DSPex V2 Foundation Specification

Overview

This directory contains the complete specification for DSPex V2 Foundation phase - a ground-up reimplementation of DSPex using Snakepit as the core dependency for Python process management.

Documents

01_requirements.md

Comprehensive requirements specification covering:

• Functional requirements for core API, Python bridge, and native implementations
• Non-functional requirements for performance, reliability, and scalability
• Constraints and success criteria
• Risk analysis and future considerations

Key Requirements:

• Unified API hiding implementation details
• Smart routing between native and Python
• High-performance native implementations where appropriate
• Production-grade reliability and monitoring

02_design.md

Detailed technical design including:

• Complete architecture with component diagrams
• Core component specifications
• Data flow and protocol definitions
• Error handling and performance strategies
• Extension points for future growth

Key Design Decisions:

• Clean separation between API and implementation
• Multiple specialized Snakepit pools
• Protocol flexibility (JSON, MessagePack, Arrow)
• Pipeline orchestration for complex workflows

03_tasks.md

Implementation roadmap with:

• Week-by-week task breakdown
• Clear dependencies and priorities
• Detailed acceptance criteria
• Risk mitigation strategies
• Success metrics

Timeline:

• Week 1: Core infrastructure (project setup, signatures, Snakepit bridge)
• Week 2: Core functionality (router, templates, basic modules)
• Week 3: Advanced features (pipelines, sessions, protocols)
• Week 4: Production readiness (error handling, performance, documentation)

Quick Start for Developers

1. Read Requirements First: Understand what we’re building and why
2. Review Design: Familiarize yourself with the architecture
3. Check Tasks: Find your assigned tasks and dependencies
4. Follow Patterns: Use the established patterns from the design

Key Concepts

Implementation Strategy

• Native First: Implement in Elixir when it provides clear benefits
• Python for Complexity: Use Python/DSPy for complex ML operations
• Smart Routing: Automatically choose the best implementation
• Pipeline Composition: Mix native and Python seamlessly

Technology Stack

• Elixir 1.14+: Core implementation language
• Snakepit: Python process management
• DSPy 2.x: Python ML framework
• Jason: JSON encoding/decoding
• Telemetry: Metrics and monitoring

Architecture Principles

1. Clean API: Users shouldn’t know implementation details
2. Performance: Native operations should be 10x faster
3. Reliability: Graceful degradation and recovery
4. Extensibility: Easy to add new implementations
5. Observability: Comprehensive monitoring built-in

Development Workflow

1. Setup Environment:

   mix deps.get
   mix compile
   mix test
   

2. Run Development Server:

   iex -S mix
   

3. Test Your Changes:

   mix test
   mix credo
   mix dialyzer
   

4. Benchmark Performance:

   mix run bench/signatures.exs
   

Success Criteria

The Foundation phase is successful when:

• All core DSPy modules are accessible from Elixir
• Native and Python implementations work seamlessly together
• Performance targets are met (sub-100ms for simple operations)
• Test coverage exceeds 90%
• Documentation is complete and examples work

Next Steps

After completing the Foundation phase, we’ll move to:

• Phase 2: Advanced features (variables, cognitive orchestration)
• Phase 3: Production features (distributed execution, auto-scaling)
• Phase 4: Enterprise features (multi-tenancy, advanced security)

Questions?

• Technical questions: Review the design document
• Task questions: Check the tasks document
• Architecture questions: See the requirements document
• Implementation questions: Check code examples in design

This specification represents our commitment to building a production-grade ML orchestration platform that advances the state of the art in ML system coordination.