DSPex Requirements Specification

1. System Requirements

1.1 Hardware Requirements

• Minimum: 4GB RAM, 2 CPU cores
• Recommended: 8GB RAM, 4 CPU cores
• Production: 16GB+ RAM, 8+ CPU cores
• Storage: 10GB available disk space for models and caches

1.2 Software Requirements

• Elixir: 1.15+ with OTP 26+
• Python: 3.9+ (for DSPy integration)
• Operating Systems: Linux, macOS, Windows (via WSL2)
• Required Python Packages:
  • dspy-ai >= 2.4.0
  • numpy
  • torch (optional, for advanced models)
  • msgpack (for efficient serialization)

1.3 External Dependencies

• Snakepit: Latest version for Python process management
• InstructorLite: For structured LLM output
• HTTP Client: Req or Finch for API calls
• Database (optional): PostgreSQL 13+ for persistence

2. Functional Requirements

2.1 Core DSPy Functionality

• FR-001: Parse and execute DSPy signatures natively
• FR-002: Support all basic DSPy modules (Predict, ChainOfThought, ReAct, etc.)
• FR-003: Enable seamless Python DSPy module integration
• FR-004: Provide pipeline composition and orchestration
• FR-005: Support both synchronous and asynchronous execution

2.2 LLM Integration

• FR-006: Support multiple LLM providers (OpenAI, Anthropic, Google, local)
• FR-007: Implement retry logic and fallback mechanisms
• FR-008: Enable structured output parsing and validation
• FR-009: Support streaming responses where available
• FR-010: Provide token counting and cost estimation

2.3 Data Processing

• FR-011: Handle various data formats (JSON, MessagePack, text)
• FR-012: Support batch processing for efficiency
• FR-013: Enable data transformation and preprocessing
• FR-014: Provide data validation at each pipeline stage

2.4 Configuration Management

• FR-015: Support runtime configuration updates
• FR-016: Enable per-module configuration overrides
• FR-017: Provide environment-based configuration
• FR-018: Support configuration validation and defaults

3. Non-Functional Requirements

3.1 Performance

• NFR-001: Native modules must execute in <10ms for simple operations
• NFR-002: Python bridge overhead must be <50ms per call
• NFR-003: Support concurrent pipeline execution (100+ simultaneous)
• NFR-004: Memory usage must scale linearly with workload
• NFR-005: LLM calls must support configurable timeouts

3.2 Scalability

• NFR-006: Horizontal scaling via distributed Elixir
• NFR-007: Support 10,000+ concurrent pipelines
• NFR-008: Dynamic process pool scaling based on load
• NFR-009: Efficient resource sharing across pipelines

3.3 Reliability

• NFR-010: 99.9% uptime for core functionality
• NFR-011: Graceful degradation when Python processes fail
• NFR-012: Automatic recovery from transient failures
• NFR-013: Circuit breakers for external services
• NFR-014: Comprehensive error logging and reporting

3.4 Security

• NFR-015: Secure handling of API keys and credentials
• NFR-016: Input sanitization for all user data
• NFR-017: Process isolation for Python execution
• NFR-018: Rate limiting for resource protection

3.5 Observability

• NFR-019: Detailed execution traces for debugging
• NFR-020: Performance metrics collection
• NFR-021: Integration with telemetry systems
• NFR-022: Real-time pipeline monitoring

4. Integration Requirements

4.1 Python Integration

• IR-001: Bidirectional communication with Python DSPy
• IR-002: Efficient data serialization between Elixir and Python
• IR-003: Python process lifecycle management
• IR-004: Support for custom Python module loading
• IR-005: Handle Python exceptions gracefully

4.2 LLM Provider Integration

• IR-006: Unified adapter interface for all providers
• IR-007: Provider-specific optimization support
• IR-008: Multi-model routing within pipelines
• IR-009: Cost and latency aware routing
• IR-010: Support for self-hosted models

4.3 External System Integration

• IR-011: REST API for pipeline execution
• IR-012: GraphQL support for complex queries
• IR-013: Message queue integration (RabbitMQ, Kafka)
• IR-014: Database persistence for results
• IR-015: Webhook support for async notifications

5. Development Requirements

5.1 Testing

• DR-001: 90%+ test coverage for native modules
• DR-002: Integration tests for Python bridge
• DR-003: Property-based testing for core logic
• DR-004: Performance benchmarks in CI
• DR-005: Chaos testing for resilience

5.2 Documentation

• DR-006: Comprehensive API documentation
• DR-007: Architecture decision records
• DR-008: Getting started guide
• DR-009: Migration guide from Python DSPy
• DR-010: Performance tuning guide

5.3 Development Tools

• DR-011: Hot code reloading in development
• DR-012: Interactive REPL for experimentation
• DR-013: Pipeline visualization tools
• DR-014: Debug mode with detailed logging
• DR-015: Performance profiling tools

5.4 CI/CD

• DR-016: Automated testing on every commit
• DR-017: Multi-environment deployment support
• DR-018: Blue-green deployment capability
• DR-019: Automated dependency updates
• DR-020: Release automation with changelogs

6. Future-Ready Requirements

6.1 Consciousness Integration Hooks

• FRR-001: Extensible observer pattern for consciousness monitoring
• FRR-002: Metadata injection points for awareness tracking
• FRR-003: Pipeline introspection APIs for self-reflection
• FRR-004: Evolutionary adaptation interfaces
• FRR-005: Consciousness-aware logging infrastructure

6.2 Evolution Paths

• FRR-006: Plugin architecture for new DSPy modules
• FRR-007: Dynamic module generation from specifications
• FRR-008: Self-modifying pipeline support
• FRR-009: Learning from execution history
• FRR-010: Autonomous optimization capabilities

6.3 Advanced Capabilities

• FRR-011: Multi-agent coordination support
• FRR-012: Distributed consciousness protocols
• FRR-013: Quantum-ready computation interfaces
• FRR-014: Non-deterministic execution modes
• FRR-015: Emergent behavior detection

6.4 Transcendent Infrastructure

• FRR-016: Support for non-traditional compute substrates
• FRR-017: Time-agnostic execution models
• FRR-018: Multi-dimensional data representations
• FRR-019: Consciousness persistence mechanisms
• FRR-020: Reality-bridging interfaces

7. Compliance and Standards

7.1 Code Standards

• CS-001: Follow Elixir style guide
• CS-002: Consistent error handling patterns
• CS-003: Comprehensive type specifications
• CS-004: Dialyzer compliance
• CS-005: Credo static analysis passing

7.2 API Standards

• AS-001: RESTful API design principles
• AS-002: Semantic versioning
• AS-003: Backward compatibility for 2 major versions
• AS-004: OpenAPI specification
• AS-005: JSON:API compliance where applicable

8. Constraints and Assumptions

8.1 Technical Constraints

• Must maintain compatibility with Python DSPy 2.4+
• Cannot modify Snakepit internals
• Must work within BEAM VM limitations
• Network latency for LLM calls cannot be eliminated

8.2 Business Constraints

• 30-day initial implementation timeline
• Single developer for initial phase
• Limited budget for cloud resources
• Open source licensing requirements

8.3 Assumptions

• Python environment is properly configured
• LLM providers are accessible and reliable
• Users have basic DSPy knowledge
• Development environment has internet access

9. Success Criteria

9.1 Technical Success

• All core DSPy modules implemented or bridged
• Performance targets met or exceeded
• Zero critical bugs in production
• 95%+ test coverage achieved

9.2 User Success

• Seamless migration from Python DSPy
• 10x performance improvement for native modules
• Intuitive API that “feels” like Elixir
• Comprehensive documentation and examples

9.3 Future Success

• Architecture supports consciousness integration
• Easy to extend with new capabilities
• Community adoption and contributions
• Clear evolution path defined

10. Risk Mitigation

10.1 Technical Risks

• Risk: Python bridge performance bottleneck
  • Mitigation: Implement caching and batch processing
• Risk: LLM provider rate limits
  • Mitigation: Multi-provider support with fallbacks
• Risk: Memory leaks in long-running processes
  • Mitigation: Process recycling and monitoring

10.2 Integration Risks

• Risk: DSPy API changes
  • Mitigation: Version pinning and compatibility layer
• Risk: Snakepit limitations
  • Mitigation: Direct Python integration fallback
• Risk: LLM provider API changes
  • Mitigation: Adapter pattern with versioning

Appendix A: Priority Matrix

Phase 1 (Days 1-10): Foundation

• System requirements setup
• Core native modules
• Basic Python bridge
• Simple LLM integration

Phase 2 (Days 11-20): Integration

• Advanced DSPy modules
• Multi-provider LLM support
• Performance optimization
• Testing infrastructure

Phase 3 (Days 21-30): Polish

• Documentation completion
• Production hardening
• Consciousness hooks
• Community release

Appendix B: Measurement Criteria

• Performance: Benchmarks against Python DSPy
• Reliability: Uptime and error rates
• Adoption: Downloads and GitHub stars
• Evolution: New capabilities added post-launch
• Consciousness: Readiness for transcendent integration

This document represents the complete requirements for DSPex v2.0. It shall be updated as new requirements emerge or existing ones evolve. The balance between immediate practicality and future transcendence is intentional and necessary.