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import streamlit as st |
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from PyPDF2 import PdfReader |
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from langchain_text_splitters import RecursiveCharacterTextSplitter |
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import os |
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from langchain_google_genai import GoogleGenerativeAIEmbeddings |
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from langchain_community.vectorstores import Chroma |
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from langchain_google_genai import ChatGoogleGenerativeAI |
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from langchain.chains.question_answering import load_qa_chain |
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from langchain.prompts import PromptTemplate |
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from langchain_community.document_loaders import PyPDFLoader |
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from langchain_chroma import Chroma |
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import tempfile |
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st.set_page_config(page_title="Document Genie", layout="wide") |
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st.markdown(""" |
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## Document Genie: Get instant insights from your Documents |
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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. |
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### How It Works |
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Follow these simple steps to interact with the chatbot: |
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1. **Upload Your Documents**: The system accepts multiple PDF files at once, analyzing the content to provide comprehensive insights. |
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2. **Ask a Question**: After processing the documents, ask any question related to the content of your uploaded documents for a precise answer. |
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""") |
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def get_pdf(pdf_file): |
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if pdf_file: |
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temp_dir = tempfile.mkdtemp() |
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path = os.path.join(temp_dir, pdf_file.name) |
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loader = PyPDFLoader(path) |
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docs = loader.load() |
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return docs |
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def text_splitter(text): |
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text_splitter = RecursiveCharacterTextSplitter( |
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chunk_size=500, |
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chunk_overlap=20, |
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separators=["\n\n","\n"," ",".",","]) |
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chunks=text_splitter.split_text(text) |
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return chunks |
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GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") |
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def get_conversational_chain(): |
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prompt_template = """ |
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Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in |
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provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n |
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Context:\n {context}?\n |
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Question: \n{question}\n |
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Answer: |
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""" |
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model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, google_api_key=GOOGLE_API_KEY) |
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prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) |
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chain = load_qa_chain(model, chain_type="stuff", prompt=prompt) |
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return chain |
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def embedding(chunk,query): |
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embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") |
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db = Chroma.from_documents(chunk,embeddings, persist_directory="./chroma_db") |
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docs = db3.similarity_search(query) |
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chain = get_conversational_chain() |
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response = chain({"input_documents": docs, "question": query}, return_only_outputs=True) |
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st.write("Reply: ", response["output_text"]) |
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def main(): |
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st.header("Chat with your pdf💁") |
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st.title("Menu:") |
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pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=False, key="pdf_uploader") |
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query = st.text_input("Ask a Question from the PDF Files", key="query") |
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if st.button("Submit & Process", key="process_button"): |
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with st.spinner("Processing..."): |
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raw_text = get_pdf(pdf_docs) |
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text_chunks = text_splitter(raw_text) |
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if query: |
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embedding(text_chunks,query) |
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st.success("Done") |
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if __name__ == "__main__": |
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main() |
