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 Architectural Components
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- Milvus: A Purpose-Built Vector Database for Scalable Similarity Search, Xin Wang, Jun Yao, Xiangyu Wang, Xiaodong Li, Sheng Wang, Bo Li, Jingjing Xu, Chunyu Zhang, Bo Yang, Fan Wu, Guozheng Li, and Xiangdong Meng, 2021 Proceedings of the VLDB Endowment, Vol. 14 (VLDB Endowment) DOI: 10.14778/3476311.3476344 - Describes the architecture of Milvus, a prominent vector database, detailing its components for storage, indexing, and query processing.
- A Comprehensive Survey on Approximate Nearest Neighbor Search Algorithms, Jinru Cao, Haicheng Chen, Yong Wang, Jinlong Lin, and Jingyu Yang, 2022 Electronics, Vol. 11 (MDPI) DOI: 10.3390/electronics11071039 - A comprehensive overview of various Approximate Nearest Neighbor (ANN) search algorithms, their principles, and applications, for understanding vector database indexing.
- Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze, 2008 (Cambridge University Press) - A foundational textbook covering vector space models, similarity measures like cosine similarity and Euclidean distance, and information retrieval concepts relevant to vector search.