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Vector Databases Fundamentals Guide

``` EN: Learn how vector databases store and retrieve embeddings for RAG systems — from architecture and indexing algorithms to hands-on ChromaDB implementation and production decision-making. ES: Aprende cómo funcionan las vector databases por dentro, por qué las necesitas para sistemas RAG y cómo elegir la correcta para tu proyecto. Esta guía te lleva desde los fundamentos (arquitectura, algoritmos de indexing, features esenciales) hasta implementación hands-on con ChromaDB y un proyecto integrador de RAG con 1,000+ documentos y API FastAPI. Al terminar, sabrás implementar storage y retrieval de vectores para producción — y tendrás criterio para decidir entre ChromaDB, Pinecone, Weaviate y Qdrant según tus requisitos reales. ```

67

Lessons

8

Modules

🎓

Bootcamp access

Lo que aprenderás

✓Understand why RAG systems need vector databases (and when SQL, NoSQL, or numpy are enough)
✓Explain how vector databases work internally: architecture, HNSW, IVF, and PQ indexing algorithms
✓Identify essential features for production RAG: metadata filtering, hybrid search, multi-tenancy, batch operations
✓Implement ChromaDB from setup to optimized similarity search with metadata filtering
✓Build a complete RAG system: document ingestion, indexing, retrieval, and generation with FastAPI
✓Compare Pinecone, Weaviate, Qdrant, and Milvus — features, pricing, and architecture trade-offs
✓Apply a decision framework to choose the right vector database for any project
✓Design production strategies: scaling, monitoring, backups, migrations, and cost optimization

¿Para quién es?

  • •AI Engineers building RAG systems who need to store and retrieve embeddings at scale
  • •Developers who completed the Embeddings Deep Dive Guide and want to implement vector storage for production
  • •Backend engineers evaluating vector database options (ChromaDB vs Pinecone vs Weaviate) for their AI projects
  • •Students preparing for Week 7-9 of the AI Engineering Bootcamp (RAG implementation modules)

Requisitos

  • •Embeddings Deep Dive Guide completed (Guide #6): how to generate embeddings, distance metrics, semantic search with numpy
  • •AI Semantics Guide completed (Guide #5): what vectors are, cosine similarity, keyword vs semantic search
  • •Python intermediate: functions, classes, async/await, pip packages
  • •Basic familiarity with REST APIs (requests, endpoints, JSON)

Course content

1Módulo 1: Por qué Vector Databases para AI Engineers8 lessons
2Módulo 2: Cómo funcionan Vector Databases (Conceptual)8 lessons
3Módulo 3: Features Esenciales para RAG8 lessons
4Módulo 4: ChromaDB Setup y Pipeline RAG Completo11 lessons
5Módulo 5: Landscape de Vector Databases para AI Engineers8 lessons
6Módulo 6: Decision Matrix para AI Engineers8 lessons
7Módulo 7: Production Considerations para RAG8 lessons
8Módulo 8: Proyecto Integrador - RAG System con ChromaDB8 lessons
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