mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 21:37:20 +08:00
9c525b7fa5
新功能： - 优化 PDF 文件的 OCR，过滤无意义的小图片 by @liunux4odoo #2525 - 支持 Gemini 在线模型 by @yhfgyyf #2630 - 支持 GLM4 在线模型 by @zRzRzRzRzRzRzR - elasticsearch更新https连接 by @xldistance #2390 - 增强对PPT、DOC知识库文件的OCR识别 by @596192804 #2013 - 更新 Agent 对话功能 by @zRzRzRzRzRzRzR - 每次创建对象时从连接池获取连接，避免每次执行方法时都新建连接 by @Lijia0 #2480 - 实现 ChatOpenAI 判断token有没有超过模型的context上下文长度 by @glide-the - 更新运行数据库报错和项目里程碑 by @zRzRzRzRzRzRzR #2659 - 更新配置文件/文档/依赖 by @imClumsyPanda @zRzRzRzRzRzRzR - 添加日文版 readme by @eltociear #2787 修复： - langchain 更新后，PGVector 向量库连接错误 by @HALIndex #2591 - Minimax's model worker 错误 by @xyhshen - ES库无法向量检索.添加mappings创建向量索引 by MSZheng20 #2688
97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
from langchain.docstore.document import Document
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from configs import EMBEDDING_MODEL, logger
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from server.model_workers.base import ApiEmbeddingsParams
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from server.utils import BaseResponse, get_model_worker_config, list_embed_models, list_online_embed_models
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from fastapi import Body
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from fastapi.concurrency import run_in_threadpool
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from typing import Dict, List
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online_embed_models = list_online_embed_models()
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def embed_texts(
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texts: List[str],
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embed_model: str = EMBEDDING_MODEL,
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to_query: bool = False,
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) -> BaseResponse:
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'''
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对文本进行向量化。返回数据格式：BaseResponse(data=List[List[float]])
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'''
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try:
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if embed_model in list_embed_models(): # 使用本地Embeddings模型
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from server.utils import load_local_embeddings
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embeddings = load_local_embeddings(model=embed_model)
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return BaseResponse(data=embeddings.embed_documents(texts))
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if embed_model in list_online_embed_models(): # 使用在线API
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config = get_model_worker_config(embed_model)
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worker_class = config.get("worker_class")
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embed_model = config.get("embed_model")
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worker = worker_class()
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if worker_class.can_embedding():
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params = ApiEmbeddingsParams(texts=texts, to_query=to_query, embed_model=embed_model)
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resp = worker.do_embeddings(params)
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return BaseResponse(**resp)
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return BaseResponse(code=500, msg=f"指定的模型 {embed_model} 不支持 Embeddings 功能。")
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except Exception as e:
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logger.error(e)
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return BaseResponse(code=500, msg=f"文本向量化过程中出现错误：{e}")
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async def aembed_texts(
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texts: List[str],
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embed_model: str = EMBEDDING_MODEL,
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to_query: bool = False,
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) -> BaseResponse:
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'''
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对文本进行向量化。返回数据格式：BaseResponse(data=List[List[float]])
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'''
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try:
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if embed_model in list_embed_models(): # 使用本地Embeddings模型
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from server.utils import load_local_embeddings
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embeddings = load_local_embeddings(model=embed_model)
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return BaseResponse(data=await embeddings.aembed_documents(texts))
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if embed_model in list_online_embed_models(): # 使用在线API
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return await run_in_threadpool(embed_texts,
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texts=texts,
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embed_model=embed_model,
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to_query=to_query)
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except Exception as e:
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logger.error(e)
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return BaseResponse(code=500, msg=f"文本向量化过程中出现错误：{e}")
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def embed_texts_endpoint(
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texts: List[str] = Body(..., description="要嵌入的文本列表", examples=[["hello", "world"]]),
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embed_model: str = Body(EMBEDDING_MODEL,
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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。"),
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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。"),
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) -> BaseResponse:
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'''
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对文本进行向量化，返回 BaseResponse(data=List[List[float]])
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'''
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return embed_texts(texts=texts, embed_model=embed_model, to_query=to_query)
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def embed_documents(
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docs: List[Document],
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embed_model: str = EMBEDDING_MODEL,
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to_query: bool = False,
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) -> Dict:
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"""
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将 List[Document] 向量化，转化为 VectorStore.add_embeddings 可以接受的参数
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"""
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texts = [x.page_content for x in docs]
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metadatas = [x.metadata for x in docs]
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embeddings = embed_texts(texts=texts, embed_model=embed_model, to_query=to_query).data
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if embeddings is not None:
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return {
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"texts": texts,
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"embeddings": embeddings,
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"metadatas": metadatas,
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}
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