Voyage

Milvus 通过 VoyageEmbeddingFunction 类与 Voyage 模型集成。该类提供了使用 Voyage 模型对文档和查询进行编码的方法，并将嵌入返回为与 Milvus 索引兼容的密集向量。要使用该功能，请通过在Voyage平台上创建账户从Voyage获取 API 密钥。

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VoyageEmbeddingFunction ：

from pymilvus.model.dense import VoyageEmbeddingFunction

voyage_ef = VoyageEmbeddingFunction(
    model_name="voyage-lite-02-instruct", # Defaults to `voyage-2`
    api_key=VOYAGE_API_KEY # Provide your Voyage API key
)

参数：

• model_name (字符串）用于编码的 Voyage 模型名称。可以指定任何可用的 Voyage 模型名称，例如 , 等。如果不指定此参数，则将使用 。有关可用模型的列表，请参阅voyage-law-2 voyage-code-2 voyage-2 Voyage 官方文档。
• api_key (字符串）访问 Voyage API 的 API 密钥。有关如何创建 API 密钥的信息，请参阅API 密钥和 Python 客户端。

要为文档创建嵌入，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 = voyage_ef.encode_documents(docs)

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

预期输出类似于下图：

Embeddings: [array([ 0.02582654, -0.00907086, -0.04604037, ..., -0.01227521,
        0.04420955, -0.00038829]), array([ 0.03844212, -0.01597065, -0.03728884, ..., -0.02118733,
        0.03349845,  0.0065346 ]), array([ 0.05143557, -0.01096631, -0.02690451, ..., -0.02416254,
        0.07658645,  0.03064499])]
Dim: 1024 (1024,)

要为查询创建嵌入式内容，请使用encode_queries() 方法：

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

query_embeddings = voyage_ef.encode_queries(queries)

print("Embeddings:", query_embeddings)
print("Dim", voyage_ef.dim, query_embeddings[0].shape)

预期输出类似于下面的内容：

Embeddings: [array([ 0.01733501, -0.0230672 , -0.05208827, ..., -0.00957995,
        0.04493361,  0.01485138]), array([ 0.05937521, -0.00729363, -0.02184347, ..., -0.02107683,
        0.05706626,  0.0263358 ])]
Dim 1024 (1024,)