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50 lines
2.3 KiB
Markdown
50 lines
2.3 KiB
Markdown
+++
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title = "vector-db"
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priority = 5
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status = "todo"
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ticket_type = "project"
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dependencies = ["21d9be", "584e0c", "99e1d9", "d9f850", "6ec5ff", "37cdd5", "081a55", "5674ce", "4c961f", "1ef9f4", "e8be9a", "5ed295"]
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## Project: Vector Database Self-Guided Course
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This is the top-level project ticket for `edu/src/vector-db.md` — a self-guided mdbook course on vector databases in the **Vibed Learning** site (`edu/`).
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The course is modelled on `edu/src/markov.md`. It teaches vector databases through 12 sections across 4 parts, mixing reading lessons and hands-on Rust programming exercises using Turso (`libsql` crate) and sqlite-vec for local vector storage.
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## Course structure
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| # | Title | Status |
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|---|---|---|
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| §1 | What Is a Vector? | Written in full |
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| §2 | Embeddings | Stub [nbd:584e0c] |
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| §3 | Vector Similarity | Stub [nbd:99e1d9] |
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| §4 | What Is a Vector Database? | Stub [nbd:d9f850] |
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| §5 | Under the Hood: ANN Algorithms | Stub [nbd:6ec5ff] |
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| §6 | Setting Up | Written in full |
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| §7 | Exercise 1 — Storing and Retrieving Vectors | Stub [nbd:081a55] |
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| §8 | Exercise 2 — K-Nearest Neighbor Search | Stub [nbd:5674ce] |
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| §9 | Generating Embeddings in Rust | Stub [nbd:4c961f] |
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| §10 | Exercise 3 — Semantic Document Search | Stub [nbd:1ef9f4] |
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| §11 | Exercise 4 — Recommendation Engine | Stub [nbd:e8be9a] |
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| §12 | Exercise 5 — Retrieval-Augmented Generation | Stub [nbd:5ed295] |
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## Filling a stub
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1. Open `edu/src/vector-db.md`
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2. Find the section (e.g. `### 2. Embeddings`)
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3. Replace the stub line (`🚧 Full content tracked in [nbd:...]`) with full content
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4. Run `mdbook build` from `edu/` — must pass cleanly
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5. Mark the section ticket done
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## Tech stack used in exercises
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- **Runtime:** Tokio async
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- **DB crate:** `libsql = "0.9"` (Turso / libSQL Rust client)
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- **Vector support:** sqlite-vec, built into libsql — no extra install
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- **Embeddings:** `fastembed` crate (local) or OpenAI-compatible HTTP API
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- **Local connection:** `Builder::new_local("vectors.db").build().await?`
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- **Vector column type:** `F32_BLOB(d)` where d = embedding dimension
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- **KNN query:** `vector_top_k('table', vector('[...]'), k)` table-valued function
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- **Distance function:** `vector_distance_cos(a, b)` — 0 = identical, 2 = opposite
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This project ticket closes when all 12 section tickets are done and `mdbook build` passes. |