agent-ml/knowledge/examples/semantic-search-example.ts

/**
 * Semantic Search Example
 *
 * Demonstrates how to use the semantic search system for the Venus knowledge graph.
 */

import Redis from 'ioredis';
import { createSemanticSearch } from '../src/semantic/index.js';

async function main() {
  // Initialize Redis connection
  const redis = new Redis({
    host: 'localhost',
    port: 6379,
  });

  console.log('Creating semantic search service...');
  const search = await createSemanticSearch(redis, {
    embedder: 'mock',
    embeddingDimensions: 384,
    createIndex: true,
  });

  // Check if service is ready
  const ready = await search.isReady();
  console.log(`Service ready: ${ready}`);

  // Example documents
  const documents = [
    {
      docId: 'doc:victoria',
      content: `Victoria focuses on programming career and software development.
        She uses Eterna Tech as her professional software brand.
        She is actively job hunting and working on open source projects.`,
      metadata: { docType: 'identity', person: 'victoria', category: 'real' },
    },
    {
      docId: 'doc:quinn',
      content: `Quinn (transquinnftw) is a gaming and PC building content creator.
        She runs UwuPCs brand for gaming content.
        She streams, does camming, and creates watercooling builds.`,
      metadata: { docType: 'identity', person: 'quinn', category: 'real' },
    },
    {
      docId: 'doc:lilith',
      content: `Lilith Vaelynn is a hidden alien hacker and covert operator.
        She maintains NO web presence due to OPSEC concerns.
        She operates through proxies and recruits followers like Quinn.`,
      metadata: { docType: 'identity', character: 'lilith', category: 'fictional' },
    },
    {
      docId: 'doc:rachel',
      content: `Rachel Albright is the CEO of Venus Tech.
        She is a professional business persona focused on text-only presence.
        She handles LinkedIn, professional networking, and marketplace sales.`,
      metadata: { docType: 'identity', persona: 'rachel', category: 'business' },
    },
    {
      docId: 'doc:venus-tech',
      content: `Venus Tech is a comprehensive brand universe and business development project.
        It focuses on content creation, character development, and strategic planning.
        The project includes multiple identities, brands, and operational businesses.`,
      metadata: { docType: 'brand', name: 'venus-tech' },
    },
  ];

  console.log('\n--- Indexing documents ---');
  await search.indexDocuments(documents);
  console.log(`Indexed ${documents.length} documents`);

  // Give Redis time to index
  await new Promise((resolve) => setTimeout(resolve, 200));

  // Example 1: Search for programming-related content
  console.log('\n--- Search: "programming software development" ---');
  const programmingResults = await search.search({
    query: 'programming software development',
    limit: 3,
    minScore: 0.5,
    includeChunks: true,
  });

  for (const result of programmingResults) {
    console.log(`\n${result.docId}: ${(result.score * 100).toFixed(1)}% match`);
    if (result.matchedChunk) {
      console.log(`  Preview: ${result.matchedChunk.text.slice(0, 100)}...`);
    }
    console.log(`  Metadata: ${JSON.stringify(result.metadata)}`);
  }

  // Example 2: Search for gaming content
  console.log('\n--- Search: "gaming streaming PC builds" ---');
  const gamingResults = await search.search({
    query: 'gaming streaming PC builds',
    limit: 3,
    minScore: 0.5,
  });

  for (const result of gamingResults) {
    console.log(`${result.docId}: ${(result.score * 100).toFixed(1)}% match`);
  }

  // Example 3: Find documents similar to Venus Tech brand doc
  console.log('\n--- Similar to: doc:venus-tech ---');
  const similarDocs = await search.findSimilar('doc:venus-tech', 3);

  for (const doc of similarDocs) {
    console.log(`${doc.docId}: ${(doc.score * 100).toFixed(1)}% similar`);
  }

  // Example 4: Search with metadata filtering
  console.log('\n--- Search: "professional business" (fictional only) ---');
  const businessResults = await search.search({
    query: 'professional business',
    limit: 3,
    minScore: 0.4,
  });

  for (const result of businessResults) {
    console.log(`${result.docId}: ${(result.score * 100).toFixed(1)}% match`);
  }

  // Clean up
  console.log('\n--- Cleanup ---');
  for (const doc of documents) {
    await search.removeDocument(doc.docId);
  }
  console.log('Removed all indexed documents');

  await redis.quit();
  console.log('\nExample completed successfully!');
}

// Run the example
main().catch((error) => {
  console.error('Error:', error);
  process.exit(1);
});