vector-databases
Original：🇺🇸 English
Translated
Use when "vector database", "embedding storage", "similarity search", "semantic search", "Chroma", "ChromaDB", "FAISS", "Qdrant", "RAG retrieval", "k-NN search", "vector index", "HNSW", "IVF"
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Sourceeyadsibai/ltk
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NPX Install
npx skill4agent add eyadsibai/ltk vector-databasesTags
Translated version includes tags in frontmatterSKILL.md Content
View Translation Comparison →Vector Databases
Store and search embeddings for RAG, semantic search, and similarity applications.
Comparison
| Database | Best For | Filtering | Scale | Managed Option |
|---|---|---|---|---|
| Chroma | Local dev, prototyping | Yes | < 1M | No |
| FAISS | Max speed, GPU, batch | No | Billions | No |
| Qdrant | Production, hybrid search | Yes | Millions | Yes |
| Pinecone | Fully managed | Yes | Billions | Yes (only) |
| Weaviate | Hybrid search, GraphQL | Yes | Millions | Yes |
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.
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.
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.
Index Algorithm Concepts
| Algorithm | How It Works | Trade-off |
|---|---|---|
| Flat | Compare to every vector | Perfect recall, slow |
| IVF | Cluster vectors, search nearby clusters | Good recall, fast |
| HNSW | Graph of neighbors | Best recall/speed ratio |
| PQ | Compress vectors | Memory efficient, lower recall |
Decision Guide
| Requirement | Recommendation |
|---|---|
| Quick prototype | Chroma |
| Metadata filtering | Chroma, Qdrant, Pinecone |
| Billions of vectors | FAISS |
| GPU acceleration | FAISS |
| Production deployment | Qdrant or Pinecone |
| Fully managed | Pinecone |
| On-premise control | Qdrant, Chroma |
Resources
- Chroma: https://docs.trychroma.com
- FAISS: https://github.com/facebookresearch/faiss
- Qdrant: https://qdrant.tech/documentation/
- Pinecone: https://docs.pinecone.io