Building a RAG Knowledge Base with OpenAI and Pinecone

What is RAG and Why Does it Matter?

Retrieval-Augmented Generation (RAG) grounds AI answers in your own documents. Instead of relying on the model's training data, you chunk your content, embed it, and retrieve the most relevant pieces before generation. The result: accurate answers with source citations, which is why multi-tenant RAG knowledge bases are transforming customer support platforms.

Chunking and Embeddings

Document quality determines answer quality. Split documents into semantic chunks of 500–1,000 tokens with overlap, then embed each chunk with a model like text-embedding-3-small. Store the vectors in a vector database such as Pinecone, pgvector, or Qdrant.

• Preserve headings and structure during chunking
• Store metadata (document id, page, source URL) alongside vectors
• Use chunk overlap to avoid splitting sentences mid-thought

Retrieval and Generation

On every query, embed the question, retrieve the top-K matching chunks, and build a prompt that instructs the model to answer only from the provided context. Ask the model to cite the source document for each claim. Stream the response and render citations as clickable links in the UI.

Evaluation and Hardening

Measure retrieval quality with hit-rate and answer relevance. Add metadata filters so retrieval respects tenants and permissions — a critical requirement in multi-tenant RAG platforms. Cache common questions to cut token costs and latency.