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Chat-Pdf-With-Search-Assistant / app.py

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	from langchain.agents import AgentType, Tool, initialize_agent
	from langchain.callbacks import StreamlitCallbackHandler
	from langchain.chains import RetrievalQA
	from langchain.chains.conversation.memory import ConversationBufferMemory
	from utils.ask_human import CustomAskHumanTool
	from utils.model_params import get_model_params
	from utils.prompts import create_agent_prompt, create_qa_prompt
	from PyPDF2 import PdfReader
	from langchain.vectorstores import FAISS
	from langchain.embeddings import HuggingFaceEmbeddings
	from langchain.embeddings import HuggingFaceHubEmbeddings
	from langchain import HuggingFaceHub
	import torch
	import streamlit as st
	from langchain.utilities import SerpAPIWrapper
	from langchain.tools import DuckDuckGoSearchRun
	import os
	hf_token = os.environ['HF_TOKEN']
	serp_token = os.environ['SERP_TOKEN']
	repo_id = "sentence-transformers/all-mpnet-base-v2"
	from datetime import datetime
	from langchain.tools import Tool




	HUGGINGFACEHUB_API_TOKEN= hf_token
	hf = HuggingFaceHubEmbeddings(
	repo_id=repo_id,
	task="feature-extraction",
	huggingfacehub_api_token= HUGGINGFACEHUB_API_TOKEN,
	)





	llm = HuggingFaceHub(
	repo_id='mistralai/Mistral-7B-Instruct-v0.2',
	huggingfacehub_api_token = HUGGINGFACEHUB_API_TOKEN,


	)



	from langchain.text_splitter import CharacterTextSplitter, TokenTextSplitter
	from langchain.vectorstores import Chroma
	from langchain.chains import RetrievalQA
	from langchain import PromptTemplate

	### PAGE ELEMENTS

	# st.set_page_config(
	# page_title="RAG Agent Demo",
	# page_icon="🦜",
	# layout="centered",
	# initial_sidebar_state="collapsed",
	# )
	# st.markdown("### Leveraging the User to Improve Agents in RAG Use Cases")


	def main():

	st.set_page_config(page_title="Ask your PDF powered by Search Agents")
	st.header("Ask your PDF powered by Search Agents 💬")

	# upload file
	pdf = st.file_uploader("Upload your PDF and chat with Agent", type="pdf")

	# extract the text
	if pdf is not None:
	pdf_reader = PdfReader(pdf)
	text = ""
	for page in pdf_reader.pages:
	text += page.extract_text()

	# Split documents and create text snippets

	text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
	texts = text_splitter.split_text(text)

	embeddings = hf
	knowledge_base = FAISS.from_texts(texts, embeddings)

	retriever = knowledge_base.as_retriever(search_kwargs={"k":3})




	qa_chain = RetrievalQA.from_chain_type(
	llm=llm,
	chain_type="stuff",
	retriever=retriever,
	return_source_documents=False,
	chain_type_kwargs={
	"prompt": create_qa_prompt(),
	},
	)

	conversational_memory = ConversationBufferMemory(
	memory_key="chat_history", k=3, return_messages=True
	)

	# tool for db search
	db_search_tool = Tool(
	name="dbRetrievalTool",
	func=qa_chain,
	description="""Use this tool to answer document related questions. The input to this tool should be the question.""",
	)

	# search = SerpAPIWrapper(serpapi_api_key=serp_token)

	# google_searchtool= Tool(
	# name="Current Search",
	# func=search.run,
	# description="use this tool to answer real time or current search related questions.",
	# )
	search = DuckDuckGoSearchRun()
	search_tool = Tool(
	name="search",
	func=search,
	description="use this tool to answer real time or current search related questions."
	)
	# Define a new tool that returns the current datetime
	datetime_tool = Tool(
	name="Datetime",
	func=lambda x: datetime.now().isoformat(),
	description="Returns the current datetime",
	)
	# tool for asking human
	human_ask_tool = CustomAskHumanTool()
	# agent prompt
	prefix, format_instructions, suffix = create_agent_prompt()
	mode = "Agent with AskHuman tool"

	# initialize agent
	agent = initialize_agent(
	agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
	tools=[db_search_tool,search_tool,datetime_tool],
	llm=llm,
	verbose=True,
	max_iterations=5,
	early_stopping_method="generate",
	memory=conversational_memory,
	agent_kwargs={
	"prefix": prefix,
	"format_instructions": format_instructions,
	"suffix": suffix,
	},
	handle_parsing_errors=True,

	)

	# question form
	with st.form(key="form"):
	user_input = st.text_input("Ask your question")
	submit_clicked = st.form_submit_button("Submit Question")

	# output container
	output_container = st.empty()
	if submit_clicked:
	# st_callback = StreamlitCallbackHandler(st.container())
	# response = agent.run(user_input,callbacks = [st_callback])
	response = agent.run(user_input)
	st.write(response)
	# output_container = output_container.container()
	# output_container.chat_message("user").write(user_input)
	# with st.chat_message("assistant"):
	# st_callback = StreamlitCallbackHandler(st.container())
	# response = agent.run(user_input, callbacks=[st_callback])
	# st.write(response)

	# answer_container = output_container.chat_message("assistant", avatar="🦜")
	# st_callback = StreamlitCallbackHandler(answer_container,)

	# answer = agent.run(user_input, callbacks=[st_callback])

	# answer_container = output_container.container()
	# answer_container.chat_message("assistant").write(answer)



	if __name__ == '__main__':
	main()