286 lines
10 KiB
Python
286 lines
10 KiB
Python
import nltk
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(__file__)))
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from configs import VERSION
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from configs.model_config import NLTK_DATA_PATH
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from configs.server_config import OPEN_CROSS_DOMAIN
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import argparse
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import uvicorn
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from fastapi import Body
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from fastapi.middleware.cors import CORSMiddleware
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from starlette.responses import RedirectResponse
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from server.chat.chat import chat
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from server.chat.openai_chat import openai_chat
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from server.chat.search_engine_chat import search_engine_chat
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from server.chat.completion import completion
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from server.chat.feedback import chat_feedback
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from server.embeddings_api import embed_texts_endpoint
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from server.llm_api import (list_running_models, list_config_models,
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change_llm_model, stop_llm_model,
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get_model_config, list_search_engines)
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from server.utils import (BaseResponse, ListResponse, FastAPI, MakeFastAPIOffline,
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get_server_configs, get_prompt_template)
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from typing import List, Literal
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nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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async def document():
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return RedirectResponse(url="/docs")
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def create_app(run_mode: str = None):
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app = FastAPI(
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title="Langchain-Chatchat API Server",
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version=VERSION
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)
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MakeFastAPIOffline(app)
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# Add CORS middleware to allow all origins
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# 在config.py中设置OPEN_DOMAIN=True，允许跨域
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# set OPEN_DOMAIN=True in config.py to allow cross-domain
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if OPEN_CROSS_DOMAIN:
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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mount_app_routes(app, run_mode=run_mode)
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return app
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def mount_app_routes(app: FastAPI, run_mode: str = None):
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app.get("/",
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response_model=BaseResponse,
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summary="swagger 文档")(document)
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# Tag: Chat
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app.post("/chat/fastchat",
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tags=["Chat"],
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summary="与llm模型对话(直接与fastchat api对话)",
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)(openai_chat)
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app.post("/chat/chat",
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tags=["Chat"],
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summary="与llm模型对话(通过LLMChain)",
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)(chat)
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app.post("/chat/search_engine_chat",
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tags=["Chat"],
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summary="与搜索引擎对话",
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)(search_engine_chat)
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app.post("/chat/feedback",
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tags=["Chat"],
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summary="返回llm模型对话评分",
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)(chat_feedback)
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# 知识库相关接口
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mount_knowledge_routes(app)
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# 摘要相关接口
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mount_filename_summary_routes(app)
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# LLM模型相关接口
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app.post("/llm_model/list_running_models",
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tags=["LLM Model Management"],
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summary="列出当前已加载的模型",
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)(list_running_models)
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app.post("/llm_model/list_config_models",
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tags=["LLM Model Management"],
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summary="列出configs已配置的模型",
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)(list_config_models)
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app.post("/llm_model/get_model_config",
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tags=["LLM Model Management"],
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summary="获取模型配置（合并后）",
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)(get_model_config)
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app.post("/llm_model/stop",
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tags=["LLM Model Management"],
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summary="停止指定的LLM模型（Model Worker)",
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)(stop_llm_model)
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app.post("/llm_model/change",
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tags=["LLM Model Management"],
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summary="切换指定的LLM模型（Model Worker)",
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)(change_llm_model)
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# 服务器相关接口
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app.post("/server/configs",
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tags=["Server State"],
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summary="获取服务器原始配置信息",
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)(get_server_configs)
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app.post("/server/list_search_engines",
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tags=["Server State"],
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summary="获取服务器支持的搜索引擎",
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)(list_search_engines)
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@app.post("/server/get_prompt_template",
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tags=["Server State"],
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summary="获取服务区配置的 prompt 模板")
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def get_server_prompt_template(
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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"),
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name: str = Body("default", description="模板名称"),
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) -> str:
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return get_prompt_template(type=type, name=name)
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# 其它接口
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app.post("/other/completion",
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tags=["Other"],
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summary="要求llm模型补全(通过LLMChain)",
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)(completion)
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app.post("/other/embed_texts",
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tags=["Other"],
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summary="将文本向量化，支持本地模型和在线模型",
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)(embed_texts_endpoint)
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def mount_knowledge_routes(app: FastAPI):
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from server.chat.knowledge_base_chat import knowledge_base_chat
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from server.chat.file_chat import upload_temp_docs, file_chat
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from server.chat.agent_chat import agent_chat
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from server.knowledge_base.kb_api import list_kbs, create_kb, delete_kb
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from server.knowledge_base.kb_doc_api import (list_files, upload_docs, delete_docs,
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update_docs, download_doc, recreate_vector_store,
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search_docs, DocumentWithScore, update_info)
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app.post("/chat/knowledge_base_chat",
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tags=["Chat"],
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summary="与知识库对话")(knowledge_base_chat)
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app.post("/chat/file_chat",
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tags=["Knowledge Base Management"],
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summary="文件对话"
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)(file_chat)
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app.post("/chat/agent_chat",
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tags=["Chat"],
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summary="与agent对话")(agent_chat)
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# Tag: Knowledge Base Management
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app.get("/knowledge_base/list_knowledge_bases",
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tags=["Knowledge Base Management"],
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response_model=ListResponse,
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summary="获取知识库列表")(list_kbs)
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app.post("/knowledge_base/create_knowledge_base",
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tags=["Knowledge Base Management"],
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response_model=BaseResponse,
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summary="创建知识库"
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)(create_kb)
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app.post("/knowledge_base/delete_knowledge_base",
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tags=["Knowledge Base Management"],
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response_model=BaseResponse,
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summary="删除知识库"
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)(delete_kb)
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app.get("/knowledge_base/list_files",
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tags=["Knowledge Base Management"],
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response_model=ListResponse,
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summary="获取知识库内的文件列表"
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)(list_files)
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app.post("/knowledge_base/search_docs",
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tags=["Knowledge Base Management"],
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response_model=List[DocumentWithScore],
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summary="搜索知识库"
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)(search_docs)
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app.post("/knowledge_base/upload_docs",
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tags=["Knowledge Base Management"],
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response_model=BaseResponse,
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summary="上传文件到知识库，并/或进行向量化"
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)(upload_docs)
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app.post("/knowledge_base/delete_docs",
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tags=["Knowledge Base Management"],
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response_model=BaseResponse,
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summary="删除知识库内指定文件"
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)(delete_docs)
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app.post("/knowledge_base/update_info",
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tags=["Knowledge Base Management"],
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response_model=BaseResponse,
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summary="更新知识库介绍"
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)(update_info)
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app.post("/knowledge_base/update_docs",
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tags=["Knowledge Base Management"],
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response_model=BaseResponse,
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summary="更新现有文件到知识库"
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)(update_docs)
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app.get("/knowledge_base/download_doc",
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tags=["Knowledge Base Management"],
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summary="下载对应的知识文件")(download_doc)
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app.post("/knowledge_base/recreate_vector_store",
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tags=["Knowledge Base Management"],
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summary="根据content中文档重建向量库，流式输出处理进度。"
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)(recreate_vector_store)
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app.post("/knowledge_base/upload_temp_docs",
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tags=["Knowledge Base Management"],
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summary="上传文件到临时目录，用于文件对话。"
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)(upload_temp_docs)
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def mount_filename_summary_routes(app: FastAPI):
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from server.knowledge_base.kb_summary_api import (summary_file_to_vector_store, recreate_summary_vector_store,
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summary_doc_ids_to_vector_store)
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app.post("/knowledge_base/kb_summary_api/summary_file_to_vector_store",
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tags=["Knowledge kb_summary_api Management"],
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summary="单个知识库根据文件名称摘要"
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)(summary_file_to_vector_store)
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app.post("/knowledge_base/kb_summary_api/summary_doc_ids_to_vector_store",
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tags=["Knowledge kb_summary_api Management"],
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summary="单个知识库根据doc_ids摘要",
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response_model=BaseResponse,
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)(summary_doc_ids_to_vector_store)
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app.post("/knowledge_base/kb_summary_api/recreate_summary_vector_store",
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tags=["Knowledge kb_summary_api Management"],
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summary="重建单个知识库文件摘要"
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)(recreate_summary_vector_store)
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def run_api(host, port, **kwargs):
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if kwargs.get("ssl_keyfile") and kwargs.get("ssl_certfile"):
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uvicorn.run(app,
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host=host,
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port=port,
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ssl_keyfile=kwargs.get("ssl_keyfile"),
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ssl_certfile=kwargs.get("ssl_certfile"),
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)
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else:
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uvicorn.run(app, host=host, port=port)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(prog='langchain-ChatGLM',
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description='About langchain-ChatGLM, local knowledge based ChatGLM with langchain'
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' ｜ 基于本地知识库的 ChatGLM 问答')
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parser.add_argument("--host", type=str, default="0.0.0.0")
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parser.add_argument("--port", type=int, default=7861)
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parser.add_argument("--ssl_keyfile", type=str)
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parser.add_argument("--ssl_certfile", type=str)
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# 初始化消息
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args = parser.parse_args()
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args_dict = vars(args)
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app = create_app()
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run_api(host=args.host,
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port=args.port,
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ssl_keyfile=args.ssl_keyfile,
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ssl_certfile=args.ssl_certfile,
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)
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