mirror of https://github.com/THUDM/ChatGLM-6B
112 lines
3.4 KiB
Python
112 lines
3.4 KiB
Python
import os
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from typing import List, Dict, Tuple, Any
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import streamlit as st
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import pandas as pd
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import os
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores import Chroma
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.chains import (
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ChatVectorDBChain,
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QAWithSourcesChain,
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VectorDBQAWithSourcesChain,
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)
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from langchain.prompts.prompt import PromptTemplate
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from langchain.docstore.document import Document
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from langchain.vectorstores.faiss import FAISS
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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AIMessagePromptTemplate,
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HumanMessagePromptTemplate,
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)
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from transformers import AutoTokenizer, AutoModel
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# Set up OpenAI API key
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# This is solely for the purpose of semantic search part of langchain vector search.
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# Completion is still purely done using ChatGLM model.
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os.environ["OPENAI_API_KEY"] = ""
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@st.cache_resource()
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def get_chat_glm():
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tokenizer = AutoTokenizer.from_pretrained(
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"THUDM/chatglm-6b-int4", trust_remote_code=True
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)
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model = (
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AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
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.half()
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.cuda()
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)
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model = model.eval()
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return model, tokenizer
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def chat_with_agent(user_input, temperature=0.2, max_tokens=800, chat_history=[]):
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model, tokenizer = get_chat_glm()
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response, updated_history = model.chat(
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tokenizer,
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user_input,
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history=chat_history,
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temperature=temperature,
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max_length=max_tokens,
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)
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return response, updated_history
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# Langchian related features
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def init_wiki_agent(
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index_dir,
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max_token=800,
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temperature=0.3,
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):
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embeddings = OpenAIEmbeddings()
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if index_dir:
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vectorstore = FAISS.load_local(index_dir, embeddings=embeddings)
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else:
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raise ValueError("Need saved vector store location")
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system_template = """使用以下文段, 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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# qa = ChatVectorDBChain.from_llm(llm=ChatOpenAI(temperature=temperature, max_tokens=max_token),
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# vectorstore=vectorstore,
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# qa_prompt=prompt)
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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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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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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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from chatglm_llm import ChatGLM_G
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qa = ChatVectorDBChain.from_llm(
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llm=ChatGLM_G(),
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vectorstore=vectorstore,
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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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qa.return_source_documents = True
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qa.top_k_docs_for_context = 3
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return qa
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def get_wiki_agent_answer(query, qa, chat_history=[]):
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result = qa({"question": query, "chat_history": chat_history})
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return result
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