BGE M3

BGE-M3因其在多语言、多功能和多粒度方面的能力而得名。BGE-M3 能够支持 100 多种语言，为多语言和跨语言检索任务树立了新的标杆。它在单一框架内执行密集检索、多向量检索和稀疏检索的独特能力，使其成为广泛的信息检索（IR）应用的理想选择。

Milvus 使用BGEM3EmbeddingFunction类与 BGE M3 模型集成。该类处理嵌入的计算，并以与 Milvus 兼容的格式返回，用于索引和搜索。要使用此功能，必须安装 FlagEmbedding。

要使用此功能，请安装必要的依赖项：

pip install --upgrade pymilvus
pip install "pymilvus[model]"

然后，实例化BGEM3EmbeddingFunction：

from pymilvus.model.hybrid import BGEM3EmbeddingFunction

bge_m3_ef = BGEM3EmbeddingFunction(
    model_name='BAAI/bge-m3', # Specify the model name
    device='cpu', # Specify the device to use, e.g., 'cpu' or 'cuda:0'
    use_fp16=False # Specify whether to use fp16. Set to `False` if `device` is `cpu`.
)

参数：

• model_name（字符串）

  用于编码的模型名称。默认值为BAAI/bge-m3。

• 设备（字符串）

  要使用的设备，cpu表示 CPU，cuda:n表示第 n 个 GPU 设备。

• use_fp16（bool）

  是否使用 16 位浮点精度（fp16）。当设备为cpu 时指定为False。

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 = 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)

预期输出类似于下图：

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)

要为查询创建嵌入式代码，请使用encode_queries()方法：

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

query_embeddings = bge_m3_ef.encode_queries(queries)

# Print embeddings
print("Embeddings:", query_embeddings)
# Print dimension of dense embeddings
print("Dense query dim:", bge_m3_ef.dim["dense"], query_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 query dim:", bge_m3_ef.dim["sparse"], list(query_embeddings["sparse"])[0].shape)

预期输出类似于下图：

Embeddings: {'dense': [array([-0.02024024, -0.01514386,  0.02380808, ...,  0.00234648,
       -0.00264978, -0.04317448], dtype=float32), array([ 0.00648045, -0.0081542 , -0.02717067, ..., -0.00380103,
        0.04200587, -0.01274772], dtype=float32)], 'sparse': <2x250002 sparse array of type '<class 'numpy.float32'>'
        with 14 stored elements in Compressed Sparse Row format>}
Dense query dim: 1024 (1024,)
Sparse query dim: 250002 (1, 250002)