Understanding the logic behind 'Chat with PDF' apps by building a Retrieval-Augmented Generation agent manually.

Everyone is talking about "Chat with PDF" apps. I wanted to build one, not by using a wrapper library that hides everything, but by understanding the logic underneath. This is my journey building a RAG (Retrieval-Augmented Generation) agent.

The Problem with LLMs
Large Language Models like GPT-4 are amazing, but they have two flaws:

1. They don't know my private data.
2. They hallucinate (make things up) when they don't know the answer.

The Architecture
RAG solves this by giving the LLM a "textbook" to study before answering.

1. Chunking: I split my document into small paragraphs (chunks).
2. Embeddings: I used an embedding model (text-embedding-3-small) to turn those text chunks into lists of numbers (vectors). Similar concepts have mathematically similar vectors.
3. Vector Store: I stored these vectors in a database (I used Pinecone, but a simple local JSON file works for testing).

The Flow
When a user asks: "What is the refund policy?"

1. I convert that question into a vector.
2. I search my database for the chunk of text mathematically closest to that question vector.
3. I find the text: "Refunds are processed within 14 days."
4. I send a prompt to GPT-4: "Using this context: 'Refunds are processed within 14 days', answer the question: 'What is the refund policy?'"

It felt like magic when it finally worked. The hardest part wasn't the AI—it was cleaning the data before feeding it in!