import streamlit as st from PyPDF2 import PdfReader from langchain_text_splitters import RecursiveCharacterTextSplitter import os from langchain_google_genai import GoogleGenerativeAIEmbeddings from langchain_community.vectorstores import Chroma from langchain_google_genai import ChatGoogleGenerativeAI from langchain.chains.question_answering import load_qa_chain from langchain.prompts import PromptTemplate from langchain_community.document_loaders import PyPDFLoader from langchain_chroma import Chroma import tempfile from langchain_cohere import CohereEmbeddings #st.set_page_config(page_title="Document Genie", layout="wide") #st.markdown(""" ### PDFChat: Get instant insights from your PDF #This chatbot is built using the Retrieval-Augmented Generation (RAG) framework, leveraging Google's Generative AI model Gemini-PRO. It processes uploaded PDF documents by breaking them down into manageable chunks, creates a searchable vector store, and generates accurate answers to user queries. This advanced approach ensures high-quality, contextually relevant responses for an efficient and effective user experience. #### How It Works #Follow these simple steps to interact with the chatbot: #1. **Upload Your Document**: The system accepts a PDF file at one time, analyzing the content to provide comprehensive insights. #2. **Ask a Question**: After processing the document, ask any question related to the content of your uploaded document for a precise answer. #""") #def get_pdf(pdf_docs): # loader = PyPDFLoader(pdf_docs) # docs = loader.load() # return docs def get_pdf(uploaded_file): if uploaded_file : temp_file = "./temp.pdf" # Delete the existing temp.pdf file if it exists if os.path.exists(temp_file): os.remove(temp_file) with open(temp_file, "wb") as file: file.write(uploaded_file.getvalue()) file_name = uploaded_file.name loader = PyPDFLoader(temp_file) docs = loader.load() return docs def text_splitter(text): text_splitter = RecursiveCharacterTextSplitter( # Set a really small chunk size, just to show. chunk_size=100000, chunk_overlap=50000, separators=["\n\n","\n"," ",".",","]) chunks=text_splitter.split_documents(text) return chunks GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COHERE_API_KEY = os.getenv("COHERE_API_KEY") def get_conversational_chain(): prompt_template = """ Given the following extracted parts of a long document and a question, create a final answer. Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in provided context just say, "answer is not available in the context", and then ignore the context and add the answer from your knowledge like a simple llm prompt. Try to give atleast the basic information.Donot return blank answer.\n\n Make sure to understand the question and answer as per the question. If the question involves terms like detailed or explained , give answer which involves complete detail about the question.\n\n Context:\n {context}?\n Question: \n{question}\n Answer: """ #model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, google_api_key=GOOGLE_API_KEY) model = ChatGoogleGenerativeAI(model="gemini-1.0-pro-latest", temperature=0.3, google_api_key=GOOGLE_API_KEY) prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) chain = load_qa_chain(model, chain_type="stuff", prompt=prompt) return chain def embedding(chunk,query): #embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") embeddings = CohereEmbeddings(model="embed-english-v3.0") db = Chroma.from_documents(chunk,embeddings) doc = db.similarity_search(query) print(doc) chain = get_conversational_chain() response = chain({"input_documents": doc, "question": query}, return_only_outputs=True) print(response) return response["output_text"] #st.write("Reply: ", response["output_text"]) if 'messages' not in st.session_state: st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}] st.header("Chat with your pdf💁") with st.sidebar: st.title("PDF FILE UPLOAD:") pdf_docs = st.file_uploader("Upload your PDF File and Click on the Submit & Process Button", accept_multiple_files=False, key="pdf_uploader") query = st.chat_input("Ask a Question from the PDF File") if query: raw_text = get_pdf(pdf_docs) text_chunks = text_splitter(raw_text) st.session_state.messages.append({'role': 'user', "content": query}) response = embedding(text_chunks,query) st.session_state.messages.append({'role': 'assistant', "content": response}) for message in st.session_state.messages: with st.chat_message(message['role']): st.write(message['content'])