mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 21:37:20 +08:00
65592a45c3
新功能：
- 支持在线 Embeddings：zhipu-api, qwen-api, minimax-api, qianfan-api
- API 增加 /other/embed_texts 接口
- init_database.py 增加 --embed-model 参数，可以指定使用的嵌入模型（本地或在线均可）
- 对于 FAISS 知识库，支持多向量库，默认位置：{KB_PATH}/vector_store/{embed_model}
- Lite 模式支持所有知识库相关功能。此模式下最主要的限制是：
- 不能使用本地 LLM 和 Embeddings 模型
- 知识库不支持 PDF 文件
- init_database.py 重建知识库时不再默认情况数据库表，增加 clear-tables 参数手动控制。
- API 和 WEBUI 中 score_threshold 参数范围改为 [0, 2]，以更好的适应在线嵌入模型
问题修复：
- API 中 list_config_models 会删除 ONLINE_LLM_MODEL 中的敏感信息，导致第二轮API请求错误
开发者：
- 统一向量库的识别：以（kb_name,embed_model）为判断向量库唯一性的依据，避免 FAISS 知识库缓存加载逻辑错误
- KBServiceFactory.get_service_by_name 中添加 default_embed_model 参数，用于在构建新知识库时设置 embed_model
- 优化 kb_service 中 Embeddings 操作：
- 统一加载接口： server.utils.load_embeddings，利用全局缓存避免各处 Embeddings 传参
- 统一文本嵌入接口：server.knowledge_base.kb_service.base.[embed_texts, embed_documents]
- 重写 normalize 函数，去除对 scikit-learn/scipy 的依赖
248 lines
8.7 KiB
Python
248 lines
8.7 KiB
Python
import nltk
|
||
import sys
|
||
import os
|
||
|
||
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
|
||
|
||
from configs import VERSION
|
||
from configs.model_config import NLTK_DATA_PATH
|
||
from configs.server_config import OPEN_CROSS_DOMAIN
|
||
import argparse
|
||
import uvicorn
|
||
from fastapi import Body
|
||
from fastapi.middleware.cors import CORSMiddleware
|
||
from starlette.responses import RedirectResponse
|
||
from server.chat.chat import chat
|
||
from server.chat.openai_chat import openai_chat
|
||
from server.chat.search_engine_chat import search_engine_chat
|
||
from server.chat.completion import completion
|
||
from server.embeddings_api import embed_texts_endpoint
|
||
from server.llm_api import (list_running_models, list_config_models,
|
||
change_llm_model, stop_llm_model,
|
||
get_model_config, list_search_engines)
|
||
from server.utils import (BaseResponse, ListResponse, FastAPI, MakeFastAPIOffline,
|
||
get_server_configs, get_prompt_template)
|
||
from typing import List, Literal
|
||
|
||
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
|
||
|
||
|
||
async def document():
|
||
return RedirectResponse(url="/docs")
|
||
|
||
|
||
def create_app(run_mode: str = None):
|
||
app = FastAPI(
|
||
title="Langchain-Chatchat API Server",
|
||
version=VERSION
|
||
)
|
||
MakeFastAPIOffline(app)
|
||
# Add CORS middleware to allow all origins
|
||
# 在config.py中设置OPEN_DOMAIN=True，允许跨域
|
||
# set OPEN_DOMAIN=True in config.py to allow cross-domain
|
||
if OPEN_CROSS_DOMAIN:
|
||
app.add_middleware(
|
||
CORSMiddleware,
|
||
allow_origins=["*"],
|
||
allow_credentials=True,
|
||
allow_methods=["*"],
|
||
allow_headers=["*"],
|
||
)
|
||
mount_app_routes(app, run_mode=run_mode)
|
||
return app
|
||
|
||
|
||
def mount_app_routes(app: FastAPI, run_mode: str = None):
|
||
app.get("/",
|
||
response_model=BaseResponse,
|
||
summary="swagger 文档")(document)
|
||
|
||
# Tag: Chat
|
||
app.post("/chat/fastchat",
|
||
tags=["Chat"],
|
||
summary="与llm模型对话(直接与fastchat api对话)",
|
||
)(openai_chat)
|
||
|
||
app.post("/chat/chat",
|
||
tags=["Chat"],
|
||
summary="与llm模型对话(通过LLMChain)",
|
||
)(chat)
|
||
|
||
app.post("/chat/search_engine_chat",
|
||
tags=["Chat"],
|
||
summary="与搜索引擎对话",
|
||
)(search_engine_chat)
|
||
|
||
# 知识库相关接口
|
||
mount_knowledge_routes(app)
|
||
|
||
# LLM模型相关接口
|
||
app.post("/llm_model/list_running_models",
|
||
tags=["LLM Model Management"],
|
||
summary="列出当前已加载的模型",
|
||
)(list_running_models)
|
||
|
||
app.post("/llm_model/list_config_models",
|
||
tags=["LLM Model Management"],
|
||
summary="列出configs已配置的模型",
|
||
)(list_config_models)
|
||
|
||
app.post("/llm_model/get_model_config",
|
||
tags=["LLM Model Management"],
|
||
summary="获取模型配置（合并后）",
|
||
)(get_model_config)
|
||
|
||
app.post("/llm_model/stop",
|
||
tags=["LLM Model Management"],
|
||
summary="停止指定的LLM模型（Model Worker)",
|
||
)(stop_llm_model)
|
||
|
||
app.post("/llm_model/change",
|
||
tags=["LLM Model Management"],
|
||
summary="切换指定的LLM模型（Model Worker)",
|
||
)(change_llm_model)
|
||
|
||
# 服务器相关接口
|
||
app.post("/server/configs",
|
||
tags=["Server State"],
|
||
summary="获取服务器原始配置信息",
|
||
)(get_server_configs)
|
||
|
||
app.post("/server/list_search_engines",
|
||
tags=["Server State"],
|
||
summary="获取服务器支持的搜索引擎",
|
||
)(list_search_engines)
|
||
|
||
@app.post("/server/get_prompt_template",
|
||
tags=["Server State"],
|
||
summary="获取服务区配置的 prompt 模板")
|
||
def get_server_prompt_template(
|
||
type: Literal["llm_chat", "knowledge_base_chat", "search_engine_chat", "agent_chat"]=Body("llm_chat", description="模板类型，可选值：llm_chat，knowledge_base_chat，search_engine_chat，agent_chat"),
|
||
name: str = Body("default", description="模板名称"),
|
||
) -> str:
|
||
return get_prompt_template(type=type, name=name)
|
||
|
||
# 其它接口
|
||
app.post("/other/completion",
|
||
tags=["Other"],
|
||
summary="要求llm模型补全(通过LLMChain)",
|
||
)(completion)
|
||
|
||
app.post("/other/embed_texts",
|
||
tags=["Other"],
|
||
summary="将文本向量化，支持本地模型和在线模型",
|
||
)(embed_texts_endpoint)
|
||
|
||
|
||
def mount_knowledge_routes(app: FastAPI):
|
||
from server.chat.knowledge_base_chat import knowledge_base_chat
|
||
from server.chat.agent_chat import agent_chat
|
||
from server.knowledge_base.kb_api import list_kbs, create_kb, delete_kb
|
||
from server.knowledge_base.kb_doc_api import (list_files, upload_docs, delete_docs,
|
||
update_docs, download_doc, recreate_vector_store,
|
||
search_docs, DocumentWithScore, update_info)
|
||
|
||
app.post("/chat/knowledge_base_chat",
|
||
tags=["Chat"],
|
||
summary="与知识库对话")(knowledge_base_chat)
|
||
|
||
app.post("/chat/agent_chat",
|
||
tags=["Chat"],
|
||
summary="与agent对话")(agent_chat)
|
||
|
||
# Tag: Knowledge Base Management
|
||
app.get("/knowledge_base/list_knowledge_bases",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=ListResponse,
|
||
summary="获取知识库列表")(list_kbs)
|
||
|
||
app.post("/knowledge_base/create_knowledge_base",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=BaseResponse,
|
||
summary="创建知识库"
|
||
)(create_kb)
|
||
|
||
app.post("/knowledge_base/delete_knowledge_base",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=BaseResponse,
|
||
summary="删除知识库"
|
||
)(delete_kb)
|
||
|
||
app.get("/knowledge_base/list_files",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=ListResponse,
|
||
summary="获取知识库内的文件列表"
|
||
)(list_files)
|
||
|
||
app.post("/knowledge_base/search_docs",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=List[DocumentWithScore],
|
||
summary="搜索知识库"
|
||
)(search_docs)
|
||
|
||
app.post("/knowledge_base/upload_docs",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=BaseResponse,
|
||
summary="上传文件到知识库，并/或进行向量化"
|
||
)(upload_docs)
|
||
|
||
app.post("/knowledge_base/delete_docs",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=BaseResponse,
|
||
summary="删除知识库内指定文件"
|
||
)(delete_docs)
|
||
|
||
app.post("/knowledge_base/update_info",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=BaseResponse,
|
||
summary="更新知识库介绍"
|
||
)(update_info)
|
||
app.post("/knowledge_base/update_docs",
|
||
tags=["Knowledge Base Management"],
|
||
response_model=BaseResponse,
|
||
summary="更新现有文件到知识库"
|
||
)(update_docs)
|
||
|
||
app.get("/knowledge_base/download_doc",
|
||
tags=["Knowledge Base Management"],
|
||
summary="下载对应的知识文件")(download_doc)
|
||
|
||
app.post("/knowledge_base/recreate_vector_store",
|
||
tags=["Knowledge Base Management"],
|
||
summary="根据content中文档重建向量库，流式输出处理进度。"
|
||
)(recreate_vector_store)
|
||
|
||
|
||
def run_api(host, port, **kwargs):
|
||
if kwargs.get("ssl_keyfile") and kwargs.get("ssl_certfile"):
|
||
uvicorn.run(app,
|
||
host=host,
|
||
port=port,
|
||
ssl_keyfile=kwargs.get("ssl_keyfile"),
|
||
ssl_certfile=kwargs.get("ssl_certfile"),
|
||
)
|
||
else:
|
||
uvicorn.run(app, host=host, port=port)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = argparse.ArgumentParser(prog='langchain-ChatGLM',
|
||
description='About langchain-ChatGLM, local knowledge based ChatGLM with langchain'
|
||
' ｜ 基于本地知识库的 ChatGLM 问答')
|
||
parser.add_argument("--host", type=str, default="0.0.0.0")
|
||
parser.add_argument("--port", type=int, default=7861)
|
||
parser.add_argument("--ssl_keyfile", type=str)
|
||
parser.add_argument("--ssl_certfile", type=str)
|
||
# 初始化消息
|
||
args = parser.parse_args()
|
||
args_dict = vars(args)
|
||
|
||
app = create_app()
|
||
mount_knowledge_routes(app)
|
||
|
||
run_api(host=args.host,
|
||
port=args.port,
|
||
ssl_keyfile=args.ssl_keyfile,
|
||
ssl_certfile=args.ssl_certfile,
|
||
)
|