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| # basic imports | |
| import os, spaces | |
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| # langchain imports | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings | |
| # api key config | |
| os.environ['GOOGLE_API_KEY'] = os.getenv('geminiapi') | |
| # define LLM | |
| llm_gemini = ChatGoogleGenerativeAI(model="gemini-pro") | |
| # define vector store file name | |
| file_name = 'all_vec_db' | |
| ## function for loading pdfs | |
| def pdf_loader(files): | |
| all_text = '' | |
| for file in files: | |
| pdf_reader = PdfReader(file) | |
| for page in pdf_reader.pages: | |
| all_text += page.extract_text() | |
| return all_text | |
| ## function for chunking | |
| def chunk_creator(all_text): | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=50) | |
| chunks = text_splitter.split_text(all_text) | |
| return chunks | |
| ## function for creating embeddings | |
| def embedding_creator(chunks): | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
| # Setting up vector db | |
| vecstore = FAISS.from_texts(chunks, embeddings) | |
| vecstore.save_local(file_name) | |
| ## function for retrieving similar vectors from db | |
| def retrieve_similar(query): | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
| vector_db = FAISS.load_local(file_name, embeddings, allow_dangerous_deserialization=True) | |
| similar_texts = vector_db.similarity_search(query) | |
| return similar_texts | |
| ## function for getting answer from the LLM | |
| def retrieve_answer_LLM(docs,query): | |
| p_template = ''' | |
| 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", don't provide the wrong answer\n\n | |
| Context:\n {context}?\n | |
| Question: \n{question}\n | |
| Answer: | |
| ''' | |
| prompt = PromptTemplate(template=p_template, input_variables=['context','question']) | |
| chain = load_qa_chain(llm_gemini, chain_type='stuff', prompt=prompt) | |
| response = chain({"input_documents":docs,"question": query}, | |
| return_only_outputs=True) | |
| return response["output_text"] | |
| # Page config | |
| st.header("PDF QnA BOT🤖") | |
| st.sidebar.title("Add PDFs") | |
| uploaded_files = st.sidebar.file_uploader("Browse", accept_multiple_files=True) | |
| upload_button = st.sidebar.button("Upload") | |
| sidebar_placeholder = st.sidebar.empty() | |
| st.subheader("Enter Query:") | |
| user_query = st.text_input("Talk to the files!") | |
| submit_button = st.button("Submit") | |
| # action on file upload | |
| if upload_button: | |
| sidebar_placeholder.text("Reading Files...") | |
| all_pdfs_text = pdf_loader(uploaded_files) | |
| sidebar_placeholder.text("Chunking Text...") | |
| text_chunked = chunk_creator(all_pdfs_text) | |
| sidebar_placeholder.text("Creating Text Embeddings...") | |
| embedding_creator(text_chunked) | |
| sidebar_placeholder.text("Files read successfully!") | |
| # action on query submission | |
| if submit_button: | |
| pbar = st.progress(25,text='Retrieving similar Chunks...') | |
| docs = retrieve_similar(user_query) | |
| pbar.progress(50,"Querying LLM...") | |
| result = retrieve_answer_LLM(docs, user_query) | |
| pbar.progress(100,"Displaying Results") | |
| st.subheader("Results:") | |
| st.write(result) | |