70 lines
2.8 KiB
Python
70 lines
2.8 KiB
Python
|
from langchain.docstore.document import Document
|
|||
|
|
from configs import EMBEDDING_MODEL, logger
|
|||
|
|
from server.model_workers.base import ApiEmbeddingsParams
|
|||
|
|
from server.utils import BaseResponse, get_model_worker_config, list_embed_models, list_online_embed_models
|
|||
|
|
from fastapi import Body
|
|||
|
|
from typing import Dict, List
|
|||
|
|
|
|||
|
|
|
|||
|
|
online_embed_models = list_online_embed_models()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def embed_texts(
|
|||
|
|
texts: List[str],
|
|||
|
|
embed_model: str = EMBEDDING_MODEL,
|
|||
|
|
to_query: bool = False,
|
|||
|
|
) -> BaseResponse:
|
|||
|
|
'''
|
|||
|
|
对文本进行向量化。返回数据格式：BaseResponse(data=List[List[float]])
|
|||
|
|
TODO: 也许需要加入缓存机制，减少 token 消耗
|
|||
|
|
'''
|
|||
|
|
try:
|
|||
|
|
if embed_model in list_embed_models(): # 使用本地Embeddings模型
|
|||
|
|
from server.utils import load_local_embeddings
|
|||
|
|
|
|||
|
|
embeddings = load_local_embeddings(model=embed_model)
|
|||
|
|
return BaseResponse(data=embeddings.embed_documents(texts))
|
|||
|
|
|
|||
|
|
if embed_model in list_online_embed_models(): # 使用在线API
|
|||
|
|
config = get_model_worker_config(embed_model)
|
|||
|
|
worker_class = config.get("worker_class")
|
|||
|
|
worker = worker_class()
|
|||
|
|
if worker_class.can_embedding():
|
|||
|
|
params = ApiEmbeddingsParams(texts=texts, to_query=to_query)
|
|||
|
|
resp = worker.do_embeddings(params)
|
|||
|
|
return BaseResponse(**resp)
|
|||
|
|
|
|||
|
|
return BaseResponse(code=500, msg=f"指定的模型 {embed_model} 不支持 Embeddings 功能。")
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(e)
|
|||
|
|
return BaseResponse(code=500, msg=f"文本向量化过程中出现错误：{e}")
|
|||
|
|
|
|||
|
|
def embed_texts_endpoint(
|
|||
|
|
texts: List[str] = Body(..., description="要嵌入的文本列表", examples=[["hello", "world"]]),
|
|||
|
|
embed_model: str = Body(EMBEDDING_MODEL, description=f"使用的嵌入模型，除了本地部署的Embedding模型，也支持在线API({online_embed_models})提供的嵌入服务。"),
|
|||
|
|
to_query: bool = Body(False, description="向量是否用于查询。有些模型如Minimax对存储/查询的向量进行了区分优化。"),
|
|||
|
|
) -> BaseResponse:
|
|||
|
|
'''
|
|||
|
|
对文本进行向量化，返回 BaseResponse(data=List[List[float]])
|
|||
|
|
'''
|
|||
|
|
return embed_texts(texts=texts, embed_model=embed_model, to_query=to_query)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def embed_documents(
|
|||
|
|
docs: List[Document],
|
|||
|
|
embed_model: str = EMBEDDING_MODEL,
|
|||
|
|
to_query: bool = False,
|
|||
|
|
) -> Dict:
|
|||
|
|
"""
|
|||
|
|
将 List[Document] 向量化，转化为 VectorStore.add_embeddings 可以接受的参数
|
|||
|
|
"""
|
|||
|
|
texts = [x.page_content for x in docs]
|
|||
|
|
metadatas = [x.metadata for x in docs]
|
|||
|
|
embeddings = embed_texts(texts=texts, embed_model=embed_model, to_query=to_query).data
|
|||
|
|
if embeddings is not None:
|
|||
|
|
return {
|
|||
|
|
"texts": texts,
|
|||
|
|
"embeddings": embeddings,
|
|||
|
|
"metadatas": metadatas,
|
|||
|
|
}
|