194 lines
9.4 KiB
Python
194 lines
9.4 KiB
Python
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import asyncio
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import json
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from typing import AsyncIterable, List, Optional
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from urllib.parse import urlencode
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from fastapi import Body, Request
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from fastapi.concurrency import run_in_threadpool
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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from langchain.prompts.chat import ChatPromptTemplate
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from sse_starlette.sse import EventSourceResponse
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from configs import (TEMPERATURE,
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USE_RERANKER,
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RERANKER_MODEL,
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RERANKER_MAX_LENGTH,
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LLM_MODELS,
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MODEL_PATH,
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MAX_TOKENS)
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from server.chat.utils import History
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from server.knowledge_base.kb_service.base import KBServiceFactory
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from server.reranker.reranker import LangchainReranker
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from server.utils import BaseResponse, get_prompt_template
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from server.utils import embedding_device
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from server.utils import wrap_done, get_ChatOpenAI
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async def article_overview(query: str = Body("你好", description="用户输入", examples=["你好"]),
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knowledge_base_name: str = Body(..., description="知识库名称", examples=["t_policy_total_bce_v1"]),
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stream: bool = Body(False, description="流式输出"),
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model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
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temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
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max_tokens: Optional[int] = Body(
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MAX_TOKENS,
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description="限制LLM生成Token数量，默认None代表模型最大值"
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),
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prompt_name: str = Body(
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"Article Overview",
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description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
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),
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source_name_list: List[str] = Body([], description="资源列表"),
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request: Request = None,
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):
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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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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query = "帮我对以下文件进行总结 ：" + ",".join(source_name_list)
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if len(source_name_list) > 1:
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prompt_name = "Article Overview2"
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else:
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prompt_name = "Article Overview"
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async def article_overview_iterator(
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query: str,
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model_name: str = model_name,
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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nonlocal max_tokens
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callback = AsyncIteratorCallbackHandler()
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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model = get_ChatOpenAI(
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model_name=model_name,
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temperature=temperature,
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max_tokens=max_tokens,
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callbacks=[callback],
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)
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docs = []
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docs = await run_in_threadpool(kb.get_doc_by_sources_name,
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source_name_list=source_name_list)
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# 加入reranker
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if USE_RERANKER:
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reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL, "BAAI/bge-reranker-large")
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print("-----------------model path------------------")
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print(reranker_model_path)
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reranker_model = LangchainReranker(top_n=3,
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device=embedding_device(),
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max_length=RERANKER_MAX_LENGTH,
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model_name_or_path=reranker_model_path
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)
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print("---------before rerank------------------")
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print(docs)
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docs = reranker_model.compress_documents(documents=docs,
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query=query)
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print("---------after rerank------------------")
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print(docs)
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# context = "\n".join([doc.page_content for doc in docs])
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# 相关信息把标题和内容进行整合
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if knowledge_base_name == 't_policy_total_bce_v1':
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knowledge = []
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for doc in docs:
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if doc.metadata["_type"] == "title":
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knowledge.append(doc.page_content + "\n" + doc.metadata['content'])
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if doc.metadata["_type"] == "content":
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knowledge.append(doc.metadata['title'] + "\n" + doc.page_content)
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context = "\n\n".join(knowledge)
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# 非政策知识库
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else:
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context = "\n".join([doc.page_content for doc in docs])
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print(f"context:{context}\n")
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if len(docs) == 0: # 如果没有找到相关文档，使用empty模板
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prompt_template = get_prompt_template("knowledge_base_chat", "empty")
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else:
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prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
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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([input_msg])
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print(f"chat_prompt:{chat_prompt}\n")
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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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# 政策知识库
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if knowledge_base_name == 't_policy_total_bce_v1':
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for inum, doc in enumerate(docs):
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# 获取标题以及详情地址（url）
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filename = doc.metadata.get("title")
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detail_url = doc.metadata.get("source")
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# parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
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# base_url = request.base_url
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# url = f"{base_url}knowledge_base/download_doc?" + parameters
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# text = f"""出处 [{inum + 1}] [{filename}]({url}) \n\n{doc.page_content}\n\n"""
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if filename:
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text = f"""出处: [{filename}]({detail_url}) \n\n"""
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else:
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text = f"""出处: [{"原文地址"}]({detail_url}) \n\n"""
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source_documents.append(text)
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# 非政策知识库
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else:
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for inum, doc in enumerate(docs):
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filename = doc.metadata.get("source")
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parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
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base_url = request.base_url
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url = f"{base_url}knowledge_base/download_doc?" + parameters
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if filename:
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text = f"""出处: [{filename}]({url}) \n\n"""
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else:
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text = f"""出处: [{"原文地址"}]({url}) \n\n"""
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source_documents.append(text)
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if len(source_documents) == 0: # 没有找到相关文档
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source_documents.append(f"<span style='color:red'>未找到相关文档,该回答为大模型自身能力解答！</span>")
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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}, ensure_ascii=False)
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yield json.dumps({"docs": source_documents}, 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": answer,
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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 EventSourceResponse(article_overview_iterator(query, model_name=model_name, prompt_name=prompt_name))
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class ArticleOverview:
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query = "请给我对文件进行一下总结"
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def __init__(self):
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self._PROMPT_TEMPLATE = """
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'<角色> 你是由浪潮开发的知冶大模型中所选定的文件综述助手。</角色> \n\n'
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'Your task is to write a detailed summary of the provided {{context}} file. Ensure that your summary is '
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'longer than 300 words and captures the essence of the content. Focus on the main points, '
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'key findings, and any important implications or conclusions. Maintain an unbiased tone and avoid relying '
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'on stereotypes. Organize the summary in a clear and coherent manner, using appropriate headings or '
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'bullet points if necessary. Remember to keep the summary concise while preserving the core information. '
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'Let\'s start with a brief overview of the file\'s main topic and then delve into the specifics.'
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'PLEASE ALWAYS RESPOND IN CHINESE!\n'
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'<已知信息>{{ context }}</已知信息>\n'
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'<问题>{{ question }}</问题>\n',
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"""
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self.PROMPT = PromptTemplate(
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input_variables=["question", "database_names"],
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template=self._PROMPT_TEMPLATE,
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)
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def query_out(self, knowledge_base_name: str, source_name_list: list):
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self.query = "帮我对以下文件进行总结 ：" + ",".join(source_name_list)
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return article_overview(self.query,
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knowledge_base_name=knowledge_base_name,
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source_name_list=source_name_list
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)
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