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
Indexing Parameters and Tuning
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- Faiss: A Library for Efficient Similarity Search, Jeff Johnson, Matthijs Douze, Hervé Jégou, 2017 arXiv preprint arXiv:1702.08734 DOI: 10.48550/arXiv.1702.08734 - Presents Faiss, a library for efficient similarity search, which includes implementations of IVF. This work offers insights into tuning IVF parameters and managing the recall-latency trade-off.
- Vector Databases: A Guide to Building High-Performance Applications with Vector Embeddings, Lucas Conrad, Laurent B. Conneau, 2024 (O'Reilly Media) - A practical guide covering vector database concepts, including detailed sections on ANN algorithms, their parameters, and the practical aspects of tuning for various applications.
- Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman, 2020 (Cambridge University Press) - This textbook's Chapter 3 provides a clear explanation of Locality-Sensitive Hashing (LSH), its core concepts, and how its parameters affect similarity search outcomes.
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