Day 20: Vector Databases & Indexing - Production Vector Storage for RAG
Автор: Hands On Course Demo
Загружено: 2026-01-12
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Today’s Build
We’re building a production-ready vector database system that replaces L19’s in-memory dictionary with ChromaDB:
Local ChromaDB instance with persistent storage and collection management
Gemini AI embedding pipeline that converts documents to 768-dimensional vectors
Similarity search engine with metadata filtering and ranking capabilities
Real-time dashboard showing indexed documents, search results, and performance metrics
Multi-collection architecture supporting different embedding strategies
From L19: We extend the naive RAG’s document chunker and retrieval interface, replacing the simple dict with a professional vector store that handles millions of embeddings.
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