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
Introduction to Dimensionality Reduction
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- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, Leland McInnes, John Healy, Nathaniel Saul, Lukas Großberger, 2018 Journal of Open Source Software, Vol. 3 (The Open Journal) DOI: 10.21105/joss.00861 - The original research paper that introduces Uniform Manifold Approximation and Projection (UMAP), a non-linear dimensionality reduction technique.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A widely used textbook that provides a thorough treatment of Principal Component Analysis (PCA), the 'curse of dimensionality,' and various other dimensionality reduction methods, offering a strong statistical basis.
- Pattern Recognition and Machine Learning, Christopher Bishop, 2006 (Springer) - A comprehensive textbook detailing Principal Component Analysis (PCA) and a range of other linear and non-linear dimensionality reduction techniques, including their underlying mathematical principles.