encode_queries()

此操作接收查询字符串列表，并将每个查询编码为向量嵌入。

请求语法

python

encode_queries(
    queries: List[str], 
) -> List[np.array]

参数：

• queries (List[str])

  字符串值列表，其中每个字符串表示一个将传递给嵌入模型进行编码的查询。模型将为列表中的每个字符串生成一个嵌入向量。

返回类型：

List[np.array]

返回值：

一个列表，其中每个元素都是一个 NumPy 数组。

异常：

• ImportError

  当未安装 OpenAI 模块时，将引发此异常。

示例

python

from pymilvus import model

openai_ef = model.dense.OpenAIEmbeddingFunction(
    model_name='text-embedding-3-large', # Specify the model name
    dimensions=512 # Set the embedding dimensionality according to MRL feature.
)

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)

# 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,)