Home Blog Learn

All Courses

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

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

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

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

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

Vector Databases and Semantic Search Implementation

Prerequisites: Basic Python, Data Concepts

Level: Intermediate

What You'll Learn

Vector Embeddings
Understand the generation and application of various vector embedding models for different data types.
Vector Database Architecture
Explain the core components and design principles of modern vector databases.
ANN Algorithms
Compare and contrast different Approximate Nearest Neighbor search algorithms and their trade-offs.
Semantic Search Pipelines
Design and construct pipelines for indexing data and performing semantic queries.
Practical Implementation
Utilize popular vector database libraries and platforms to build a functional semantic search application.
Performance Tuning
Evaluate and tune indexing parameters for optimal search performance and resource usage.

© 2025 ApX Machine Learning

;