Vector Databases
What this article covers
- What vector databases are and their use cases.
- Comparison of Chroma and Qdrant.
- Links to dedicated articles on Chroma and Qdrant.
Introduction
Vector databases store embeddings and enable fast similarity search. They’re essential for RAG because they retrieve text passages that match your prompt. For local deployment, Chroma and Qdrant stand out.
Contents
- Chroma - Simple, Python-friendly vector database.
- Qdrant - Scalable vector database with REST and gRPC APIs.
- Weaviate - Vector database with GraphQL API and hybrid search.
- Milvus - High-performance vector database for large-scale deployments.
- pgvector - Vector search as a PostgreSQL extension.
- FAISS - Meta’s library for high-performance vector search.
Comparison
| Database | Strength | Getting started |
|---|---|---|
| Chroma | Easy to integrate into Python projects | Very straightforward |
| Qdrant | Scalable, production-ready | Straightforward |
FAQ - Frequently asked questions
Which vector database should I use as a beginner?
Chroma is very beginner-friendly if you work with Python. Qdrant is more robust if you need to scale later.
Are these databases free?
Both have open-source versions that you can run locally at no cost.