Part 1: Introduction to Vector Databases

← Back to Series Overview | Part 2: Vector Embeddings →

My Documentation Search Nightmare

It was 3 AM, and I was debugging why our engineering documentation search was completely useless.

The problem:

• User searches: "how do I deploy a container"

• Our docs literally had a page titled: "Kubernetes Deployment Guide"

• Search returned ZERO results

Why? Our PostgreSQL full-text search looked for exact word matches:

• Query: deploy, container

• Doc title: kubernetes, deployment, guide

• Match: NONE

I tried expanding synonyms manually (container → docker → kubernetes), but it was impossible to maintain. Our documentation had hundreds of pages.

User satisfaction was 41%. Developers were frustrated. The wiki was useless.

Then I discovered vector embeddings and semantic search.

The Transformation

I implemented pgvector and stored document embeddings. Same query, different results:

// Old keyword search
SELECT * FROM docs WHERE to_tsvector(content) @@ to_tsquery('deploy & container');
// Result: 0 rows

// New vector search
SELECT * FROM docs ORDER BY embedding <-> $1 LIMIT 5;
// Result: 5 highly relevant pages including "Kubernetes Deployment Guide"

Search satisfaction jumped to 92% within a week.

The magic? Vector embeddings understand that:

• "deploy container" ≈ "kubernetes deployment"

• "fix bug" ≈ "debugging" ≈ "troubleshoot"

• "API authentication" ≈ "REST security" ≈ "OAuth implementation"

This article explains what vector databases are, why they matter, and when to use them.

What Is a Vector Database?

Simple definition: A database optimized for storing and searching multi-dimensional numeric arrays that represent the "meaning" of data.

Traditional Database Storage

CREATE TABLE products (
  id SERIAL PRIMARY KEY,
  name TEXT,
  description TEXT,
  price DECIMAL
);

-- Search: exact matches or text patterns
SELECT * FROM products WHERE name ILIKE '%laptop%';

Limitation: Only finds products with the word "laptop" in the name.

Vector Database Storage

CREATE TABLE products (
  id SERIAL PRIMARY KEY,
  name TEXT,
  description TEXT,
  price DECIMAL,
  embedding VECTOR(1536)  -- pgvector: stores 1536-dimensional vector
);

-- Search: semantic similarity
SELECT * FROM products ORDER BY embedding <-> $query_vector LIMIT 10;

Power: Finds products semantically similar to your query, even with different words.

Query: "portable computer for coding" Results:

1. "MacBook Pro 14" Developer Edition"

2. "ThinkPad X1 Carbon Programming Laptop"

3. "Dell XPS 13 for Software Engineers"

None of these titles contain "portable," "computer," or "coding," but they're semantically similar.

How Vector Databases Work (The Simple Version)

Step 1: Convert text to vectors (embeddings)

import { OpenAI } from 'openai';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function getEmbedding(text: string): Promise<number[]> {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: text,
  });
  
  return response.data[0].embedding; // Returns array of 1536 numbers
}

// Example
const embedding = await getEmbedding("How to deploy containers");
console.log(embedding.slice(0, 5)); 
// [-0.0123, 0.456, -0.789, 0.234, -0.567, ...]

What are these numbers? Coordinates in 1536-dimensional space that represent the "meaning" of the text.

Step 2: Store vectors in database

import { PrismaClient } from '@prisma/client';

const prisma = new PrismaClient();

async function storeDocument(title: string, content: string) {
  const text = `${title} ${content}`;
  const embedding = await getEmbedding(text);
  
  await prisma.document.create({
    data: {
      title,
      content,
      embedding: embedding, // pgvector stores as vector type
    },
  });
}

Step 3: Search by similarity

async function searchDocuments(query: string, limit: number = 5) {
  // Convert query to vector
  const queryEmbedding = await getEmbedding(query);
  
  // Find similar vectors using cosine distance
  const results = await prisma.$queryRaw`
    SELECT id, title, content,
           1 - (embedding <=> ${queryEmbedding}::vector) as similarity
    FROM documents
    ORDER BY embedding <=> ${queryEmbedding}::vector
    LIMIT ${limit}
  `;
  
  return results;
}

// Usage
const docs = await searchDocuments("deploying containers");
console.log(docs);
// [
//   { title: "Kubernetes Deployment Guide", similarity: 0.87 },
//   { title: "Docker Container Basics", similarity: 0.82 },
//   { title: "Container Orchestration with ECS", similarity: 0.79 },
// ]

The <=> operator calculates cosine distance between vectors. Closer vectors = more similar meaning.

Real-World Use Cases I've Implemented

1. Documentation Search (The Problem I Started With)

Challenge: 500+ internal documentation pages, keyword search was useless.

Solution: Embedded all docs with text-embedding-3-small, stored in pgvector.

Results:

• Search quality: 41% → 92%

• Average results: 0.3 → 4.8 relevant pages

• Time to find answer: 8 minutes → 45 seconds

TypeScript implementation:

interface Doc {
  id: number;
  title: string;
  content: string;
  url: string;
  embedding: number[];
}

async function semanticDocSearch(query: string) {
  const queryVector = await getEmbedding(query);
  
  const results = await prisma.$queryRaw<Doc[]>`
    SELECT 
      id, title, content, url,
      1 - (embedding <=> ${queryVector}::vector) as similarity
    FROM documentation
    WHERE 1 - (embedding <=> ${queryVector}::vector) > 0.7
    ORDER BY embedding <=> ${queryVector}::vector
    LIMIT 10
  `;
  
  return results;
}

2. Customer Support Ticket Routing

Challenge: Route 1000+ daily tickets to correct team based on content, not just keywords.

Solution: Embedded ticket descriptions, found similar historical tickets, routed to same team.

Results:

• Correct routing: 73% → 91%

• First response time: 4 hours → 1.5 hours

• Mis-routed tickets: 27% → 9%

Key insight: "Payment not working" and "Card declined" are similar, even with different words.

async function routeTicket(ticketContent: string) {
  const ticketVector = await getEmbedding(ticketContent);
  
  // Find 5 most similar solved tickets
  const similar = await prisma.$queryRaw`
    SELECT team, category, COUNT(*) as count
    FROM tickets
    WHERE status = 'solved'
    ORDER BY embedding <=> ${ticketVector}::vector
    LIMIT 5
  `;
  
  // Route to most common team in similar tickets
  const recommendedTeam = similar[0].team;
  return recommendedTeam;
}

3. E-commerce Product Recommendations

Challenge: Show "Similar Products" that actually make sense.

Solution: Embedded product descriptions, find nearest neighbors.

Results:

• Click-through rate: 2.1% → 8.7%

• Add-to-cart from recommendations: 3x increase

• Revenue from recommendations: $45k → $189k monthly

async function getSimilarProducts(productId: number) {
  const product = await prisma.product.findUnique({
    where: { id: productId },
    select: { embedding: true },
  });
  
  const similar = await prisma.$queryRaw`
    SELECT id, name, price, image_url,
           1 - (embedding <=> ${product.embedding}::vector) as similarity
    FROM products
    WHERE id != ${productId}
      AND 1 - (embedding <=> ${product.embedding}::vector) > 0.75
    ORDER BY embedding <=> ${product.embedding}::vector
    LIMIT 6
  `;
  
  return similar;
}

When to Use Vector Databases

✅ Perfect Use Cases

Semantic search - Find documents by meaning, not keywords

• Documentation, knowledge bases, wikis

• Customer support articles

• Legal document discovery

• Research paper search

Recommendations - Find similar items

• Product recommendations

• Content recommendations (articles, videos)

• "Customers who bought this also bought..."

• Job recommendations

RAG (Retrieval-Augmented Generation) - Give LLMs relevant context

• Chatbots with company knowledge

• Code completion with codebase context

• Question answering from documents

Duplicate detection - Find semantically duplicate content

• Duplicate support tickets

• Similar bug reports

• Plagiarism detection

• Content moderation

Classification - Route or categorize by similarity

• Support ticket routing

• Email categorization

• Intent classification

❌ When NOT to Use Vectors

Exact matches - Use traditional indexes

-- Use standard B-tree index, not vectors
SELECT * FROM users WHERE email = '[email protected]';

Aggregations and analytics - Use columnar databases

-- Use traditional database, not vectors
SELECT category, COUNT(*), AVG(price) FROM products GROUP BY category;

Transactional workloads - Use RDBMS

-- Use standard database transactions
UPDATE accounts SET balance = balance - 100 WHERE id = 123;

High cardinality filtering - Vectors don't replace WHERE clauses

-- Combine vector search WITH traditional filters
SELECT * FROM products
WHERE price < 1000 AND category = 'electronics'
ORDER BY embedding <=> $vector
LIMIT 10;

Vector Databases vs Traditional Databases

Traditional Database: Exact Matching

// Find products with keyword "laptop"
const results = await prisma.product.findMany({
  where: {
    name: { contains: 'laptop' },
  },
});

Finds: Products with "laptop" in name Misses: "notebook computer," "portable workstation," "MacBook"

Vector Database: Semantic Matching

// Find products semantically similar to "laptop"
const vector = await getEmbedding('laptop');
const results = await prisma.$queryRaw`
  SELECT * FROM products
  ORDER BY embedding <=> ${vector}::vector
  LIMIT 10
`;

Finds: Laptops, notebooks, MacBooks, portable computers (all semantically similar) Power: Understands meaning, not just exact words

Why I Chose pgvector (PostgreSQL Extension)

I evaluated Pinecone, Weaviate, Qdrant, Milvus, and pgvector. Here's why I use pgvector for most projects:

✅ Advantages

One database, not two

// Single database handles both structured data AND vectors
await prisma.$transaction([
  prisma.order.create({ data: orderData }),
  prisma.product.update({ 
    where: { id: productId },
    data: { embedding: newEmbedding },
  }),
]);

ACID transactions - Vectors stay consistent with your data Familiar SQL - Use existing PostgreSQL knowledge Lower costs - No separate vector database service ($0 → $200/month saved) Simpler operations - One database to backup, monitor, scale Type safety - Prisma generates TypeScript types

⚠️ Limitations

Performance at massive scale - pgvector is great up to ~100M vectors. Beyond that, specialized DBs win.

Limited vector operations - Can't do complex graph traversals like some vector DBs.

Index flexibility - Fewer index types than specialized solutions.

For 90% of applications, pgvector is perfect. Start here, scale later if needed.

Vector Similarity Metrics (Quick Overview)

Vector databases use mathematical distance functions to find similar vectors:

Cosine Similarity (Most Common)

Measures angle between vectors. Good for: Text, embeddings from ML models.

// pgvector operator: <=> (cosine distance)
const results = await prisma.$queryRaw`
  SELECT *, 1 - (embedding <=> ${queryVector}::vector) as similarity
  FROM documents
  ORDER BY embedding <=> ${queryVector}::vector
  LIMIT 5
`;

Range: 0 (opposite) to 1 (identical)

Euclidean Distance (L2)

Straight-line distance between points. Good for: Spatial data, coordinates.

// pgvector operator: <-> (L2 distance)
const results = await prisma.$queryRaw`
  SELECT *, embedding <-> ${queryVector}::vector as distance
  FROM locations
  ORDER BY embedding <-> ${queryVector}::vector
  LIMIT 5
`;

Range: 0 (identical) to ∞ (very different)

Dot Product

Magnitude AND angle. Good for: Normalized vectors, some ML models.

// pgvector operator: <#> (negative dot product)
const results = await prisma.$queryRaw`
  SELECT *, (embedding <#> ${queryVector}::vector) * -1 as similarity
  FROM images
  ORDER BY embedding <#> ${queryVector}::vector
  LIMIT 5
`;

For text embeddings (OpenAI, Sentence Transformers), use cosine similarity.

My First Vector Search in 20 Lines

Here's the complete code to implement semantic search:

import { PrismaClient } from '@prisma/client';
import { OpenAI } from 'openai';

const prisma = new PrismaClient();
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function semanticSearch(query: string) {
  // 1. Convert query to vector
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: query,
  });
  const queryVector = response.data[0].embedding;
  
  // 2. Find similar documents
  const results = await prisma.$queryRaw`
    SELECT id, title, content,
           1 - (embedding <=> ${queryVector}::vector) as similarity
    FROM documents
    WHERE 1 - (embedding <=> ${queryVector}::vector) > 0.7
    ORDER BY embedding <=> ${queryVector}::vector
    LIMIT 5
  `;
  
  return results;
}

// Usage
const docs = await semanticSearch("How do I deploy containers?");
console.log(docs);

That's it. 20 lines for semantic search that understands meaning.

What's Next

In this article, you learned:

• ✅ What vector databases are and why they matter

• ✅ How they differ from traditional databases

• ✅ Real use cases I've implemented

• ✅ When to use vectors vs traditional search

• ✅ Why I chose pgvector

• ✅ Basic semantic search in 20 lines

Next: We'll dive deep into how embeddings actually work, how to generate them, and the math behind similarity search.

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← Back to Series Overview | Part 2: Vector Embeddings Fundamentals →