A lightweight, optimized vector database built on Val Town's SQLite (powered by Turso/libSQL) with Nebius Qwen3-Embedding-8B embeddings.

• 4096-dimensional embeddings via Nebius Qwen3-Embedding-8B
• Semantic search with cosine similarity
• Distance scores in search results (0 = identical, 1 = orthogonal, 2 = opposite)
• Smart re-embedding - only re-embeds when text changes (content hash check)
• Optimized index for storage efficiency

~22KB per record → ~45,000 records per 1GB

curl -X POST https://YOUR_ENDPOINT/upsert \
  -H "Content-Type: application/json" \
  -d '{"id": "doc-1", "text": "Dogs are loyal pets", "meta": {"category": "animals"}}'

curl -X POST https://YOUR_ENDPOINT/search \
  -H "Content-Type: application/json" \
  -d '{"query": "furry pets", "k": 5}'

Only return results with distance ≤ 0.5 (more relevant):

curl -X POST https://YOUR_ENDPOINT/search \
  -H "Content-Type: application/json" \
  -d '{"query": "furry pets", "k": 10, "maxDistance": 0.5}'

The distance field uses cosine distance:

Recommended: Use maxDistance: 0.5 to filter out noise.

You can import vectordb.ts directly in other Val Town projects:

import * as db from "https://esm.town/v/kamenxrider/slimarmor/vectordb.ts";

// Setup (creates table + index if not exists)
await db.setup();

// Upsert
await db.upsert("my-doc", "Some text to embed", { category: "notes" });

// Search
const results = await db.search("search query", 10, 0.5);
// Returns: [{ id, text, meta, distance }, ...]

// Delete
await db.remove("my-doc");

// Stats
const stats = await db.stats();
// Returns: { count: 5, estimated_storage_mb: 0.11 }

• Runtime: Val Town (Deno)
• Database: Val Town SQLite (Turso/libSQL)
• Embeddings: Nebius Qwen3-Embedding-8B (4096 dims)
• Vector Index: DiskANN with cosine similarity

MIT