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
Understanding Vector Dimensionality
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- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - Chapter 7, 'Model Assessment and Selection,' and Chapter 17, 'High-Dimensional Problems: p >> N,' explain the challenges of high-dimensional data, including sparsity and computational cost.
- Distance Concentration in High Dimensions and its Implications for Nearest Neighbor Search, K. S. Beyer, R. Goldstein, R. Ramakrishnan, and U. Shaft, 1999 Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data (ACM) DOI: 10.1145/304181.304188 - This paper explains the phenomenon of distance concentration, where distances between points become less discriminative in high-dimensional spaces, directly impacting similarity search.
- Vector Databases: A Guide to Enterprise Applications, Mark Palmer, Alexandru C. Ioan, 2023 (O'Reilly Media) - This book discusses the practical implications of vector dimensionality for storage, computational cost, and search performance within vector database systems.
- Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Daniel Jurafsky and James H. Martin, 2025 - Chapters on word embeddings (e.g., Chapter 6 in the 3rd ed. draft) discuss properties and choices of embedding dimensionality, highlighting trade-offs for NLP tasks.