Caution
๐ A Hands-on Tutorial on Vector Database Principles and Practices โ From Zero to Production
EasyVecDB is a systematic learning project on vector databases designed for developers and researchers.
It covers the full lifecycle from foundational concepts and algorithmic principles to production-level application deployment, focusing on three main directions:
- ๐งฉ Theory Fundamentals: Understand the principles, architecture, and indexing mechanisms of vector databases
- โ๏ธ Hands-on Practice: Master the usage and optimization of Milvus / Faiss / Annoy
- ๐ก Project Cases: Build complete projects from scratch, including RAG systems, embedding-based retrieval, and clustering visualization
The project is divided into Fundamentals and Practice sections, corresponding to the navigation structure below:
| Section | Key Content | Status |
|---|---|---|
| Part I: Fundamentals (Base) | Vector DB principles, embeddings, and search basics | |
| Chapter 1 Project Introduction | Project goals and learning roadmap | โ |
| Chapter 2 Why Vector Databases | Retrieval bottlenecks and similarity search | โ |
| Chapter 3 Vector Embedding Basics | Word2Vec, Transformer embeddings | โ |
| Chapter 4 Vector Search Basics | Brute-force search and similarity metrics | โ |
| Chapter 5 ANN Search Algorithms | IVF, PQ, HNSW, LSH, Annoy principles & code | โ |
| Chapter 6 Build Your Own Vector Database | Minimal vector DB implementation | โ |
| Part II: Annoy Tutorial | Lightweight ANN search library | |
| Chapter 1 Annoy Introduction & Setup | Installation and core concepts | โ |
| Chapter 2 Annoy Core API | Index building, querying, parameter tuning | โ |
| Chapter 3 Annoy Advanced Tips & Best Practices | Performance optimization, engineering practices | โ |
| Part III: Faiss Tutorial | High-performance vector search engine | |
| Chapter 1 FAISS Introduction & Setup | Installation and core concepts | โ |
| Chapter 2 FAISS Core Indexes | Flat, IVF, PQ, HNSW indexes | โ |
| Chapter 3 Advanced FAISS Features | Composite indexes, GPU, batch search | โ |
| Chapter 4 FAISS Performance Tuning | Recall, latency, memory optimization | โ |
| Chapter 5 FAISS Engineering Practices | Service deployment and real-world cases | โ |
| Part IV: Milvus Tutorial | Distributed vector database & engineering | |
| Chapter 1 Milvus Introduction: Concepts & Architecture | Architecture and core components | โ |
| Chapter 2 Milvus Core Concepts | Collection, Partition, Index | โ |
| Chapter 3 Milvus Basic Operations | Data ingestion, query, index management | โ |
| Chapter 4 Milvus AI Applications: Hybrid Search with BM25 | RAG and hybrid retrieval | โ |
| Chapter 5 Milvus AI Applications: Image Retrieval | Image retrieval system | โ |
| Chapter 6 Milvus Advanced Topics | Internal architecture, reranker, Milvus Lite, MinerU | โ |
| Part V: AI Applications Based on Vector Databases | ||
| Project 1 Recommendation Recall with Annoy | DSSM + Annoy vector recall | โ |
| Project 2 RAG with FAISS | RAG using FAISS | โ |
| Project 3 Agent with Milvus | Agent system using Milvus | โ |
| Project 4 RAG with Milvus & ArangoDB | Hybrid RAG system | โ |
| Part VI: Supplementary Topics | Related advanced topics | |
| Vector Fundamentals | Vector math and basics | โ |
| FusionANNS Architecture | GPU-accelerated retrieval | โ |
| Meta-Chunking Strategy | Intelligent text chunking | โ |
| Theoretical Limits of Retrieval | Performance boundaries | โ |
| RabitQ Indexing | High-dimensional quantization | โ |
| Clustering Algorithms | Clustering overview | โ |
โณ Continuously updating...
๐ This project aims to help you master vector databases from principles โ practice โ deployment.
.
โโโ docs Vector database tutorials and documentation
โโโ data Common example datasets
โโโ src Project-related source code
โโโ tmp Temporary files
Related Competition
- If you find any issues, feel free to open an Issue. If there is no response, you may contact the Support Team.
- If youโd like to contribute, submit a Pull Request. If there is no response, you may also contact the Support Team.
- If you are interested in starting a new Datawhale project, please follow the Datawhale Open Source Project Guide.
- Muxiaoxiong โ Project Lead (Datawhale Member)
- Liu Xiao โ Contributor (Datawhale Teaching Assistant)
- Ke Muling โ Contributor (Datawhale Member)
- Zhao Xinlong โ Contributor (Datawhale Teaching Assistant)
- Chen Fuyuan โ Contributor (Datawhale Member)
- Thanks to @Sm1les for the support and help
- Thanks to all contributors who made this project possible โค๏ธ
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