import { assert, assertEquals, } from "https://deno.land/std@0.224.0/testing/asserts.ts"; /** * Proof of Concept: Embedded Vector Database (Mocking sqlite-vec & TurboQuant) * * This module demonstrates the concept of hashing semantic concepts into vectors * and performing cosine similarity to achieve fuzzy retrieval of associative memory * without needing an external vector database. */ // Simple mock for cosine similarity of 1D arrays function cosineSimilarity(vecA: number[], vecB: number[]): number { let dotProduct = 0; let normA = 0; let normB = 0; for (let i = 0; i < vecA.length; i++) { dotProduct += vecA[i] * vecB[i]; normA += vecA[i] ** 2; normB += vecB[i] ** 2; } if (normA === 0 || normB === 0) return 0; return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB)); } // Mock of quantized vectors (e.g. TurboQuant 2-bit/4-bit compression) const MOCK_VECTOR_DB = [ { id: "docs-1", text: "How to run the server", vector: [0.8, 0.1, 0.1, 0.0] }, { id: "docs-2", text: "Database connection logic", vector: [0.1, 0.9, 0.2, 0.1], }, { id: "telemetry-1", text: "Server latency spikes", vector: [0.2, 0.1, 0.9, 0.3], }, ]; if (import.meta.main) { console.log("Running Embedded Vector Database PoC tests..."); try { // Query representing "I have a slow server issue" const queryVector = [0.3, 0.0, 0.9, 0.2]; console.log("Querying Vector DB..."); const results = MOCK_VECTOR_DB.map((doc) => ({ ...doc, score: cosineSimilarity(queryVector, doc.vector), })).sort((a, b) => b.score - a.score); console.log( "Top result:", results[0].text, `(Score: ${results[0].score.toFixed(2)})`, ); assert( results[0].score > 0.8, "The telemetry doc should be the highest match", ); assertEquals(results[0].id, "telemetry-1"); console.log( "✅ Embedded Vector DB PoC successful: Fuzzy semantic match found.", ); } catch (err) { console.error("❌ Embedded Vector DB PoC failed:", err); Deno.exit(1); } }