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from langchain.agents import AgentType, Tool, initialize_agent |
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from langchain.callbacks import StreamlitCallbackHandler |
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from langchain.chains import RetrievalQA |
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from langchain.chains.conversation.memory import ConversationBufferMemory |
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from utils.ask_human import CustomAskHumanTool |
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from utils.model_params import get_model_params |
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from utils.prompts import create_agent_prompt, create_qa_prompt |
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from PyPDF2 import PdfReader |
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from langchain.vectorstores import FAISS |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.embeddings import HuggingFaceHubEmbeddings |
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from langchain import HuggingFaceHub |
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import torch |
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import streamlit as st |
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from langchain.utilities import SerpAPIWrapper |
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from langchain.tools import DuckDuckGoSearchRun |
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import os |
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hf_token = os.environ['HF_TOKEN'] |
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serp_token = os.environ['SERP_TOKEN'] |
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repo_id = "sentence-transformers/all-mpnet-base-v2" |
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HUGGINGFACEHUB_API_TOKEN= hf_token |
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hf = HuggingFaceHubEmbeddings( |
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repo_id=repo_id, |
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task="feature-extraction", |
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huggingfacehub_api_token= HUGGINGFACEHUB_API_TOKEN, |
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) |
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llm = HuggingFaceHub( |
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repo_id='mistralai/Mistral-7B-Instruct-v0.2', |
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huggingfacehub_api_token = HUGGINGFACEHUB_API_TOKEN, |
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) |
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from langchain.text_splitter import CharacterTextSplitter, TokenTextSplitter |
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from langchain.vectorstores import Chroma |
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from langchain.chains import RetrievalQA |
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from langchain import PromptTemplate |
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def main(): |
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st.set_page_config(page_title="Ask your PDF powered by Search Agents") |
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st.header("Ask your PDF powered by Search Agents π¬") |
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pdf = st.file_uploader("Upload your PDF and chat with Agent", type="pdf") |
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if pdf is not None: |
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pdf_reader = PdfReader(pdf) |
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text = "" |
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for page in pdf_reader.pages: |
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text += page.extract_text() |
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text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0) |
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texts = text_splitter.split_text(text) |
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embeddings = hf |
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knowledge_base = FAISS.from_texts(texts, embeddings) |
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retriever = knowledge_base.as_retriever(search_kwargs={"k":3}) |
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qa_chain = RetrievalQA.from_chain_type( |
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llm=llm, |
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chain_type="stuff", |
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retriever=retriever, |
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return_source_documents=False, |
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chain_type_kwargs={ |
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"prompt": create_qa_prompt(), |
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}, |
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) |
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conversational_memory = ConversationBufferMemory( |
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memory_key="chat_history", k=3, return_messages=True |
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) |
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db_search_tool = Tool( |
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name="dbRetrievalTool", |
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func=qa_chain, |
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description="""Use this tool to answer document related questions. The input to this tool should be the question.""", |
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) |
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search = DuckDuckGoSearchRun() |
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search_tool = Tool( |
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name="search", |
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func=search, |
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description="use this tool to answer real time or current search related questions." |
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) |
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human_ask_tool = CustomAskHumanTool() |
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prefix, format_instructions, suffix = create_agent_prompt() |
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mode = "Agent with AskHuman tool" |
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agent = initialize_agent( |
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agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, |
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tools=[db_search_tool,search_tool], |
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llm=llm, |
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verbose=True, |
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max_iterations=5, |
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early_stopping_method="generate", |
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memory=conversational_memory, |
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agent_kwargs={ |
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"prefix": prefix, |
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"format_instructions": format_instructions, |
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"suffix": suffix, |
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}, |
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handle_parsing_errors=True, |
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) |
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with st.form(key="form"): |
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user_input = st.text_input("Ask your question") |
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submit_clicked = st.form_submit_button("Submit Question") |
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output_container = st.empty() |
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if submit_clicked: |
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st_callback = StreamlitCallbackHandler(st.container()) |
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response = agent_run(user_input),callbacks = [st_callback]) |
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st.write(response) |
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if __name__ == '__main__': |
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main() |