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
Core Concepts of ANN
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- Approximate Nearest Neighbor Search in High Dimensions, Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt, 2018 Algorithms and Data Structures. WADS 2017. Lecture Notes in Computer Science, vol 10999, Vol. 10999 (Springer) DOI: 10.1007/978-3-319-94770-9_3 - An updated and comprehensive survey on approximate nearest neighbor search, detailing its motivation, challenges, and various algorithmic approaches.
- Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman, 2020 (Cambridge University Press) - Chapter 3 introduces the problem of finding similar items efficiently, motivating approximate methods for high-dimensional data.
- Benchmarking Approximate Nearest Neighbor Search Algorithms for High-Dimensional Vectors, Bernhard Aumüller, Erik Heinlein, Alexander Reiser, Thomas Seidl, 2017 Proceedings of the VLDB Endowment, Vol. 10 (VLDB Endowment) DOI: 10.14778/3137628.3137637 - Discusses essential metrics like recall, latency, and QPS for evaluating and comparing ANN algorithms, which is directly relevant to understanding the accuracy-speed trade-off.