Vector Databases and Semantic Search Implementation
Chapter 1: Embeddings and Vector Spaces
From Data to Vectors: A Refresher
Survey of Embedding Models
Understanding Vector Dimensionality
Introduction to Dimensionality Reduction
Measuring Similarity in Vector Space
Hands-on Practical: Generating and Comparing Embeddings
Quiz for Chapter 1
Chapter 2: Introducing Vector Databases
What Defines a Vector Database?
Core Architectural Components
Data Models and Schemas
Vector Operations: CRUD
Metadata Filtering
Scaling Considerations
Hands-on Practical: Basic Vector DB Interaction
Quiz for Chapter 2
Chapter 3: Approximate Nearest Neighbor (ANN) Search
The Need for Approximation
Core Concepts of ANN
Algorithm Overview: HNSW
Algorithm Overview: IVF
Algorithm Overview: LSH
Indexing Parameters and Tuning
Evaluating ANN Performance
Hands-on Practical: Experimenting with Index Parameters
Quiz for Chapter 3
Chapter 4: Building Semantic Search Systems
Semantic vs. Keyword Search Revisited
Architecture of a Semantic Search Pipeline
Data Preparation and Chunking Strategies
Query Processing and Embedding
Result Ranking and Re-ranking
Implementing Hybrid Search
Evaluating Semantic Search Relevance
Hands-on Practical: Designing a Search Query Flow
Quiz for Chapter 4
Chapter 5: Vector Databases in Practice
Choosing a Vector Database Platform
Working with Pinecone Client
Working with Weaviate Client
Working with Milvus Client
Working with ChromaDB Client
Indexing Large Datasets Efficiently
Monitoring and Maintenance
Hands-on Practical: Build a Small Semantic Search App
Quiz for Chapter 5
Implementing Hybrid Search
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- Reciprocal Rank Fusion (RRF): A Multi-System Combiner, Jeffrey A. Allan, Bruce Croft, 2009 Proceedings of the Eighteenth Text REtrieval Conference (TREC 2009) (ACM) DOI: 10.1145/1879792.1879809 - This is the original paper introducing Reciprocal Rank Fusion (RRF), a method for combining ranked lists from multiple search systems.
- Okapi at TREC-3: Automatic Query Construction and Other Experiments, Stephen E. Robertson, Stephen Walker, Michael Hancock, Ann Beaulieu, Mike Gatford, 1994 Proceedings of the Third Text REtrieval Conference (TREC 3) (National Institute of Standards and Technology (NIST)) - This paper formally introduced the BM25 ranking function, a widely used algorithm for keyword-based information retrieval.
- Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, 2008 (Cambridge University Press) - A foundational textbook covering information retrieval principles, including keyword models like BM25 and strategies for combining search results.