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https://github.com/RYDE-WORK/Langchain-Chatchat.git
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1、自动读取knowledge_based_chatglm.py中LLM及embedding模型枚举，选择后点击setting进行模型加载，可随时切换模型进行测试 2、可手动调节保留对话历史长度，可根据显存大小自行调节 3、添加上传文件功能，通过下拉框选择已上传的文件，点击loading加载文件，过程中可随时更换加载的文件 4、底部添加use via API可对接到自己系统 TODO： 1、添加模型加载进度条 2、添加输出内容及错误提示 3、国家化语言切换 4、引用标注 5、添加插件系统（可基础lora训练等）
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
from langchain.chains import RetrievalQA
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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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# 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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# 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": "THUDM/chatglm-6b",
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"chatglm-6b-int4": "THUDM/chatglm-6b-int4",
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# "chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
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}
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chatglm = None
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embeddings = None
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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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def init_knowledge_vector_store(filepath):
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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_knowledge_based_answer(query, vector_store, chat_history=[]):
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global chatglm,embeddings
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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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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.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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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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