codefuse-chatbot/dev_opsgpt/service/api.py

218 lines
7.0 KiB
Python
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

import nltk
import argparse
import uvicorn, os, sys
from fastapi.middleware.cors import CORSMiddleware
from starlette.responses import RedirectResponse
from typing import List
src_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
)
sys.path.append(src_dir)
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
from dev_opsgpt.chat import LLMChat, SearchChat, KnowledgeChat
from dev_opsgpt.service.kb_api import *
from dev_opsgpt.service.cb_api import *
from dev_opsgpt.utils.server_utils import BaseResponse, ListResponse, FastAPI, MakeFastAPIOffline
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
from dev_opsgpt.chat import LLMChat, SearchChat, KnowledgeChat, ToolChat, DataChat, CodeChat
llmChat = LLMChat()
searchChat = SearchChat()
knowledgeChat = KnowledgeChat()
toolChat = ToolChat()
dataChat = DataChat()
codeChat = CodeChat()
async def document():
return RedirectResponse(url="/docs")
def create_app():
app = FastAPI(
title="DevOps-ChatBot 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=["*"],
)
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)")(llmChat.chat)
app.post("/chat/knowledge_base_chat",
tags=["Chat"],
summary="与知识库对话")(knowledgeChat.chat)
app.post("/chat/search_engine_chat",
tags=["Chat"],
summary="与搜索引擎对话")(searchChat.chat)
app.post("/chat/tool_chat",
tags=["Chat"],
summary="与搜索引擎对话")(toolChat.chat)
app.post("/chat/data_chat",
tags=["Chat"],
summary="与搜索引擎对话")(dataChat.chat)
app.post("/chat/code_chat",
tags=["Chat"],
summary="与代码库对话")(codeChat.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_docs)
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_doc)
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_doc)
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_doc)
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)
app.post("/code_base/create_code_base",
tags=["Code Base Management"],
summary="新建 code_base"
)(create_cb)
app.post("/code_base/delete_code_base",
tags=["Code Base Management"],
summary="删除 code_base"
)(delete_cb)
app.post("/code_base/code_base_chat",
tags=["Code Base Management"],
summary="删除 code_base"
)(delete_cb)
app.get("/code_base/list_code_bases",
tags=["Code Base Management"],
summary="列举 code_base",
response_model=ListResponse
)(list_cbs)
# # LLM模型相关接口
# app.post("/llm_model/list_models",
# tags=["LLM Model Management"],
# summary="列出当前已加载的模型",
# )(list_llm_models)
# 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)
return app
app = create_app()
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='DevOps-ChatBot',
description='About DevOps-ChatBot, local knowledge based LLM with langchain'
' 基于本地知识库的 LLM 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)
run_api(host=args.host,
port=args.port,
ssl_keyfile=args.ssl_keyfile,
ssl_certfile=args.ssl_certfile,
)