In this lab, you'll build a high-performance vector database from scratch in Rust. Implement vector embeddings, similarity search algorithms, efficient indexing structures like HNSW, and create a production-ready system for semantic search and retrieval-augmented generation (RAG).
Rust
Vector Database
Build a vector database for semantic search. Implement embeddings, similarity search, and efficient indexing algorithms.
6h 40min
12 modules
Advanced
What You'll Build
Learning Objectives
Understand vector embeddings and similarity metrics
Implement efficient vector storage and retrieval
Build HNSW (Hierarchical Navigable Small World) index
Optimize for high-dimensional vector search
Create a REST API for vector operations
Handle concurrent queries and updates
Prerequisites
Advanced Rust programming skills
Understanding of data structures and algorithms
Linear algebra and vector mathematics basics
Experience with concurrent programming
Course Modules
1
Vector Mathematics Foundations
Implement vector operations, distance metrics (cosine, euclidean, dot product), and normalization.
2
Storage Layer Design
Design efficient vector storage, implement serialization, and create a persistence layer with RocksDB.
3
Brute Force Search
Implement basic nearest neighbor search as a baseline, optimize with SIMD operations.
4
HNSW Index Structure
Understand and implement the HNSW algorithm for approximate nearest neighbor search.
5
Index Building & Updates
Implement efficient index building, handle incremental updates, and manage deletions.
6
Query Optimization
Optimize search performance, implement query caching, and tune HNSW parameters.
7
Concurrent Operations
Add thread-safe operations, implement read-write locking, and handle concurrent queries and updates.
8
API Layer
Build REST API for insert, search, update, and delete operations. Add batch processing.
9
Metadata & Filtering
Add metadata support, implement pre and post-filtering, and enable hybrid search.
10
Benchmarking & Testing
Create comprehensive benchmarks, compare against existing solutions, and optimize bottlenecks.
11
Memory Management
Optimize memory usage, implement memory-mapped files, and handle large datasets efficiently.
12
Production Features
Add monitoring, health checks, backup/restore, and prepare for production deployment.
