Vector Databases
- 110 installs
- 7 repo stars
- Updated January 15, 2026
- eyadsibai/ltk
Design embeddings storage, similarity search, and upsert/query flows when building RAG, semantic search, or recommendation backends.
About
Vector-databases from eyadsibai/ltk guides implementation of embedding-backed storage: choose index types, model dimensions, metadata filters, and query/upsert patterns so agents and services deliver reliable semantic search and retrieval over large corpora.
- Embedding index setup
- Similarity query patterns
- Upsert and metadata filters
- RAG backend guidance
- LTK data toolkit
Vector Databases by the numbers
- 110 all-time installs (skills.sh)
- Ranked #317 of 911 Databases skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
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| Installs | 110 |
|---|---|
| repo stars | ★ 7 |
| Last updated | January 15, 2026 |
| Repository | eyadsibai/ltk ↗ |
What it does
Design embeddings storage, similarity search, and upsert/query flows when building RAG, semantic search, or recommendation backends.
Files
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>
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