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" 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." ) # 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], 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]) 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()