Building a RAG System: Chat with Your Business Data (Part 6)

Computers don't understand text. They understand numbers. To let an AI "search" your documents, we need to convert your text into lists of numbers called Embeddings.

Imagine a 3D space.

The word "Dog" is at coordinate [1, 1, 1].

The word "Puppy" is at [1, 1, 2].

The word "Car" is at [9, 9, 9].

"Dog" is close to "Puppy". "Dog" is far from "Car". Vector Search algorithms calculate the Cosine Similarity (distance) between these points.

The Workflow: 1. Ingestion: Load your PDF. 2. Chunking: Split it into 1000-character paragraphs. 3. Embedding: Send each paragraph to OpenAI's \`text-embedding-3-small\` model. It returns a vector. 4. Upsert: Save that vector + the text into Pinecone (Vector DB).