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import streamlit as st
import os
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from data_extraction import Extraction
import nest_asyncio
from chunking import Chunker
from embeddings import Embeddings
from vectorstore import VectorDB
from retriever import Retriever, CreateBM25Retriever
from llm import LLM
from langchain_core.prompts import PromptTemplate
from chain import Chain
from streamlit_chat import message
if 'responses' not in st.session_state:
st.session_state['responses'] = ["How can I assist you?"]
if 'requests' not in st.session_state:
st.session_state['requests'] = []
if 'buffer_memory' not in st.session_state:
st.session_state.buffer_memory=ConversationBufferWindowMemory(k=3,return_messages=True)
nest_asyncio.apply()
ext = Extraction('fast')
chnk = Chunker(chunk_size=1000, chunk_overlap=200)
emb = Embeddings("hf", "all-MiniLM-L6-v2")
_llm = LLM('vertex').get_llm()
ch = Chain(_llm, st.session_state.buffer_memory)
conversation = ch.get_chain_with_history()
def query_refiner(conversation, query):
prompt=f"Given the following user query and historical user queries, rephrase the users current query to form a meaningful and clear question.Previously user has asked the following: \n{conversation}\n\n User's Current Query: {query}. What will be the refined query? Only provide the query without any extra details or explanations.",
ans = _llm.invoke(prompt).content
return ans
def get_conversation_string():
conversation_string = ""
for i in range(len(st.session_state['responses'])-1):
conversation_string += "Human: "+st.session_state['requests'][i] + "\n"
# conversation_string += "Bot: "+ st.session_state['responses'][i+1] + "\n"
return conversation_string
def main():
inp_dir = "./inputs"
db = 'pinecone'
db_dir = 'pineconedb'
st.set_page_config("Chat PDF")
st.header("Chat with PDF")
response_container = st.container()
textcontainer = st.container()
ret = None
with textcontainer:
query = st.text_input("Query: ", key="input")
if query:
if ret is None:
ret = Retriever(db, db_dir, emb.embedding, 'ensemble', 5)
with st.spinner("typing..."):
conversation_string = get_conversation_string()
if len(st.session_state['responses']) != 0:
refined_query = query_refiner(conversation_string, query)
else:
refined_query = query
st.subheader("Refined Query:")
st.write(refined_query)
context, context_list = ret.get_context(refined_query)
response = conversation.predict(input=f"Context:\n {context} \n\n Query:\n{query}")
# response += '\n' + "Source: " + src
st.session_state.requests.append(query)
st.session_state.responses.append(response)
with response_container:
if st.session_state['responses']:
for i in range(len(st.session_state['responses'])):
message(st.session_state['responses'][i],key=str(i))
if i < len(st.session_state['requests']):
message(st.session_state["requests"][i], is_user=True,key=str(i)+ '_user')
with st.sidebar:
st.title("Menu:")
pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
pdfs = []
if pdf_docs:
for pdf_file in pdf_docs:
filename = pdf_file.name
path = os.path.join(inp_dir,filename)
with open(path, "wb") as f:
f.write(pdf_file.getvalue())
pdfs.append(path)
with st.spinner("Processing..."):
texts, metas = ext.get_text(pdfs)
docs = chnk.get_chunks(texts, metas)
vs = VectorDB(db, emb.embedding, db_dir, docs=docs)
bm = CreateBM25Retriever(docs)
st.success("Done")
if __name__ == "__main__":
main() |