Welcome to the VectorFlow documentation! This comprehensive guide will help you understand, use, and extend VectorFlow.
Perfect for new users who want to get up and running quickly.
- Installation Guide - Set up VectorFlow on your system
- Quick Start Tutorial - Build your first vector search application
- Basic Concepts - Understand vector databases and key terminology
Deep dive into how VectorFlow is built.
- Architecture Overview - High-level system design
- Storage System - Vector and metadata storage internals
- Indexing Algorithms - Flat and HNSW index implementation
- API Layer - REST API design and patterns
Complete reference for all APIs and interfaces.
- REST API Reference - HTTP endpoints documentation
- Python API Reference - Python SDK documentation
- Data Models - Request/response schemas
For users who want to go deeper.
- HNSW Algorithm - Detailed explanation of HNSW indexing
- Write-Ahead Log - Crash recovery and durability
- Distributed Deployment - Sharding and scaling
- Performance Tuning - Optimization guide
Real-world examples and use cases.
- Semantic Search - Build a semantic search engine
- RAG Pipeline - Retrieval-Augmented Generation
- Recommendation System - Build recommendations
Help improve VectorFlow.
- Contributing Guide - How to contribute
- Development Setup - Set up development environment
- Code Style Guide - Coding standards
...get started quickly → Quick Start Tutorial
...understand how it works → Architecture Overview
...use the REST API → REST API Reference
...optimize performance → Performance Tuning
...deploy in production → Distributed Deployment
...build a RAG application → RAG Pipeline Example
...contribute to the project → Contributing Guide
- Read Basic Concepts
- Follow Quick Start Tutorial
- Try Basic Usage Example
- Explore REST API Reference
- Study Architecture Overview
- Read Storage System
- Understand Indexing Algorithms
- Build a Semantic Search application
- Deep dive into HNSW Algorithm
- Learn Write-Ahead Log internals
- Explore Distributed Deployment
- Master Performance Tuning
Use your browser's search (Ctrl+F / Cmd+F) to find specific topics:
- Vector embeddings: Start with Basic Concepts
- HNSW parameters: See HNSW Algorithm
- API endpoints: Check REST API Reference
- Performance: Read Performance Tuning
- Crash recovery: Learn about Write-Ahead Log
- Scaling: Study Distributed Deployment
If you can't find what you're looking for:
- Check the examples - Real code is often the best documentation
- Search issues - Someone may have asked before
- Ask a question - Create a GitHub discussion
- Read the code - VectorFlow is exceptionally well-commented
Throughout this documentation:
- Code blocks show runnable examples
- Notes provide additional context
- Warnings highlight important considerations
- Tips offer best practices
Example:
# This is a code example
from vectorflow import VectorStore
store = VectorStore(dimension=128)Note: Make sure to install dependencies first
Warning: Don't use in production without proper testing
Tip: Use HNSW for large datasets (>10K vectors)
This documentation is actively maintained. Last updated: March 2026
To suggest improvements:
- Open an issue on GitHub
- Submit a pull request
- Join the discussion forum
- HNSW: Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
- Product Quantization: Product quantization for nearest neighbor search
- RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- FAISS: Facebook AI Similarity Search
- Annoy: Approximate Nearest Neighbors Oh Yeah
- ScaNN: Scalable Nearest Neighbors by Google
- hnswlib: Fast HNSW implementation in C++
Happy reading! 📖