Explicit Cache Management for LLM Inference

A developer-centric approach to managing context and documents in Large Language Model applications

🚀 Quick Start

// Cache a document once
"[System Cache: legal_doc] Here's the contract: [DOCUMENT CONTENT]"

// Reference it in conversations  
"[System Cache Reference: legal_doc] What are the key terms?"
"[System Cache Reference: legal_doc] Are there any liability clauses?"

// Clean up when done
"[System Clean Cache: legal_doc]"

✨ Key Features

• Explicit Control: Developers manage cache lifecycle directly
• Session Isolation: Clean session boundaries prevent cache pollution
• Syntax-Driven: Simple, intuitive syntax for cache operations
• Framework Agnostic: Works with any LLM inference engine
• Mobile Optimized: Particularly valuable for resource-constrained devices

🎯 Problem This Solves

Current LLM applications face major challenges:

• Bandwidth Waste: Resending large documents with every request
• Cost Inefficiency: Paying to reprocess the same content repeatedly
• Poor Mobile Performance: Memory constraints make document handling difficult
• Cache Opacity: No control over what gets cached or when it's cleared
• Inconsistent APIs: Different caching approaches across inference engines

💡 Our Solution

Explicit cache management through intuitive syntax:

// Session management
"[System Start Session]"                    // Clean session start
"[System Clean Cache]"                      // Clear all caches

// Content caching  
"[System Cache: doc1] [LARGE DOCUMENT]"     // Cache with ID
"[System Cache Reference: doc1] Question?"  // Reference cached content

// Advanced operations
"[System Cache: temp, ttl: 3600] content"   // Cache with expiration
"[System Cache Reference: doc1,doc2] ?"     // Multi-document queries
"[System Clean Cache: doc1,doc2]"           // Selective cleanup

📚 Documentation

• Core Specification - Complete technical spec
• Getting Started - Quick implementation guide
• API Reference - Full syntax reference
• Use Cases - Common patterns and examples

🛠 Implementations

• Python Reference - Complete Python implementation
• JavaScript/Node.js - Web and Node.js support
• Integration Guides - llama.cpp, vLLM, MLC-LLM

🏗 Architecture

[Developer App]
       ↓
[Cache Parser] ← Processes [System Cache: id] syntax
       ↓
[Cache Manager] ← Stores content + computed KV states
       ↓
[Inference Engine] ← llama.cpp, vLLM, MLC-LLM, etc.
       ↓
[LLM Model] ← Unchanged model weights

📊 Performance Impact

Document Q&A Example:

• Without caching: 50KB document × 10 queries = 500KB transferred
• With explicit caching: 50KB document × 1 + 10 small queries = ~55KB transferred
• Bandwidth savings: ~90%
• Response time: 2-5x faster (cached KV states)
• API costs: Significantly reduced

🎯 Status

Current Phase: Core Specification & Reference Implementation
Next Phase: Academic publication (COLM 2025)
Target: Industry adoption across LLM inference engines

🔮 Vision

We envision a future where:

• Every LLM inference engine supports explicit cache management
• Developers have fine-grained control over context and memory
• Mobile LLM apps can efficiently handle large documents
• Cache management becomes a standard part of LLM application architecture

🤝 Contributing

We welcome contributions! Areas where we need help:

• Reference implementations for different inference engines
• Performance benchmarks and comparisons
• Mobile optimization strategies
• Security and isolation enhancements
• Documentation and examples

See CONTRIBUTING.md for detailed guidelines.

📞 Community

• Discussions: GitHub Discussions
• Issues: Bug reports and feature requests
• Twitter: Updates and announcements [@your-handle]

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

This project was inspired by the challenges faced by developers building document-heavy LLM applications and the need for standardized cache management across inference engines.

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"Explicit is better than implicit" - The Zen of Python

This project addresses the lack of explicit cache management in current LLM inference systems, providing developers with fine-grained control over context caching for improved performance and cost efficiency.