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 Large Datasets Efficiently
Was this section helpful?
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Presents foundational concepts for building scalable systems, including efficient data ingestion, batch processing, and distributed system design.
- Fluent Python: Clear, Concise, and Effective Programming, Luciano Ramalho, 2022 (O'Reilly Media) - Offers practical guidance on Python's concurrency mechanisms, including
concurrent.futuresfor thread and process pools, which is relevant for parallel batch insertion. - Transformers documentation on Performance and scalability, Hugging Face, 2024 - Provides guidance on optimizing embedding model inference, including batching inputs, using GPUs, and selecting appropriate models for efficiency.