使用 Langfuse 对 RAG 查询进行 Tracing

本教程演示如何使用 Langfuse 对 RAG 查询进行 Tracing。示例使用 LlamaIndex 构建 RAG Pipeline，并使用 Milvus Lite 存储和检索文档。

本快速入门将演示如何使用 Milvus Lite 作为 Vector Store 设置 LlamaIndex 应用，以及如何使用 Langfuse LlamaIndex 集成对应用进行 Tracing。

Langfuse 是一个开源 LLM 工程平台，可帮助团队协作调试、分析和迭代 LLM 应用程序。所有平台功能均已集成，以加快开发工作流程。

Milvus Lite 是 Milvus 的轻量级版本，Milvus 是一个开源向量数据库，可通过 Embedding 和相似性搜索为人工智能应用提供支持。

设置

确保已安装 llama-index 和 langfuse。

Shell

pip install llama-index langfuse llama-index-vector-stores-milvus --upgrade

初始化集成。从 Langfuse 项目设置 中获取 API 密钥，并用密钥值替换 public_key secret_key。本示例使用 OpenAI 进行嵌入和聊天完成，因此还需要在环境变量中指定 OpenAI 密钥。

Python

import os

# Get keys for your project from the project settings page
# https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region

# Your openai key
os.environ["OPENAI_API_KEY"] = ""

Python

from llama_index.core import Settings
from llama_index.core.callbacks import CallbackManager
from langfuse.llama_index import LlamaIndexCallbackHandler

langfuse_callback_handler = LlamaIndexCallbackHandler()
Settings.callback_manager = CallbackManager([langfuse_callback_handler])

使用 Milvus Lite 索引

Python

from llama_index.core import Document

doc1 = Document(text="""
Maxwell "Max" Silverstein, a lauded movie director, screenwriter, and producer, was born on October 25, 1978, in Boston, Massachusetts. A film enthusiast from a young age, his journey began with home movies shot on a Super 8 camera. His passion led him to the University of Southern California (USC), majoring in Film Production. Eventually, he started his career as an assistant director at Paramount Pictures. Silverstein's directorial debut, “Doors Unseen,” a psychological thriller, earned him recognition at the Sundance Film Festival and marked the beginning of a successful directing career.
""")
doc2 = Document(text="""
Throughout his career, Silverstein has been celebrated for his diverse range of filmography and unique narrative technique. He masterfully blends suspense, human emotion, and subtle humor in his storylines. Among his notable works are "Fleeting Echoes," "Halcyon Dusk," and the Academy Award-winning sci-fi epic, "Event Horizon's Brink." His contribution to cinema revolves around examining human nature, the complexity of relationships, and probing reality and perception. Off-camera, he is a dedicated philanthropist living in Los Angeles with his wife and two children.
""")

Python

# Example index construction + LLM query

from llama_index.core import VectorStoreIndex
from llama_index.core import StorageContext
from llama_index.vector_stores.milvus import MilvusVectorStore

vector_store = MilvusVectorStore(
    uri="tmp/milvus_demo.db", dim=1536, overwrite=False
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(
    [doc1,doc2], storage_context=storage_context
)

查询

Python

# Query
response = index.as_query_engine().query("What did he do growing up?")
print(response)

Python

# Chat
response = index.as_chat_engine().chat("What did he do growing up?")
print(response)

在 Langfuse 中查看 Trace

Python

# As we want to immediately see result in Langfuse, we need to flush the callback handler
langfuse_callback_handler.flush()

完成后，可在 Langfuse 项目中查看索引和查询的 Trace。

示例 Trace（公开链接）：

1. 查询
2. 查询（聊天）

Langfuse 中的 Trace：

对更多高级功能感兴趣？

请参阅完整的 集成文档，了解更多高级功能和使用方法：

• 与 Langfuse Python SDK 和其他集成的互操作性
• 为 Trace 添加自定义元数据和属性