Lesson 6: Vector Databases & Retrieval

Advanced Architectures for Vertical AI Agents ~3 min read 60 min hands-on

Semantic Retrieval Platform

This integrated project unifies vector database indexing, adaptive document chunking, hybrid retrieval, and cross-encoder reranking into a single production-style platform. Documents are segmented, embedded, and stored in a ChromaDB HNSW index while a synchronized BM25 lexical index enables keyword search alongside dense retrieval. Queries pass through reciprocal rank fusion with metadata filtering before an optional reranker reorders the candidate pool. A React operations console exposes every stage — ingestion, chunking comparison, search modes, ranking pipelines, and operational metrics.

Objective

Build and operate a self-contained platform that combines:

  • vector database indexing with HNSW cosine search

  • adaptive document chunking and embedding generation

  • hybrid retrieval with reciprocal rank fusion

  • metadata filtering across vector and keyword channels

  • cross-encoder reranking with cache-backed scoring

Delivered System

  • Child project: semantic-retrieval-platform

  • Runtime surfaces:

  • FastAPI retrieval backend

  • React operations console

  • Docker Compose stack (API, console, Redis)

Core Capabilities

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