About this ebook
When you search for something on Spotify and it finds music you actually like, that is vector search. When you ask ChatGPT a question about your company's internal documents and it finds the right information, that is vector search. When an e-commerce site recommends products that are genuinely relevant to what you were just looking at, that is vector search.
Vector databases are the infrastructure layer that makes intelligent AI applications possible — and most developers building LLM applications are either doing this wrong, doing it inefficiently, or not doing it at all.
This book fixes that.
Vector Databases is a complete, code-first guide to understanding, implementing, and deploying vector search for AI applications — from the mathematics of embeddings to production-scale RAG architecture, covering the tools that actually matter in 2026.
Inside you will find:
The complete foundation — how embeddings convert meaning into geometry, how distance metrics measure similarity, and how indexing algorithms like HNSW make billion-scale search possible in milliseconds
Four major vector database platforms covered in depth — Chroma for fast local development, Pinecone for managed production-scale deployments, Weaviate for hybrid search and multimodal capabilities, and FAISS for maximum-performance custom implementations — each with complete working Python code
RAG implementation from scratch — document processing strategies, chunking approaches, complete retrieval pipelines, advanced techniques including HyDE and multi-query retrieval, and systematic evaluation with precision and recall metrics
Production architecture — metadata filtering, hybrid search combining keyword and vector approaches, horizontal sharding, caching, multi-tenant systems, and real-world patterns for enterprise deployments handling millions of documents
Multimodal embeddings with CLIP for image-text search, fine-tuning embedding models on your own domain data, and recommendation systems built on vector similarity
Three complete real-world applications: enterprise semantic search, content recommendation, and multi-tenant enterprise RAG with access control
Every chapter includes working Python code you can run immediately — built against real APIs and real databases, not simplified pseudocode
If you are building LLM applications without understanding vector databases, you are building on a foundation you do not understand.
This book gives you that foundation — and everything built on top of it.
By Fatima Ahmad. Available on Google Play Books.
