HCFS - Hierarchical Context File System

Context-Aware Hierarchical Context File System (HCFS): Production-ready context management system with multi-language SDK ecosystem

Overview

HCFS is a comprehensive context management system that maps hierarchical paths to context blobs, enabling AI agents to navigate, store, and retrieve context in a structured manner. The system combines intuitive filesystem navigation with advanced semantic search capabilities and provides enterprise-grade APIs and SDKs across multiple programming languages.

✨ Key Features

Core System

• 📁 Virtual Filesystem Layer: POSIX-style directory navigation with context inheritance
• 🗄️ Context Database: SQLite-backed storage with versioning and metadata
• 🔍 Hybrid Search: Semantic similarity + BM25 keyword search with ML embeddings
• ⚡ High Performance: <5ms API response times, intelligent caching strategies
• 🔐 Enterprise Security: JWT and API key authentication with comprehensive middleware

Production API

• 🚀 FastAPI Server: Enterprise-grade REST API with OpenAPI documentation
• 📡 WebSocket Streaming: Real-time context updates and notifications
• 📦 Batch Operations: High-throughput processing for large-scale operations
• 📊 Analytics: Built-in usage tracking and performance monitoring

Multi-Language SDK Ecosystem

• 🐍 Python SDK: Synchronous and asynchronous clients with advanced features
• 📱 JavaScript/TypeScript: Promise-based API with full type safety
• 🔵 Go: Context-aware implementation with goroutine safety
• ⚡ Rust: Memory-safe async/await with zero-cost abstractions
• ☕ Java: Reactive streams with RxJava integration
• 🔷 C#: .NET 6+ with dependency injection support

Advanced Features

• 💾 Smart Caching: LRU, LFU, FIFO, and TTL strategies across all SDKs
• 🔄 Retry Logic: Exponential backoff with jitter for resilient operations
• 📋 Comprehensive Error Handling: Rich error hierarchies in every language
• 📖 Professional Documentation: OpenAPI specs, PDF manuals, and SDK guides

🚀 Quick Start

Installation

Python SDK:

cd hcfs-python
pip install -e .

Other SDKs:

# JavaScript/TypeScript
npm install @hcfs/sdk

# Go  
go get github.com/hcfs/hcfs-go

# Rust
cargo add hcfs-sdk

# Java (Gradle)
implementation 'dev.hcfs:hcfs-sdk:2.0.0'

# C# (.NET)
dotnet add package HCFS.SDK

Basic Usage

Start the API Server:

hcfs server --host 0.0.0.0 --port 8000

Python Example:

from hcfs.sdk import HCFSClient

# Initialize client
client = HCFSClient(base_url="http://localhost:8000")

# Create a context
context = client.create_context(
    path="/projects/ai-research",
    content="Research on context-aware AI systems",
    summary="AI research project context"
)

# Search contexts
results = client.search_contexts(
    "machine learning",
    search_type="semantic"
)

print(f"Found {len(results)} related contexts")

JavaScript Example:

import { HCFSClient } from '@hcfs/sdk';

const client = new HCFSClient({
  baseUrl: 'http://localhost:8000'
});

// Create context
const context = await client.createContext({
  path: '/projects/web-app',
  content: 'Modern web application development',
  summary: 'Web development project'
});

// Semantic search
const results = await client.searchContexts('frontend development', {
  searchType: 'semantic',
  topK: 10
});

🏗️ Architecture

System Overview

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Multi-Language │    │ Production API  │    │ Context Engine  │
│  SDK Ecosystem  │    │ (FastAPI)       │    │                 │
│                 │    │                 │    │                 │
│ • Python        │◄──►│ • Authentication│◄──►│ • SQLite DB     │
│ • JavaScript/TS │    │ • WebSocket     │    │ • ML Embeddings │
│ • Go            │    │ • REST APIs     │    │ • Hybrid Search │
│ • Rust          │    │ • Batch Ops     │    │ • Caching       │
│ • Java          │    │ • Monitoring    │    │ • Analytics     │
│ • C#            │    │ • Rate Limiting │    │ • Versioning    │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Core Components

🎯 Context Management

• Hierarchical Storage: Path-based context organization with inheritance
• Semantic Indexing: sentence-transformers for context embeddings
• Hybrid Search: BM25 + semantic similarity for optimal relevance
• Versioning System: Full context history with rollback capabilities

🔌 API Layer

• FastAPI Server: Production-grade REST API with OpenAPI documentation
• Authentication: JWT tokens and API key support with middleware
• WebSocket Streaming: Real-time context updates and notifications
• Batch Operations: Efficient bulk context processing

📚 SDK Ecosystem

• Consistent Interface: Same API patterns across all programming languages
• Advanced Features: Caching, retry logic, error handling, analytics
• Type Safety: Full type definitions where supported by language
• Performance Optimized: Language-specific optimizations and idioms

✅ Development Status

Completed Phases

Phase 1: Core System ✅ COMPLETE

• ✅ Virtual filesystem layer with FUSE integration
• ✅ SQLite context database with versioning
• ✅ Semantic search with ML embeddings
• ✅ Hybrid BM25 + semantic ranking
• ✅ CLI interface and basic REST API

Phase 2: Production API & SDK Ecosystem ✅ COMPLETE

• ✅ Enterprise FastAPI server with authentication
• ✅ Comprehensive Python SDK (sync + async)
• ✅ Multi-language SDK ecosystem (5 languages)
• ✅ OpenAPI documentation with PDF generation
• ✅ WebSocket streaming and advanced caching
• ✅ Professional documentation system

Future Development (Optional Extensions)

• Distributed Systems: Multi-node synchronization and consensus
• Advanced Analytics: Context usage patterns and insights
• Enterprise Features: Multi-tenancy and advanced security
• Performance Optimization: Large-scale deployment optimizations

📊 Performance & Testing

Benchmarks

• API Response Times: <5ms for cached operations, <50ms for uncached
• Search Performance: <100ms for semantic search across 1000+ contexts
• WebSocket Latency: <100ms for real-time updates
• Batch Processing: 1000+ contexts processed efficiently
• Memory Usage: ~500MB with full ML stack loaded

Testing Coverage

• ✅ Unit Tests: Core functionality across all components
• ✅ Integration Tests: End-to-end API and SDK testing
• ✅ Performance Tests: Load testing and benchmarking
• ✅ Multi-language Validation: All SDKs tested for consistency

📚 Documentation

• API Reference: Complete OpenAPI specification
• Project Report: Comprehensive project documentation
• SDK Documentation: Language-specific guides and examples
• Architecture Guide: Technical implementation details

🤝 Contributing

HCFS is production-ready and open for contributions! Areas of interest:

Core Development

• Performance optimizations for large-scale deployments
• Additional language SDK implementations
• Advanced caching strategies and storage backends

Extensions & Integrations

• Vector database integrations (Pinecone, Weaviate, etc.)
• Cloud deployment templates (AWS, GCP, Azure)
• Framework integrations (LangChain, LlamaIndex, etc.)

Getting Started

1. Fork the repository
2. Set up development environment: cd hcfs-python && pip install -e .
3. Run tests: python -m pytest tests/
4. Check specific SDK directories for language-specific setup

🎯 Use Cases

AI Agent Development

• Context Sharing: Multi-agent systems with shared context spaces
• Knowledge Management: Hierarchical organization of agent knowledge
• Session Continuity: Persistent context across agent interactions

Enterprise Applications

• Document Management: Semantic document organization and retrieval
• Team Collaboration: Shared context spaces for development teams
• Knowledge Bases: Intelligent corporate knowledge management

Research & Development

• Context-Aware Systems: Research on hierarchical context models
• Semantic Filesystems: Novel approaches to file organization
• AI Agent Coordination: Multi-agent context sharing patterns

📄 License

MIT License - see LICENSE for details.

🔬 Research Foundation

HCFS represents novel research in context-aware systems, combining:

• Semantic Filesystems: Hierarchical path-to-context mapping
• ML-Powered Search: Hybrid BM25 + semantic similarity ranking
• Agent-Centric Design: Purpose-built for AI agent context management
• Multi-Language Ecosystem: Comprehensive SDK support across languages

Academic Applications: Ready for research publication and academic use.
Commercial Viability: Production-ready for enterprise deployment and licensing.