125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
from langchain.chains import RetrievalQA
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from langchain.prompts import PromptTemplate
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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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import sentence_transformers
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import torch
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import os
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import readline
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# Global Parameters
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EMBEDDING_MODEL = "text2vec"
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VECTOR_SEARCH_TOP_K = 6
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LLM_MODEL = "chatglm-6b"
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LLM_HISTORY_LEN = 3
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DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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# Show reply with source text from input document
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REPLY_WITH_SOURCE = True
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embedding_model_dict = {
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"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
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"ernie-base": "nghuyong/ernie-3.0-base-zh",
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"text2vec": "GanymedeNil/text2vec-large-chinese",
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}
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llm_model_dict = {
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"chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
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"chatglm-6b-int4": "THUDM/chatglm-6b-int4",
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"chatglm-6b": "THUDM/chatglm-6b",
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}
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def init_cfg(LLM_MODEL, EMBEDDING_MODEL, LLM_HISTORY_LEN, V_SEARCH_TOP_K=6):
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global chatglm, embeddings, VECTOR_SEARCH_TOP_K
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VECTOR_SEARCH_TOP_K = V_SEARCH_TOP_K
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chatglm = ChatGLM()
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chatglm.load_model(model_name_or_path=llm_model_dict[LLM_MODEL])
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chatglm.history_len = LLM_HISTORY_LEN
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embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL],)
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embeddings.client = sentence_transformers.SentenceTransformer(embeddings.model_name,
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device=DEVICE)
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def init_knowledge_vector_store(filepath:str):
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if not os.path.exists(filepath):
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print("路径不存在")
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return None
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elif os.path.isfile(filepath):
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file = os.path.split(filepath)[-1]
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try:
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loader = UnstructuredFileLoader(filepath, mode="elements")
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docs = loader.load()
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print(f"{file} 已成功加载")
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except:
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print(f"{file} 未能成功加载")
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return None
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elif os.path.isdir(filepath):
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docs = []
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for file in os.listdir(filepath):
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fullfilepath = os.path.join(filepath, file)
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try:
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loader = UnstructuredFileLoader(fullfilepath, mode="elements")
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docs += loader.load()
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print(f"{file} 已成功加载")
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except:
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print(f"{file} 未能成功加载")
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vector_store = FAISS.from_documents(docs, embeddings)
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return vector_store
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def get_knowledge_based_answer(query, vector_store, chat_history=[]):
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global chatglm, embeddings
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prompt_template = """基于以下已知信息，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 "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。
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已知内容:
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{context}
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问题:
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{question}"""
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prompt = PromptTemplate(
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template=prompt_template,
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input_variables=["context", "question"]
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)
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chatglm.history = chat_history
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knowledge_chain = RetrievalQA.from_llm(
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llm=chatglm,
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retriever=vector_store.as_retriever(search_kwargs={"k": VECTOR_SEARCH_TOP_K}),
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prompt=prompt
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)
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knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
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input_variables=["page_content"], template="{page_content}"
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)
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knowledge_chain.return_source_documents = True
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result = knowledge_chain({"query": query})
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chatglm.history[-1][0] = query
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return result, chatglm.history
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if __name__ == "__main__":
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init_cfg(LLM_MODEL, EMBEDDING_MODEL, LLM_HISTORY_LEN)
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vector_store = None
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while not vector_store:
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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_knowledge_based_answer(query=query,
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vector_store=vector_store,
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chat_history=history)
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if REPLY_WITH_SOURCE:
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print(resp)
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else:
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print(resp["result"])
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