OpenAI

Milvus 通过OpenAIEmbeddingFunction类与 OpenAI 的模型集成。该类提供了使用预训练的 OpenAI 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OpenAI平台上创建账户从OpenAI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OpenAIEmbeddingFunction：

from pymilvus import model

openai_ef = model.dense.OpenAIEmbeddingFunction(
    model_name='text-embedding-3-large', # Specify the model name
    api_key='YOUR_API_KEY', # Provide your OpenAI API key
    dimensions=512 # Set the embedding dimensionality
)

参数：

• model_name（字符串）

  用于编码的 OpenAI 模型名称。有效选项为text-embedding-3-small、text- embedding- 3-large 和text-embedding-ada-002（默认）。

• api_key（字符串）

  访问 OpenAI API 的 API 密钥。

• 维数（int）

  输出嵌入结果的维数。仅支持text-embedding-3及更高版本的模型。

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 = openai_ef.encode_documents(docs)

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

预期输出类似于下图：

Embeddings: [array([ 1.76741909e-02, -2.04964578e-02, -1.09788161e-02, -5.27223349e-02,
        4.23139781e-02, -6.64533582e-03,  4.21088142e-03,  1.04644023e-01,
        5.10009527e-02,  5.32827862e-02, -3.26061808e-02, -3.66494283e-02,
...
       -8.93232748e-02,  6.68255147e-03,  3.55093405e-02, -5.09071983e-02,
        3.74144339e-03,  4.72541340e-02,  2.11916920e-02,  1.00753829e-02,
       -5.76633997e-02,  9.68257990e-03,  4.62721288e-02, -4.33261096e-02])]
Dim: 512 (512,)

要为查询创建嵌入信息，请使用encode_queries()方法：

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

query_embeddings = openai_ef.encode_queries(queries)

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

预期输出类似于下图：

Embeddings: [array([ 0.00530251, -0.01907905, -0.01672608, -0.05030033,  0.01635982,
       -0.03169853, -0.0033602 ,  0.09047844,  0.00030747,  0.11853652,
       -0.02870182, -0.01526102,  0.05505067,  0.00993909, -0.07165466,
...
       -9.78106782e-02, -2.22669560e-02,  1.21873049e-02, -4.83198799e-02,
        5.32377362e-02, -1.90469325e-02,  5.62430918e-02,  1.02650477e-02,
       -6.21757433e-02,  7.88027793e-02,  4.91846527e-04, -1.51633881e-02])]
Dim 512 (512,)