Vector Database Integration for AI Applications

Vector databases are the backbone of modern AI applications, enabling semantic search, RAG, and personalization. This guide covers embeddings, similarity search, indexing strategies, and production patterns for vector databases.

What are Vector Databases?

Embeddings 101

Convert text to numerical vectors that capture meaning:

// Generate embedding
const embedding = await openai.embeddings.create({
  model: "text-embedding-3-small",
  input: "How do I reset my password?"
});

// Result: [0.023, -0.142, 0.891, ... ] (1536 dimensions)
// Similar questions will have similar vectors

Popular Vector Databases

Similarity Search

Indexing Strategies

• • HNSW: Fast approximate search, great for production
• • IVF: Cluster-based, good for huge datasets
• • Flat: Exact search, slow but accurate

Production Best Practices

Conclusion

Vector databases unlock semantic search capabilities essential for modern AI apps. Choose the right database for your scale, optimize embeddings, and implement efficient indexing for fast, relevant results.