Chatting with Multiple PDF Documents using Google Gemini

Overview

In this lecture, we learn to create an end-to-end project that enables chatting with multiple PDF documents using Google Gemini. We'll use LangChain, an important tool for integrating Google Gemini Pro, to develop an application. Key features of this project include:

• Using vector embeddings
• Storing embeddings in a local database or external databases like Pinecone or Cassandra DB
• Utilizing Facebook’s vector embedding technique

Project Demo

1. Upload PDFs: Users can upload multiple PDFs (limit 200 MB each). Example PDFs include research papers like "Attention is All You Need" and "YOLO".
2. Vector Embedding Conversion: The PDFs are converted into vector embeddings and stored in a local database or other databases.
3. Query PDFs: Users can ask questions about the PDFs. The system retrieves relevant information by querying the vector embeddings.

Example Queries

• "What is scaled dot product attention?"
• "Provide a summary about multi-head attention."
• "Provide a detailed summary about the 2007 YOLO error analysis."

Developing the Application

Follow a step-by-step process which includes creating an environment, installing necessary libraries, and executing code.

Steps

1. Initialize the Project Environment:
   • Create a virtual environment: conda create -n yourenvname python=3.x
   • Activate the environment: conda activate yourenvname
2. Set Environment Variables:
   • Obtain and set your Google API Key for the Google Gemini Pro.
3. Install Required Libraries:
   • streamlet, google-generative-ai, python-dotenv, langchain, pypdf2, chromadb, and faiss-cpu.
4. PDF Handling:
   • Read PDFs: Extract text from PDFs using PyPDF2.
   • Chunk Text: Split the extracted text into smaller, manageable chunks.
   • Create Vector Store: Convert the text chunks into vector embeddings using Google Generative AI Embeddings.

Coding the Application

• Load Dependencies: Ensure to import libraries like Streamlit, LangChain, Faiss, and other required modules.
• Read and Chunk PDFs: Define functions to read PDF texts and split texts into smaller chunks.
• Vector Embedding: Convert text chunks into vector embeddings.
• Conversational Chain: Define methods for user interaction with the PDFs by setting up a prompt template and managing interactions.
• Streamlit Application: Develop a front-end using Streamlit to allow users to upload PDFs, query them, and view responses.

Example Code

PDF Text Extraction

from PyPDF2 import PdfReader
def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        reader = PdfReader(pdf)
        for page in reader.pages:
            text += page.extract_text()
    return text

Split Text into Chunks

def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    chunks = text_splitter.split_text(text)
    return chunks

Convert to Vector Embeddings

def get_vector_store(text_chunks, embeddings):
    vector_store = faiss.from_texts(text_chunks, embedding=embeddings)
    return vector_store

App Execution

if __name__ == '__main__':
    main()

Querying and Response

• Load the embeddings
• Search for similarity
• Provide context-based responses to user queries

Conclusion

This project demonstrates how to create an interactive application to chat with multiple PDF documents using innovative tools like Google Gemini Pro, LangChain, and vector embeddings. This end-to-end pipeline can optimally handle large PDF documents and derive meaningful interactions.

Takeaways: Learn to handle PDFs, create embeddings, and develop a conversational AI interface effectively.