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"

2installs

Sourceeyadsibai/ltk

Added on2026-02-16

NPX Install

npx skill4agent add eyadsibai/ltk vector-databases

SKILL.md Content

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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