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name: vector-databases description: Use when "vector database", "embedding storage", "similarity search", "semantic search", "Chroma", "ChromaDB", "FAISS", "Qdrant", "RAG retrieval", "k-NN search", "vector index", "HNSW", "IVF" version: 1.0.0

Vector Databases

Store and search embeddings for RAG, semantic search, and similarity applications.

Comparison

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Chroma

Embedded vector database for prototyping. No server needed.

Strengths: Zero-config, auto-embedding, metadata filtering, persistent storage Limitations: Not for production scale, single-node only

Key concept: Collections hold documents + embeddings + metadata. Auto-embeds text if no vectors provided.

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FAISS (Facebook AI)

Pure vector similarity - no metadata, no filtering, maximum speed.

Index types:

• Flat: Exact search, small datasets (< 10K)
• IVF: Inverted file, medium datasets (10K - 1M)
• HNSW: Graph-based, good recall/speed tradeoff
• PQ: Product quantization, memory efficient for billions

Strengths: Fastest, GPU support, scales to billions Limitations: No filtering, no metadata, vectors only

Key concept: Choose index based on dataset size. Trade accuracy for speed with approximate search.

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Qdrant

Production-ready with rich filtering and hybrid search.

Strengths: Payload filtering, horizontal scaling, cloud option, gRPC API Limitations: More complex setup than Chroma

Key concept: "Payloads" are metadata attached to vectors. Filter during search, not after.

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Index Algorithm Concepts

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Decision Guide

Resources

• Chroma: https://docs.trychroma.com
• FAISS: https://github.com/facebookresearch/faiss
• Qdrant: https://qdrant.tech/documentation/
• Pinecone: https://docs.pinecone.io