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The science behind vector search
14 students
The science behind vector search
Build Production RAG Pipelines with Hybrid Search, BM25, ColBERT Re-ranking, and Semantic Chunking
Created byDaniel Romero
Last updated 12/2025
English
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
Explore related topics
Course content
4 sections • 9 lectures • 1h 24m total length
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 search
How Reciprocal Rank Fusion combines multiple ranking methods
Why ColBERT's late interaction approach delivers superior relevance
How semantic chunking improves embedding quality
How to build production-ready ingestion pipelines with real-world data sources
This 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