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
From Data to Vectors: A Refresher
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- Efficient Estimation of Word Representations in Vector Space, Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013 International Conference on Learning Representations (ICLR) Workshop DOI: 10.48550/arXiv.1301.3781 - This paper introduces Word2Vec, a method for learning distributed word representations that capture semantic and syntactic relationships, forming a basis for vector embeddings.
- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems 30 (NIPS 2017) DOI: 10.48550/arXiv.1706.03762 - This seminal paper presents the Transformer architecture, a core component of many advanced embedding models used today, by introducing the self-attention mechanism.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook covering the theoretical foundations and practical aspects of deep learning, including how neural networks learn meaningful data representations (embeddings).
- Stanford CS224N: Natural Language Processing with Deep Learning, Diyi Yang, Tatsunori Hashimoto, Winter 2025 - A university course offering lecture materials and resources on modern NLP, covering word vectors, neural networks, and transformer models for generating embeddings.