba-tokenizer.md): Jieba Tokenizer（分词器）通过将中文文本分解为单词来进行处理。 - [Lindera](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/tokenizer/lindera-tokenizer.md): lindera Tokenizer（分词器）执行基于字典的形态分析。对于日语、韩语和汉语等单词不以空格分隔的语言，它是一个不错的选择。 - [Standard Tokenizer](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/tokenizer/standard-tokenizer.md): Milvus 中的标准 Tokenizer 根据空格和标点符号分割文本，因此适用于大多数语言。 - [Whitespace](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/analyzer/tokenizer/whitespace-tokenizer.md): 只要单词之间有空格，空白 Tokenizer（分词器）就会将文本划分为术语。 - [数组字段](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/array_data_type.md): ARRAY 字段存储同一数据类型元素的有序 Collection。下面举例说明 ARRAY 字段如何存储数据： - [二进制向量](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/binary-vector.md): 二进制向量是一种特殊的数据表示形式，它将传统的高维浮点向量转换成只包含 0 和 1 的二进制向量。这种转换不仅压缩了向量的大小，还降低了存储和计算成本，同时保留了语义信息。当对非关键特征的精度要求不高时，二进制向量可以有效保持原始浮点向量的大部分完整性和实用性。 - [稠密向量](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/dense-vector.md): 稠密向量是广泛应用于机器学习和数据分析的数字数据表示。它们由包含实数的数组组成，其中大部分或所有元素都不为零。与稀疏向量相比，稠密向量在同一维度上包含更多信息，因为每个维度都持有有意义的值。这种表示方法能有效捕捉复杂的模式和关系，使数据在高维空间中更容易分析和处理。稠密向量通常有固定的维数，从几十到几百甚至上千不等，具体取决于具体的应用和要求。 - [动态字段](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/enable-dynamic-field.md): Collection 的 Schema 中定义的所有字段都必须包含在要插入的实体中。如果希望某些字段是可选的，可以考虑启用动态字段。本主题将介绍如何启用和使用动态字段。 - [可空和默认值](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/nullable-and-default.md): Milvus 允许你为标量字段（主键字段除外）设置可空属性和默认值。对于标记为 nullable=True 的字段，你可以在插入数据时跳过该字段，或直接将其设置为空值，系统会将其视为空值而不会导致错误。当字段有默认值时，如果在插入过程中没有为该字段指定数据，系统将自动应用该值。 - [数字字段](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/number.md): 数字字段是存储数值的标量字段。这些数值可以是整数（整数）或十进制数（浮点数）。它们通常用于表示数量、测量值或任何需要进行数学处理的数据。 - [主键字段与 AutoID](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/primary-field.md): 主键字段唯一标识一个实体。本页介绍如何添加两种不同数据类型的主键字段，以及如何启用 Milvus 自动分配主键字段值。 - [Schema 概述](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema): Schema 定义了 Collection 的数据结构。在创建 Collection 之前，你需要设计好它的 Schema。本页将帮助你理解 Collection Schema，并自行设计一个示例 Schema。 - [稀疏向量](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/sparse_vector.md): 稀疏向量是信息检索和自然语言处理中捕捉表层术语匹配的重要方法。虽然稠密向量在语义理解方面表现出色，但稀疏向量往往能提供更可预测的匹配结果，尤其是在搜索特殊术语或文本标识符时。 - [字符串字段](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/string.md): 在 Milvus 中，VARCHAR 是用于存储字符串数据的数据类型。定义 VARCHAR 字段时，有两个参数是必须的： - [JSON 字段](https://www.xlonglab.com/docs/v2.5.x/userGuide/schema/use-json-fields.md): JSON 字段是一种标量字段，与 Embedding 一起以键值对的形式存储附加信息。下面是一个以 JSON 格式存储数据的示例： - [ARRAY Operators](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/boolean/array-operators.md): Milvus 提供功能强大的运算符来查询数组字段，允许你根据数组内容过滤和检索实体。 - [基本运算符](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/boolean/basic-operators.md): Milvus 提供丰富的基本运算符，帮助您高效地过滤和查询数据。这些运算符允许您根据标量字段、数字计算、逻辑条件等来完善搜索条件。了解如何使用这些运算符对于建立精确查询和最大限度地提高搜索效率至关重要。 - [过滤说明](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/boolean): Milvus 提供强大的过滤功能，可对数据进行精确查询。过滤表达式允许你针对特定的标量字段，用不同的条件细化搜索结果。本指南介绍如何在 Milvus 中使用过滤表达式，并以`query` 操作为例进行说明。您还可以在搜索和删除请求中应用这些过滤器。 - [过滤器模板](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/boolean/filtering-templating.md): 在 Milvus 中，元素众多的复杂过滤表达式，尤其是涉及中日韩字符等非 ASCII 字符的过滤表达式，会严重影响查询性能。为了解决这个问题，Milvus 引入了过滤表达式模板机制，旨在通过减少解析复杂表达式所花费的时间来提高效率。本页介绍了在搜索、查询和删除操作中使用过滤表达式模板的方法。 - [JSON 运算符](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/boolean/json-operators.md): Milvus 支持用于查询和过滤 JSON 字段的高级运算符，使其成为管理复杂结构化数据的完美工具。这些运算符可实现对 JSON 文档的高效查询，允许您根据 JSON 字段中的特定元素、值或条件检索实体。本节将指导您在 Milvus 中使用特定于 JSON 的运算符，并提供实际示例来说明其功能。 - [Clustering Compaction](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/clustering-compaction.md): Clustering Compaction 根据 Clustering Key 重新组织 Segment 中的 Entity，以缩小搜索范围并提升大型 Collection 的查询性能。 - [Consistency Level](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/consistency.md): Milvus 支持 Strong、Bounded、Session 和 Eventually 四种 Consistency Level，其中 Bounded 为默认值。 - [从 Elasticsearch 查询迁移到 Milvus](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/elasticsearch-queries-to-milvus.md): 基于 Apache Lucene 构建的 Elasticsearch 是领先的开源搜索引擎。然而，它在现代人工智能应用中面临着各种挑战，包括更新成本高、实时性差、分片管理效率低、非云原生设计以及资源需求过高。作为云原生向量数据库，Milvus 通过解耦存储和计算、高效的高维数据索引以及与现代基础设施的无缝集成，克服了这些问题。… - [过滤搜索](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/filtered-search.md): ANN 搜索能找到与指定 Embedding 最相似的 Embedding。不过，搜索结果不一定总是正确的。您可以在搜索请求中包含过滤条件，这样 Milvus 就会在进行 ANN 搜索前进行元数据过滤，将搜索范围从整个 Collection 缩小到只搜索符合指定过滤条件的实体。 - [全文搜索](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/full-text-search.md): 全文搜索是一种在文本数据集中检索包含特定术语或短语的文档，然后根据相关性对结果进行排序的功能。该功能克服了语义搜索的局限性（语义搜索可能会忽略精确的术语），确保您获得最准确且与上下文最相关的结果。此外，它还能接受原始文本输入，自动将文本数据转换为稀疏嵌入，无需手动生成 Embedding，从而简化了向量搜索。 - [查询](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/get-and-scalar-query.md): 除 ANN 搜索外，Milvus 还支持通过查询进行元数据过滤。本页将介绍如何使用查询、获取和查询迭代器来执行元数据过滤。 - [分组搜索](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/grouping-search.md): 分组搜索允许 Milvus 按指定字段中的值对搜索结果进行分组，以便在更高层次上汇总数据。例如，你可以使用基本的 ANN 搜索来查找与手头这本书类似的书籍，但你也可以使用分组搜索来查找可能涉及该书所讨论主题的书籍类别。本主题将介绍如何使用分组搜索以及主要注意事项。 - [文本匹配](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/keyword-match.md): Milvus 中的文本匹配功能可根据特定术语进行精确的文档检索。该功能主要用于满足特定条件的过滤搜索，并可结合标量过滤来细化查询结果，允许在符合标量标准的向量内进行相似性搜索。 - [度量类型](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/metric.md): 相似度量用于衡量向量之间的相似性。选择合适的距离度量有助于显著提高分类和聚类性能。 - [使用 mmap](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/mmap.md): 内存映射（Mmap）实现了对磁盘上大型文件的直接内存访问，使 Milvus 可以在内存和硬盘中同时存储索引和数据。这种方法有助于根据访问频率优化数据放置策略，在不明显影响搜索性能的情况下扩大 Collection 的存储容量。本页帮助你了解 Milvus 如何使用 mmap 实现快速高效的数据存储和检索。 - [多向量混合搜索](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/multi-vector-search.md): 在许多应用中，可以通过标题和描述等丰富的信息集或文本、图像和音频等多种模式来搜索对象。例如，如果文本或图片与搜索查询的语义相符，就可以搜索包含一段文本和一张图片的推文。混合搜索将这些不同领域的搜索结合在一起，从而增强了搜索体验。Milvus 允许在多个向量字段上进行搜索，同时进行多个近似近邻（ANN）搜索，从而支持这种搜索。… - [Phrase Match](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/phrase-match.md): 短语匹配可让您搜索包含精确短语查询词的文档。默认情况下，单词必须以相同的顺序出现，并且彼此直接相邻。例如，查询 "机器人机器学习 "会匹配"...典型的机器人机器学习模型... "这样的文本，其中 "机器人"、"机器 "和 "学习 "依次出现，中间没有其他词。 - [范围搜索](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/range-search.md): 范围搜索将返回实体的距离或得分限制在特定范围内，从而提高搜索结果的相关性。本页将帮助您了解什么是范围搜索以及进行范围搜索的步骤。 - [Reranker](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/reranking.md): 混合搜索通过多个同时进行的 ANN 搜索获得更精确的搜索结果。多次搜索会返回多组结果，这就需要 Reranking 策略来帮助合并结果并进行 rerank，并返回一组结果。本指南将介绍 Milvus 支持的重排策略，并提供选择适当重排策略的提示。 - [基本向量搜索](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/single-vector-search.md): 近似近邻（ANN）搜索以记录 Embedding 排序顺序的索引文件为基础，根据接收到的搜索请求中携带的查询向量定位 Embedding 子集，将查询向量与子群中的向量进行比较，并返回最相似的结果。通过 ANN 搜索，Milvus 提供了高效的搜索体验。本页将帮助您了解如何进行基本的 ANN 搜索。 - [使用 Partition Key](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/use-partition-key.md): Partition Key 是一种基于 Partition 的搜索优化解决方案。通过指定一个特定的标量字段作为 Partition Key，并在搜索过程中根据 Partition Key 指定过滤条件，可以将搜索范围缩小到几个 Partition，从而提高搜索效率。本文将介绍如何使用 Partition Key 及相关注意事项。 - [搜索迭代器](https://www.xlonglab.com/docs/v2.5.x/userGuide/search-query-get/with-iterators.md): ANN Search 对单次查询可调用的实体数量有最大限制，因此仅使用基本 ANN Search 可能无法满足大规模检索的需求。对于 topK 超过 16,384 的 ANN Search 请求，建议考虑使用 SearchIterator。本节将介绍如何使用 SearchIterator 以及相关注意事项。 - [安装 Birdwatcher](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/birdwatcher_install_guides.md): 了解如何安装 Birdwatcher 以调试 Milvus。 - [Birdwatcher](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/birdwatcher_overview.md): Birdwatcher 是 Milvus 2.x 的调试工具。它连接到 etcd 并检查 Milvus 系统的状态。 - [使用 Birdwatcher](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/birdwatcher_usage_guides.md): 了解如何使用 Birdwatcher 调试 Milvus。 - [Milvus CLI 命令参考](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/cli_commands.md): 使用命令与 Milvus 互动。 - [Milvus 命令行界面](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/cli_overview.md): Milvus 命令行界面（CLI）是一种命令行工具，支持数据库连接、数据操作和数据导入导出。 - [安装 Milvus CLI](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/install_cli.md): 了解如何安装 Milvus CLI。 - [跨存储桶在实例间迁移](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-backup/common-cases/cross-bucket-backup-and-restore.md): 本主题详细介绍如何从一个 Milvus 实例备份 Collection，并将其恢复到同一 Object Storage 中使用不同存储桶的另一个 Milvus 实例。 - [在跨 S3 环境的实例间迁移](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-backup/common-cases/multi-storage-backup-and-restore.md): 本主题详细介绍了从一个 Milvus 实例备份一个 Collection 并将其还原到另一个实例的过程 - [在一个存储桶中的不同实例之间迁移（不同的根路径）](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-backup/common-cases/shared-bucket-backup-and-restore.md): 本主题将详细介绍使用共享存储桶存储对象时，从一个 Milvus 实例备份一个 Collection 并将其恢复到另一个实例的过程 - [在一个实例中备份和还原](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-backup/common-cases/single-instance-backup-and-restore.md): 本主题将详细介绍在同一 Milvus 实例中备份 Collection 并从备份中还原的过程 - [使用 API 备份和恢复数据](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-backup/milvus_backup_api.md): 了解如何通过 API 使用 Milvus 备份功能 - [使用命令备份和恢复数据](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-backup/milvus_backup_cli.md): 学习如何通过 CLI 使用 Milvus 备份 - [Milvus 备份](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-backup/milvus_backup_overview.md): Milvus-Backup 是一款允许用户备份和恢复 Milvus 数据的工具。 - [监控](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-cdc/cdc-monitoring.md): Milvus CDC 通过 Grafana 面板提供全面的监控功能。 - [部署 CDC 服务器](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-cdc/deploy-cdc-server.md): 本指南提供了部署 Milvus-CDC 服务器的分步流程。 - [管理 CDC 任务](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-cdc/manage-cdc-tasks.md): 捕获数据更改 (CDC) 任务可实现从源 Milvus 实例到目标 Milvus 实例的数据同步。 - [CDC 概览](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-cdc/milvus-cdc-overview.md): Milvus CDC 是一个用户友好型工具，可以捕获和同步 Milvus 实例中的增量数据。 - [Milvus WebUI](https://www.xlonglab.com/docs/v2.5.x/userGuide/tools/milvus-webui.md): Milvus Web UI 是 Milvus 的图形化管理工具。它以简单直观的界面提高了系统的可观察性。你可以 ### API Reference - [About Milvus GO SDK | Go API · About](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/About.md) - [AddPrivilegesToGroup() | Go API · Authentication/AddPrivilegesToGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/AddPrivilegesToGroup.md) - [BackupRBAC() | Go API · Authentication/BackupRBAC](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/BackupRBAC.md) - [CreatePrivilegeGroup() | Go API · Authentication/CreatePrivilegeGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/CreatePrivilegeGroup.md) - [CreateRole() | Go API · Authentication/CreateRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/CreateRole.md) - [CreateUser() | Go API · Authentication/CreateUser](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/CreateUser.md) - [DescribeRole() | Go API · Authentication/DescribeRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/DescribeRole.md) - [DescribeUser() | Go API · Authentication/DescribeUser](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/DescribeUser.md) - [DropPrivilegeGroup() | Go API · Authentication/DropPrivilegeGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/DropPrivilegeGroup.md) - [DropRole() | Go API · Authentication/DropRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/DropRole.md) - [DropUser() | Go API · Authentication/DropUser](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/DropUser.md) - [GrantPrivilege() | Go API · Authentication/GrantPrivilege](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/GrantPrivilege.md) - [GrantPrivilegeV2() | Go API · Authentication/GrantPrivilegeV2](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/GrantPrivilegeV2.md) - [GrantRole() | Go API · Authentication/GrantRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/GrantRole.md) - [GrantV2() | Go API · Authentication/GrantV2](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/GrantV2.md) - [ListPrivilegeGroups() | Go API · Authentication/ListPrivilegeGroups](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/ListPrivilegeGroups.md) - [ListRoles() | Go API · Authentication/ListRoles](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/ListRoles.md) - [ListUsers() | Go API · Authentication/ListUsers](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/ListUsers.md) - [OperatePrivilegeGroup() | Go API · Authentication/OperatePrivilegeGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/OperatePrivilegeGroup.md) - [RemovePrivilegesFromGroup() | Go API · Authentication/RemovePrivilegesFromGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/RemovePrivilegesFromGroup.md) - [RestoreRBAC() | Go API · Authentication/RestoreRBAC](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/RestoreRBAC.md) - [RevokePrivilege() | Go API · Authentication/RevokePrivilege](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/RevokePrivilege.md) - [RevokePrivilegeV2() | Go API · Authentication/RevokePrivilegeV2](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/RevokePrivilegeV2.md) - [RevokeRole() | Go API · Authentication/RevokeRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/RevokeRole.md) - [RevokeV2() | Go API · Authentication/RevokeV2](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/RevokeV2.md) - [UpdatePassword() | Go API · Authentication/UpdatePassword](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Authentication/UpdatePassword.md) - [Close() | Go API · Client/Close](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Client/Close.md) - [New() | Go API · Client/New](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Client/New.md) - [AddCollectionField() | Go API · Collection/AddCollectionField](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/AddCollectionField.md) - [AlterAlias() | Go API · Collection/AlterAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/AlterAlias.md) - [AlterCollectionFieldProperty() | Go API · Collection/AlterCollectionFieldProperty](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/AlterCollectionFieldProperty.md) - [AlterCollectionProperties() | Go API · Collection/AlterCollectionProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/AlterCollectionProperties.md) - [CreateAlias() | Go API · Collection/CreateAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/CreateAlias.md) - [CreateCollection() | Go API · Collection/CreateCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/CreateCollection.md) - [DescribeAlias() | Go API · Collection/DescribeAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/DescribeAlias.md) - [DescribeCollection() | Go API · Collection/DescribeCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/DescribeCollection.md) - [DescribeReplica() | Go API · Collection/DescribeReplica](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/DescribeReplica.md) - [DropAlias() | Go API · Collection/DropAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/DropAlias.md) - [DropCollection() | Go API · Collection/DropCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/DropCollection.md) - [DropCollectionProperties() | Go API · Collection/DropCollectionProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/DropCollectionProperties.md) - [GetCollectionStats() | Go API · Collection/GetCollectionStats](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/GetCollectionStats.md) - [HasCollection() | Go API · Collection/HasCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/HasCollection.md) - [ListAliases() | Go API · Collection/ListAliases](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/ListAliases.md) - [ListCollections() | Go API · Collection/ListCollections](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/ListCollections.md) - [RenameCollection() | Go API · Collection/RenameCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/RenameCollection.md) - [RunAnalyzer() | Go API · Collection/RunAnalyzer](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Collection/RunAnalyzer.md) - [BulkImport() | Go API · DataImport/BulkImport](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/DataImport/BulkImport.md) - [GetImportProgress() | Go API · DataImport/GetImportProgress](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/DataImport/GetImportProgress.md) - [ListImportJobs() | Go API · DataImport/ListImportJobs](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/DataImport/ListImportJobs.md) - [AlterDatabaseProperties() | Go API · Database/AlterDatabaseProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Database/AlterDatabaseProperties.md) - [CreateDatabase() | Go API · Database/CreateDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Database/CreateDatabase.md) - [DescribeDatabase() | Go API · Database/DescribeDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Database/DescribeDatabase.md) - [DropDatabase() | Go API · Database/DropDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Database/DropDatabase.md) - [DropDatabaseProperties() | Go API · Database/DropDatabaseProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Database/DropDatabaseProperties.md) - [ListDatabase() | Go API · Database/ListDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Database/ListDatabase.md) - [UseDatabase() | Go API · Database/UseDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Database/UseDatabase.md) - [AlterIndexProperties() | Go API · Management/AlterIndexProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/AlterIndexProperties.md) - [Compact() | Go API · Management/Compact](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/Compact.md) - [CreateIndex() | Go API · Management/CreateIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/CreateIndex.md) - [DescribeIndex() | Go API · Management/DescribeIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/DescribeIndex.md) - [DropIndex() | Go API · Management/DropIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/DropIndex.md) - [DropIndexProperties() | Go API · Management/DropIndexProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/DropIndexProperties.md) - [GetCompactionState() | Go API · Management/GetCompactionState](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/GetCompactionState.md) - [GetLoadState() | Go API · Management/GetLoadState](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/GetLoadState.md) - [ListIndexes() | Go API · Management/ListIndexes](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/ListIndexes.md) - [LoadCollection() | Go API · Management/LoadCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/LoadCollection.md) - [LoadPartitions() | Go API · Management/LoadPartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/LoadPartitions.md) - [RefreshLoad() | Go API · Management/RefreshLoad](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/RefreshLoad.md) - [ReleaseCollection() | Go API · Management/ReleaseCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/ReleaseCollection.md) - [ReleasePartitions() | Go API · Management/ReleasePartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Management/ReleasePartitions.md) - [CreatePartition() | Go API · Partition/CreatePartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Partition/CreatePartition.md) - [DropPartition() | Go API · Partition/DropPartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Partition/DropPartition.md) - [GetPartitionStats() | Go API · Partition/GetPartitionStats](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Partition/GetPartitionStats.md) - [HasPartition() | Go API · Partition/HasPartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Partition/HasPartition.md) - [ListPartitions() | Go API · Partition/ListPartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Partition/ListPartitions.md) - [CreateResourceGroup() | Go API · ResourceGroup/CreateResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/ResourceGroup/CreateResourceGroup.md) - [DescribeResourceGroup() | Go API · ResourceGroup/DescribeResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/ResourceGroup/DescribeResourceGroup.md) - [DropResourceGroup() | Go API · ResourceGroup/DropResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/ResourceGroup/DropResourceGroup.md) - [ListResourceGroups() | Go API · ResourceGroup/ListResourceGroups](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/ResourceGroup/ListResourceGroups.md) - [TransferReplica() | Go API · ResourceGroup/TransferReplica](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/ResourceGroup/TransferReplica.md) - [UpdateResourceGroup() | Go API · ResourceGroup/UpdateResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/ResourceGroup/UpdateResourceGroup.md) - [Delete() | Go API · Vector/Delete](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/Delete.md) - [Flush() | Go API · Vector/Flush](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/Flush.md) - [Get() | Go API · Vector/Get](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/Get.md) - [HybridSearch() | Go API · Vector/HybridSearch](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/HybridSearch.md) - [Insert() | Go API · Vector/Insert](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/Insert.md) - [Query() | Go API · Vector/Query](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/Query.md) - [Search() | Go API · Vector/Search](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/Search.md) - [SearchIterator() | Go API · Vector/SearchIterator](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/SearchIterator.md) - [Upsert() | Go API · Vector/Upsert](https://www.xlonglab.com/docs/v2.5.x/api-reference/go/Vector/Upsert.md) - [About | Java API · About](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/About.md) - [alterAlias() | Java API · v1/Alias/alterAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Alias/alterAlias.md) - [createAlias() | Java API · v1/Alias/createAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Alias/createAlias.md) - [dropAlias() | Java API · v1/Alias/dropAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Alias/dropAlias.md) - [listAliases() | Java API · v1/Alias/listAliases](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Alias/listAliases.md) - [createCredential() | Java API · v1/Authentication/createCredential](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Authentication/createCredential.md) - [deleteCredential() | Java API · v1/Authentication/deleteCredential](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Authentication/deleteCredential.md) - [listCredUsers() | Java API · v1/Authentication/listCredUsers](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Authentication/listCredUsers.md) - [updateCredential() | Java API · v1/Authentication/updateCredential](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Authentication/updateCredential.md) - [bulkInsert() | Java API · v1/BulkInsertOps/bulkInsert](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/BulkInsertOps/bulkInsert.md) - [getBulkInsertState() | Java API · v1/BulkInsertOps/getBulkInsertState](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/BulkInsertOps/getBulkInsertState.md) - [listBulkInsertTasks() | Java API · v1/BulkInsertOps/listBulkInsertTasks](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/BulkInsertOps/listBulkInsertTasks.md) - [LocalBulkWriter | Java API · v1/BulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/BulkWriter/LocalBulkWriter.md) - [RemoteBulkWriter | Java API · v1/BulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/BulkWriter/RemoteBulkWriter.md) - [alterCollection() | Java API · v1/Collection/alterCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/alterCollection.md) - [createCollection() | Java API · v1/Collection/createCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/createCollection.md) - [delete() | Java API · v1/Collection/delete](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/delete.md) - [describeCollection() | Java API · v1/Collection/describeCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/describeCollection.md) - [dropCollection() | Java API · v1/Collection/dropCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/dropCollection.md) - [flush() | Java API · v1/Collection/flush](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/flush.md) - [getCollectionStatistics() | Java API · v1/Collection/getCollectionStatistics](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/getCollectionStatistics.md) - [getLoadState() | Java API · v1/Collection/getLoadState](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/getLoadState.md) - [getLoadingProgress() | Java API · v1/Collection/getLoadingProgress](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/getLoadingProgress.md) - [getPersistentSegmentInfo() | Java API · v1/Collection/getPersistentSegmentInfo](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/getPersistentSegmentInfo.md) - [getQuerySegmentInfo() | Java API · v1/Collection/getQuerySegmentInfo](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/getQuerySegmentInfo.md) - [getReplicas() | Java API · v1/Collection/getReplicas](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/getReplicas.md) - [insert() | Java API · v1/Collection/insert](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/insert.md) - [insertAsync() | Java API · v1/Collection/insertAsync](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/insertAsync.md) - [loadCollection() | Java API · v1/Collection/loadCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/loadCollection.md) - [releaseCollection() | Java API · v1/Collection/releaseCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/releaseCollection.md) - [renameCollection() | Java API · v1/Collection/renameCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/renameCollection.md) - [showCollections() | Java API · v1/Collection/showCollections](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/showCollections.md) - [upsert() | Java API · v1/Collection/upsert](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/upsert.md) - [upsertAsync() | Java API · v1/Collection/upsertAsync](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Collection/upsertAsync.md) - [MilvusClient | Java API · v1/Connections/MilvusClient](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Connections/MilvusClient.md) - [MilvusClientV1Pool | Java API · v1/Connections/MilvusClientV1Pool](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Connections/MilvusClientV1Pool.md) - [alterDatabase() | Java API · v1/Database/alterDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Database/alterDatabase.md) - [createDatabase() | Java API · v1/Database/createDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Database/createDatabase.md) - [dropDatabase() | Java API · v1/Database/dropDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Database/dropDatabase.md) - [listDatabases() | Java API · v1/Database/listDatabases](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Database/listDatabases.md) - [createCollection() | Java API · v1/HighlevelAPI/createCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/HighlevelAPI/createCollection.md) - [delete() | Java API · v1/HighlevelAPI/delete](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/HighlevelAPI/delete.md) - [get() | Java API · v1/HighlevelAPI/get](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/HighlevelAPI/get.md) - [insert() | Java API · v1/HighlevelAPI/insert](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/HighlevelAPI/insert.md) - [listCollections() | Java API · v1/HighlevelAPI/listCollections](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/HighlevelAPI/listCollections.md) - [query() | Java API · v1/HighlevelAPI/query](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/HighlevelAPI/query.md) - [search() | Java API · v1/HighlevelAPI/search](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/HighlevelAPI/search.md) - [alterIndex() | Java API · v1/Index/alterIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Index/alterIndex.md) - [createIndex() | Java API · v1/Index/createIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Index/createIndex.md) - [describeIndex() | Java API · v1/Index/describeIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Index/describeIndex.md) - [dropIndex() | Java API · v1/Index/dropIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Index/dropIndex.md) - [getIndexBuildProgress() | Java API · v1/Index/getIndexBuildingProgress](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Index/getIndexBuildingProgress.md) - [getIndexState() | Java API · v1/Index/getIndexState](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Index/getIndexState.md) - [getCompactionState() | Java API · v1/Management/getCompactionState](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Management/getCompactionState.md) - [getCompactionStateWithPlans() | Java API · v1/Management/getCompactionStateWithPlans](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Management/getCompactionStateWithPlans.md) - [getFlushAllState() | Java API · v1/Management/getFlushAllState](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Management/getFlushAllState.md) - [getFlushState() | Java API · v1/Management/getFlushState](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Management/getFlushState.md) - [getMetrics() | Java API · v1/Management/getMetrics](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Management/getMetrics.md) - [loadBalance() | Java API · v1/Management/loadBalance](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Management/loadBalance.md) - [manualCompact() | Java API · v1/Management/manualCompact](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Management/manualCompact.md) - [ConsistencyLevelEnum | Java API · v1/Misc/ConsistencyLevelEnum](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Misc/ConsistencyLevelEnum.md) - [Constant | Java API · v1/Misc/Constant](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Misc/Constant.md) - [DataType | Java API · v1/Misc/DataType](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Misc/DataType.md) - [IndexType | Java API · v1/Misc/IndexType](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Misc/IndexType.md) - [LogLevel | Java API · v1/Misc/LogLevel](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Misc/LogLevel.md) - [MetricType | Java API · v1/Misc/MetricType](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Misc/MetricType.md) - [R | Java API · v1/Misc/RT](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Misc/RT.md) - [createPartition() | Java API · v1/Partition/createPartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Partition/createPartition.md) - [dropPartition() | Java API · v1/Partition/dropPartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Partition/dropPartition.md) - [getPartitionStatistics() | Java API · v1/Partition/getPartitionStatistics](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Partition/getPartitionStatistics.md) - [hasPartition() | Java API · v1/Partition/hasPartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Partition/hasPartition.md) - [loadPartitions() | Java API · v1/Partition/loadPartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Partition/loadPartitions.md) - [releasePartitions() | Java API · v1/Partition/releasePartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Partition/releasePartitions.md) - [showPartitions() | Java API · v1/Partition/showPartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/Partition/showPartitions.md) - [hybridSearch() | Java API · v1/QuerySearch/hybridSearch](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/QuerySearch/hybridSearch.md) - [query() | Java API · v1/QuerySearch/query](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/QuerySearch/query.md) - [queryAsync() | Java API · v1/QuerySearch/queryAsync](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/QuerySearch/queryAsync.md) - [queryIterator() | Java API · v1/QuerySearch/queryIterator](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/QuerySearch/queryIterator.md) - [search() | Java API · v1/QuerySearch/search](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/QuerySearch/search.md) - [searchAsync() | Java API · v1/QuerySearch/searchAsync](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/QuerySearch/searchAsync.md) - [searchIterator() | Java API · v1/QuerySearch/searchIterator](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/QuerySearch/searchIterator.md) - [addUserToRole() | Java API · v1/RBAC/addUserToRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/addUserToRole.md) - [createRole() | Java API · v1/RBAC/createRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/createRole.md) - [dropRole() | Java API · v1/RBAC/dropRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/dropRole.md) - [grantRolePrivilege() | Java API · v1/RBAC/grantRolePrivilege](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/grantRolePrivilege.md) - [removeUserFromRole() | Java API · v1/RBAC/removeUserFromRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/removeUserFromRole.md) - [revokeRolePrivilege() | Java API · v1/RBAC/revokeRolePrivilege](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/revokeRolePrivilege.md) - [selectGrantForRole() | Java API · v1/RBAC/selectGrantForRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/selectGrantForRole.md) - [selectGrantForRoleAndObject() | Java API · v1/RBAC/selectGrantForRoleAndObject](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/selectGrantForRoleAndObject.md) - [selectRole() | Java API · v1/RBAC/selectRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/selectRole.md) - [selectUser() | Java API · v1/RBAC/selectUser](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/RBAC/selectUser.md) - [createResourceGroup() | Java API · v1/ResourceGroup/createResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/ResourceGroup/createResourceGroup.md) - [describeResourceGroup() | Java API · v1/ResourceGroup/describeResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/ResourceGroup/describeResourceGroup.md) - [dropResourceGroup() | Java API · v1/ResourceGroup/dropResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/ResourceGroup/dropResourceGroup.md) - [listResourceGroups() | Java API · v1/ResourceGroup/listResourceGroups](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/ResourceGroup/listResourceGroups.md) - [transferNode() | Java API · v1/ResourceGroup/transferNode](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/ResourceGroup/transferNode.md) - [transferReplica() | Java API · v1/ResourceGroup/transferReplica](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/ResourceGroup/transferReplica.md) - [checkHealth() | Java API · v1/System/checkHealth](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/System/checkHealth.md) - [getVersion() | Java API · v1/System/getVersion](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v1/System/getVersion.md) - [addPrivilegesToGroup() | Java API · v2/Authentication/addPrivilegesToGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/addPrivilegesToGroup.md) - [createPrivilegeGroup() | Java API · v2/Authentication/createPrivilegeGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/createPrivilegeGroup.md) - [createRole() | Java API · v2/Authentication/createRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/createRole.md) - [createUser() | Java API · v2/Authentication/createUser](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/createUser.md) - [describeRole() | Java API · v2/Authentication/describeRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/describeRole.md) - [describeUser() | Java API · v2/Authentication/describeUser](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/describeUser.md) - [dropPrivilegeGroup() | Java API · v2/Authentication/dropPrivilegeGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/dropPrivilegeGroup.md) - [dropRole() | Java API · v2/Authentication/dropRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/dropRole.md) - [dropUser() | Java API · v2/Authentication/dropUser](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/dropUser.md) - [grantPrivilege() | Java API · v2/Authentication/grantPrivilege](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/grantPrivilege.md) - [grantPrivilegeV2() | Java API · v2/Authentication/grantPrivilegeV2](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/grantPrivilegeV2.md) - [grantRole() | Java API · v2/Authentication/grantRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/grantRole.md) - [listPrivilegeGroups() | Java API · v2/Authentication/listPrivilegeGroups](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/listPrivilegeGroups.md) - [listRoles() | Java API · v2/Authentication/listRoles](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/listRoles.md) - [listUsers() | Java API · v2/Authentication/listUsers](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/listUsers.md) - [removePrivilegesFromGroup() | Java API · v2/Authentication/removePrivilegesFromGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/removePrivilegesFromGroup.md) - [revokePrivilege() | Java API · v2/Authentication/revokePrivilege](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/revokePrivilege.md) - [revokePrivilegeV2() | Java API · v2/Authentication/revokePrivilegeV2](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/revokePrivilegeV2.md) - [revokeRole() | Java API · v2/Authentication/revokeRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/revokeRole.md) - [updatePassword() | Java API · v2/Authentication/updatePassword](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Authentication/updatePassword.md) - [MilvusClientV2 | Java API · v2/Client/MilvusClientV2](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Client/MilvusClientV2.md) - [MilvusClientV2Pool | Java API · v2/Client/MilvusClientV2Pool](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Client/MilvusClientV2Pool.md) - [CollectionSchema | Java API · v2/Collections/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/CollectionSchema/CollectionSchema.md) - [addField() | Java API · v2/Collections/CollectionSchema/addField](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/CollectionSchema/addField.md) - [addFunction() | Java API · v2/Collections/CollectionSchema/addFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/CollectionSchema/addFunction.md) - [getField() | Java API · v2/Collections/CollectionSchema/getField](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/CollectionSchema/getField.md) - [ConsistencyLevel | Java API · v2/Collections/ConsistencyLevel](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/ConsistencyLevel.md) - [DataType | Java API · v2/Collections/DataType](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/DataType.md) - [FieldSchema | Java API · v2/Collections/FieldSchema](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/FieldSchema.md) - [Function | Java API · v2/Collections/Function](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/Function.md) - [FunctionType | Java API · v2/Collections/FunctionType](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/FunctionType.md) - [ListCollectionsV2() | Java API · v2/Collections/ListCollectionsV2](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/ListCollectionsV2.md) - [alterAlias() | Java API · v2/Collections/alterAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/alterAlias.md) - [alterCollectionField() | Java API · v2/Collections/alterCollectionField](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/alterCollectionField.md) - [alterCollectionProperties() | Java API · v2/Collections/alterCollectionProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/alterCollectionProperties.md) - [createAlias() | Java API · v2/Collections/createAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/createAlias.md) - [createCollection() | Java API · v2/Collections/createCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/createCollection.md) - [CreateSchema() | Java API · v2/Collections/createSchema](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/createSchema.md) - [describeAlias() | Java API · v2/Collections/describeAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/describeAlias.md) - [describeCollection() | Java API · v2/Collections/describeCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/describeCollection.md) - [describeReplicas() | Java API · v2/Collections/describeReplicas](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/describeReplicas.md) - [dropAlias() | Java API · v2/Collections/dropAlias](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/dropAlias.md) - [dropCollection() | Java API · v2/Collections/dropCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/dropCollection.md) - [dropCollectionFieldProperties() | Java API · v2/Collections/dropCollectionFieldProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/dropCollectionFieldProperties.md) - [dropCollectionProperties() | Java API · v2/Collections/dropCollectionProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/dropCollectionProperties.md) - [getCollectionStats() | Java API · v2/Collections/getCollectionStats](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/getCollectionStats.md) - [hasCollection() | Java API · v2/Collections/hasCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/hasCollection.md) - [listAliases() | Java API · v2/Collections/listAliases](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/listAliases.md) - [listCollections() | Java API · v2/Collections/listCollections](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/listCollections.md) - [renameCollection() | Java API · v2/Collections/renameCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Collections/renameCollection.md) - [BulkFileType | Java API · v2/DataImport/BulkFileType](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/BulkFileType.md) - [bulkImport() | Java API · v2/DataImport/BulkImport/bulkImport](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/BulkImport/bulkImport.md) - [getImportProgress() | Java API · v2/DataImport/BulkImport/getImportProgress](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/BulkImport/getImportProgress.md) - [listImportJobs() | Java API · v2/DataImport/BulkImport/listImportJobs](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/BulkImport/listImportJobs.md) - [LocalBulkWriter | Java API · v2/DataImport/LocalBulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/LocalBulkWriter/LocalBulkWriter.md) - [append_row() | Java API · v2/DataImport/LocalBulkWriter/append_row](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/LocalBulkWriter/append_row.md) - [close() | Java API · v2/DataImport/LocalBulkWriter/close](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/LocalBulkWriter/close.md) - [commit() | Java API · v2/DataImport/LocalBulkWriter/commit](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/LocalBulkWriter/commit.md) - [getBatchFiles() | Java API · v2/DataImport/LocalBulkWriter/getBatchFiles](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/LocalBulkWriter/getBatchFiles.md) - [RemoteBulkWriter | Java API · v2/DataImport/RemoteBulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/RemoteBulkWriter/RemoteBulkWriter.md) - [append_row() | Java API · v2/DataImport/RemoteBulkWriter/append_row](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/RemoteBulkWriter/append_row.md) - [close() | Java API · v2/DataImport/RemoteBulkWriter/close](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/RemoteBulkWriter/close.md) - [commit() | Java API · v2/DataImport/RemoteBulkWriter/commit](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/RemoteBulkWriter/commit.md) - [getBatchFiles() | Java API · v2/DataImport/RemoteBulkWriter/getBatchFiles](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/DataImport/RemoteBulkWriter/getBatchFiles.md) - [alterDatabaseProperties() | Java API · v2/Database/alterDatabaseProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Database/alterDatabaseProperties.md) - [createDatabase() | Java API · v2/Database/createDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Database/createDatabase.md) - [currentUsedDatabase() | Java API · v2/Database/currentUsedDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Database/currentUsedDatabase.md) - [describeDatabase() | Java API · v2/Database/describeDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Database/describeDatabase.md) - [dropDatabase() | Java API · v2/Database/dropDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Database/dropDatabase.md) - [dropDatabaseProperties() | Java API · v2/Database/dropDatabaseProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Database/dropDatabaseProperties.md) - [listDatabases() | Java API · v2/Database/listDatabases](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Database/listDatabases.md) - [useDatabase() | Java API · v2/Database/useDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Database/useDatabase.md) - [IndexParam | Java API · v2/Management/IndexParam](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Management/IndexParam.md) - [IndexType | Java API · v2/Management/IndexType](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Management/IndexType.md) - [MetricType | Java API · v2/Management/MetricType](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Management/MetricType.md) - [alterIndexProperties() | Java API · v2/Management/alterIndexProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Management/alterIndexProperties.md) - [checkHealth() | Java API · v2/Management/checkHealth](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Management/checkHealth.md) - 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[runAnalyzer() | Java API · v2/Vector/runAnalyzer](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Vector/runAnalyzer.md) - [search() | Java API · v2/Vector/search](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Vector/search.md) - [searchIterator() | Java API · v2/Vector/searchIterator](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Vector/searchIterator.md) - [upsert() | Java API · v2/Vector/upsert](https://www.xlonglab.com/docs/v2.5.x/api-reference/java/v2/Vector/upsert.md) - [About Milvus-sdk-node | Node.js API · About](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/About.md) - [addPrivilegesToGroup() | Node.js API · Authentication/addPrivilegesToGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Authentication/addPrivilegesToGroup.md) - [addUserToRole() | Node.js API · Authentication/addUserToRole](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Authentication/addUserToRole.md) - [backupRBAC() | Node.js API · Authentication/backupRBAC](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Authentication/backupRBAC.md) - 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[dropDatabaseProperties() | Node.js API · Database/dropDatabaseProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Database/dropDatabaseProperties.md) - [listDatabases() | Node.js API · Database/listDatabases](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Database/listDatabases.md) - [useDatabase() | Node.js API · Database/useDatabase](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Database/useDatabase.md) - [IndexType | Node.js API · Management/IndexType](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/IndexType.md) - [MetricType | Node.js API · Management/MetricType](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/MetricType.md) - [alterIndexProperties() | Node.js API · Management/alterIndexProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/alterIndexProperties.md) - [compact() | Node.js API · Management/compact](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/compact.md) - [createIndex() | Node.js API · Management/createIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/createIndex.md) - [describeIndex() | Node.js API · Management/describeIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/describeIndex.md) - [dropIndex() | Node.js API · Management/dropIndex](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/dropIndex.md) - [dropIndexProperties() | Node.js API · Management/dropIndexProperties](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/dropIndexProperties.md) - [flush() | Node.js API · Management/flush](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/flush.md) - [flushSync() | Node.js API · Management/flushSync](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/flushSync.md) - [getCompactionState() | Node.js API · Management/getCompactionState](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/getCompactionState.md) - 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[loadCollectionSync() | Node.js API · Management/loadCollectionSync](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/loadCollectionSync.md) - [refreshLoad() | Node.js API · Management/refreshLoad](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/refreshLoad.md) - [releaseCollection() | Node.js API · Management/releaseCollection](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Management/releaseCollection.md) - [createPartition() | Node.js API · Partitions/createPartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Partitions/createPartition.md) - [dropPartition() | Node.js API · Partitions/dropPartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Partitions/dropPartition.md) - [getPartitionStatistics() | Node.js API · Partitions/getPartitionStatistics](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Partitions/getPartitionStatistics.md) - [hasPartition() | Node.js API · Partitions/hasPartition](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Partitions/hasPartition.md) - [listPartitions() | Node.js API · Partitions/listPartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Partitions/listPartitions.md) - [loadPartitions() | Node.js API · Partitions/loadPartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Partitions/loadPartitions.md) - [loadPartitionsSync() | Node.js API · Partitions/loadPartitionsSync](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Partitions/loadPartitionsSync.md) - [releasePartitions() | Node.js API · Partitions/releasePartitions](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Partitions/releasePartitions.md) - [ResourceGroupConfig | Node.js API · ResourceGroup/ResourceGroupConfig](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/ResourceGroup/ResourceGroupConfig.md) - [createResourceGroup() | Node.js API · ResourceGroup/createResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/ResourceGroup/createResourceGroup.md) - [describeResourceGroup() | Node.js API · ResourceGroup/describeResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/ResourceGroup/describeResourceGroup.md) - [dropResourceGroup() | Node.js API · ResourceGroup/dropResourceGroup](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/ResourceGroup/dropResourceGroup.md) - [listResourceGroups() | Node.js API · ResourceGroup/listResourceGroups](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/ResourceGroup/listResourceGroups.md) - [transferReplica() | Node.js API · ResourceGroup/transferReplica](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/ResourceGroup/transferReplica.md) - [updateResourceGroups() | Node.js API · ResourceGroup/updateResourceGroups](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/ResourceGroup/updateResourceGroups.md) - [bulkInsert() | Node.js API · Vector/bulkInsert](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/bulkInsert.md) - [count() | Node.js API · Vector/count](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/count.md) - [delete() | Node.js API · Vector/delete](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/delete.md) - [get() | Node.js API · Vector/get](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/get.md) - [hybridSearch() | Node.js API · Vector/hybridSearch](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/hybridSearch.md) - [insert() | Node.js API · Vector/insert](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/insert.md) - [query() | Node.js API · Vector/query](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/query.md) - [queryIterator() | Node.js API · Vector/queryIterator](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/queryIterator.md) - [search() | Node.js API · Vector/search](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/search.md) - [searchIterator() | Node.js API · Vector/searchIterator](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/searchIterator.md) - [upsert() | Node.js API · Vector/upsert](https://www.xlonglab.com/docs/v2.5.x/api-reference/node/Vector/upsert.md) - [About PyMilvus | Python API · About](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/About.md) - [BulkFileType | Python API · DataImport/BulkFileType](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/BulkFileType.md) - [bulk_import() | Python API · DataImport/BulkImport/bulk_import](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/BulkImport/bulk_import.md) - [get_import_progress() | Python API · DataImport/BulkImport/get_import_progress](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/BulkImport/get_import_progress.md) - [list_import_jobs() | Python API · DataImport/BulkImport/list_import_jobs](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/BulkImport/list_import_jobs.md) - [LocalBulkWriter | Python API · DataImport/LocalBulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/LocalBulkWriter/LocalBulkWriter.md) - [append_row() | Python API · DataImport/LocalBulkWriter/append_row](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/LocalBulkWriter/append_row.md) - [commit() | Python API · DataImport/LocalBulkWriter/commit](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/LocalBulkWriter/commit.md) - [AzureConnectParam | Python API · DataImport/RemoteBulkWriter/AzureConnectParam](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/AzureConnectParam.md) - [RemoteBulkWriter | Python API · DataImport/RemoteBulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/RemoteBulkWriter.md) - [S3ConnectParam | Python API · DataImport/RemoteBulkWriter/S3ConnectParam](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/S3ConnectParam.md) - [append_row() | Python API · DataImport/RemoteBulkWriter/append_row](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/append_row.md) - [commit() | Python API · DataImport/RemoteBulkWriter/commit](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/commit.md) - [BGEM3EmbeddingFunction | Python API · EmbeddingModels/BGEM3EmbeddingFunction/BGEM3EmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/BGEM3EmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/encode_queries.md) - [CohereEmbeddingFunction | Python API · EmbeddingModels/CohereEmbeddingFunction/CohereEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/CohereEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/CohereEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/CohereEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/CohereEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/encode_queries.md) - [GeminiEmbeddingFunction | Python API · EmbeddingModels/GeminiEmbeddingFunction/GeminiEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/GeminiEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/GeminiEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/GeminiEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/GeminiEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/GeminiEmbeddingFunction/encode_queries.md) - [InstructorEmbeddingFunction | Python API · EmbeddingModels/InstructorEmbeddingFunction/InstructorEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/InstructorEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/InstructorEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/InstructorEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/InstructorEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/encode_queries.md) - [JinaEmbeddingFunction | Python API · EmbeddingModels/JinaEmbeddingFunction/JinaEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/JinaEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/JinaEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/JinaEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/JinaEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/encode_queries.md) - [MGTEEmbeddingFunction | Python API · EmbeddingModels/MGTEEmbeddingFunction/MGTEEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/MGTEEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/MGTEEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/MGTEEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/MGTEEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/encode_queries.md) - [MistralAIEmbeddingFunction | Python API · EmbeddingModels/MistralAIEmbeddingFunction/MistralAIEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/MistralAIEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/encode_queries.md) - [Model2VecEmbeddingFunction | Python API · EmbeddingModels/Model2VecEmbeddingFunction/Model2VecEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/Model2VecEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/Model2VecEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/Model2VecEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/Model2VecEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/Model2VecEmbeddingFunction/encode_queries.md) - [NomicEmbeddingFunction | Python API · EmbeddingModels/NomicEmbeddingFunction/NomicEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/NomicEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/NomicEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/NomicEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/NomicEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/encode_queries.md) - [OnnxEmbeddingFunction | Python API · EmbeddingModels/OnnxEmbeddingFunction/OnnxEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/OnnxEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/OnnxEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/OnnxEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/OnnxEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/encode_queries.md) - [OpenAIEmbeddingFunction | Python API · EmbeddingModels/OpenAIEmbeddingFunction/OpenAIEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/OpenAIEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/encode_queries.md) - [SentenceTransformerEmbeddingFunction | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/SentenceTransformerEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/SentenceTransformerEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_queries.md) - [SpladeEmbeddingFunction | Python API · EmbeddingModels/SpladeEmbeddingFunction/SpladeEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/SpladeEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/SpladeEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/SpladeEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/SpladeEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/encode_queries.md) - [VoyageEmbeddingFunction | Python API · EmbeddingModels/VoyageEmbeddingFunction/VoyageEmbeddingFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/VoyageEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/VoyageEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/VoyageEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/encode_documents.md) - 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[describe_user() | Python API · MilvusClient/Authentication/describe_user](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/describe_user.md) - [drop_privilege_group() | Python API · MilvusClient/Authentication/drop_privilege_group](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/drop_privilege_group.md) - [drop_role() | Python API · MilvusClient/Authentication/drop_role](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/drop_role.md) - [drop_user() | Python API · MilvusClient/Authentication/drop_user](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/drop_user.md) - [grant_privilege() | Python API · MilvusClient/Authentication/grant_privilege](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/grant_privilege.md) - [grant_privilege_v2() | Python API · MilvusClient/Authentication/grant_privilege_v2](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/grant_privilege_v2.md) - [grant_role() | Python API · MilvusClient/Authentication/grant_role](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/grant_role.md) - [list_privilege_groups() | Python API · MilvusClient/Authentication/list_privilege_groups](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/list_privilege_groups.md) - [list_roles() | Python API · MilvusClient/Authentication/list_roles](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/list_roles.md) - [list_users() | Python API · MilvusClient/Authentication/list_users](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/list_users.md) - [remove_privileges_from_group() | Python API · MilvusClient/Authentication/remove_privileges_from_group](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/remove_privileges_from_group.md) - [revoke_privilege_v2() | Python API · MilvusClient/Authentication/revoke_privilege_v2](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Authentication/revoke_privilege_v2.md) - 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[CollectionSchema | Python API · MilvusClient/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/CollectionSchema/CollectionSchema.md) - [add_field() | Python API · MilvusClient/CollectionSchema/add_field](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/CollectionSchema/add_field.md) - [construct_from_dict() | Python API · MilvusClient/CollectionSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/CollectionSchema/construct_from_dict.md) - [run_analyzer() | Python API · MilvusClient/CollectionSchema/run_analyzer](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/CollectionSchema/run_analyzer.md) - [to_dict() | Python API · MilvusClient/CollectionSchema/to_dict](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/CollectionSchema/to_dict.md) - [verify() | Python API · MilvusClient/CollectionSchema/verify](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/CollectionSchema/verify.md) - [DataType | Python API · MilvusClient/Collections/DataType](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/DataType.md) - [FunctionType | Python API · MilvusClient/Collections/FunctionType](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/FunctionType.md) - [IndexType | Python API · MilvusClient/Collections/IndexType](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/IndexType.md) - [alter_alias() | Python API · MilvusClient/Collections/alter_alias](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/alter_alias.md) - [alter_collection_field() | Python API · MilvusClient/Collections/alter_collection_field](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/alter_collection_field.md) - [alter_collection_properties() | Python API · MilvusClient/Collections/alter_collection_properties](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/alter_collection_properties.md) - [create_alias() | Python API · MilvusClient/Collections/create_alias](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/create_alias.md) - [create_collection() | Python API · MilvusClient/Collections/create_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/create_collection.md) - [create_schema() | Python API · MilvusClient/Collections/create_schema](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/create_schema.md) - [describe_alias() | Python API · MilvusClient/Collections/describe_alias](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/describe_alias.md) - [describe_collection() | Python API · MilvusClient/Collections/describe_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/describe_collection.md) - [drop_alias() | Python API · MilvusClient/Collections/drop_alias](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/drop_alias.md) - [drop_collection() | Python API · MilvusClient/Collections/drop_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/drop_collection.md) - [drop_collection_properties() | Python API · MilvusClient/Collections/drop_collection_properties](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/drop_collection_properties.md) - [get_collection_stats() | Python API · MilvusClient/Collections/get_collection_stats](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/get_collection_stats.md) - [has_collection() | Python API · MilvusClient/Collections/has_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/has_collection.md) - [list_aliases() | Python API · MilvusClient/Collections/list_aliases](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/list_aliases.md) - [list_collections() | Python API · MilvusClient/Collections/list_collections](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/list_collections.md) - [rename_collection() | Python API · MilvusClient/Collections/rename_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Collections/rename_collection.md) - [alter_database_properties() | Python API · MilvusClient/Database/alter_database_properties](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Database/alter_database_properties.md) - [create_database() | Python API · MilvusClient/Database/create_database](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Database/create_database.md) - [describe_database() | Python API · MilvusClient/Database/describe_database](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Database/describe_database.md) - [drop_database() | Python API · MilvusClient/Database/drop_database](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Database/drop_database.md) - [drop_database_properties() | Python API · MilvusClient/Database/drop_database_properties](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Database/drop_database_properties.md) - [list_databases() | Python API · MilvusClient/Database/list_databases](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Database/list_databases.md) - [using_database() | Python API · MilvusClient/Database/using_database](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Database/using_database.md) - [Function | Python API · MilvusClient/Function/Function](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Function/Function.md) - [add_function() | Python API · MilvusClient/Function/add_function](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Function/add_function.md) - [construct_from_dict() | Python API · MilvusClient/Function/construct_from_dict](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Function/construct_from_dict.md) - [to_dict() | Python API · MilvusClient/Function/to_dict](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Function/to_dict.md) - 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[load_collection() | Python API · MilvusClient/Management/load_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Management/load_collection.md) - [prepare_index_params() | Python API · MilvusClient/Management/prepare_index_params](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Management/prepare_index_params.md) - [refresh_load() | Python API · MilvusClient/Management/refresh_load](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Management/refresh_load.md) - [release_collection() | Python API · MilvusClient/Management/release_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Management/release_collection.md) - [create_partition() | Python API · MilvusClient/Partitions/create_partition](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Partitions/create_partition.md) - [drop_partition() | Python API · MilvusClient/Partitions/drop_partition](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/MilvusClient/Partitions/drop_partition.md) - 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[create_partition() | Python API · ORM/Collection/create_partition](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/create_partition.md) - [delete() | Python API · ORM/Collection/delete](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/delete.md) - [describe() | Python API · ORM/Collection/describe](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/describe.md) - [drop() | Python API · ORM/Collection/drop](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/drop.md) - [drop_index() | Python API · ORM/Collection/drop_index](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/drop_index.md) - [drop_partition() | Python API · ORM/Collection/drop_partition](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/drop_partition.md) - [flush() | Python API · ORM/Collection/flush](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/flush.md) - [get_compaction_plans() | Python API · ORM/Collection/get_compaction_plans](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/get_compaction_plans.md) - [get_compaction_state() | Python API · ORM/Collection/get_compaction_state](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/get_compaction_state.md) - [get_replicas() | Python API · ORM/Collection/get_replicas](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/get_replicas.md) - [has_index() | Python API · ORM/Collection/has_index](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/has_index.md) - [has_partition() | Python API · ORM/Collection/has_partition](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/has_partition.md) - [hybrid_search() | Python API · ORM/Collection/hybrid_search](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/hybrid_search.md) - [index() | Python API · ORM/Collection/index](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/index.md) - [insert() | Python API · ORM/Collection/insert](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/insert.md) - [load() | Python API · ORM/Collection/load](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/load.md) - [partition() | Python API · ORM/Collection/partition](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/partition.md) - [query() | Python API · ORM/Collection/query](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/query.md) - [query_iterator() | Python API · ORM/Collection/query_iterator](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/query_iterator.md) - [release() | Python API · ORM/Collection/release](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/release.md) - [search() | Python API · ORM/Collection/search](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/search.md) - [search_iterator() | Python API · ORM/Collection/search_iterator](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/search_iterator.md) - [set_properties() | Python API · ORM/Collection/set_properties](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/set_properties.md) - [upsert() | Python API · ORM/Collection/upsert](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/upsert.md) - [wait_for_compaction_completed() | Python API · ORM/Collection/wait_for_compaction_completed](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Collection/wait_for_compaction_completed.md) - [CollectionSchema | Python API · ORM/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/CollectionSchema/CollectionSchema.md) - [add_field() | Python API · ORM/CollectionSchema/add_field](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/CollectionSchema/add_field.md) - [construct_from_dict() | Python API · ORM/CollectionSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/CollectionSchema/construct_from_dict.md) - [to_dict() | Python API · ORM/CollectionSchema/to_dict](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/CollectionSchema/to_dict.md) - [verify() | Python API · ORM/CollectionSchema/verify](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/CollectionSchema/verify.md) - [Connections | Python API · ORM/Connections/Connections](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Connections/Connections.md) - [add_connection() | Python API · ORM/Connections/add_connection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Connections/add_connection.md) - [connect() | Python API · ORM/Connections/connect](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Connections/connect.md) - [disconnect() | Python API · ORM/Connections/disconnect](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Connections/disconnect.md) - [get_connection_addr() | Python API · ORM/Connections/get_connection_addr](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Connections/get_connection_addr.md) - [has_connection() | Python API · ORM/Connections/has_connection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Connections/has_connection.md) - [list_connections() | Python API · ORM/Connections/list_connections](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Connections/list_connections.md) - [remove_connection() | Python API · ORM/Connections/remove_connection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Connections/remove_connection.md) - [FieldSchema | Python API · ORM/FieldSchema/FieldSchema](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/FieldSchema/FieldSchema.md) - [construct_from_dict() | Python API · ORM/FieldSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/FieldSchema/construct_from_dict.md) - [to_dict() | Python API · ORM/FieldSchema/to_dict](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/FieldSchema/to_dict.md) - [Partition | Python API · ORM/Partition/Partition](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/Partition.md) - [delete() | Python API · ORM/Partition/delete](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/delete.md) - [drop() | Python API · ORM/Partition/drop](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/drop.md) - [flush() | Python API · ORM/Partition/flush](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/flush.md) - [get_replicas() | Python API · ORM/Partition/get_replicas](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/get_replicas.md) - [insert() | Python API · ORM/Partition/insert](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/insert.md) - [load() | Python API · ORM/Partition/load](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/load.md) - [query() | Python API · ORM/Partition/query](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/query.md) - [release() | Python API · ORM/Partition/release](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/release.md) - [search() | Python API · ORM/Partition/search](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/search.md) - [upsert() | Python API · ORM/Partition/upsert](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Partition/upsert.md) - [Role | Python API · ORM/Role/Role](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/Role.md) - [add_user() | Python API · ORM/Role/add_user](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/add_user.md) - [create() | Python API · ORM/Role/create](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/create.md) - [drop() | Python API · ORM/Role/drop](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/drop.md) - [get_users() | Python API · ORM/Role/get_users](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/get_users.md) - [grant() | Python API · ORM/Role/grant](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/grant.md) - [is_exist() | Python API · ORM/Role/is_exist](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/is_exist.md) - [list_grant() | Python API · ORM/Role/list_grant](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/list_grant.md) - [list_grants() | Python API · ORM/Role/list_grants](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/list_grants.md) - [remove_user() | Python API · ORM/Role/remove_user](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/remove_user.md) - [revoke() | Python API · ORM/Role/revoke](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/Role/revoke.md) - [create_database() | Python API · ORM/db/create_database](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/db/create_database.md) - [drop_database() | Python API · ORM/db/drop_database](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/db/drop_database.md) - [list_database() | Python API · ORM/db/list_database](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/db/list_database.md) - [using_database() | Python API · ORM/db/using_database](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/db/using_database.md) - [BulkInsertState | Python API · ORM/utility/BulkInsertState](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/BulkInsertState.md) - [alter_alias() | Python API · ORM/utility/alter_alias](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/alter_alias.md) - [create_alias() | Python API · ORM/utility/create_alias](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/create_alias.md) - [create_resource_group() | Python API · ORM/utility/create_resource_group](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/create_resource_group.md) - [create_user() | Python API · ORM/utility/create_user](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/create_user.md) - [delete_user() | Python API · ORM/utility/delete_user](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/delete_user.md) - [describe_resource_group() | Python API · ORM/utility/describe_resource_group](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/describe_resource_group.md) - [do_bulk_insert() | Python API · ORM/utility/do_bulk_insert](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/do_bulk_insert.md) - [drop_alias() | Python API · ORM/utility/drop_alias](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/drop_alias.md) - [drop_collection() | Python API · ORM/utility/drop_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/drop_collection.md) - [drop_resource_group() | Python API · ORM/utility/drop_resource_group](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/drop_resource_group.md) - [flush_all() | Python API · ORM/utility/flush_all](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/flush_all.md) - [get_bulk_insert_state() | Python API · ORM/utility/get_bulk_insert_state](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/get_bulk_insert_state.md) - [get_query_segment_info() | Python API · ORM/utility/get_query_segment_info](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/get_query_segment_info.md) - [get_server_type() | Python API · ORM/utility/get_server_type](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/get_server_type.md) - [get_server_version() | Python API · ORM/utility/get_server_version](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/get_server_version.md) - [has_collection() | Python API · ORM/utility/has_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/has_collection.md) - [has_partition() | Python API · ORM/utility/has_partition](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/has_partition.md) - [hybridts_to_datetime() | Python API · ORM/utility/hybridts_to_datetime](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/hybridts_to_datetime.md) - [hybridts_to_unixtime() | Python API · ORM/utility/hybridts_to_unixtime](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/hybridts_to_unixtime.md) - [index_building_progress() | Python API · ORM/utility/index_building_progress](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/index_building_progress.md) - [list_aliases() | Python API · ORM/utility/list_aliases](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_aliases.md) - [list_bulk_insert_tasks() | Python API · ORM/utility/list_bulk_insert_tasks](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_bulk_insert_tasks.md) - [list_collections() | Python API · ORM/utility/list_collections](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_collections.md) - [list_indexes() | Python API · ORM/utility/list_indexes](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_indexes.md) - [list_resource_groups() | Python API · ORM/utility/list_resource_groups](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_resource_groups.md) - [list_roles() | Python API · ORM/utility/list_roles](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_roles.md) - [list_user() | Python API · ORM/utility/list_user](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_user.md) - [list_usernames() | Python API · ORM/utility/list_usernames](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_usernames.md) - [list_users() | Python API · ORM/utility/list_users](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/list_users.md) - [load_balance() | Python API · ORM/utility/load_balance](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/load_balance.md) - [load_state() | Python API · ORM/utility/load_state](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/load_state.md) - [loading_progress() | Python API · ORM/utility/loading_progress](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/loading_progress.md) - [mkts_from_datetime() | Python API · ORM/utility/mkts_from_datetime](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/mkts_from_datetime.md) - [mkts_from_hybridts() | Python API · ORM/utility/mkts_from_hybridts](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/mkts_from_hybridts.md) - [mkts_from_unixtime() | Python API · ORM/utility/mkts_from_unixtime](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/mkts_from_unixtime.md) - [rename_collection() | Python API · ORM/utility/rename_collection](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/rename_collection.md) - [reset_password() | Python API · ORM/utility/reset_password](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/reset_password.md) - [transfer_node() | Python API · ORM/utility/transfer_node](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/transfer_node.md) - [transfer_replica() | Python API · ORM/utility/transfer_replica](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/transfer_replica.md) - [update_password() | Python API · ORM/utility/update_password](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/update_password.md) - [wait_for_index_building_complete() | Python API · ORM/utility/wait_for_index_building_complete](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/wait_for_index_building_complete.md) - [wait_for_loading_complete() | Python API · ORM/utility/wait_for_loading_complete](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/ORM/utility/wait_for_loading_complete.md) - [BGERerankFunction | Python API · Rerankers/BGERerankFunction/BGERerankFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/BGERerankFunction/BGERerankFunction.md) - [__call__() | Python API · Rerankers/BGERerankFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/BGERerankFunction/__call__.md) - [CohereRerankFunction | Python API · Rerankers/CohereRerankFunction/CohereRerankFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/CohereRerankFunction/CohereRerankFunction.md) - [__call__() | Python API · Rerankers/CohereRerankFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/CohereRerankFunction/__call__.md) - [CrossEncoderRerankFunction | Python API · Rerankers/CrossEncoderRerankFunction/CrossEncoderRerankFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/CrossEncoderRerankFunction/CrossEncoderRerankFunction.md) - [__call__() | Python API · Rerankers/CrossEncoderRerankFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/CrossEncoderRerankFunction/__call__.md) - [JinaRerankFunction | Python API · Rerankers/JinaRerankFunction/JinaRerankFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/JinaRerankFunction/JinaRerankFunction.md) - [__call__() | Python API · Rerankers/JinaRerankFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/JinaRerankFunction/__call__.md) - [VoyageRerankFunction | Python API · Rerankers/VoyageRerankFunction/VoyageRerankFunction](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/VoyageRerankFunction/VoyageRerankFunction.md) - [__call__() | Python API · Rerankers/VoyageRerankFunction/__call__](https://www.xlonglab.com/docs/v2.5.x/api-reference/pymilvus/Rerankers/VoyageRerankFunction/__call__.md) ## v2.4.x ### 概览与基础概念 - [比较](https://www.xlonglab.com/docs/v2.4.x/about/comparison.md): 本文将 Milvus 与其他向量搜索解决方案进行比较。 - [Milvus 限额](https://www.xlonglab.com/docs/v2.4.x/about/limitations.md): 了解使用 Milvus 时的限制。 - [Milvus 采用者](https://www.xlonglab.com/docs/v2.4.x/about/milvus_adopters.md): 了解在生产应用中采用 Milvus 的公司。 - [Milvus 是什么？](https://www.xlonglab.com/docs/v2.4.x/about/overview.md): Milvus 是一个高性能、高度可扩展的向量数据库，可在从笔记本电脑到大型分布式系统等各种环境中高效运行。它既可以开源软件的形式提供，也可以云服务的形式提供。 - [Milvus 路线图](https://www.xlonglab.com/docs/v2.4.x/about/roadmap.md): Milvus 是一个开源向量数据库，旨在为人工智能应用提供支持。以下是我们的发展路线图。 ### 管理与运维 - [为 Kubernetes 上的 Milvus 分配资源](https://www.xlonglab.com/docs/v2.4.x/adminGuide/allocate.md): 了解如何在 Kubernetes 上为 Milvus 分配资源。 - [Milvus 用户认证](https://www.xlonglab.com/docs/v2.4.x/adminGuide/authenticate.md): 了解如何在 Milvus 中管理用户身份验证。 - [配置块缓存](https://www.xlonglab.com/docs/v2.4.x/adminGuide/chunk_cache.md) - [在 EC2 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/aws): 了解如何在 AWS EC2 上部署 Milvus 集群。 - [在 AWS 上为 Milvus 配置 Layer-7 Load Balancer](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/aws/aws_layer7.md): 了解如何在 AWS 上的第 7 层负载平衡器后面部署 Milvus 集群。 - [在 EKS 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/aws/eks.md): 了解如何在 EKS 上部署 Milvus 集群 - [按 IAM 角色配置 S3 访问权限](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/aws/s3.md): 了解如何使用 IAM 角色配置 s3。 - [按工作负载身份配置 Blob 存储访问权限](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/azure/abs.md): 了解如何使用 Workload Identity 配置 Blob Storage。 - [使用 Kubernetes 在 Microsoft Azure 上部署 Milvus](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/azure): 了解如何在 Azure 上部署 Milvus 集群。 - [为 Milvus 配置 ingress-nginx](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/azure/ingress.md): 了解如何使用 Milvus 配置 ingress nginx。 - [在 GKE 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/gcp): 了解如何在 GKE 上部署 Milvus 集群。 - [在 GCP 上为 Milvus 配置 Layer-7 Load Balancer](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/gcp/gcp_layer7.md): 了解如何在 GCP 上的 Layer-7 负载均衡器后面部署 Milvus 集群。 - [使用 Workload Identity 配置 GCS 访问](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/gcp/gcs.md): 了解如何使用工作负载身份配置 gcs。 - [在 OpenShift 上部署 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/adminGuide/clouds/openshift): 了解如何在 OpenShift 上部署 Milvus 集群。 - [配置 Jaeger Tracing](https://www.xlonglab.com/docs/v2.4.x/adminGuide/config_jaeger_tracing.md): 本指南介绍如何使用 Jaeger 收集 Milvus 的 Trace 数据。 - [使用 Docker Compose 配置 Milvus](https://www.xlonglab.com/docs/v2.4.x/adminGuide/configure-docker.md): 使用 Docker Compose 配置 Milvus。 - [使用 Helm Chart 配置 Milvus](https://www.xlonglab.com/docs/v2.4.x/adminGuide/configure-helm.md): 用 Helm Chart 配置 Milvus。 - [使用本地磁盘配置 Milvus QueryNode](https://www.xlonglab.com/docs/v2.4.x/adminGuide/configure-querynode-localdisk.md): 了解如何配置 Milvus QueryNode 以使用本地磁盘。 - [配置访问日志](https://www.xlonglab.com/docs/v2.4.x/adminGuide/configure_access_logs.md) - [使用 Milvus Operator 配置 Milvus](https://www.xlonglab.com/docs/v2.4.x/adminGuide/configure_operator.md): 了解如何使用 Milvus Operator 配置 Milvus。 - [使用 SASL/SSL 连接到 Kafka](https://www.xlonglab.com/docs/v2.4.x/adminGuide/connect_kafka_ssl.md): 本指南列出了几种连接 Milvus 和 Kafka 的方法，从不带 SASL/SSL 的最简单方法到带 SASL/SSL 的完全安全方法。 - [使用 Docker Compose 或 Helm 配置 Meta storage](https://www.xlonglab.com/docs/v2.4.x/adminGuide/deploy_etcd.md): 了解如何使用 Docker Compose/Helm 为 Milvus 配置 Meta storage。 - [使用 Docker Compose 或 Helm 配置消息存储](https://www.xlonglab.com/docs/v2.4.x/adminGuide/deploy_pulsar.md): 了解如何使用 Docker Compose 或 Helm 配置消息存储。 - [使用 Docker Compose 或 Helm 配置 Object Storage](https://www.xlonglab.com/docs/v2.4.x/adminGuide/deploy_s3.md): 了解如何使用 Docker Compose 或 Helm 为 Milvus 设置 S3 存储。 - [即时配置 Milvus](https://www.xlonglab.com/docs/v2.4.x/adminGuide/dynamic_config.md): 了解 Milvus 的动态配置。 - [设置 Collection 数量限制](https://www.xlonglab.com/docs/v2.4.x/adminGuide/limit_collection_counts.md) - [使用 Milvus Operator 配置消息存储](https://www.xlonglab.com/docs/v2.4.x/adminGuide/message_storage_operator.md): 了解如何使用 Milvus Operator 配置消息存储。 - [使用 Milvus Operator 配置 Meta storage](https://www.xlonglab.com/docs/v2.4.x/adminGuide/meta_storage_operator.md): 了解如何使用 Milvus Operator 配置 Meta storage。 - [创建警报](https://www.xlonglab.com/docs/v2.4.x/adminGuide/monitor/alert.md): 了解如何在 Grafana 中为 Milvus 服务创建警报。 - [配置 Grafana Loki](https://www.xlonglab.com/docs/v2.4.x/adminGuide/monitor/configure_grafana_loki.md): 本主题介绍如何使用 Loki 收集日志，并使用 Grafana 查询 Milvus 集群的日志。 - [Milvus 指标仪表板](https://www.xlonglab.com/docs/v2.4.x/adminGuide/monitor/metrics_dashboard.md): 本主题介绍 Milvus 仪表板中显示的监控指标。 - [部署监控服务](https://www.xlonglab.com/docs/v2.4.x/adminGuide/monitor): 了解如何使用 Prometheus 为 Milvus 集群部署监控服务。 - [监控概述](https://www.xlonglab.com/docs/v2.4.x/adminGuide/monitor/monitor_overview.md): 了解 Milvus 如何将 Prometheus 和 Grafana 用于监控和警报服务。 - [可视化指标](https://www.xlonglab.com/docs/v2.4.x/adminGuide/monitor/visualize.md): 了解如何在 Grafana 中可视化 Milvus 指标。 - [使用 Milvus Operator 配置 Object Storage](https://www.xlonglab.com/docs/v2.4.x/adminGuide/object_storage_operator.md): 了解如何使用 Milvus Operator 配置 Object Storage。 - [Milvus RBAC](https://www.xlonglab.com/docs/v2.4.x/adminGuide/rbac.md): 了解如何管理用户、角色和权限。 - [管理 Resource Group](https://www.xlonglab.com/docs/v2.4.x/adminGuide/resource_group.md): 了解如何管理资源组。 - [扩展 Milvus 依赖项](https://www.xlonglab.com/docs/v2.4.x/adminGuide/scale-dependencies.md) - [扩展 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/adminGuide/scaleout.md): 了解如何在 Milvus 集群中手动或自动缩放和扩展。 - [传输中的加密](https://www.xlonglab.com/docs/v2.4.x/adminGuide/tls.md): 了解如何在 Milvus 启用 TLS Proxy。 - [使用 Docker Compose 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/adminGuide/upgrade_milvus_cluster-docker.md): 了解如何使用 Docker Compose 升级 Milvus 集群。 - [使用 Helm Chart 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/adminGuide/upgrade_milvus_cluster-helm.md): 了解如何使用 Helm Chart 升级 Milvus 集群。 - [使用 Milvus Operator 升级 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/adminGuide/upgrade_milvus_cluster-operator.md): 了解如何使用 Milvus Operator 升级 Milvus 集群。 - [使用 Docker Compose 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.4.x/adminGuide/upgrade_milvus_standalone-docker.md): 了解如何使用 Docker Compose 升级 Milvus Standalone。 - [使用 Helm Chart 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.4.x/adminGuide/upgrade_milvus_standalone-helm.md): 了解如何使用 Helm Chart 升级 Milvus Standalone 单机版。 - [使用 Milvus Operator 升级 Milvus Standalone](https://www.xlonglab.com/docs/v2.4.x/adminGuide/upgrade_milvus_standalone-operator.md): 了解如何使用 Milvus Operator 升级 Milvus Standalone。 ### Embedding 模型 - [BGE M3](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-bgm-m3.md): BGE-M3 因其在多语言、多功能和多地域性方面的能力而得名。 - [BM25](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-bm25.md): BM25 是信息检索中使用的一种排序函数，用于估计文档与给定搜索查询的相关性。 - [Cohere Embedding](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-cohere.md): 本文介绍如何使用 CohereEmbeddingFunction 使用 Cohere Embedding 模型对文档和查询进行编码。 - [Instructor](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-instructor.md): 本文介绍如何使用 InstructorEmbeddingFunction，通过 Instructor Embedding 模型对文档和查询进行编码。 - [Jina AI - Embedding](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-jina.md): 本文介绍如何使用 JinaEmbeddingFunction 使用 Jina AI Embedding 模型对文档和查询进行编码。 - [mGTE](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-mgte.md): 本文介绍如何使用 MGTEEmbeddingFunction，通过 mGTE Embedding 模型对文档和查询进行编码。 - [Mistral AI](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-mistral-ai.md): 本文介绍如何使用 MistralAIEmbeddingFunction，通过 Mistral AI Embedding 模型对文档和查询进行编码。 - [Nomic](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-nomic.md): 本文介绍如何使用 NomicEmbeddingFunction 使用 Nomic Embedding 模型对文档和查询进行编码。 - [OpenAI](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-openai.md): Milvus 通过 OpenAIEmbeddingFunction 类与 OpenAI 的模型集成。 - [Sentence Transformers](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-sentence-transform.md): 本文演示了如何使用 Milvus 中的 Sentence Transformers 将文档和查询编码为稠密向量。 - [SPLADE](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-splade.md): 本文介绍如何使用 SpladeEmbeddingFunction 使用 SPLADE 模型对文档和查询进行编码。 - [Voyage](https://www.xlonglab.com/docs/v2.4.x/embeddings/embed-with-voyage.md): 本文介绍如何使用 VoyageEmbeddingFunction 使用 Voyage 模型对文档和查询进行编码。 - [Embedding Overview](https://www.xlonglab.com/docs/v2.4.x/embeddings): 了解如何为数据生成 Embedding。 ### 常见问题 - [操作常见问题](https://www.xlonglab.com/docs/v2.4.x/faq/operational_faq.md): 查找有关 Milvus Operator 常见问题的答案。 - [性能常见问题](https://www.xlonglab.com/docs/v2.4.x/faq/performance_faq.md): 查找有关搜索性能、性能增强和其他性能相关问题的常见问题答案。 - [产品常见问题](https://www.xlonglab.com/docs/v2.4.x/faq/product_faq.md): 查找有关世界上最先进的向量数据库常见问题的答案。 - [故障排除](https://www.xlonglab.com/docs/v2.4.x/faq/troubleshooting.md): 了解使用 Milvus 时可能遇到的常见问题，以及如何克服这些问题。 ### 安装与入门 - [Milvus 部署选项概览](https://www.xlonglab.com/docs/v2.4.x/getstarted/install-overview.md): Milvus 是一个高性能、可扩展的向量数据库。它支持各种规模的用例，从在 Jupyter 笔记本中本地运行的演示到处理数百亿向量的大规模 Kubernetes 集群。目前，Milvus 有三种部署选项：Milvus Lite、Milvus Standalone 和 Milvus Distributed。 - [安装 Milvus Go SDK](https://www.xlonglab.com/docs/v2.4.x/getstarted/install_SDKs/install-go.md): 了解如何安装 Milvus 的 GO SDK。 - [安装 Milvus Java SDK](https://www.xlonglab.com/docs/v2.4.x/getstarted/install_SDKs/install-java.md): 了解如何安装 Milvus 的 Java SDK。 - [安装 Milvus Nodejs SDK](https://www.xlonglab.com/docs/v2.4.x/getstarted/install_SDKs/install-node.md): 了解如何安装 Milvus 的 Node.js SDK。 - [安装 Milvus Python SDK](https://www.xlonglab.com/docs/v2.4.x/getstarted/install_SDKs/install-pymilvus.md): 了解如何安装 Milvus 的 Python SDK。 - [本地运行 Milvus Lite](https://www.xlonglab.com/docs/v2.4.x/getstarted/milvus_lite.md): 开始使用 Milvus Lite。 - [快速入门](https://www.xlonglab.com/docs/v2.4.x/getstarted/quickstart.md): 开始使用 Milvus。 - [使用 Docker Compose 运行 Milvus (Linux)](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-docker/install_standalone-docker-compose.md): 了解如何使用 Docker Compose 独立安装 Milvus。 - [在 Docker 中运行 Milvus（Linux）](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-docker/install_standalone-docker.md): 了解如何使用 Docker 独立安装 Milvus。 - [在 Docker 中运行 Milvus（Windows）](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-docker/install_standalone-windows.md): 了解如何使用 Windows 版 Docker Desktop 独立安装 Milvus。 - [使用 Docker Compose 安装 Milvus 的要求](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-docker/prerequisite-docker.md): 了解使用 Docker Compose 安装 Milvus 之前的必要准备工作。 - [使用 Helm Chart 运行支持 GPU 的 Milvus](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-gpu/install_cluster-helm-gpu.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 Docker Compose 运行支持 GPU 的 Milvus](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-gpu/install_standalone-docker-compose-gpu.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 GPU 安装 Milvus 的要求](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-gpu/prerequisite-gpu.md): 了解安装 Milvus GPU 前的必要准备工作。 - [使用 Helm 安装 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-k8s/install_cluster-helm.md): 了解如何在 Kubernetes 上安装 Milvus 集群。 - [使用 Milvus Operator 安装 Milvus 集群](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-k8s/install_cluster-milvusoperator.md): 了解如何使用 Milvus Operator 在 Kubernetes 上安装 Milvus 集群 - [在 Kubernetes 上运行 Milvus 的要求](https://www.xlonglab.com/docs/v2.4.x/getstarted/run-milvus-k8s/prerequisite-helm.md): 了解使用 Helm 安装 Milvus 之前的必要准备工作。 ### 生态集成 - [使用 Milvus + PII 屏蔽器构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/RAG_with_pii_and_milvus.md): 在本教程中，我们将向您展示如何使用 Milvus 和 PII Masker 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [检索增强生成：使用 Apify 抓取网站并将数据保存到 Milvus 以用于问题解答](https://www.xlonglab.com/docs/v2.4.x/integrations/apify_milvus_rag.md): 本教程介绍如何使用 Apify 的网站内容抓取器抓取网站，并将数据保存到 Milvus 或 Zilliz Cloud 向量数据库，以便日后用于问题解答。 - [使用 Milvus 和 Cognee 构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/build_RAG_with_milvus_and_cognee.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Cognee 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Fireworks AI 构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/build_RAG_with_milvus_and_fireworks.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Fireworks AI 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Gemini 构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/build_RAG_with_milvus_and_gemini.md): 在本教程中，我们将向您展示如何使用 Milvus 和 Gemini 构建 RAG Pipeline（Retrieval-Augmented Generation）。我们将使用 Gemini 模型根据给定的查询生成文本。我们还将使用 Milvus 来存储和检索生成的文本。 - [利用 Milvus 和 Lepton AI 构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/build_RAG_with_milvus_and_lepton.md): 在本教程中，我们将向您展示如何利用 Milvus 和 Lepton AI 构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 Ollama 构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/build_RAG_with_milvus_and_ollama.md): 在本指南中，我们将向您展示如何利用 Ollama 和 Milvus 高效、安全地构建 RAG Pipeline（Retrieval-Augmented Generation）。 - [使用 Milvus 和 SiliconFlow 构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/build_RAG_with_milvus_and_siliconflow.md): 本教程介绍如何使用 Milvus 和 SiliconFlow 构建 RAG Pipeline。 - [在 Arm 架构上构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/build_rag_on_arm.md): 在本教程中，您将学习如何在基于 Arm 的基础架构上构建检索增强生成（RAG）应用程序。在向量存储方面，我们利用了全面管理的 Milvus 向量数据库 Zilliz Cloud。Zilliz Cloud 可在 AWS、GCP 和 Azure 等主流云上使用。在本演示中，我们使用部署在 AWS 上的 Zilliz Cloud 和 Arm 机器。… - [使用 Milvus 部署 Dify](https://www.xlonglab.com/docs/v2.4.x/integrations/dify_with_milvus.md): 本教程介绍如何将 Dify 与 Milvus 集成，使用 Milvus 作为向量数据库，为知识检索与 RAG Pipeline 提供存储和检索能力。 - [使用 DeepEval 进行评估](https://www.xlonglab.com/docs/v2.4.x/integrations/evaluation_with_deepeval.md): 本指南演示了如何使用 DeepEval 评估基于 Milvus 的 RAG Pipeline。 - [使用 Arize Phoenix 进行评估](https://www.xlonglab.com/docs/v2.4.x/integrations/evaluation_with_phoenix.md): 本指南演示了如何使用 Arize Phoenix 评估基于 Milvus 的 RAG Pipeline。 - [Airbyte：开源数据移动基础架构](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_airbyte.md): Airbyte 是一种开源数据移动基础架构，用于构建提取和加载（EL）数据管道。它具有多功能性、可扩展性和易用性。Airbyte 的连接器目录 "开箱即用"，预置了 350 多个连接器。这些连接器可用于在短短几分钟内开始将数据从源复制到目标。 - [使用 Milvus 和 BentoML 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_bentoml.md): 本指南演示了如何使用 BentoCloud 上的开源 Embedding 模型和大语言模型与 Milvus 向量数据库来构建检索增强生成（RAG）应用程序。 - [使用 Milvus 与 CAMEL-AI 构建 RAG 系统](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_camel.md): 本指南介绍如何结合 CAMEL-AI 与 Milvus 构建检索增强生成（RAG）系统，并使用 Milvus 检索相关文档，为大语言模型生成提供上下文。 - [使用 Milvus 和 Cohere 进行问题解答](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_cohere.md): 本页将介绍如何使用 Milvus 作为向量数据库、Hugging Face 作为嵌入系统来搜索问题的最佳答案。 - [Milvus 与 DSPy 集成](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_dspy.md): 本指南演示了如何使用 DSPy 的 Retriever 模块之一 MilvusRM 来优化 RAG 程序。 - [使用 Milvus 部署 FastGPT](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_fastgpt.md): 本教程将指导您如何使用 Milvus 快速部署自己的专属 FastGPT 应用程序。 - [使用 Milvus 和 Haystack 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_haystack.md): 本指南演示了如何使用 Haystack 和 Milvus 建立检索增强生成（RAG）系统。 - [使用 Milvus 和 Hugging Face 进行问题解答](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_hugging-face.md): 本教程展示了如何使用 Hugging Face 作为数据加载器和嵌入生成器进行数据处理，并使用 Milvus 作为向量数据库进行语义搜索，从而构建一个问题解答系统。 - [Milvus 与 Jina 集成](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_jina.md): 本指南演示了如何使用 Jina 嵌入和 Milvus 进行相似性搜索和检索任务。 - [使用 Langfuse 对 RAG 查询进行 Tracing](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_langfuse.md): 本教程演示如何使用 Langfuse 的 LlamaIndex 集成对 RAG 查询进行 Tracing，并使用 Milvus Lite 存储和检索文档。 - [使用 Milvus 和 LlamaIndex 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_llamaindex.md): 本指南演示了如何使用 LlamaIndex 和 Milvus 建立检索增强生成（RAG）系统。 - [与 Milvus 集成的 MemGPT](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_memgpt.md): MemGPT 可轻松构建和部署有状态 LLM Agent。通过 Milvus 集成，您可以构建可连接外部数据源（RAG）的 AI Agent。 - [使用 Milvus 和 OpenAI 进行语义搜索](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_openai.md): 本页讨论向量数据库与 OpenAI 的 Embedding API 的集成。 - [使用 Milvus 进行图像搜索 - 集成](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_pytorch.md): 本页讨论使用 Milvus 进行图像搜索 - [使用 Ragas 进行评估](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_ragas.md): 本指南演示了如何使用 Ragas 评估基于 Milvus 的 RAG Pipeline。 - [使用 Milvus 和 Sentence Transformers 进行电影搜索](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_sentencetransformers.md): 本页讨论如何使用 Milvus 进行电影搜索 - [在 Snowpark Container Services 上使用 Milvus](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_snowpark.md): 本指南演示如何在 Snowpark Container Services 上启动 Milvus 演示。 - [在 AI Pipeline 中使用 Apache Spark™ 与 Milvus/Zilliz Cloud](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_spark.md): 本页讨论 Spark-Milvus 连接器。 - [用 Vanna 和 Milvus 编写 SQL](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_vanna.md): 本指南演示如何使用 Vanna 根据存储在数据库中的数据生成并执行 SQL 查询。 - [与 Milvus 和 FiftyOne 一起进行视觉搜索](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_voxel51.md): 本页讨论与 voxel51 的集成 - [使用 Milvus 和 VoyageAI 进行语义搜索](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_voyageai.md): 本页讨论向量数据库与 VoyageAI 的 Embedding API 的集成。 - [将 Milvus 与 WhyHow 整合](https://www.xlonglab.com/docs/v2.4.x/integrations/integrate_with_whyhow.md): 本指南演示了如何使用 whyhow.ai 和 Milvus Lite 进行基于规则的检索。 - [集成概述](https://www.xlonglab.com/docs/v2.4.x/integrations/integrations_overview.md): 本页为您提供与 Milvus 和第三方工具互动的教程列表。 - [使用 Kafka Connect 连接 Milvus](https://www.xlonglab.com/docs/v2.4.x/integrations/kafka-connect-milvus.md): 在本快速入门指南中，我们将展示如何设置开源 kafka 和 Zilliz Cloud 以摄取向量数据。 - [使用 Milvus 的知识表](https://www.xlonglab.com/docs/v2.4.x/integrations/knowledge_table_with_milvus.md): 默认情况下，知识表使用 Milvus 数据库来存储和检索提取的数据。这样，用户就可以利用 Milvus 的强大功能轻松搜索、过滤和分析数据。在本教程中，我们将介绍如何开始使用 Knowledge Table 和 Milvus。 - [使用 Milvus 的 Kotaemon RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/kotaemon_with_milvus.md): 本教程将指导您如何使用 Milvus 定制 kotaemon 应用程序。 - [将 Milvus 用作向量存储库](https://www.xlonglab.com/docs/v2.4.x/integrations/langchain/basic_usage_langchain.md): 本笔记本介绍如何使用与 Milvus 向量数据库相关的功能。 - [使用 Milvus 和 LangChain 的检索增强生成（RAG）](https://www.xlonglab.com/docs/v2.4.x/integrations/langchain/integrate_with_langchain.md): 本指南演示了如何使用 LangChain 和 Milvus 构建检索-增强生成（RAG）系统。 - [Milvus Hybrid Search Retriever](https://www.xlonglab.com/docs/v2.4.x/integrations/langchain/milvus_hybrid_search_retriever.md): 本笔记本介绍如何使用与 Milvus 向量数据库相关的功能。 - [Multi-agent Systems with Mistral AI, Milvus and Llama-agents](https://www.xlonglab.com/docs/v2.4.x/integrations/llama_agents_metadata.md): 在本笔记本中，我们将探讨不同的想法：将数据存储到 Milvus 中，使用带有 Mistral 模型的 llama-index 进行数据查询，创建自动数据搜索与读取 Agent，以及开发根据用户查询进行元数据过滤 Agent。 - [开始使用 Dynamiq 和 Milvus](https://www.xlonglab.com/docs/v2.4.x/integrations/milvus_rag_with_dynamiq.md): 在本教程中，我们将探讨如何将 Dynamiq 与 Milvus（专为 RAG 工作流打造的高性能向量数据库）无缝结合使用。Milvus 擅长 Embedding 的高效存储、索引和检索，是要求快速、精确访问上下文数据的人工智能系统不可或缺的组件。 - [使用 Milvus、vLLM 和 Llama 3.1 构建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/milvus_rag_with_vllm.md): 本博客将向您展示如何使用 Milvus、vLLM 和 Llama 3.1 构建并运行 RAG。更具体地说，我将向你展示如何在 Milvus 中将文本信息嵌入并存储为 Embedding，并将此向量存储作为知识库来高效检索与用户问题相关的文本块。 - [开始使用 Mem0 和 Milvus](https://www.xlonglab.com/docs/v2.4.x/integrations/quickstart_mem0_with_milvus.md): 在本教程中，我们将介绍基本的 Mem0 内存管理操作--添加、检索、更新、搜索、删除和跟踪内存历史记录--使用 Milvus，这是一个高性能、开源的向量数据库，可支持高效存储和检索。本实践介绍将指导您完成基础内存操作，帮助您利用 Mem0 和 Milvus 构建个性化的人工智能交互。 - [使用 Langflow 和 Milvus 构建 RAG 系统](https://www.xlonglab.com/docs/v2.4.x/integrations/rag_with_langflow.md): 本指南演示了如何使用 Langflow 与 Milvus 一起构建 RAG Pipeline。 - [利用 Milvus 和 Unstructured 创建 RAG](https://www.xlonglab.com/docs/v2.4.x/integrations/rag_with_milvus_and_unstructured.md): 在本教程中，我们将使用 Unstructured 来摄取 PDF 文档，然后使用 Milvus 构建 RAG Pipeline。 - [在 DocsGPT 中使用 Milvus](https://www.xlonglab.com/docs/v2.4.x/integrations/use_milvus_in_docsgpt.md): 在本教程中，我们将向您展示如何使用 Milvus 作为 DocsGPT 的后台向量数据库。 - [在 PrivateGPT 中使用 Milvus](https://www.xlonglab.com/docs/v2.4.x/integrations/use_milvus_in_private_gpt.md): 在本教程中，我们将向您展示如何使用 Milvus 作为 PrivateGPT 的后台向量数据库。 - [将 Milvus 与 SambaNova 结合使用](https://www.xlonglab.com/docs/v2.4.x/integrations/use_milvus_with_sambanova.md): 本教程利用 SambaNova 人工智能入门套件中的 Milvus 集成，构建了一个类似于 RAG（检索-增强生成）的企业知识检索系统，用于基于企业私有文档的检索和回答。 - [高级视频搜索：利用 Twelve Labs 和 Milvus 进行语义检索](https://www.xlonglab.com/docs/v2.4.x/integrations/video_search_with_twelvelabs_and_milvus.md): 了解如何通过将 Twelve Labs 用于生成多模态嵌入的 Embedding API 与 Milvus 集成来创建语义视频搜索应用程序。它涵盖了从设置开发环境到实现混合搜索和时态视频分析等高级功能的整个过程，为构建复杂的视频内容分析和检索系统奠定了全面的基础。 ### 参考资料 - [Milvus 架构概述](https://www.xlonglab.com/docs/v2.4.x/reference/architecture/architecture_overview.md): Milvus 提供快速、可靠、稳定的向量数据库，专为相似性搜索和人工智能而建。 - [数据处理](https://www.xlonglab.com/docs/v2.4.x/reference/architecture/data_processing.md): 了解 Milvus 的数据处理程序。 - [存储与计算分离](https://www.xlonglab.com/docs/v2.4.x/reference/architecture/four_layers.md): Milvus 的存储与计算分离结构。 - [主要组件](https://www.xlonglab.com/docs/v2.4.x/reference/architecture/main_components.md): 了解 Milvus Standalone 和集群的主要组件。 - [使用 ARRAY 字段](https://www.xlonglab.com/docs/v2.4.x/reference/array_data_type.md) - [Milvus 2.2 基准测试报告](https://www.xlonglab.com/docs/v2.4.x/reference/benchmark.md): 了解 Milvus 的基准结果。 - [Bitset](https://www.xlonglab.com/docs/v2.4.x/reference/bitset.md): 了解 Milvus 中的 Bitset。 - [标量过滤规则](https://www.xlonglab.com/docs/v2.4.x/reference/boolean.md): 了解 Milvus 中的布尔表达规则。 - [Consistency Level](https://www.xlonglab.com/docs/v2.4.x/reference/consistency.md): Milvus 支持 Strong、Bounded、Session 和 Eventually 四种 Consistency Level，其中 Bounded 为默认值。 - [Coordinator HA](https://www.xlonglab.com/docs/v2.4.x/reference/coordinator_ha.md): 了解 Milvus Coordinator 积极待命的动机和程序。 - [On-disk Index](https://www.xlonglab.com/docs/v2.4.x/reference/disk_index.md): Milvus 的磁盘索引机制。 - [术语](https://www.xlonglab.com/docs/v2.4.x/reference/glossary.md) - [GPU 索引](https://www.xlonglab.com/docs/v2.4.x/reference/gpu_index.md): Milvus 的 GPU 索引机制。 - [内存索引](https://www.xlonglab.com/docs/v2.4.x/reference): Milvus 的索引机制。 - [Knowhere](https://www.xlonglab.com/docs/v2.4.x/reference/knowhere.md): 了解 Milvus 的 Knowhere。 - [相似度量](https://www.xlonglab.com/docs/v2.4.x/reference/metric.md): Milvus 支持多种相似性度量，包括欧氏距离、内积、Jaccard 等。 - [支持 MMap 的数据存储](https://www.xlonglab.com/docs/v2.4.x/reference/mmap.md): MMap 可以在单个节点上提供更多数据。 - [多租户战略](https://www.xlonglab.com/docs/v2.4.x/reference/multi_tenancy.md): Milvus 中的多租户。 - [In-Memory Replica](https://www.xlonglab.com/docs/v2.4.x/reference/replica.md): 了解 Milvus 中的内存复制。 - [Reranking](https://www.xlonglab.com/docs/v2.4.x/reference/reranking.md): 本专题将介绍重排过程，解释其意义以及两种重排方法的实施。 - [标量索引](https://www.xlonglab.com/docs/v2.4.x/reference/scalar_index.md): Milvus 标量索引。 - [管理 Schema](https://www.xlonglab.com/docs/v2.4.x/reference/schema.md): 了解如何在 Milvus 中定义 Schema。 - [稀疏向量](https://www.xlonglab.com/docs/v2.4.x/reference/sparse_vector.md): 了解如何在 Milvus 中使用稀疏向量。 - [common 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_common.md): 了解如何为 Milvus 配置通用功能。 - [DataCoord 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_datacoord.md): 了解如何为 Milvus 配置 dataCoord。 - [DataNode 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_datanode.md): 了解如何为 Milvus 配置 dataNode。 - [etcd 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_etcd.md): 了解如何为 Milvus 配置 etcd。 - [GPU 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_gpu.md): 了解如何为 Milvus 配置 gpu。 - [gRPC 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_grpc.md): 了解如何为 Milvus 配置 grpc。 - [IndexCoord 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_indexcoord.md): 了解如何为 Milvus 配置 indexCoord。 - [IndexNode 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_indexnode.md): 了解如何为 Milvus 配置 indexNode。 - [本地存储配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_localstorage.md): 了解如何为 Milvus 配置 localStorage。 - [日志配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_log.md): 了解如何为 Milvus 配置日志。 - [Meta storage 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_metastore.md): 了解如何为 Milvus 配置 Meta storage。 - [MinIO 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_minio.md): 了解如何为 Milvus 配置 minio。 - [MQ 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_mq.md): 了解如何为 Milvus 配置 mq。 - [消息通道配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_msgchannel.md): 了解如何为 Milvus 配置 msgChannel。 - [NATS 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_natsmq.md): 了解如何为 Milvus 配置 natsmq。 - [Proxy 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_proxy.md): 了解如何为 Milvus 配置 Proxy。 - [Pulsar 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_pulsar.md): 了解如何为 Milvus 配置 Pulsar。 - [QueryCoord 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_querycoord.md): 了解如何为 Milvus 配置 queryCoord。 - [QueryNode 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_querynode.md): 了解如何为 Milvus 配置 queryNode。 - [配额与限制配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_quotaandlimits.md): 了解如何为 Milvus 配置 quotaAndLimits。 - [RocksMQ 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_rocksmq.md): 了解如何为 Milvus 配置 rocksmq。 - [RootCoord 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_rootcoord.md): 了解如何为 Milvus 配置 rootCoord。 - [TiKV 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_tikv.md): 了解如何为 Milvus 配置 tikv。 - [TLS 配置参数](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_tls.md): 了解 Milvus Proxy 对外服务的 TLS 模式、服务器证书、私钥和 CA 证书路径配置。 - [Trace 配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/configure_trace.md): 了解如何为 Milvus 配置 Trace。 - [系统配置](https://www.xlonglab.com/docs/v2.4.x/reference/sys_config/system_configuration.md): 了解 Milvus 的系统配置。 - [时间同步](https://www.xlonglab.com/docs/v2.4.x/reference/time_sync.md): 了解 Milvus 的时间同步系统。 - [Timestamp](https://www.xlonglab.com/docs/v2.4.x/reference/timestamp.md): 了解时间戳的概念以及 Milvus 向量数据库中与时间戳相关的四个主要参数。 - [User、Privilege 与 Role](https://www.xlonglab.com/docs/v2.4.x/reference/users_and_roles.md): 了解基于角色的访问控制（RBAC）中用户、角色、对象和权限的定义。 ### release_notes.md - [发行说明](https://www.xlonglab.com/docs/v2.4.x/release_notes.md): Milvus 发行说明 ### Reranker - [BGE](https://www.xlonglab.com/docs/v2.4.x/rerankers/rerankers-bge.md): Milvus 通过`BGERerankFunction`类支持 BGE reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Reranker Cohere](https://www.xlonglab.com/docs/v2.4.x/rerankers/rerankers-cohere.md): Milvus 通过 `CohereRerankFunction` 类支持 Cohere Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [交叉编码器](https://www.xlonglab.com/docs/v2.4.x/rerankers/rerankers-cross-encoder.md): Milvus 通过 "CrossEncoderRerankFunction "类支持交叉编码器 Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Jina AI - Reranker](https://www.xlonglab.com/docs/v2.4.x/rerankers/rerankers-jina.md): Milvus 通过 "JinaRerankFunction "类支持 Jina Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 - [Reranker 概览](https://www.xlonglab.com/docs/v2.4.x/rerankers/rerankers-overview.md): PyMilvus 模型库集成了 Reranker 函数，用于优化初始搜索返回结果的顺序。 - [Reranker Voyage](https://www.xlonglab.com/docs/v2.4.x/rerankers/rerankers-voyage.md): Milvus 通过 "VoyageRerankFunction "类支持 Voyage Reranker 模型。该功能可让您有效地对查询-文档对的相关性进行评分。 ### 教程 - [音频相似性搜索](https://www.xlonglab.com/docs/v2.4.x/tutorials/audio_similarity_search.md): 利用 Milvus 建立音频相似性搜索系统。 - [用 Milvus 创建 RAG](https://www.xlonglab.com/docs/v2.4.x/tutorials/build-rag-with-milvus.md): 使用 Milvus 构建 RAG Pipeline，完成文档检索、上下文增强和大语言模型生成。 - [使用 Milvus 进行上下文检索](https://www.xlonglab.com/docs/v2.4.x/tutorials/contextual_retrieval_with_milvus.md): 上下文检索（Contextual Retrieval）是 Anthropic 提出的一种先进的检索方法，旨在解决当前检索增强生成（RAG）解决方案中出现的块的语义隔离问题。在当前实用的 RAG 范式中，文档被分成若干个块，使用向量数据库进行查询搜索，检索出最相关的块。然后，LLM 使用这些检索到的分块对查询做出响应。… - [DNA 序列分类](https://www.xlonglab.com/docs/v2.4.x/tutorials/dna_sequence_classification.md): 利用 Milvus 建立 DNA 序列分类系统。 - [使用 Milvus 进行全文搜索](https://www.xlonglab.com/docs/v2.4.x/tutorials/full_text_search_with_milvus.md): Milvus 2.5 发布后，全文搜索功能使用户能够根据关键字或短语高效搜索文本，提供强大的文本检索功能。这一功能提高了搜索的准确性，并可与基于 Embedding 的检索无缝结合，实现混合搜索，从而在单个查询中同时获得基于语义和关键字的结果。在本笔记本中，我们将展示 Milvus 全文搜索的基本用法。 - [Funnel Search with Matryoshka Embeddings](https://www.xlonglab.com/docs/v2.4.x/tutorials/funnel_search_with_matryoshka.md): 在本笔记本中，我们将研究如何将 Matryoshka 嵌入与 Milvus 一起用于语义搜索。我们展示了一种名为 "漏斗搜索 "的算法，它允许我们在嵌入维度的一小部分子集上执行相似性搜索，而不会导致召回率急剧下降。 - [使用 Milvus 构建 Graph RAG](https://www.xlonglab.com/docs/v2.4.x/tutorials/graph_rag_with_milvus.md): 使用 Milvus 构建 Graph RAG - [利用 Milvus 进行 HDBSCAN 聚类](https://www.xlonglab.com/docs/v2.4.x/tutorials/hdbscan_clustering_with_milvus.md): 在本笔记本中，我们将使用 BGE-M3 Embedding 模型从新闻标题数据集中提取嵌入，利用 Milvus 高效计算嵌入之间的距离以帮助 HDBSCAN 进行聚类，然后使用 UMAP 方法将结果可视化以进行分析。本笔记本是 Milvus 对 Dylan Castillo 文章的改编。 - [如何提高 RAG Pipeline 的性能](https://www.xlonglab.com/docs/v2.4.x/tutorials/how_to_enhance_your_rag.md): 随着 RAG 应用的日益普及，人们越来越关注如何提高其性能。本文介绍了优化 RAG Pipeline 的所有可能方法，并提供了相应的图解，帮助您快速了解主流的 RAG 优化策略。 - [使用 Milvus 进行混合搜索](https://www.xlonglab.com/docs/v2.4.x/tutorials/hybrid_search_with_milvus.md): 使用 Milvus 进行混合搜索 - [重复数据删除](https://www.xlonglab.com/docs/v2.4.x/tutorials/image_deduplication_system.md): 使用 Milvus 构建重复数据删除图像系统。 - [使用 Milvus 搜索图像](https://www.xlonglab.com/docs/v2.4.x/tutorials/image_similarity_search.md): 使用 Milvus 进行图像搜索 - [用 Milvus 推荐电影](https://www.xlonglab.com/docs/v2.4.x/tutorials/movie_recommendation_with_milvus.md): 在本笔记本中，我们将探索如何使用 OpenAI 生成电影描述的 Embedding，并在 Milvus 中利用这些 Embedding 描述来推荐符合您偏好的电影。为了增强搜索结果，我们将利用过滤功能来执行元数据搜索。本示例中使用的数据集来自 Hugging Face 数据集，包含 8000 多个电影条目，为电影推荐提供了丰富的选择。 - [使用 Milvus 的多模态 RAG](https://www.xlonglab.com/docs/v2.4.x/tutorials/multimodal_rag_with_milvus.md): 使用 Milvus 的多模态 RAG - [问答系统](https://www.xlonglab.com/docs/v2.4.x/tutorials/question_answering_system.md): 使用 Milvus 建立问题解答系统。 - [推荐系统](https://www.xlonglab.com/docs/v2.4.x/tutorials/recommendation_system.md): 利用 Milvus 建立个性化推荐系统。 - [文本到图像搜索引擎](https://www.xlonglab.com/docs/v2.4.x/tutorials/text_image_search.md): 使用 Milvus 建立文本到图像搜索引擎。 - [文本搜索引擎](https://www.xlonglab.com/docs/v2.4.x/tutorials/text_search_engine.md): 使用 Milvus 建立文本搜索引擎。 - [教程概览](https://www.xlonglab.com/docs/v2.4.x/tutorials/tutorials-overview.md): 本页为您提供与 Milvus 互动的教程列表。 - [使用 ColPali 与 Milvus 一起进行多模态检索](https://www.xlonglab.com/docs/v2.4.x/tutorials/use_ColPali_with_milvus.md): 在本笔记本中，为了通用起见，我们将这种多向量表示法称为 "ColBERT Embedding"。不过，实际使用的模型是 ColPali 模型。我们将演示如何使用 Milvus 进行多向量检索。在此基础上，我们将介绍如何使用 ColPali 根据给定查询检索网页。 - [向量可视化](https://www.xlonglab.com/docs/v2.4.x/tutorials/vector_visualization.md): 在本例中，我们将展示如何使用 t-SN 可视化 Milvus 中的嵌入（向量）。 - [视频相似性搜索](https://www.xlonglab.com/docs/v2.4.x/tutorials/video_similarity_search.md): 利用 Milvus 建立视频相似性搜索系统。 ### 用户指南 - [Clustering Compaction](https://www.xlonglab.com/docs/v2.4.x/userGuide/clustering-compaction.md): Clustering Compaction 根据 Clustering Key 重新组织 Segment 中的 Entity，以缩小搜索范围并提升大型 Collection 的查询性能。 - [导入数据](https://www.xlonglab.com/docs/v2.4.x/userGuide/data-import/import-data.md): 本页演示导入准备好的数据的程序。 - [准备源数据](https://www.xlonglab.com/docs/v2.4.x/userGuide/data-import/prepare-source-data.md): 本页讨论了在开始批量将数据插入 Collection 之前应该考虑的一些问题。 - [启用动态字段](https://www.xlonglab.com/docs/v2.4.x/userGuide/enable-dynamic-field.md) - [插入、upsert 和删除](https://www.xlonglab.com/docs/v2.4.x/userGuide/insert-update-delete.md): 本指南将向您介绍 Collection 中的数据操作，包括插入、upsert 和删除。 - [管理 Collection](https://www.xlonglab.com/docs/v2.4.x/userGuide/manage-collections.md) - [标量字段索引](https://www.xlonglab.com/docs/v2.4.x/userGuide/manage-indexes/index-scalar-fields.md): 本指南将指导您为整数、字符串等字段创建和配置标量索引。 - [索引向量字段](https://www.xlonglab.com/docs/v2.4.x/userGuide/manage-indexes/index-vector-fields.md): 本指南将指导您完成创建和管理 Collection 中向量字段索引的基本操作。 - [使用 GPU 建立索引](https://www.xlonglab.com/docs/v2.4.x/userGuide/manage-indexes/index-with-gpu.md): 本指南介绍如何在 Milvus 中建立支持 GPU 的索引，以提高搜索性能。 - [管理 Partition](https://www.xlonglab.com/docs/v2.4.x/userGuide/manage-partitions.md) - [Database](https://www.xlonglab.com/docs/v2.4.x/userGuide/manage_databases.md) - [获取和标量查询](https://www.xlonglab.com/docs/v2.4.x/userGuide/search-query-get/get-and-scalar-query.md): 本指南演示如何通过 ID 获取实体并进行标量筛选。 - [混合搜索](https://www.xlonglab.com/docs/v2.4.x/userGuide/search-query-get/multi-vector-search.md): 本指南演示如何在 Milvus 中执行混合搜索，并了解结果的 Reranking。 - [单向量搜索](https://www.xlonglab.com/docs/v2.4.x/userGuide/search-query-get/single-vector-search.md): 本文介绍如何使用单个查询向量搜索 Milvus Collection 中的向量。 - [使用迭代器](https://www.xlonglab.com/docs/v2.4.x/userGuide/search-query-get/with-iterators.md): Milvus 提供搜索和查询迭代器，用于迭代大量实体的结果。 - [安装 Birdwatcher](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/birdwatcher_install_guides.md): 了解如何安装 Birdwatcher 以调试 Milvus。 - [Birdwatcher](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/birdwatcher_overview.md): Birdwatcher 是 Milvus 2.x 的调试工具。它连接到 etcd 并检查 Milvus 系统的状态。 - [使用 Birdwatcher](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/birdwatcher_usage_guides.md): 了解如何使用 Birdwatcher 调试 Milvus。 - [Milvus CLI 命令参考](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/cli_commands.md): 使用命令与 Milvus 互动。 - [Milvus 命令行界面](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/cli_overview.md): Milvus 命令行界面（CLI）是一种命令行工具，支持数据库连接、数据操作和数据导入导出。 - [安装 Milvus CLI](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/install_cli.md): 了解如何安装 Milvus CLI。 - [监控](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/milvus-cdc/cdc-monitoring.md): Milvus CDC 通过 Grafana 面板提供全面的监控功能。 - [部署 CDC 服务器](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/milvus-cdc/deploy-cdc-server.md): 本指南提供了部署 Milvus-CDC 服务器的分步流程。 - [管理 CDC 任务](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/milvus-cdc/manage-cdc-tasks.md): 捕获数据更改 (CDC) 任务可实现从源 Milvus 实例到目标 Milvus 实例的数据同步。 - [CDC 概览](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/milvus-cdc/milvus-cdc-overview.md): Milvus CDC 是一个用户友好型工具，可以捕获和同步 Milvus 实例中的增量数据。 - [使用 API 备份和恢复数据](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/milvus_backup_api.md): 了解如何通过 API 使用 Milvus 备份功能 - [使用命令备份和恢复数据](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/milvus_backup_cli.md): 学习如何通过 CLI 使用 Milvus 备份 - [Milvus 备份](https://www.xlonglab.com/docs/v2.4.x/userGuide/tools/milvus_backup_overview.md): Milvus-Backup 是一款允许用户备份和恢复 Milvus 数据的工具。 - [使用 JSON 字段](https://www.xlonglab.com/docs/v2.4.x/userGuide/use-json-fields.md) - [使用 Partition Key](https://www.xlonglab.com/docs/v2.4.x/userGuide/use-partition-key.md) ### API Reference - [About Milvus GO SDK | Go API · About](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/About.md) - [Columns | Go API · Collection/Columns](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/Columns.md) - [CreateCollection() | Go API · Collection/CreateCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/CreateCollection.md) - [Delete() | Go API · Collection/Delete](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/Delete.md) - [DeleteByPks() | Go API · Collection/DeleteByPks](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/DeleteByPks.md) - [DescribeCollection() | Go API · Collection/DescribeCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/DescribeCollection.md) - [DropCollection() | Go API · Collection/DropCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/DropCollection.md) - [Flush() | Go API · Collection/Flush](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/Flush.md) - [GetLoadState() | Go API · Collection/GetLoadState](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/GetLoadState.md) - [Insert() | Go API · Collection/Insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/Insert.md) - [InsertRows() | Go API · Collection/InsertRows](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/InsertRows.md) - [ListCollections() | Go API · Collection/ListCollections](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/ListCollections.md) - [LoadCollection() | Go API · Collection/LoadCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/LoadCollection.md) - [Query() | Go API · Collection/Query](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/Query.md) - [ReleaseCollection() | Go API · Collection/ReleaseCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/ReleaseCollection.md) - [Search() | Go API · Collection/Search](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/Search.md) - [SearchParams | Go API · Collection/SearchParams](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/SearchParams.md) - [Upsert() | Go API · Collection/Upsert](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/Upsert.md) - [Vectors | Go API · Collection/Vectors](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Collection/Vectors.md) - [Close() | Go API · Connections/Close](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Connections/Close.md) - [NewClient() | Go API · Connections/NewClient](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Connections/NewClient.md) - [CreateIndex() | Go API · Index/CreateIndex](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Index/CreateIndex.md) - [DescribeIndex() | Go API · Index/DescribeIndex](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Index/DescribeIndex.md) - [DropIndex() | Go API · Index/DropIndex](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Index/DropIndex.md) - [GetIndexBuildProgress() | Go API · Index/GetIndexBuildProgress](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Index/GetIndexBuildProgress.md) - [Indexes | Go API · Index/Indexes](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Index/Indexes.md) - [CreatePartition() | Go API · Partition/CreatePartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Partition/CreatePartition.md) - [DropPartition() | Go API · Partition/DropPartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Partition/DropPartition.md) - [HasPartition() | Go API · Partition/HasPartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Partition/HasPartition.md) - [LoadPartitions() | Go API · Partition/LoadPartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Partition/LoadPartitions.md) - [ReleasePartitions() | Go API · Partition/ReleasePartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Partition/ReleasePartitions.md) - [ShowPartitions() | Go API · Partition/ShowPartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/go/Partition/ShowPartitions.md) - [About | Java API · About](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/About.md) - [alterAlias() | Java API · v1/Alias/alterAlias](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Alias/alterAlias.md) - [createAlias() | Java API · v1/Alias/createAlias](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Alias/createAlias.md) - [dropAlias() | Java API · v1/Alias/dropAlias](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Alias/dropAlias.md) - [listAliases() | Java API · v1/Alias/listAliases](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Alias/listAliases.md) - [createCredential() | Java API · v1/Authentication/createCredential](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Authentication/createCredential.md) - [deleteCredential() | Java API · v1/Authentication/deleteCredential](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Authentication/deleteCredential.md) - [listCredUsers() | Java API · v1/Authentication/listCredUsers](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Authentication/listCredUsers.md) - [updateCredential() | Java API · v1/Authentication/updateCredential](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Authentication/updateCredential.md) - [bulkInsert() | Java API · v1/BulkInsertOps/bulkInsert](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/BulkInsertOps/bulkInsert.md) - [getBulkInsertState() | Java API · v1/BulkInsertOps/getBulkInsertState](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/BulkInsertOps/getBulkInsertState.md) - [listBulkInsertTasks() | Java API · v1/BulkInsertOps/listBulkInsertTasks](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/BulkInsertOps/listBulkInsertTasks.md) - [LocalBulkWriter | Java API · v1/BulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/BulkWriter/LocalBulkWriter.md) - [RemoteBulkWriter | Java API · v1/BulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/BulkWriter/RemoteBulkWriter.md) - [alterCollection() | Java API · v1/Collection/alterCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/alterCollection.md) - [createCollection() | Java API · v1/Collection/createCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/createCollection.md) - [delete() | Java API · v1/Collection/delete](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/delete.md) - [describeCollection() | Java API · v1/Collection/describeCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/describeCollection.md) - [dropCollection() | Java API · v1/Collection/dropCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/dropCollection.md) - [flush() | Java API · v1/Collection/flush](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/flush.md) - [getCollectionStatistics() | Java API · v1/Collection/getCollectionStatistics](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/getCollectionStatistics.md) - [getLoadState() | Java API · v1/Collection/getLoadState](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/getLoadState.md) - [getLoadingProgress() | Java API · v1/Collection/getLoadingProgress](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/getLoadingProgress.md) - [getPersistentSegmentInfo() | Java API · v1/Collection/getPersistentSegmentInfo](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/getPersistentSegmentInfo.md) - [getQuerySegmentInfo() | Java API · v1/Collection/getQuerySegmentInfo](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/getQuerySegmentInfo.md) - [getReplicas() | Java API · v1/Collection/getReplicas](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/getReplicas.md) - [insert() | Java API · v1/Collection/insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/insert.md) - [insertAsync() | Java API · v1/Collection/insertAsync](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/insertAsync.md) - [loadCollection() | Java API · v1/Collection/loadCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/loadCollection.md) - [releaseCollection() | Java API · v1/Collection/releaseCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/releaseCollection.md) - [renameCollection() | Java API · v1/Collection/renameCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/renameCollection.md) - [showCollections() | Java API · v1/Collection/showCollections](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/showCollections.md) - [upsert() | Java API · v1/Collection/upsert](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/upsert.md) - [upsertAsync() | Java API · v1/Collection/upsertAsync](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Collection/upsertAsync.md) - [MilvusClient | Java API · v1/Connections/MilvusClient](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Connections/MilvusClient.md) - [MilvusClientV1Pool | Java API · v1/Connections/MilvusClientV1Pool](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Connections/MilvusClientV1Pool.md) - [alterDatabase() | Java API · v1/Database/alterDatabase](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Database/alterDatabase.md) - [createDatabase() | Java API · v1/Database/createDatabase](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Database/createDatabase.md) - [dropDatabase() | Java API · v1/Database/dropDatabase](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Database/dropDatabase.md) - [listDatabases() | Java API · v1/Database/listDatabases](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Database/listDatabases.md) - [createCollection() | Java API · v1/HighlevelAPI/createCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/HighlevelAPI/createCollection.md) - [delete() | Java API · v1/HighlevelAPI/delete](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/HighlevelAPI/delete.md) - [get() | Java API · v1/HighlevelAPI/get](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/HighlevelAPI/get.md) - [insert() | Java API · v1/HighlevelAPI/insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/HighlevelAPI/insert.md) - [listCollections() | Java API · v1/HighlevelAPI/listCollections](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/HighlevelAPI/listCollections.md) - [query() | Java API · v1/HighlevelAPI/query](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/HighlevelAPI/query.md) - [search() | Java API · v1/HighlevelAPI/search](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/HighlevelAPI/search.md) - [alterIndex() | Java API · v1/Index/alterIndex](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Index/alterIndex.md) - [createIndex() | Java API · v1/Index/createIndex](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Index/createIndex.md) - [describeIndex() | Java API · v1/Index/describeIndex](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Index/describeIndex.md) - [dropIndex() | Java API · v1/Index/dropIndex](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Index/dropIndex.md) - [getIndexBuildProgress() | Java API · v1/Index/getIndexBuildingProgress](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Index/getIndexBuildingProgress.md) - [getIndexState() | Java API · v1/Index/getIndexState](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Index/getIndexState.md) - [getCompactionState() | Java API · v1/Management/getCompactionState](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Management/getCompactionState.md) - [getCompactionStateWithPlans() | Java API · v1/Management/getCompactionStateWithPlans](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Management/getCompactionStateWithPlans.md) - [getFlushAllState() | Java API · v1/Management/getFlushAllState](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Management/getFlushAllState.md) - [getFlushState() | Java API · v1/Management/getFlushState](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Management/getFlushState.md) - [getMetrics() | Java API · v1/Management/getMetrics](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Management/getMetrics.md) - [loadBalance() | Java API · v1/Management/loadBalance](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Management/loadBalance.md) - [manualCompact() | Java API · v1/Management/manualCompact](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Management/manualCompact.md) - [ConsistencyLevelEnum | Java API · v1/Misc/ConsistencyLevelEnum](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Misc/ConsistencyLevelEnum.md) - [Constant | Java API · v1/Misc/Constant](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Misc/Constant.md) - [DataType | Java API · v1/Misc/DataType](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Misc/DataType.md) - [IndexType | Java API · v1/Misc/IndexType](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Misc/IndexType.md) - [LogLevel | Java API · v1/Misc/LogLevel](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Misc/LogLevel.md) - [MetricType | Java API · v1/Misc/MetricType](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Misc/MetricType.md) - [R | Java API · v1/Misc/RT](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Misc/RT.md) - [createPartition() | Java API · v1/Partition/createPartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Partition/createPartition.md) - [dropPartition() | Java API · v1/Partition/dropPartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Partition/dropPartition.md) - [getPartitionStatistics() | Java API · v1/Partition/getPartitionStatistics](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Partition/getPartitionStatistics.md) - [hasPartition() | Java API · v1/Partition/hasPartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Partition/hasPartition.md) - [loadPartitions() | Java API · v1/Partition/loadPartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Partition/loadPartitions.md) - [releasePartitions() | Java API · v1/Partition/releasePartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Partition/releasePartitions.md) - [showPartitions() | Java API · v1/Partition/showPartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/Partition/showPartitions.md) - [hybridSearch() | Java API · v1/QuerySearch/hybridSearch](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/QuerySearch/hybridSearch.md) - [query() | Java API · v1/QuerySearch/query](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/QuerySearch/query.md) - [queryAsync() | Java API · v1/QuerySearch/queryAsync](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/QuerySearch/queryAsync.md) - [queryIterator() | Java API · v1/QuerySearch/queryIterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/QuerySearch/queryIterator.md) - [search() | Java API · v1/QuerySearch/search](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/QuerySearch/search.md) - [searchAsync() | Java API · v1/QuerySearch/searchAsync](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/QuerySearch/searchAsync.md) - [searchIterator() | Java API · v1/QuerySearch/searchIterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/QuerySearch/searchIterator.md) - [addUserToRole() | Java API · v1/RBAC/addUserToRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/addUserToRole.md) - [createRole() | Java API · v1/RBAC/createRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/createRole.md) - [dropRole() | Java API · v1/RBAC/dropRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/dropRole.md) - [grantRolePrivilege() | Java API · v1/RBAC/grantRolePrivilege](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/grantRolePrivilege.md) - [removeUserFromRole() | Java API · v1/RBAC/removeUserFromRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/removeUserFromRole.md) - [revokeRolePrivilege() | Java API · v1/RBAC/revokeRolePrivilege](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/revokeRolePrivilege.md) - [selectGrantForRole() | Java API · v1/RBAC/selectGrantForRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/selectGrantForRole.md) - [selectGrantForRoleAndObject() | Java API · v1/RBAC/selectGrantForRoleAndObject](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/selectGrantForRoleAndObject.md) - [selectRole() | Java API · v1/RBAC/selectRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/selectRole.md) - [selectUser() | Java API · v1/RBAC/selectUser](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/RBAC/selectUser.md) - [createResourceGroup() | Java API · v1/ResourceGroup/createResourceGroup](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/ResourceGroup/createResourceGroup.md) - [describeResourceGroup() | Java API · v1/ResourceGroup/describeResourceGroup](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/ResourceGroup/describeResourceGroup.md) - [dropResourceGroup() | Java API · v1/ResourceGroup/dropResourceGroup](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/ResourceGroup/dropResourceGroup.md) - [listResourceGroups() | Java API · v1/ResourceGroup/listResourceGroups](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/ResourceGroup/listResourceGroups.md) - [transferNode() | Java API · v1/ResourceGroup/transferNode](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/ResourceGroup/transferNode.md) - [transferReplica() | Java API · v1/ResourceGroup/transferReplica](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/ResourceGroup/transferReplica.md) - [checkHealth() | Java API · v1/System/checkHealth](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/System/checkHealth.md) - [getVersion() | Java API · v1/System/getVersion](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v1/System/getVersion.md) - [addPrivilegesToGroup() | Java API · v2/Authentication/addPrivilegesToGroup](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/addPrivilegesToGroup.md) - [createPrivilegeGroup() | Java API · v2/Authentication/createPrivilegeGroup](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/createPrivilegeGroup.md) - [createRole() | Java API · v2/Authentication/createRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/createRole.md) - [createUser() | Java API · v2/Authentication/createUser](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/createUser.md) - [describeRole() | Java API · v2/Authentication/describeRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/describeRole.md) - [describeUser() | Java API · v2/Authentication/describeUser](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/describeUser.md) - [dropPrivilegeGroup() | Java API · v2/Authentication/dropPrivilegeGroup](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/dropPrivilegeGroup.md) - [dropRole() | Java API · v2/Authentication/dropRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/dropRole.md) - [dropUser() | Java API · v2/Authentication/dropUser](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/dropUser.md) - [grantPrivilege() | Java API · v2/Authentication/grantPrivilege](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/grantPrivilege.md) - [grantPrivilegeV2() | Java API · v2/Authentication/grantPrivilegeV2](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/grantPrivilegeV2.md) - [grantRole() | Java API · v2/Authentication/grantRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/grantRole.md) - [listPrivilegeGroups() | Java API · v2/Authentication/listPrivilegeGroups](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/listPrivilegeGroups.md) - [listRoles() | Java API · v2/Authentication/listRoles](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/listRoles.md) - [listUsers() | Java API · v2/Authentication/listUsers](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/listUsers.md) - [removePrivilegesFromGroup() | Java API · v2/Authentication/removePrivilegesFromGroup](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/removePrivilegesFromGroup.md) - [revokePrivilege() | Java API · v2/Authentication/revokePrivilege](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/revokePrivilege.md) - [revokePrivilegeV2() | Java API · v2/Authentication/revokePrivilegeV2](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/revokePrivilegeV2.md) - [revokeRole() | Java API · v2/Authentication/revokeRole](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/revokeRole.md) - [updatePassword() | Java API · v2/Authentication/updatePassword](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Authentication/updatePassword.md) - [MilvusClientV2 | Java API · v2/Client/MilvusClientV2](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Client/MilvusClientV2.md) - [MilvusClientV2Pool | Java API · v2/Client/MilvusClientV2Pool](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Client/MilvusClientV2Pool.md) - [CollectionSchema | Java API · v2/Collections/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/CollectionSchema/CollectionSchema.md) - [addField() | Java API · v2/Collections/CollectionSchema/addField](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/CollectionSchema/addField.md) - [getField() | Java API · v2/Collections/CollectionSchema/getField](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/CollectionSchema/getField.md) - [ConsistencyLevel | Java API · v2/Collections/ConsistencyLevel](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/ConsistencyLevel.md) - [DataType | Java API · v2/Collections/DataType](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/DataType.md) - [FieldSchema | Java API · v2/Collections/FieldSchema](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/FieldSchema.md) - [alterAlias() | Java API · v2/Collections/alterAlias](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/alterAlias.md) - [alterCollectionField() | Java API · v2/Collections/alterCollectionField](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/alterCollectionField.md) - [alterCollectionProperties() | Java API · v2/Collections/alterCollectionProperties](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/alterCollectionProperties.md) - [createAlias() | Java API · v2/Collections/createAlias](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/createAlias.md) - [createCollection() | Java API · v2/Collections/createCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Collections/createCollection.md) - 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[hybridSearch() | Java API · v2/Vector/hybridSearch](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Vector/hybridSearch.md) - [insert() | Java API · v2/Vector/insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Vector/insert.md) - [query() | Java API · v2/Vector/query](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Vector/query.md) - [queryIterator() | Java API · v2/Vector/queryIterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Vector/queryIterator.md) - [search() | Java API · v2/Vector/search](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Vector/search.md) - [searchIterator() | Java API · v2/Vector/searchIterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Vector/searchIterator.md) - [upsert() | Java API · v2/Vector/upsert](https://www.xlonglab.com/docs/v2.4.x/api-reference/java/v2/Vector/upsert.md) - [About Milvus-sdk-node | Node.js API · About](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/About.md) - 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[loadCollectionSync() | Node.js API · Management/loadCollectionSync](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Management/loadCollectionSync.md) - [refreshLoad() | Node.js API · Management/refreshLoad](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Management/refreshLoad.md) - [releaseCollection() | Node.js API · Management/releaseCollection](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Management/releaseCollection.md) - [createPartition() | Node.js API · Partitions/createPartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Partitions/createPartition.md) - [dropPartition() | Node.js API · Partitions/dropPartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Partitions/dropPartition.md) - [getPartitionStatistics() | Node.js API · Partitions/getPartitionStatistics](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Partitions/getPartitionStatistics.md) - [hasPartition() | Node.js API · Partitions/hasPartition](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Partitions/hasPartition.md) - [listPartitions() | Node.js API · Partitions/listPartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Partitions/listPartitions.md) - [loadPartitions() | Node.js API · Partitions/loadPartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Partitions/loadPartitions.md) - [loadPartitionsSync() | Node.js API · Partitions/loadPartitionsSync](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Partitions/loadPartitionsSync.md) - [releasePartitions() | Node.js API · Partitions/releasePartitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Partitions/releasePartitions.md) - [count() | Node.js API · Vector/count](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/count.md) - [delete() | Node.js API · Vector/delete](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/delete.md) - [get() | Node.js API · Vector/get](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/get.md) - [hybridSearch() | Node.js API · Vector/hybridSearch](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/hybridSearch.md) - [insert() | Node.js API · Vector/insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/insert.md) - [query() | Node.js API · Vector/query](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/query.md) - [queryIterator() | Node.js API · Vector/queryIterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/queryIterator.md) - [search() | Node.js API · Vector/search](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/search.md) - [upsert() | Node.js API · Vector/upsert](https://www.xlonglab.com/docs/v2.4.x/api-reference/node/Vector/upsert.md) - [About PyMilvus | Python API · About](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/About.md) - [BulkFileType | Python API · DataImport/BulkFileType](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/BulkFileType.md) - [bulk_import() | Python API · DataImport/BulkImport/bulk_import](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/BulkImport/bulk_import.md) - [get_import_progress() | Python API · DataImport/BulkImport/get_import_progress](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/BulkImport/get_import_progress.md) - [list_import_jobs() | Python API · DataImport/BulkImport/list_import_jobs](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/BulkImport/list_import_jobs.md) - [LocalBulkWriter | Python API · DataImport/LocalBulkWriter/LocalBulkWriter](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/LocalBulkWriter/LocalBulkWriter.md) - [append_row() | Python API · DataImport/LocalBulkWriter/append_row](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/LocalBulkWriter/append_row.md) - [commit() | Python API · DataImport/LocalBulkWriter/commit](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/LocalBulkWriter/commit.md) - [AzureConnectParam | Python API · DataImport/RemoteBulkWriter/AzureConnectParam](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/AzureConnectParam.md) - [RemoteBulkWriter | Python API · DataImport/RemoteBulkWriter/RemoteBulkWriter](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/RemoteBulkWriter.md) - [S3ConnectParam | Python API · DataImport/RemoteBulkWriter/S3ConnectParam](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/S3ConnectParam.md) - [append_row() | Python API · DataImport/RemoteBulkWriter/append_row](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/append_row.md) - [commit() | Python API · DataImport/RemoteBulkWriter/commit](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/DataImport/RemoteBulkWriter/commit.md) - [BGEM3EmbeddingFunction | Python API · EmbeddingModels/BGEM3EmbeddingFunction/BGEM3EmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/BGEM3EmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/BGEM3EmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/BGEM3EmbeddingFunction/__call__.md) - 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[encode_queries() | Python API · EmbeddingModels/CohereEmbeddingFunction/encode_queries()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/CohereEmbeddingFunction/encode_queries().md) - [InstructorEmbeddingFunction | Python API · EmbeddingModels/InstructorEmbeddingFunction/InstructorEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/InstructorEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/InstructorEmbeddingFunction/__call__()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/__call__().md) - [encode_documents() | Python API · EmbeddingModels/InstructorEmbeddingFunction/encode_documents()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/encode_documents().md) - [encode_queries() | Python API · EmbeddingModels/InstructorEmbeddingFunction/encode_queries()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/InstructorEmbeddingFunction/encode_queries().md) - [JinaEmbeddingFunction | Python API · EmbeddingModels/JinaEmbeddingFunction/JinaEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/JinaEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/JinaEmbeddingFunction/__call__()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/__call__().md) - [encode_documents() | Python API · EmbeddingModels/JinaEmbeddingFunction/encode_documents()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/encode_documents().md) - [encode_queries() | Python API · EmbeddingModels/JinaEmbeddingFunction/encode_queries()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/JinaEmbeddingFunction/encode_queries().md) - [MGTEEmbeddingFunction | Python API · EmbeddingModels/MGTEEmbeddingFunction/MGTEEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/MGTEEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/MGTEEmbeddingFunction/__call__()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/__call__().md) - [encode_documents() | Python API · EmbeddingModels/MGTEEmbeddingFunction/encode_documents()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/encode_documents().md) - [encode_queries() | Python API · EmbeddingModels/MGTEEmbeddingFunction/encode_queries()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/MGTEEmbeddingFunction/encode_queries().md) - [MistralAIEmbeddingFunction | Python API · EmbeddingModels/MistralAIEmbeddingFunction/MistralAIEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/MistralAIEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/__call__()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/__call__().md) - [encode_documents() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/encode_documents()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/encode_documents().md) - [encode_queries() | Python API · EmbeddingModels/MistralAIEmbeddingFunction/encode_queries()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/MistralAIEmbeddingFunction/encode_queries().md) - [NomicEmbeddingFunction | Python API · EmbeddingModels/NomicEmbeddingFunction/NomicEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/NomicEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/NomicEmbeddingFunction/__call__()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/__call__().md) - [encode_documents() | Python API · EmbeddingModels/NomicEmbeddingFunction/encode_documents()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/encode_documents().md) - [encode_queries() | Python API · EmbeddingModels/NomicEmbeddingFunction/encode_queries()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/NomicEmbeddingFunction/encode_queries().md) - [OnnxEmbeddingFunction | Python API · EmbeddingModels/OnnxEmbeddingFunction/OnnxEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/OnnxEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/OnnxEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/OnnxEmbeddingFunction/encode_documents()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/encode_documents().md) - [encode_queries() | Python API · EmbeddingModels/OnnxEmbeddingFunction/encode_queries()](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/OnnxEmbeddingFunction/encode_queries().md) - [OpenAIEmbeddingFunction | Python API · EmbeddingModels/OpenAIEmbeddingFunction/OpenAIEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/OpenAIEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/OpenAIEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/OpenAIEmbeddingFunction/encode_queries.md) - [SentenceTransformerEmbeddingFunction | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/SentenceTransformerEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/SentenceTransformerEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/SentenceTransformerEmbeddingFunction/encode_queries.md) - [SpladeEmbeddingFunction | Python API · EmbeddingModels/SpladeEmbeddingFunction/SpladeEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/SpladeEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/SpladeEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/SpladeEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/encode_documents.md) - [encode_queries() | Python API · EmbeddingModels/SpladeEmbeddingFunction/encode_queries](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/SpladeEmbeddingFunction/encode_queries.md) - [VoyageEmbeddingFunction | Python API · EmbeddingModels/VoyageEmbeddingFunction/VoyageEmbeddingFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/VoyageEmbeddingFunction.md) - [__call__() | Python API · EmbeddingModels/VoyageEmbeddingFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/__call__.md) - [encode_documents() | Python API · EmbeddingModels/VoyageEmbeddingFunction/encode_documents](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/EmbeddingModels/VoyageEmbeddingFunction/encode_documents.md) - 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[grant_role() | Python API · MilvusClient/Authentication/grant_role](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/grant_role.md) - [list_privilege_groups() | Python API · MilvusClient/Authentication/list_privilege_groups](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/list_privilege_groups.md) - [list_roles() | Python API · MilvusClient/Authentication/list_roles](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/list_roles.md) - [list_users() | Python API · MilvusClient/Authentication/list_users](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/list_users.md) - [remove_privileges_from_group() | Python API · MilvusClient/Authentication/remove_privileges_from_group](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/remove_privileges_from_group.md) - [revoke_privilege_v2() | Python API · MilvusClient/Authentication/revoke_privilege_v2](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/revoke_privilege_v2.md) - [revoke_privileges() | Python API · MilvusClient/Authentication/revoke_privileges](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/revoke_privileges.md) - [revoke_role() | Python API · MilvusClient/Authentication/revoke_role](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/revoke_role.md) - [update_password() | Python API · MilvusClient/Authentication/update_password](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Authentication/update_password.md) - [MilvusClient | Python API · MilvusClient/Client/MilvusClient](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Client/MilvusClient.md) - [close() | Python API · MilvusClient/Client/close](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Client/close.md) - [CollectionSchema | Python API · MilvusClient/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/CollectionSchema/CollectionSchema.md) - [add_field() | Python API · MilvusClient/CollectionSchema/add_field](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/CollectionSchema/add_field.md) - [construct_from_dict() | Python API · MilvusClient/CollectionSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/CollectionSchema/construct_from_dict.md) - [to_dict() | Python API · MilvusClient/CollectionSchema/to_dict](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/CollectionSchema/to_dict.md) - [verify() | Python API · MilvusClient/CollectionSchema/verify](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/CollectionSchema/verify.md) - [DataType | Python API · MilvusClient/Collections/DataType](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/DataType.md) - [IndexType | Python API · MilvusClient/Collections/IndexType](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/IndexType.md) - [alter_alias() | Python API · MilvusClient/Collections/alter_alias](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/alter_alias.md) - [alter_collection_field() | Python API · MilvusClient/Collections/alter_collection_field](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/alter_collection_field.md) - [alter_collection_properties() | Python API · MilvusClient/Collections/alter_collection_properties](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/alter_collection_properties.md) - [create_alias() | Python API · MilvusClient/Collections/create_alias](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/create_alias.md) - [create_collection() | Python API · MilvusClient/Collections/create_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/create_collection.md) - [create_schema() | Python API · MilvusClient/Collections/create_schema](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/create_schema.md) - [describe_alias() | Python API · MilvusClient/Collections/describe_alias](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/describe_alias.md) - [describe_collection() | Python API · MilvusClient/Collections/describe_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/describe_collection.md) - [drop_alias() | Python API · MilvusClient/Collections/drop_alias](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/drop_alias.md) - [drop_collection() | Python API · MilvusClient/Collections/drop_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/drop_collection.md) - [drop_collection_properties() | Python API · MilvusClient/Collections/drop_collection_properties](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/drop_collection_properties.md) - [get_collection_stats() | Python API · MilvusClient/Collections/get_collection_stats](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/get_collection_stats.md) - [has_collection() | Python API · MilvusClient/Collections/has_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/has_collection.md) - [list_aliases() | Python API · MilvusClient/Collections/list_aliases](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/list_aliases.md) - [list_collections() | Python API · MilvusClient/Collections/list_collections](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/list_collections.md) - [rename_collection() | Python API · MilvusClient/Collections/rename_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Collections/rename_collection.md) - [add_index() | Python API · MilvusClient/Management/add_index](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/add_index.md) - [compact() | Python API · MilvusClient/Management/compact](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/compact.md) - [create_index() | Python API · MilvusClient/Management/create_index](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/create_index.md) - [describe_index() | Python API · MilvusClient/Management/describe_index](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/describe_index.md) - [drop_index() | Python API · MilvusClient/Management/drop_index](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/drop_index.md) - [flush() | Python API · MilvusClient/Management/flush](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/flush.md) - [get_compact_state() | Python API · MilvusClient/Management/get_compact_state](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/get_compact_state.md) - [get_load_state() | Python API · MilvusClient/Management/get_load_state](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/get_load_state.md) - [list_indexes() | Python API · MilvusClient/Management/list_indexes](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/list_indexes.md) - [load_collection() | Python API · MilvusClient/Management/load_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/load_collection.md) - [prepare_index_params() | Python API · MilvusClient/Management/prepare_index_params](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/prepare_index_params.md) - [refresh_load() | Python API · MilvusClient/Management/refresh_load](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/refresh_load.md) - [release_collection() | Python API · MilvusClient/Management/release_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Management/release_collection.md) - [create_partition() | Python API · MilvusClient/Partitions/create_partition](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Partitions/create_partition.md) - [drop_partition() | Python API · MilvusClient/Partitions/drop_partition](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Partitions/drop_partition.md) - [get_partition_stats() | Python API · MilvusClient/Partitions/get_partition_stats](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Partitions/get_partition_stats.md) - [has_partition() | Python API · MilvusClient/Partitions/has_partition](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Partitions/has_partition.md) - [list_partitions() | Python API · MilvusClient/Partitions/list_partitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Partitions/list_partitions.md) - [load_partitions() | Python API · MilvusClient/Partitions/load_partitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Partitions/load_partitions.md) - [release_partitions() | Python API · MilvusClient/Partitions/release_partitions](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Partitions/release_partitions.md) - [delete() | Python API · MilvusClient/Vector/delete](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Vector/delete.md) - [get() | Python API · MilvusClient/Vector/get](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Vector/get.md) - [insert() | Python API · MilvusClient/Vector/insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Vector/insert.md) - [query() | Python API · MilvusClient/Vector/query](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Vector/query.md) - [query_iterator() | Python API · MilvusClient/Vector/query_iterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Vector/query_iterator.md) - [search() | Python API · MilvusClient/Vector/search](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Vector/search.md) - [search_iterator() | Python API · MilvusClient/Vector/search_iterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Vector/search_iterator.md) - [upsert() | Python API · MilvusClient/Vector/upsert](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/MilvusClient/Vector/upsert.md) - [Collection | Python API · ORM/Collection/Collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/Collection.md) - [compact() | Python API · ORM/Collection/compact](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/compact.md) - [construct_from_dataframe() | Python API · ORM/Collection/construct_from_dataframe](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/construct_from_dataframe.md) - [create_index() | Python API · ORM/Collection/create_index](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/create_index.md) - [create_partition() | Python API · ORM/Collection/create_partition](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/create_partition.md) - [delete() | Python API · ORM/Collection/delete](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/delete.md) - [describe() | Python API · ORM/Collection/describe](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/describe.md) - [drop() | Python API · ORM/Collection/drop](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/drop.md) - [drop_index() | Python API · ORM/Collection/drop_index](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/drop_index.md) - [drop_partition() | Python API · ORM/Collection/drop_partition](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/drop_partition.md) - [flush() | Python API · ORM/Collection/flush](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/flush.md) - [get_compaction_plans() | Python API · ORM/Collection/get_compaction_plans](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/get_compaction_plans.md) - [get_compaction_state() | Python API · ORM/Collection/get_compaction_state](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/get_compaction_state.md) - [get_replicas() | Python API · ORM/Collection/get_replicas](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/get_replicas.md) - [has_index() | Python API · ORM/Collection/has_index](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/has_index.md) - [has_partition() | Python API · ORM/Collection/has_partition](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/has_partition.md) - [hybrid_search() | Python API · ORM/Collection/hybrid_search](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/hybrid_search.md) - [index() | Python API · ORM/Collection/index](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/index.md) - [insert() | Python API · ORM/Collection/insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/insert.md) - [load() | Python API · ORM/Collection/load](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/load.md) - [partition() | Python API · ORM/Collection/partition](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/partition.md) - [query() | Python API · ORM/Collection/query](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/query.md) - [query_iterator() | Python API · ORM/Collection/query_iterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/query_iterator.md) - [release() | Python API · ORM/Collection/release](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/release.md) - [search() | Python API · ORM/Collection/search](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/search.md) - [search_iterator() | Python API · ORM/Collection/search_iterator](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/search_iterator.md) - [set_properties() | Python API · ORM/Collection/set_properties](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/set_properties.md) - [upsert() | Python API · ORM/Collection/upsert](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/upsert.md) - [wait_for_compaction_completed() | Python API · ORM/Collection/wait_for_compaction_completed](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Collection/wait_for_compaction_completed.md) - [CollectionSchema | Python API · ORM/CollectionSchema/CollectionSchema](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/CollectionSchema/CollectionSchema.md) - [add_field() | Python API · ORM/CollectionSchema/add_field](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/CollectionSchema/add_field.md) - [construct_from_dict() | Python API · ORM/CollectionSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/CollectionSchema/construct_from_dict.md) - [to_dict() | Python API · ORM/CollectionSchema/to_dict](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/CollectionSchema/to_dict.md) - [verify() | Python API · ORM/CollectionSchema/verify](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/CollectionSchema/verify.md) - [Connections | Python API · ORM/Connections/Connections](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Connections/Connections.md) - [add_connection() | Python API · ORM/Connections/add_connection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Connections/add_connection.md) - [connect() | Python API · ORM/Connections/connect](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Connections/connect.md) - [disconnect() | Python API · ORM/Connections/disconnect](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Connections/disconnect.md) - [get_connection_addr() | Python API · ORM/Connections/get_connection_addr](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Connections/get_connection_addr.md) - [has_connection() | Python API · ORM/Connections/has_connection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Connections/has_connection.md) - [list_connections() | Python API · ORM/Connections/list_connections](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Connections/list_connections.md) - [remove_connection() | Python API · ORM/Connections/remove_connection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Connections/remove_connection.md) - [FieldSchema | Python API · ORM/FieldSchema/FieldSchema](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/FieldSchema/FieldSchema.md) - [construct_from_dict() | Python API · ORM/FieldSchema/construct_from_dict](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/FieldSchema/construct_from_dict.md) - [to_dict() | Python API · ORM/FieldSchema/to_dict](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/FieldSchema/to_dict.md) - [Partition | Python API · ORM/Partition/Partition](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/Partition.md) - [delete() | Python API · ORM/Partition/delete](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/delete.md) - [drop() | Python API · ORM/Partition/drop](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/drop.md) - [flush() | Python API · ORM/Partition/flush](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/flush.md) - [get_replicas() | Python API · ORM/Partition/get_replicas](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/get_replicas.md) - [insert() | Python API · ORM/Partition/insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/insert.md) - [load() | Python API · ORM/Partition/load](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/load.md) - [query() | Python API · ORM/Partition/query](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/query.md) - [release() | Python API · ORM/Partition/release](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/release.md) - [search() | Python API · ORM/Partition/search](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/search.md) - [upsert() | Python API · ORM/Partition/upsert](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Partition/upsert.md) - [Role | Python API · ORM/Role/Role](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/Role.md) - [add_user() | Python API · ORM/Role/add_user](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/add_user.md) - [create() | Python API · ORM/Role/create](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/create.md) - [drop() | Python API · ORM/Role/drop](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/drop.md) - [get_users() | Python API · ORM/Role/get_users](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/get_users.md) - [grant() | Python API · ORM/Role/grant](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/grant.md) - [is_exist() | Python API · ORM/Role/is_exist](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/is_exist.md) - [list_grant() | Python API · ORM/Role/list_grant](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/list_grant.md) - [list_grants() | Python API · ORM/Role/list_grants](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/list_grants.md) - [remove_user() | Python API · ORM/Role/remove_user](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/remove_user.md) - [revoke() | Python API · ORM/Role/revoke](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/Role/revoke.md) - [create_database() | Python API · ORM/db/create_database](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/db/create_database.md) - [drop_database() | Python API · ORM/db/drop_database](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/db/drop_database.md) - [list_database() | Python API · ORM/db/list_database](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/db/list_database.md) - [using_database() | Python API · ORM/db/using_database](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/db/using_database.md) - [BulkInsertState | Python API · ORM/utility/BulkInsertState](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/BulkInsertState.md) - [alter_alias() | Python API · ORM/utility/alter_alias](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/alter_alias.md) - [create_alias() | Python API · ORM/utility/create_alias](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/create_alias.md) - [create_resource_group() | Python API · ORM/utility/create_resource_group](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/create_resource_group.md) - [create_user() | Python API · ORM/utility/create_user](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/create_user.md) - [delete_user() | Python API · ORM/utility/delete_user](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/delete_user.md) - [describe_resource_group() | Python API · ORM/utility/describe_resource_group](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/describe_resource_group.md) - [do_bulk_insert() | Python API · ORM/utility/do_bulk_insert](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/do_bulk_insert.md) - [drop_alias() | Python API · ORM/utility/drop_alias](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/drop_alias.md) - [drop_collection() | Python API · ORM/utility/drop_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/drop_collection.md) - [drop_resource_group() | Python API · ORM/utility/drop_resource_group](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/drop_resource_group.md) - [flush_all() | Python API · ORM/utility/flush_all](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/flush_all.md) - [get_bulk_insert_state() | Python API · ORM/utility/get_bulk_insert_state](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/get_bulk_insert_state.md) - [get_query_segment_info() | Python API · ORM/utility/get_query_segment_info](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/get_query_segment_info.md) - 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[list_usernames() | Python API · ORM/utility/list_usernames](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/list_usernames.md) - [list_users() | Python API · ORM/utility/list_users](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/list_users.md) - [load_balance() | Python API · ORM/utility/load_balance](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/load_balance.md) - [load_state() | Python API · ORM/utility/load_state](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/load_state.md) - [loading_progress() | Python API · ORM/utility/loading_progress](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/loading_progress.md) - [mkts_from_datetime() | Python API · ORM/utility/mkts_from_datetime](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/mkts_from_datetime.md) - [mkts_from_hybridts() | Python API · ORM/utility/mkts_from_hybridts](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/mkts_from_hybridts.md) - [mkts_from_unixtime() | Python API · ORM/utility/mkts_from_unixtime](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/mkts_from_unixtime.md) - [rename_collection() | Python API · ORM/utility/rename_collection](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/rename_collection.md) - [reset_password() | Python API · ORM/utility/reset_password](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/reset_password.md) - [transfer_node() | Python API · ORM/utility/transfer_node](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/transfer_node.md) - [transfer_replica() | Python API · ORM/utility/transfer_replica](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/transfer_replica.md) - [update_password() | Python API · ORM/utility/update_password](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/update_password.md) - [wait_for_index_building_complete() | Python API · ORM/utility/wait_for_index_building_complete](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/wait_for_index_building_complete.md) - [wait_for_loading_complete() | Python API · ORM/utility/wait_for_loading_complete](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/ORM/utility/wait_for_loading_complete.md) - [BGERerankFunction | Python API · Rerankers/BGERerankFunction/BGERerankFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/BGERerankFunction/BGERerankFunction.md) - [__call__() | Python API · Rerankers/BGERerankFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/BGERerankFunction/__call__.md) - [CohereRerankFunction | Python API · Rerankers/CohereRerankFunction/CohereRerankFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/CohereRerankFunction/CohereRerankFunction.md) - [__call__() | Python API · Rerankers/CohereRerankFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/CohereRerankFunction/__call__.md) - [CrossEncoderRerankFunction | Python API · Rerankers/CrossEncoderRerankFunction/CrossEncoderRerankFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/CrossEncoderRerankFunction/CrossEncoderRerankFunction.md) - [__call__() | Python API · Rerankers/CrossEncoderRerankFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/CrossEncoderRerankFunction/__call__.md) - [JinaRerankFunction | Python API · Rerankers/JinaRerankFunction/JinaRerankFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/JinaRerankFunction/JinaRerankFunction.md) - [__call__() | Python API · Rerankers/JinaRerankFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/JinaRerankFunction/__call__.md) - [VoyageRerankFunction | Python API · Rerankers/VoyageRerankFunction/VoyageRerankFunction](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/VoyageRerankFunction/VoyageRerankFunction.md) - [__call__() | Python API · Rerankers/VoyageRerankFunction/__call__](https://www.xlonglab.com/docs/v2.4.x/api-reference/pymilvus/Rerankers/VoyageRerankFunction/__call__.md) ## 来源与更新 - 上游仓库: https://github.com/milvus-io/web-content.git - 上游提交: 444eea863da4d4af7e9d8410af37a790704ab60c - 上游提交时间: 2026-10-12T04:18:45Z - 本站为社区维护的中文文档站，不是 Milvus 官方网站。