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Create app.py
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app.py
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import streamlit as st
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from langchain.document_loaders import PyPDFLoader, DirectoryLoader
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from langchain import PromptTemplate
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.llms import CTransformers
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from langchain.chains import RetrievalQA
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import chainlit as cl
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DB_FAISS_PATH = 'vectorstore/db_faiss'
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custom_prompt_template = """Use the following pieces of information to answer the user's question.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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Context: {context}
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Question: {question}
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Only return the helpful answer below and nothing else.
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Helpful answer:
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"""
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def set_custom_prompt():
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"""
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Prompt template for QA retrieval for each vectorstore
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"""
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prompt = PromptTemplate(template=custom_prompt_template,
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input_variables=['context', 'question'])
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return prompt
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# Retrieval QA Chain
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def retrieval_qa_chain(llm, prompt, db):
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qa_chain = RetrievalQA.from_chain_type(llm=llm,
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chain_type='stuff',
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retriever=db.as_retriever(search_kwargs={'k': 2}),
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return_source_documents=True,
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chain_type_kwargs={'prompt': prompt}
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)
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return qa_chain
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# Loading the model
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def load_llm():
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# Load the locally downloaded model here
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llm = CTransformers(
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model="llama-2-7b-chat.ggmlv3.q8_0.bin",
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model_type="llama",
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max_new_tokens=512,
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temperature=0.5
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)
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return llm
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# QA Model Function
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def qa_bot():
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
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model_kwargs={'device': 'cpu'})
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db = FAISS.load_local(DB_FAISS_PATH, embeddings)
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llm = load_llm()
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qa_prompt = set_custom_prompt()
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qa = retrieval_qa_chain(llm, qa_prompt, db)
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return qa
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def main():
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st.title("AI ChatBot LLM")
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qa_result = qa_bot()
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user_input = st.text_input("Enter your question:")
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if st.button("Ask"):
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response = qa_result({'query': user_input})
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answer = response["result"]
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sources = response["source_documents"]
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st.write("Answer:", answer)
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if sources:
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st.write("Sources:", sources)
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else:
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st.write("No sources found")
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if __name__ == "__main__":
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main()
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