110 lines
4.5 KiB
Python
110 lines
4.5 KiB
Python
from langchain.document_loaders.github import GitHubIssuesLoader
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from fastapi import Body
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from fastapi.responses import StreamingResponse
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from configs.model_config import (llm_model_dict, LLM_MODEL, SEARCH_ENGINE_TOP_K, PROMPT_TEMPLATE)
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from server.chat.utils import wrap_done
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from server.utils import BaseResponse
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from langchain.chat_models import ChatOpenAI
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from langchain import LLMChain
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from typing import AsyncIterable
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import asyncio
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from langchain.prompts.chat import ChatPromptTemplate
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from typing import List, Optional, Literal
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from server.chat.utils import History
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from langchain.docstore.document import Document
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import json
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import os
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from functools import lru_cache
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from datetime import datetime
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GITHUB_PERSONAL_ACCESS_TOKEN = os.environ.get("GITHUB_PERSONAL_ACCESS_TOKEN")
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@lru_cache(1)
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def load_issues(tick: str):
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'''
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set tick to a periodic value to refresh cache
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'''
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loader = GitHubIssuesLoader(
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repo="chatchat-space/langchain-chatglm",
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access_token=GITHUB_PERSONAL_ACCESS_TOKEN,
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include_prs=True,
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state="all",
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)
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docs = loader.load()
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return docs
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def
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def github_chat(query: str = Body(..., description="用户输入", examples=["本项目最新进展"]),
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top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
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include_prs: bool = Body(True, description="是否包含PR"),
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state: Literal['open', 'closed', 'all'] = Body(None, description="Issue/PR状态"),
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creator: str = Body(None, description="创建者"),
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history: List[History] = Body([],
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description="历史对话",
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examples=[[
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{"role": "user",
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"content": "介绍一下本项目"},
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{"role": "assistant",
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"content": "LangChain-Chatchat (原 Langchain-ChatGLM): 基于 Langchain 与 ChatGLM 等大语言模型的本地知识库问答应用实现。"}]]
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),
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stream: bool = Body(False, description="流式输出"),
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):
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if GITHUB_PERSONAL_ACCESS_TOKEN is None:
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return BaseResponse(code=404, msg=f"使用本功能需要 GITHUB_PERSONAL_ACCESS_TOKEN")
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async def chat_iterator(query: str,
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search_engine_name: str,
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top_k: int,
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history: Optional[List[History]],
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) -> AsyncIterable[str]:
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callback = AsyncIteratorCallbackHandler()
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model = ChatOpenAI(
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streaming=True,
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verbose=True,
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callbacks=[callback],
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openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
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openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
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model_name=LLM_MODEL
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)
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docs = lookup_search_engine(query, search_engine_name, top_k)
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context = "\n".join([doc.page_content for doc in docs])
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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# Begin a task that runs in the background.
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task = asyncio.create_task(wrap_done(
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chain.acall({"context": context, "question": query}),
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callback.done),
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)
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source_documents = [
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f"""出处 [{inum + 1}] [{doc.metadata["source"]}]({doc.metadata["source"]}) \n\n{doc.page_content}\n\n"""
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for inum, doc in enumerate(docs)
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]
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if stream:
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async for token in callback.aiter():
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# Use server-sent-events to stream the response
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yield json.dumps({"answer": token,
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"docs": source_documents},
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ensure_ascii=False)
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else:
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answer = ""
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async for token in callback.aiter():
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answer += token
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yield json.dumps({"answer": token,
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"docs": source_documents},
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ensure_ascii=False)
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await task
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return StreamingResponse(search_engine_chat_iterator(query, search_engine_name, top_k, history),
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media_type="text/event-stream")
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