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