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：

包含文档嵌入的字典。

初始化 BGEM3EmbeddingFunction 时，如果 return_dense、return_sparse 和 return_colbert_vecs 设置为 True，则返回的字典将包含键 dense、sparse 和 colbert_vecs，分别对应稠密嵌入、稀疏词嵌入和 ColBERT 向量。

异常：

• ImportError

  未安装 FlagEmbedding 模块时，将引发此异常。

示例

python

from pymilvus import model

bge_m3_ef = model.hybrid.BGEM3EmbeddingFunction(
    model_name='BAAI/bge-m3', # Specify t`he model name
    device='cpu', # Specify the device to use, e.g., 'cpu' or 'cuda:0'
    use_fp16=False # Whether to use fp16. `False` for `device='cpu'`.
)

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 = bge_m3_ef.encode_documents(docs)

# Print embeddings
print("Embeddings:", docs_embeddings)
# Print dimension of dense embeddings
print("Dense document dim:", bge_m3_ef.dim["dense"], docs_embeddings["dense"][0].shape)
# Since the sparse embeddings are in a 2D csr_array format, we convert them to a list for easier manipulation.
print("Sparse document dim:", bge_m3_ef.dim["sparse"], list(docs_embeddings["sparse"])[0].shape)

# Embeddings: {'dense': [array([-0.02505937, -0.00142193,  0.04015467, ..., -0.02094924,
#         0.02623661,  0.00324098], dtype=float32), array([ 0.00118463,  0.00649292, -0.00735763, ..., -0.01446293,
#         0.04243685, -0.01794822], dtype=float32), array([ 0.00415287, -0.0101492 ,  0.0009811 , ..., -0.02559666,
#         0.08084674,  0.00141647], dtype=float32)], 'sparse': <3x250002 sparse array of type '<class 'numpy.float32'>'
#   with 43 stored elements in Compressed Sparse Row format>}
# Dense document dim: 1024 (1024,)
# Sparse document dim: 250002 (1, 250002)