Vector Database 101
Welcome to my Vector Database 101 series! This comprehensive guide takes you from understanding vector embeddings to building production-ready semantic search systems with PostgreSQL and TypeScript.
My Vector Database Journey
I was building a documentation search feature. Traditional full-text search was failing miserably.
User searches "how to deploy containers" → Returns nothing. The docs said "Kubernetes deployment guide" → Exact words didn't match.
Users were frustrated. Important documentation was invisible because they used different words than we did.
Then I implemented semantic search with vector embeddings. Same query, instant results. The system understood that "deploy containers" and "Kubernetes deployment" meant the same thing.
Search satisfaction jumped from 40% to 92%.
That's when I learned: Vector databases aren't hype—they're a fundamental shift in how we understand and search data.
This series shares everything I learned building production vector search systems with PostgreSQL and TypeScript.
Series Overview
My journey from keyword search failure to semantic success
What vector databases actually solve
When to use vectors vs traditional databases
Real-world use cases I've implemented
Real example: Debugging why keyword search failed for documentation
Understanding embeddings: turning text into numbers
How embedding models work (OpenAI, Sentence Transformers)
Similarity metrics: cosine, euclidean, dot product
Visualizing vector spaces
Real example: Building a product recommendation system with embeddings
Why I chose pgvector over specialized vector DBs
Installing and configuring pgvector
Creating vector columns and indexes
HNSW vs IVFFlat indexes explained
Real example: Migrating from Pinecone to PostgreSQL to reduce costs
Complete TypeScript application setup
Generating embeddings with OpenAI API
Storing and querying vectors with Prisma
Implementing semantic search endpoints
Real example: Building a customer support knowledge base
Combining vector and keyword search
Filtering vectors with metadata
Multi-vector queries (documents + images)
Reranking and relevance tuning
Real example: E-commerce product search with filters
Understanding vector index strategies
Query performance tuning
Memory and storage optimization
Monitoring and debugging slow queries
Real example: Optimizing search latency from 2s to 50ms
Embedding versioning and migrations
Managing embedding model updates
Scaling strategies and sharding
Caching and CDN integration
Error handling and fallback strategies
Real example: Handling embedding model deprecation in production
Who This Series Is For
You should read this if you:
Build search or recommendation features
Work with AI/ML applications
Want to implement semantic/similarity search
Need to understand unstructured data
Are exploring RAG (Retrieval-Augmented Generation)
Want to add vector capabilities to existing PostgreSQL databases
You'll get the most value if you:
Have basic TypeScript/JavaScript knowledge
Understand SQL and relational databases
Have worked with REST APIs
Want practical, production-ready code
Learn better from real project experience
What Makes This Series Different
Real project experience: Every concept comes from actual production systems I've built and maintained.
PostgreSQL-first approach: Use your existing database instead of adding another service. pgvector brings vectors to PostgreSQL.
TypeScript focus: Modern, type-safe code you can use in production immediately.
Cost-conscious: I show you how to build powerful vector search without expensive managed services.
Progressive learning: Start simple, add complexity as you understand the fundamentals.
Prerequisites
TypeScript/JavaScript: Comfortable with async/await, types, and Node.js
PostgreSQL: Basic SQL knowledge and database concepts
REST APIs: Understanding HTTP and API design
OpenAI API (optional): For generating embeddings (we'll also cover local alternatives)
All code examples use:
TypeScript 5+
PostgreSQL 14+ with pgvector extension
Prisma ORM for type-safe database access
OpenAI API for embeddings (with local alternatives shown)
Tech Stack for This Series
PostgreSQL extensions:
What You'll Build
By the end of this series, you'll have built:
Semantic search API - Find similar documents by meaning
Product recommendation engine - Suggest items based on similarity
Q&A knowledge base - Customer support with semantic matching
Hybrid search system - Combine keywords and vectors
Production-ready architecture - Scalable, monitored, maintainable
My Promise
No fake scenarios. Every example is based on real systems I've built or problems I've solved.
No magic abstractions. You'll understand how vectors work, not just how to call a library.
No vendor lock-in. Use open-source PostgreSQL and pgvector. Switch embedding models anytime.
Working code. Every article includes complete, runnable TypeScript examples.
Why PostgreSQL + pgvector?
I've used Pinecone, Weaviate, Qdrant, and Milvus. Here's why I now default to pgvector:
One database: No separate vector service to manage
ACID guarantees: Transactional consistency with your data
Familiar SQL: Use existing PostgreSQL knowledge and tools
Lower costs: No per-vector pricing, use existing infrastructure
Type safety: Prisma generates TypeScript types from your schema
Simpler ops: One database to backup, monitor, and scale
For most applications (< 100M vectors), pgvector performs excellently.
How to Use This Series
Read sequentially if you're new to vector databases
Jump to specific topics if you have experience
Code along - understanding comes from building
Adapt examples to your actual use cases
Revisit sections as you encounter production challenges
Development Environment Setup
We'll set this up properly in Part 3, but here's a preview:
Let's Begin
Vector databases are transforming how we search, recommend, and understand data. They power:
ChatGPT's knowledge retrieval
Google's semantic search
Netflix's recommendations
GitHub Copilot's code suggestions
You don't need a specialized vector database to use this technology. PostgreSQL with pgvector is production-ready and powerful.
Start with Part 1: Introduction to Vector Databases →
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