Want to truly understand how PDF Question-Answering RAG systems work? This code-along tutorial is for you! We'll build a powerful chatbot that can answer your questions based on any PDF, all without relying on pre-built libraries like LangChain.
Here's what you'll master in this deep dive:
1. Downloading PDFs with Python: We'll start by fetching your desired PDF directly from the web using the efficient requests module.
2. PDF Processing & Chunking: Learn how to load and process PDFs, then strategically break them down into manageable chunks using a custom splitter for optimal embedding and semantic search.
3. Harnessing Google Gemini's Embedding Power: Unleash the cutting-edge capabilities of Google Gemini's embedding function to convert our text chunks into meaningful numerical representations for AI.
4. Building a ChromaDB Vector Database: We'll create a dedicated ChromaDB collection, incorporating our chosen embedding function for lightning-fast semantic search of our documents.
5. Ingesting Documents into ChromaDB: Seamlessly integrate our PDF chunks into the ChromaDB collection, making them instantly available for retrieval and question answering.
6. Querying ChromaDB for Relevant Passages: Watch as we craft queries and leverage ChromaDB's powerful search capabilities to pinpoint the most relevant passages from our PDF based on your questions.
7. Asking Questions with Google Gemini Pro 1.5: We'll send the retrieved passages as context, along with your specific question, to the advanced Google Gemini Pro 1.5 model for intelligent and insightful answers.
8. Generating Comprehensive Answers: Finally, see how Gemini Pro 1.5 expertly processes the information and delivers accurate answers directly from your PDF documents.
Colab Notebook :
colab.research...
Chapters:
(00:00) : Introduction and scope of today's code along
(06:00) Installing necessary libraries
(07:00) Get your Gemini API key
(08:10) Configuring Google Gemini
(10:15) Build Download PDF method
(13:26) Extract Text from PDF using PyPDF2
(18:50) Building Text Splitter from scratch
(28:00) ChromaDB Gemini Embeddings
(30:20) Adding chunks to ChromaDB
(31:55) Querying ChromaDB for relevant context
(35:10) Converting list of lists to one passage
(39:00) Create Prompt for Gemini with Context and Query
(40:15) Generating answer from Google Gemini
By building this project from scratch, you'll gain a deep understanding of:
a. Core concepts of Retrieval Augmented Generation (RAG) for document AI.
b. The power of Google Gemini for embeddings, semantic search, and question answering.
c. How to work with vector databases like ChromaDB for efficient document understanding.
d. The flexibility of building custom, transparent AI solutions without relying on pre-packaged libraries.
Ready to level up your AI skills, unlock the power of document AI, and build your own PDF Q&A chatbot? Let's dive in!
Don't forget to:
Like this video 👍 if you found it helpful.
Subscribe to the channel 🔔 for more in-depth AI tutorials and code-alongs.
Leave a comment below 💬 with your questions or project ideas!
Let's build something amazing! 🚀