Embedding World

Deep dives into text embeddings, vector databases, sparse retrieval, and semantic search. A curated collection of articles on building production-ready search systems.

Embedding Models, Vector Databases & Semantic Search

This is a curated collection of my writing on text embeddings, sparse retrieval, vector databases, and semantic search architecture.

If you’re building search systems or RAG pipelines, start here:

• HyperRun — A self-hosted AI docs widget with semantic code search
• HyperRun + FastHTML — Streaming chat architecture with HTMX and SSE
• Late Interaction — ColBERT and multi-vector retrieval explained
• BM25 from Scratch — Implementing classical sparse retrieval
• BM25 + Qdrant for Arabic — Practical sparse+dense hybrid search
• BM25 Benchmarks — Full comparison across datasets
• Vector Database Book — Notes on building vector search systems

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