Vector Databases Zero to Hero: A Complete Practical Guide for Modern AI Systems
Table of Contents Introduction Why Vectors? From Raw Data to Embeddings Core Concepts of Vector Search 3.1 Similarity Metrics 3.2 Index Types Popular Vector Database Engines 4.1 FAISS 4.2 Milvus 4.3 Pinecone 4.4 Weaviate Setting Up a Vector Database from Scratch 5.1 Data Preparation 5.2 Choosing an Index 5.3 Ingestion Pipeline Practical Query Patterns 6.1 Nearest‑Neighbour Search 6.2 Hybrid Search (Vector + Metadata) 6.3 Filtering & Pagination Scaling Considerations 7.1 Sharding & Replication 7.2 GPU vs CPU Indexing 7.3 Cost Optimisation Security, Governance, and Observability Real‑World Use Cases 9.1 Semantic Search in Documentation Portals 9.2 Recommendation Engines 9.3 Anomaly Detection in Time‑Series Data Best Practices Checklist Conclusion Resources Introduction Vector databases have moved from an academic curiosity to a cornerstone technology for modern AI systems. Whether you are building a semantic search engine, a recommendation system, or a large‑scale anomaly detector, the ability to store, index, and query high‑dimensional vectors efficiently is now a non‑negotiable requirement. ...