fix #1142: 在History中使用jinja2模板代替f-string，避免消息中含有{ }时出错
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liunux4odoo
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054ae4715d
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@ -142,12 +142,12 @@ SEARCH_ENGINE_TOP_K = 5
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# nltk 模型存储路径
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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# 基于本地知识问答的提示词模版
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# 基于本地知识问答的提示词模版（使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
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PROMPT_TEMPLATE = """【指令】根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，不允许在答案中添加编造成分，答案请使用中文。
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【已知信息】{context}
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【已知信息】{{ context }}
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【问题】{question}"""
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【问题】{{ question }}"""
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# API 是否开启跨域，默认为False，如果需要开启，请设置为True
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# is open cross domain
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@ -21,7 +21,7 @@ def chat(query: str = Body(..., description="用户输入", examples=["恼羞成
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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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history = [History(**h) if isinstance(h, dict) else h for h in history]
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history = [History.from_data(h) for h in history]
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async def chat_iterator(query: str,
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history: List[History] = [],
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@ -37,8 +37,9 @@ def chat(query: str = Body(..., description="用户输入", examples=["恼羞成
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model_name=LLM_MODEL
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)
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input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_tuple() for i in history] + [("human", "{input}")])
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[i.to_msg_template() for i in history] + [input_msg])
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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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@ -1,109 +0,0 @@
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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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@ -38,7 +38,7 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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history = [History(**h) if isinstance(h, dict) else h for h in history]
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history = [History.from_data(h) for h in history]
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async def knowledge_base_chat_iterator(query: str,
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kb: KBService,
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@ -57,8 +57,9 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
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docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
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context = "\n".join([doc.page_content for doc in docs])
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input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)
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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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[i.to_msg_template() for i in history] + [input_msg])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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@ -73,6 +73,8 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
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if search_engine_name not in SEARCH_ENGINES.keys():
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return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}")
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history = [History.from_data(h) for h in history]
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async def search_engine_chat_iterator(query: str,
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search_engine_name: str,
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top_k: int,
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@ -91,8 +93,9 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
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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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input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)
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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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[i.to_msg_template() for i in history] + [input_msg])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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@ -1,6 +1,7 @@
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import asyncio
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from typing import Awaitable
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from typing import Awaitable, List, Tuple, Dict, Union
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from pydantic import BaseModel, Field
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from langchain.prompts.chat import ChatMessagePromptTemplate
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async def wrap_done(fn: Awaitable, event: asyncio.Event):
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@ -28,3 +29,29 @@ class History(BaseModel):
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def to_msg_tuple(self):
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return "ai" if self.role=="assistant" else "human", self.content
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def to_msg_template(self, is_raw=True) -> ChatMessagePromptTemplate:
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role_maps = {
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"ai": "assistant",
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"human": "user",
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}
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role = role_maps.get(self.role, self.role)
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if is_raw: # 当前默认历史消息都是没有input_variable的文本。
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content = "{% raw %}" + self.content + "{% endraw %}"
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else:
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content = self.content
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return ChatMessagePromptTemplate.from_template(
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content,
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"jinja2",
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role=role,
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)
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@classmethod
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def from_data(cls, h: Union[List, Tuple, Dict]) -> "History":
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if isinstance(h, (list,tuple)) and len(h) >= 2:
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h = cls(role=h[0], content=h[1])
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elif isinstance(h, dict):
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h = cls(**h)
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return h
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