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#!/usr/bin/env python
# -*- coding:utf-8 _*-
"""
@author:quincy qiang
@license: Apache Licence
@file: model.py
@time: 2023/04/17
@contact: yanqiangmiffy@gamil.com
@software: PyCharm
@description: coding..
"""
from langchain.chains import RetrievalQA
from langchain.prompts.prompt import PromptTemplate
from clc.gpt_service import ChatGLMService
from clc.source_service import SourceService
class LangChainApplication(object):
def __init__(self, config):
self.config = config
self.llm_service = ChatGLMService()
self.llm_service.load_model(model_name_or_path=self.config.llm_model_name)
self.source_service = SourceService(config)
self.source_service.init_source_vector()
def get_knowledge_based_answer(self, query,
history_len=5,
temperature=0.1,
top_p=0.9,
chat_history=[]):
prompt_template = """基于以下已知信息，简洁和专业的来回答用户的问题。
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" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
已知内容:
{context}
问题:
{question}"""
prompt = PromptTemplate(template=prompt_template,
input_variables=["context", "question"])
self.llm_service.history = chat_history[-history_len:] if history_len > 0 else []
self.llm_service.temperature = temperature
self.llm_service.top_p = top_p
knowledge_chain = RetrievalQA.from_llm(
llm=self.llm_service,
retriever=self.source_service.vector_store.as_retriever(
search_kwargs={"k": 2}),
prompt=prompt)
knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
input_variables=["page_content"], template="{page_content}")
knowledge_chain.return_source_documents = True
result = knowledge_chain({"query": query})
return result
# if __name__ == '__main__':
# config = LangChainCFG()
# application = LangChainApplication(config)
# result = application.get_knowledge_based_answer('马保国是谁')
# print(result)
# application.source_service.add_document('/home/searchgpt/yq/Knowledge-ChatGLM/docs/added/马保国.txt')
# result = application.get_knowledge_based_answer('马保国是谁')
# print(result)
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