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Update generate_answer.py
Browse files- generate_answer.py +9 -6
generate_answer.py
CHANGED
@@ -1,11 +1,15 @@
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import os
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from glob import glob
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import openai
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from dotenv import load_dotenv
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.vectorstores import Chroma
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from langchain.document_loaders import PyPDFLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_community.chat_models import ChatOpenAI
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from langchain.chains import RetrievalQA
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from langchain.memory import ConversationBufferMemory
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@@ -18,13 +22,12 @@ openai.api_key = api_key
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def base_model_chatbot(messages):
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system_message = [
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{"role": "system", "content": "You are
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]
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messages = system_message + messages
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=messages
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max_tokens=1500 # Increase max_tokens limit
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)
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return response.choices[0].message['content']
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@@ -66,11 +69,11 @@ class ConversationalRetrievalChain:
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retriever=retriever,
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memory=memory,
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)
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def with_pdf_chatbot(messages):
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"""Main function to execute the QA system."""
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query = messages[-1]['content'].strip()
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qa_chain = ConversationalRetrievalChain().create_chain()
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result = qa_chain({"query": query})
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return result['result']
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### generate_answer.py
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import os
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from glob import glob
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import openai
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from dotenv import load_dotenv
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+
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.vectorstores import Chroma
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from langchain.document_loaders import PyPDFLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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+
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from langchain_community.chat_models import ChatOpenAI
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from langchain.chains import RetrievalQA
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from langchain.memory import ConversationBufferMemory
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def base_model_chatbot(messages):
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system_message = [
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{"role": "system", "content": "You are an helpful AI chatbot, that answers questions asked by User."}
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]
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messages = system_message + messages
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo", # Ensure the model is specified correctly
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messages=messages
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)
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return response.choices[0].message['content']
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retriever=retriever,
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memory=memory,
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)
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def with_pdf_chatbot(messages):
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"""Main function to execute the QA system."""
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query = messages[-1]['content'].strip()
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qa_chain = ConversationalRetrievalChain().create_chain()
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result = qa_chain({"query": query})
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return result['result']
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