Skip to main content
Intermediate60-90 minOllamaChroma or Qdrantlocal embedding model

Build a local RAG prototype with Ollama and a vector database

A privacy-oriented learning path for running a small RAG prototype locally.

Prerequisites

  • A machine with enough memory for local models
  • Command-line comfort
  • Small document set

Step-by-step tutorial

Step 1

Choose local models

Pick a chat model and embedding model that fit your hardware.

  • Check model license
  • Test latency
  • Document hardware limits
  • Avoid sensitive data until security is reviewed

Step 2

Index sample documents

Parse a small corpus and store embeddings in a local vector database.

  • Chunk documents
  • Store metadata
  • Run retrieval tests
  • Inspect missed queries

Step 3

Connect retrieval to generation

Send only the best retrieved context to the local model and cite sources.

  • Limit context size
  • Add source IDs
  • Handle no-answer cases
  • Log failures

Next steps

  • Try hybrid search
  • Measure latency
  • Move from prototype corpus to governed corpus