Delete vectorstore/db_faiss/model.py

vectorstore/db_faiss/model.py

@@ -1,105 +0,0 @@
-from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain.prompts import PromptTemplate
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain_community.llms import CTransformers
-from langchain.chains import RetrievalQA
-import chainlit as cl
-
-DB_FAISS_PATH = 'vectorstore/db_faiss'
-
-custom_prompt_template = """Use the following pieces of information to answer the user's question.
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-Context: {context}
-Question: {question}
-
-Only return the helpful answer below and nothing else.
-Helpful answer:
-"""
-
-def set_custom_prompt():
-    """
-    Prompt template for QA retrieval for each vectorstore
-    """
-    prompt = PromptTemplate(template=custom_prompt_template,
-                            input_variables=['context', 'question'])
-    return prompt
-
-#Retrieval QA Chain
-def retrieval_qa_chain(llm, prompt, db):
-    qa_chain = RetrievalQA.from_chain_type(llm=llm,
-                                       chain_type='stuff',
-                                       retriever=db.as_retriever(search_kwargs={'k': 2}),
-                                       return_source_documents=True,
-                                       chain_type_kwargs={'prompt': prompt}
-                                       )
-    return qa_chain
-
-#Loading the model
-def load_llm():
-    # Load the locally downloaded model here
-    
-    # Path to the specific GGML model file you want to use
-    model_path = "/home/ebiz/Govind/Llama2-Medical-Chatbot/llama-2-7b-chat.ggmlv3.q4_0.bin"
-    
-    
-    llm = CTransformers(
-        model = model_path,
-        model_type="llama",
-        max_new_tokens = 1024,
-        temperature = 0.5
-    )
-    return llm
-
-#QA Model Function
-def qa_bot():
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
-                                       model_kwargs={'device': 'cpu'})
-    db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
-    llm = load_llm()
-    qa_prompt = set_custom_prompt()
-    qa = retrieval_qa_chain(llm, qa_prompt, db)
-
-    return qa
-
-#output function
-def final_result(query):
-    qa_result = qa_bot()
-    response = qa_result({'query': query})
-    return response
-
-#chainlit code
-@cl.on_chat_start
-async def start():
-    chain = qa_bot()
-    msg = cl.Message(content="Starting the bot...")
-    await msg.send()
-    msg.content = "Hi, Welcome to Medical Bot. What is your query?"
-    await msg.update()
-
-    cl.user_session.set("chain", chain)
-
-@cl.on_message
-async def main(message: cl.Message):
-    chain = cl.user_session.get("chain") 
-
-    # Disable streaming to avoid duplicate answers
-    cb = cl.AsyncLangchainCallbackHandler(
-        stream_final_answer=False  # Disable streaming to prevent multiple responses
-    )
-
-    res = await chain.acall(message.content, callbacks=[cb])
-    answer = res["result"]
-    sources = res.get("source_documents", [])
-
-    # Ensure the answer is sent once and with sources if available
-    # if sources:
-    #     source_info = "\nSources:\n" + "\n".join([doc.metadata.get("source", "Unknown") for doc in sources])
-    #     answer += source_info
-    # else:
-    #     answer += "\nNo sources found"
-
-    await cl.Message(content=answer).send()
-
-