The science behind vector search

Build Production RAG Pipelines with Hybrid Search, BM25, ColBERT Re-ranking, and Semantic Chunking

What you’ll learn
Build a complete document ingestion pipeline with chunking, embedding generation, and storage
Implement Hybrid Search combining semantic search (dense vectors) with keyword search (sparse vectors/BM25) using Reciprocal Rank Fusion (RRF)
Apply re-ranking techniques with ColBERT (late interaction) to significantly improve search result relevance
Develop an intelligent SemanticChunker using HDBScan to create semantically cohesive chunks, avoiding topic mixing
Integrate with external APIs (SEC EDGAR) for automated ingestion of financial documents with structured metadata
Understand the difference between similarity and relevance in vector search systems and how to optimize for true relevance

Requirements
Standard Programming skills (our examples are in Python)
Curiosity about building AI-powered search systems
No prior experience with vector databases required — we start from scratch

Description
Why do most RAG tutorials stop at basic vector search?You’ve seen the demos: embed your documents, store them in a vector database, and run a similarity search. But when you try this in production, your retrieval scores hover around 60%, and the results aren’t always what you need. That’s because similarity and relevance are not the same thing.This course takes you beyond the basics and into the science behind vector search. You’ll learn why simple dense embeddings aren’t enough and how to build retrieval systems that actually find the most relevant information.What you’ll build:You’ll start by creating a complete ingestion pipeline with Qdrant Cloud, generating dense embeddings with FastEmbed. Then you’ll implement Hybrid Search, combining semantic understanding (dense vectors) with keyword precision (sparse vectors using BM25). Using Reciprocal Rank Fusion (RRF), you’ll merge results from both methods to get the best of both worlds.But we don’t stop there. You’ll implement re-ranking with ColBERT, a late interaction model that compares query and document tokens to achieve maximum relevance. Your search scores will jump from 60% to over 90%.You’ll also build a Semantic Chunker using HDBScan clustering to create chunks that represent single topics instead of mixed content. Finally, you’ll integrate with the SEC EDGAR API to automatically fetch and process real financial documents with structured metadata.By the end of this course, you’ll understand:Why Hybrid Search outperforms pure vector searchHow Reciprocal Rank Fusion combines multiple ranking methodsWhy ColBERT’s late interaction approach delivers superior relevanceHow semantic chunking improves embedding qualityHow to build production-ready ingestion pipelines with real-world data sourcesThis is not another beginner tutorial. This is the engineering knowledge you need to build retrieval systems that work in production.

Who this course is for
Developers who want to understand and implement advanced vector search techniques beyond basic similarity search
Engineers building RAG systems who need to improve retrieval relevance with Hybrid Search and re-ranking
Backend developers working with document processing who want to learn intelligent chunking strategies
Professionals dealing with complex documents (financial, legal, technical) who need production-ready ingestion pipelines

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