71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
from langchain.prompts.prompt import PromptTemplate
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from langchain.chains import ChatVectorDBChain
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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HumanMessagePromptTemplate,
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)
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.document_loaders import UnstructuredFileLoader
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from chatglm_llm import ChatGLM
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def init_knowledge_vector_store(filepath):
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embeddings = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese", )
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loader = UnstructuredFileLoader(filepath, mode="elements")
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docs = loader.load()
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vector_store = FAISS.from_documents(docs, embeddings)
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return vector_store
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def get_wiki_agent_answer(query, vector_store, chat_history=[]):
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system_template = """基于以下内容，简洁和专业的来回答用户的问题。
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如果无法从中得到答案，请说 "不知道" 或 "没有足够的相关信息"，不要试图编造答案。答案请使用中文。
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----------------
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{context}
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----------------
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"""
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messages = [
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SystemMessagePromptTemplate.from_template(system_template),
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HumanMessagePromptTemplate.from_template("{question}"),
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]
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prompt = ChatPromptTemplate.from_messages(messages)
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condese_propmt_template = """任务: 给一段对话和一个后续问题，将后续问题改写成一个独立的问题。确保问题是完整的，没有模糊的指代。
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----------------
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聊天记录：
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{chat_history}
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----------------
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后续问题：{question}
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----------------
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改写后的独立、完整的问题："""
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new_question_prompt = PromptTemplate.from_template(condese_propmt_template)
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chatglm = ChatGLM()
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chatglm.history = chat_history
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knowledge_chain = ChatVectorDBChain.from_llm(
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llm=chatglm,
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vectorstore=vector_store,
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qa_prompt=prompt,
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condense_question_prompt=new_question_prompt,
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)
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knowledge_chain.return_source_documents = True
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knowledge_chain.top_k_docs_for_context = 10
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result = knowledge_chain({"question": query, "chat_history": chat_history})
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return result, chatglm.history
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if __name__ == "__main__":
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filepath = input("Input your local knowledge file path 请输入本地知识文件路径：")
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vector_store = init_knowledge_vector_store(filepath)
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history = []
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while True:
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query = input("Input your question 请输入问题：")
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resp, history = get_wiki_agent_answer(query=query,
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vector_store=vector_store,
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chat_history=history)
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print(resp)
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