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gangyan/langchain-chat/server/api.py

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from server.chat.check_language import check_language, get_supported_languages
from server.chat.chat_comparison import chat_comparison_test
from server.chat.gen_title import gen_title
from server.chat.relevant_articles import relevant_articles
from server.chat.self_kb_chat import self_kb_chat
from server.chat.stop import stop
import nltk
import sys
import os
from server.chat.chat_test import chat_test, get_image
from server.chat.gen_abstract import gen_abstract
from server.chat.gen_conclusion import gen_conclusion
from server.chat.gen_keywords import gen_keywords
from server.chat.gen_paragraph import gen_paragraph
from server.chat.knowledge_chat_test import knowledge_chat_test
from server.chat.translate import tarnslate_text
from server.chat.upload import upload_file
from server.chat.utils import download_self_doc
from server.chat.word_explain import word_explain
from server.chat.write_article import write_article
from server.knowledge_base.kb_doc_api import search_self_docs, upload_docs_new
from server.translator_service.main_api import cancel_task, download_result, get_progress, translate_file
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.search_engine_chat import search_engine_chat
from server.chat.completion import completion
from server.custom.chapter_overview import chapter_overview
from server.custom.article_overview import article_overview
from server.custom.abstract_search import abstract_search
from server.custom.paper_translation import paper_translation
from server.chat.feedback import chat_feedback
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
from server.chat.rewrite import(
# con_rewrite,
# exp_write,
# abb_write,
formal_style,
party_style,
col_style,
chi_to_ens,
ens_to_chi
)
from server.chat.con_rewrite import con_rewrite
from server.chat.exp_rewrite import exp_rewrite
from server.chat.abb_rewrite import abb_rewrite
from server.chat.rew_rewrite import rew_rewrite
from server.chat.sentence_reference import sentence_reference
from contextlib import asynccontextmanager
from server.translator_service.task_manager import TaskManager
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
async def document():
return RedirectResponse(url="/docs")
@asynccontextmanager
async def lifespan(app: FastAPI):
tm = TaskManager(translate_fn=translate_file)
tm.start()
app.state.tm = tm
# 2. 手动执行所有注册的 startup 钩子(包括启动器注入的)
for fn in app.router.on_startup:
await fn()
yield
tm.shutdown()
for fn in app.router.on_shutdown:
await fn()
def create_app(run_mode: str = None):
app = FastAPI(
title="Langchain-Chatchat API Server",
version=VERSION,
lifespan=lifespan,
)
MakeFastAPIOffline(app)
# asyncio.create_task(lifespans)
# 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)
app.get("/chat/get_image",
tags=["Chat"],
summary="获取图片",
)(get_image)
app.get("/chat/get_self_file",
tags=["Chat"],
summary="获取个人知识库文件",
)(download_self_doc)
# Tag: Chat
app.post("/chat/chat_comparison",
tags=["Chat"],
summary="生成文献对比报告"
)(chat_comparison_test)
app.post("/chat/chat",
tags=["Chat"],
summary="与llm模型对话(通过LLMChain)",
)(chat_test)
app.post("/chat",
tags=["Chat"],
summary="与llm模型对话(通过LLMChain)",
)(chat)
app.post("/chat/translate_text",
tags=["Chat"],
summary="翻译",
)(tarnslate_text)
app.post("/translate/translate_file",
tags=["translate"],
summary="文件翻译",
)(translate_file)
app.get("/translate/download_file",
tags=["translate"],
summary="下载译文",
)(download_result)
app.get("/translate/progress",
tags=["translate"],
summary="获取翻译任务进度",
)(get_progress)
app.post("/translate/cancel",
tags=["translate"],
summary="取消翻译任务",
)(cancel_task)
app.post("/chat/check_language",
tags=["Chat"],
summary="语种检测接口",
)(check_language)
app.get("/chat/get_language",
tags=["Chat"],
summary="获取当前支持的语种",
)(get_supported_languages)
# Tag: Chat
app.post("/chat/stop",
tags=["Chat"],
summary="中断模型请求",
)(stop)
app.post("/chat/upload_Allfile",
tags=["Chat"],
summary="上传文件",
)(upload_file)
app.post("/chat/outlines",
tags=["Chat"],
summary="与llm模型对话生成大纲(通过LLMChain)",
)(knowledge_chat_test)
app.post("/chat/finsh_outlines",
tags=["Chat"],
summary="与llm模型对话生成全文(通过LLMChain)",
)(write_article)
app.post("/chat/chat_test",
tags=["Chat"],
summary="与llm模型对话(通过LLMChain)",
)(chat_test)
app.post("/chat/search_engine_chat",
tags=["Chat"],
summary="与搜索引擎对话",
)(search_engine_chat)
app.post("/chat/feedback",
tags=["Chat"],
summary="返回llm模型对话评分",
)(chat_feedback)
app.post("/chat/gen_title",
tags=["Chat"],
summary="生成当前对话的标题",
)(gen_title)
app.post("/rewrite/con_rewrite",
tags=["Write"],
summary="续写文本",
)(con_rewrite)
app.post("/rewrite/exp_write",
tags=["Write"],
summary="扩写文本",
)(exp_rewrite)
app.post("/rewrite/abb_write",
tags=["Write"],
summary="缩写文本",
)(abb_rewrite)
app.post("/rewrite/rew_rewrite",
tags=["Write"],
summary="重写文本",
)(rew_rewrite)
app.post("/sentence_reference",
tags=["Write"],
summary="好句子提示",
)(sentence_reference)
app.post("/gen_abstract",
tags=["Write"],
summary="摘要生成",
)(gen_abstract)
app.post("/gen_conclusion",
tags=["Write"],
summary="结论生成",
)(gen_conclusion)
app.post("/gen_keywords",
tags=["Read"],
summary="关键词生成",
)(gen_keywords)
app.post("/gen_paragraph",
tags=["Read"],
summary="章节速览",
)(gen_paragraph)
app.post("/word_explain",
tags=["Read"],
summary="名词解释",
)(word_explain)
app.post("/relevant_articles",
tags=["Read"],
summary="相关文献",
)(relevant_articles)
#新功能接口
# app.post("/rewrite/con_rewrite",
# tags=["IastStrategy"],
# summary="续写文本",
# )(con_rewrite)
#
# app.post("/rewrite/rewrite",
# tags=["IastStrategy"],
# summary="改写",
# )(rewrite)
# app.post("/rewrite/exp_write",
# tags=["IastStrategy"],
# summary="扩写",
# )(exp_write)
# app.post("/rewrite/abb_write",
# tags=["IastStrategy"],
# summary="缩写",
# )(abb_write)
# app.post("/rewrite/embellish",
# tags=["IastStrategy"],
# summary="润色",
# )(embellish)
app.post("/rewrite/formal_style",
tags=["IastStrategy"],
summary="正式风格",
)(formal_style)
app.post("/rewrite/party_style",
tags=["IastStrategy"],
summary="党政风格",
)(party_style)
app.post("/rewrite/col_style",
tags=["IastStrategy"],
summary="口语风格",
)(col_style)
app.post("/rewrite/chi_to_ens",
tags=["IastStrategy"],
summary="中译英",
)(chi_to_ens)
app.post("/rewrite/ens_to_chi",
tags=["IastStrategy"],
summary="英译中",
)(ens_to_chi)
# 知识库相关接口
mount_knowledge_routes(app)
# 摘要相关接口
mount_filename_summary_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",
"report_chat",
"search_engine_chat",
"agent_chat"
] = Body("llm_chat",
description="模板类型可选值llm_chatknowledge_base_chatsearch_engine_chatagent_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)
app.post("/knowledge_base/chapter_overview",
tags=["Other"],
summary="文件速览"
)(chapter_overview)
app.post("/knowledge_base/abstract_search",
tags=["Other"],
summary="相似摘要搜索"
)(abstract_search)
app.post("/knowledge_base/article_overview",
tags=["Other"],
summary="文件综述"
)(article_overview)
app.post("/knowledge_base/paper_translation",
tags=["Other"],
summary="论文翻译"
)(paper_translation)
def mount_knowledge_routes(app: FastAPI):
from server.chat.knowledge_base_chat import knowledge_base_chat
from server.chat.knowledge_base_chat_old import knowledge_base_chat_old
from server.chat.report_chat import report_chat
from server.chat.file_chat import upload_temp_docs, file_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, DocumentWithVSId, update_info,
update_docs_by_id, )
app.post("/chat/knowledge_base_chat",
tags=["Chat"],
summary="与知识库对话")(knowledge_base_chat)
app.post("/chat/self_kb_chat",
tags=["Chat"],
summary="与个人知识库对话")(self_kb_chat)
app.post("/chat/knowledge_base_chat_old",
tags=["Chat"],
summary="旧版与知识库对话")(knowledge_base_chat_old)
app.post("/chat/report_chat",
tags=["Chat"],
summary="与报告知识库对话")(report_chat)
app.post("/chat/file_chat",
tags=["Knowledge Base Management"],
summary="文件对话"
)(file_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[DocumentWithVSId],
summary="搜索知识库"
)(search_docs)
app.post("/knowledge_base/search_self_docs",
tags=["Knowledge Base Management"],
response_model=List[DocumentWithVSId],
summary="搜索个人知识库"
)(search_self_docs)
app.post("/knowledge_base/update_docs_by_id",
tags=["Knowledge Base Management"],
response_model=BaseResponse,
summary="直接更新知识库文档"
)(update_docs_by_id)
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/upload_docs_new",
tags=["Knowledge Base Management"],
response_model=BaseResponse,
summary="上传文件到知识库,并/或进行向量化,并获取解析结果"
)(upload_docs_new)
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)
app.post("/knowledge_base/upload_temp_docs",
tags=["Knowledge Base Management"],
summary="上传文件到临时目录,用于文件对话。"
)(upload_temp_docs)
def mount_filename_summary_routes(app: FastAPI):
from server.knowledge_base.kb_summary_api import (summary_file_to_vector_store, recreate_summary_vector_store,
summary_doc_ids_to_vector_store)
app.post("/knowledge_base/kb_summary_api/summary_file_to_vector_store",
tags=["Knowledge kb_summary_api Management"],
summary="单个知识库根据文件名称摘要"
)(summary_file_to_vector_store)
app.post("/knowledge_base/kb_summary_api/summary_doc_ids_to_vector_store",
tags=["Knowledge kb_summary_api Management"],
summary="单个知识库根据doc_ids摘要",
response_model=BaseResponse,
)(summary_doc_ids_to_vector_store)
app.post("/knowledge_base/kb_summary_api/recreate_summary_vector_store",
tags=["Knowledge kb_summary_api Management"],
summary="重建单个知识库文件摘要"
)(recreate_summary_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()
run_api(host=args.host,
port=args.port,
ssl_keyfile=args.ssl_keyfile,
ssl_certfile=args.ssl_certfile,
)