Athena - Multi-LLM Cache Augmented Generation System

Athena 2.0 is an advanced Cache Augmented Generation (CAG) system that leverages multiple specialized Language Models (LLMs) to provide accurate, contextual responses for programming, algorithms, and mathematics queries. Built with Go, it combines distributed knowledge sources with an intelligent caching system and high-performance embedding generation.

Core Architecture

Athena employs a Multi-LLM CAG architecture that coordinates specialized models for different domains while maintaining consistency and efficiency through intelligent caching.

Key Components

1. Orchestration Layer

   • Main LLM (GPT-4): High-level reasoning and response coordination
   • Task Planner: Query decomposition and subtask management
   • Multi-Task Coordinator: Parallel LLM operation management

2. Specialized LLMs

   • Code Analysis LLM (CodeLlama/StarCoder): Programming patterns and implementation
   • Math Reasoning LLM (Claude): Mathematical proofs and computations
   • Research Analysis LLM (PaLM): Academic paper processing and synthesis

3. Knowledge Sources

   • GitHub API: Code examples and programming patterns
   • Stack Exchange API: Technical solutions and best practices
   • arXiv API: Academic papers and theoretical foundations
   • Wolfram Alpha API: Mathematical computations and formal proofs

4. Cache Management

   • Vector Store: High-performance embedding storage
   • Semantic Cache: Contextual response caching
   • Cache Manager: Intelligent cache warming and invalidation

Features

Core Capabilities

• Multi-model orchestration with specialized LLMs
• Domain-specific knowledge retrieval and caching
• High-performance embedding generation with CoreML acceleration
• Intelligent task decomposition and parallel processing
• Advanced context management across models
• Comprehensive response validation and regeneration

Performance Features

• CoreML hardware acceleration for embeddings
• Intelligent batching and parallel processing
• Multi-level caching system
• Dynamic model loading based on usage patterns
• Resource-aware scaling and optimization

Development Features

• Comprehensive monitoring and logging
• Flexible configuration management
• Extensible knowledge source integration
• Clear error handling and recovery
• Detailed performance metrics

Implementation Roadmap

Phase 1: Core Infrastructure (Weeks 1-4)

1. Multi-LLM Communication Protocol

   • Design inter-LLM message format
   • Implement communication channels
   • Create fallback mechanisms

2. Knowledge Source Integration

   • Set up API clients for all sources
   • Implement rate limiting and quotas
   • Create unified query interface

3. Cache System Setup

   • Configure vector store (Milvus)
   • Set up semantic cache (Redis)
   • Implement cache manager

Phase 2: LLM Integration (Weeks 5-8)

1. Main LLM Setup

   • Implement orchestration logic
   • Create task planning system
   • Design prompt templates

2. Specialized LLMs

   • Configure domain-specific models
   • Implement model switching logic
   • Create specialized prompts

3. Response Processing

   • Build validation system
   • Implement regeneration logic
   • Create response formatter

Phase 3: Optimization (Weeks 9-12)

1. Performance Tuning

   • Optimize embedding generation
   • Implement parallel processing
   • Fine-tune caching strategies

2. Resource Management

   • Add usage monitoring
   • Implement cost optimization
   • Create scaling logic

3. Error Handling

   • Add comprehensive error recovery
   • Implement graceful degradation
   • Create monitoring alerts

Phase 4: Production Readiness (Weeks 13-16)

1. Testing and Validation

   • Create comprehensive test suite
   • Implement integration tests
   • Add performance benchmarks

2. Documentation

   • API documentation
   • Deployment guides
   • Usage examples

3. Monitoring Setup

   • Configure metrics collection
   • Set up dashboards
   • Implement alerting

Technical Requirements

System Requirements

• Go 1.21+
• Redis 7.0+
• Milvus 2.0+
• CoreML support for acceleration

API Dependencies

• OpenAI API (GPT-4)
• CodeLlama/StarCoder API
• Claude API
• PaLM API
• GitHub API
• Stack Exchange API
• arXiv API
• Wolfram Alpha API

Hardware Recommendations

• 32GB+ RAM
• 8+ CPU cores
• GPU/CoreML support
• 100GB+ SSD storage

Performance Considerations

Resource Management

1. Token Budget Allocation

   • Main LLM: 40% of budget
   • Specialized LLMs: 20% each
   • Reserve: 20% for regeneration

2. Cache Configuration

   • Vector Store: 20GB maximum
   • Semantic Cache: 10GB maximum
   • Cache invalidation: 24-hour TTL

3. API Rate Limits

   • GitHub: 5000 requests/hour
   • Stack Exchange: 300 requests/day
   • Wolfram Alpha: 2000 requests/month
   • arXiv: 100 requests/minute

Optimization Strategies

1. Caching

   • Implement two-level cache (memory + disk)
   • Use LRU eviction policy
   • Maintain cache hit ratio > 80%

2. Batching

   • Dynamic batch sizes based on load
   • Maximum batch size: 32 requests
   • Batch timeout: 100ms

3. Parallel Processing

   • Maximum concurrent LLMs: 4
   • Thread pool size: CPU cores * 2
   • Worker queue depth: 1000

Configuration

Create a .env file with the following settings:

# LLM Configuration
MAIN_LLM_MODEL=gpt-4
CODE_LLM_MODEL=codellama
MATH_LLM_MODEL=claude
RESEARCH_LLM_MODEL=palm

# API Keys
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
GITHUB_TOKEN=
STACK_EXCHANGE_KEY=
WOLFRAM_ALPHA_KEY=

# Cache Settings
VECTOR_CACHE_SIZE=20GB
SEMANTIC_CACHE_SIZE=10GB
CACHE_TTL=24h

# Performance
MAX_CONCURRENT_LLMS=4
BATCH_SIZE=32
BATCH_TIMEOUT=100ms

# Hardware Acceleration
ENABLE_COREML=true
REQUIRE_ANE=false

Usage Examples

Basic Query

client := athena.NewClient(config)

response, err := client.Query(ctx, &QueryRequest{
    Text: "Explain the time complexity of quicksort",
    MaxTokens: 1000,
})

Advanced Usage

// Configure specialized processing
opts := &QueryOptions{
    RequireMathValidation: true,
    EnableCodeExecution: true,
    MaxResponseTime: 30 * time.Second,
}

response, err := client.Query(ctx, &QueryRequest{
    Text: "Prove the correctness of quicksort",
    Options: opts,
})

Contributing

See CONTRIBUTING.md for detailed guidelines.

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see LICENSE for details.