Getting Started with Vector Databases

Vector databases are revolutionizing how we store and search unstructured data. In this comprehensive guide, we'll explore what vector databases are, why they matter, and how to get started with them.

What is a Vector Database?

A vector database is a specialized database designed to store, index, and search vector embeddings - mathematical representations of data in high-dimensional space. Unlike traditional databases that store structured data in rows and columns, vector databases excel at finding semantic similarity between data points.

Why Use Vector Databases?

Vector databases have become essential for modern AI applications:

1. Semantic Search - Find similar items based on meaning
2. RAG (Retrieval Augmented Generation) - Power AI assistants with relevant context
3. Recommendation Systems - Suggest similar products or content
4. Anomaly Detection - Identify outliers in your data
5. Image/Video Search - Find visually similar content

How Do Vector Embeddings Work?

Vector embeddings are created by machine learning models that convert data (text, images, audio) into arrays of numbers:

// Example: Text to vector embedding
const text = "Vector databases are awesome";
const embedding = await model.embed(text);

console.log(embedding);
// Output: [0.123, -0.456, 0.789, ..., 0.234] (1536 dimensions)

These embeddings capture semantic meaning - similar items have similar vectors, which can be measured using mathematical distance metrics.

Similarity Search Basics

The core operation in vector databases is similarity search - finding vectors closest to a query vector:

// Search for similar documents
const results = await vectorDB.search({
  vector: queryEmbedding,
  limit: 10,
  filter: { category: "tutorial" }
});

// Results ranked by similarity
results.forEach(result => {
  console.log(result.text, result.score);
});

Common Distance Metrics

Vector similarity can be measured in different ways:

• Cosine Similarity - Best for normalized vectors
• Euclidean Distance - Measures straight-line distance
• Dot Product - Fast but sensitive to magnitude

Getting Started with Vectrify

Here's a simple example using Vectrify:

import { VectrifyClient } from '@vectrify/sdk';

// Initialize client
const client = new VectrifyClient({
  apiKey: process.env.VECTRIFY_API_KEY
});

// Create a collection
const collection = await client.createCollection({
  name: "my-documents",
  dimension: 1536,
  metric: "cosine"
});

// Insert vectors
await collection.insert([
  {
    id: "doc1",
    vector: embedding1,
    metadata: { title: "Introduction to AI" }
  },
  {
    id: "doc2",
    vector: embedding2,
    metadata: { title: "Advanced Machine Learning" }
  }
]);

// Search
const results = await collection.search({
  vector: queryVector,
  limit: 5
});

Best Practices

When working with vector databases, keep these tips in mind:

1. Choose the right embedding model - Different models for different use cases
2. Optimize vector dimensions - Balance between accuracy and performance
3. Use metadata filters - Combine semantic and structured search
4. Monitor performance - Track query latency and accuracy
5. Version your embeddings - Keep track of model versions

Real-World Use Cases

1. Semantic Search Engine

Build a search engine that understands user intent:

const query = "how to improve app performance";
const embedding = await embedModel.embed(query);
const results = await collection.search({ vector: embedding });

// Returns relevant docs about optimization, speed, efficiency

2. RAG for Chatbots

Power AI assistants with relevant context:

async function askQuestion(question: string) {
  // Find relevant context
  const context = await collection.search({
    vector: await embed(question),
    limit: 3
  });
  
  // Generate answer with context
  const answer = await llm.generate({
    prompt: question,
    context: context.map(r => r.metadata.text)
  });
  
  return answer;
}

3. Product Recommendations

Recommend similar products based on user behavior:

const userHistory = await getUserPurchases(userId);
const userEmbedding = aggregateEmbeddings(userHistory);

const recommendations = await collection.search({
  vector: userEmbedding,
  filter: { inStock: true },
  limit: 10
});

Next Steps

Now that you understand the basics, here's what to explore next:

• Advanced Indexing - Learn about HNSW, IVF, and other algorithms
• Hybrid Search - Combine vector and keyword search
• Production Optimization - Scale to billions of vectors
• Integration Patterns - Integrate with your existing stack

Conclusion

Vector databases are the foundation of modern AI applications. By storing and searching semantic information efficiently, they enable powerful features like semantic search, RAG, and recommendations.

Ready to build your first vector-powered application?