encode_documents()

此操作接收文档并将其编码为向量嵌入。

请求语法

python

encode_documents(
    documents: List[str], 
) -> Dict

参数：

• documents (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文档。模型会为列表中的每个字符串生成一个嵌入向量。

返回类型：

Dict

返回值：

一个包含编码后嵌入的字典，包括稠密嵌入和稀疏嵌入。

异常：

None

示例

python

from pymilvus.model.hybrid import MGTEEmbeddingFunction

ef = MGTEEmbeddingFunction()

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 = ef.encode_documents(docs)

# Print embeddings
print("Embeddings:", docs_embeddings)
# Print dimension of embeddings
print(ef.dim)

# Embeddings: {'dense': [tensor([-4.9149e-03,  1.6553e-02, -9.5524e-03, -2.1800e-02,  1.2075e-02,
#          1.8500e-02, -3.0632e-02,  5.5909e-02,  8.7365e-02,  1.8763e-02,
#          2.1708e-03, -2.7530e-02, -1.1523e-01,  6.5810e-03, -6.4674e-02,
#          6.7966e-02,  1.3005e-01,  1.1942e-01, -1.2174e-02, -4.0426e-02,
#         ...
#          2.0129e-02, -2.3657e-02,  2.2626e-02,  2.1858e-02, -1.9181e-02,
#          6.0706e-02, -2.0558e-02, -4.2050e-02], device='mps:0')], 'sparse': <Compressed Sparse Row sparse array of dtype 'float64'
#         with 41 stored elements and shape (3, 250002)>}

# {'dense': 768, 'sparse': 250002}