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
Algorithm Overview: IVF
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- Searching in one billion vectors, Hervé Jégou, Matthijs Douze, Cordelia Schmid, 2011 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33 (IEEE) DOI: 10.1109/TPAMI.2010.57 - Introduces the IVFADC method, which combines Inverted File Indexing with Product Quantization and Asymmetric Distance Computation for efficient similarity search in large datasets.
- Product Quantization for Nearest Neighbor Search, Hervé Jégou, Matthijs Douze, Cordelia Schmid, 2011 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33 (IEEE Computer Society) DOI: 10.1109/TPAMI.2010.57 - Presents the Product Quantization technique for compressing high-dimensional vectors and efficiently estimating their distances, a cornerstone for methods like IVF-PQ.
- FAISS Wiki: IVFPQ, Meta AI Research, 2025 - Official documentation describing the IVF and IVF-PQ implementations within the FAISS library, providing technical details and parameter explanations for practical use.