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Parent(s):
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Upload 4 files
Browse files- app.py +14 -0
- langchain_bot.py +39 -0
- requirements.txt +0 -0
- system.json +18 -0
app.py
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import gradio as gr
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import os
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from langchain_bot import chain
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chat_history=[]
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def chat_messages(prompt,chat_history):
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response = chain.invoke({"question":prompt})
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return response['answer']
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gr.ChatInterface(
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chat_messages
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).launch(debug=True)
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langchain_bot.py
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import os
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import json
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import MessagesPlaceholder
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnablePassthrough
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from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
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from langchain.memory.buffer_window import ConversationBufferWindowMemory
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from langchain_core.prompts import PromptTemplate
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from langchain.memory import VectorStoreRetrieverMemo
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### Contextualize question ###
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from langchain.chains import create_history_aware_retriever, create_retrieval_chain
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settings= json.load(open("system.json","r"))
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from upstash_vector import Index
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from langchain_community.vectorstores.upstash import UpstashVectorStore
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index = Index(os.environ["UPSTASH_VECTOR_REST_URL"],os.environ["UPSTASH_VECTOR_REST_TOKEN"])
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vectorStore = UpstashVectorStore(
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embedding=True, index=index,
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)
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retriever = vectorStore.as_retriever(search_kwargs={"k": settings["k"]})
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#LLM setup
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LLM= ChatOpenAI(model=settings["model"], temperature=settings["temp"])
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#Setup prompt template
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QUESTION_PROMPT = PromptTemplate(
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template=settings["prompt_temp"], # プロンプトテンプレートをセット
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input_variables=["context", "question"] # プロンプトに挿入する変数
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)
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# Conversation memory
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memory = ConversationBufferWindowMemory(
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memory_key=settings["MEMORY_KEY"], # Memory key メモリーのキー名
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output_key="answer", #output key 出力ののキー名
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k=8, #saved conversation number 保持する会話の履歴数
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return_messages=True, #get chat list チャット履歴をlistで取得する場合はTrue
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)
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requirements.txt
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system.json
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[{
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"model":"gpt-3.5-turbo-1106",
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"temp":0.2,
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"MEMORY_KEY":"chat_history",
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"k":20,
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"prompt_temp":"""
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You are an AI Chatbot from precious Plastic your job is to answer Question about recycling plastic.
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You can return links to in the answer as well as image if you want
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Us the following context to help in answering the Question.
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------
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{context}
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------
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Question: {question}
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Do not:
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・ Do not make thing up that you do not know, if you dont know, say that you dont know \
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"""
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}]
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