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