Sentence Transformers

Milvus 通过Sentence Transformers Embedding Function（SentenceTransformerEmbeddingFunction）类与Sentence Transformers预训练模型集成。该类提供了使用预训练的 Sentence Transformers 模型对文档和查询进行编码的方法，并将嵌入作为与 Milvus 索引兼容的稠密向量返回。

要使用该功能，请安装必要的依赖项：

pip install --upgrade pymilvus
pip install "pymilvus[model]"

然后，实例化SentenceTransformerEmbeddingFunction：

from pymilvus import model

sentence_transformer_ef = model.dense.SentenceTransformerEmbeddingFunction(
    model_name='all-MiniLM-L6-v2', # Specify the model name
    device='cpu' # Specify the device to use, e.g., 'cpu' or 'cuda:0'
)

参数：

• model_name*（字符串）*

  用于编码的 Sentence Transformers 模型名称。默认值为all-MiniLM-L6-v2。您可以使用任何一个 Sentence Transformers 预训练模型。有关可用模型的列表，请参阅预训练模型。

• 设备*（字符串）*

  要使用的设备，cpu表示 CPU，cuda:n表示第 n 个 GPU 设备。

要创建文档嵌入，请使用**encode_documents()**方法：

docs = [
    "Artificial intelligence was founded as an academic discipline in 1956.",
    "Alan Turing was the first person to conduct substantial research in AI.",
    "Born in Maida Vale, London, Turing was raised in southern England.",
]

docs_embeddings = sentence_transformer_ef.encode_documents(docs)

# Print embeddings
print("Embeddings:", docs_embeddings)
# Print dimension and shape of embeddings
print("Dim:", sentence_transformer_ef.dim, docs_embeddings[0].shape)

预期输出类似于下图：

Embeddings: [array([-3.09392996e-02, -1.80662833e-02,  1.34775648e-02,  2.77156215e-02,
       -4.86349640e-03, -3.12581174e-02, -3.55921760e-02,  5.76934684e-03,
        2.80773244e-03,  1.35783911e-01,  3.59678417e-02,  6.17732145e-02,
...
       -4.61330153e-02, -4.85207550e-02,  3.13997865e-02,  7.82178566e-02,
       -4.75336798e-02,  5.21207601e-02,  9.04406682e-02, -5.36676683e-02],
      dtype=float32)]
Dim: 384 (384,)

要为查询创建嵌入式代码，请使用**encode_queries()**方法：

queries = ["When was artificial intelligence founded",
           "Where was Alan Turing born?"]

query_embeddings = sentence_transformer_ef.encode_queries(queries)

# Print embeddings
print("Embeddings:", query_embeddings)
# Print dimension and shape of embeddings
print("Dim:", sentence_transformer_ef.dim, query_embeddings[0].shape)

预期输出类似于下图：

Embeddings: [array([-2.52114702e-02, -5.29330298e-02,  1.14570223e-02,  1.95571519e-02,
       -2.46500354e-02, -2.66519729e-02, -8.48201662e-03,  2.82961670e-02,
       -3.65092754e-02,  7.50745758e-02,  4.28900979e-02,  7.18822703e-02,
...
       -6.76431581e-02, -6.45996556e-02, -4.67132553e-02,  4.78532910e-02,
       -2.31596199e-03,  4.13446948e-02,  1.06935494e-01, -1.08258888e-01],
      dtype=float32)]
Dim: 384 (384,)