create_db.py

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS

DATA_PATH="data/"
DB_FAISS_PATH = "vectorstores/db_faiss/"

def create_vector_db():
 loader=DirectoryLoader(DATA_PATH,glob="*.pdf",loader_cls=PyPDFLoader)
 documents=loader.load()
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
 texts=text_splitter.split_documents(documents)

 embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
  model_kwargs={'device':'cpu'})
 db=FAISS.from_documents(texts,embeddings)
 db.save_local(DB_FAISS_PATH)



if __name__=="__main__":
 create_vector_db()
 
 
 
 ## Below for chainlit
 from langchain import PromptTemplate
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import RetrievalQA
import chainlit as cl

DB_FAISS_PATH = "vectorstores/db_faiss/"

custom_prompt_template='''Use the following pieces of information to answer the users question. 
If you don't know the answer, please just say that you don't know the answer. Don't make up an answer.

Context:{context}
question:{question}

Only returns the helpful anser below and nothing else.
Helpful answer
'''

def set_custom_prompt():
 '''
 Prompt template for QA retrieval for each vector store
 '''
 prompt =PromptTemplate(template=custom_prompt_template, input_variables=['context','question'])

 return prompt


def load_llm():
 llm = CTransformers(
  model='llama-2-7b-chat.ggmlv3.q8_0.bin',
  model_type='llama',
  max_new_tokens=512,
  temperature=0.5
  )
 return llm


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

def qa_bot(vector):
 embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
  model_kwargs={'device':'cpu'})
 db = FAISS.load_local(vector,embeddings)
 llm=load_llm()
 qa_prompt=set_custom_prompt()
 qa = retrieval_qa_chain(llm,qa_prompt,db)
 return qa 


def final_result(query):
 qa_result=qa_bot()
 response=qa_result({'query':query})
 return response 

## chainlit here
@cl.on_chat_start
async def start():
 chain=qa_bot()
 msg=cl.Message(content="Firing up the shariah bot...")
 await msg.send()
 msg.content= "Hi, welcome to sharia bot. What is your query?"
 await msg.update()
 cl.user_session.set("chain",chain)


@cl.on_message
async def main(message):
 chain=cl.user_session.get("chain")
 cb = cl.AsyncLangchainCallbackHandler(
  stream_final_answer=True, answer_prefix_tokens=["FINAL","ANSWER"]
  )
 cb.ansert_reached=True
 res=await chain.acall(message, callbacks=[cb])
 answer=res["result"]
 sources=res["source_documents"]

 if sources:
  answer+=f"\nSources: "+str(str(sources))
 else:
  answer+=f"\nNo Sources found"

 await cl.Message(content=answer).send() 


# what are the characteristics of charge card?