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100a claude

Documentation for 100a_claude from the Ds ex repository.

I’ll analyze the DSPEx MVP design and create a comprehensive implementation plan.Now let me create the detailed technical specification for the foundational module - DSPEx.Signature:Based on my analysis of the DSPy codebase and the proposed DSPEx design, I’ve created a comprehensive implementation plan and detailed specification for the foundational DSPEx.Signature module.

Key Insights from the Analysis:

  1. Clean Separation: The Elixir design properly separates concerns - signatures are pure compile-time contracts, while adapters handle the runtime formatting complexity.

  2. BEAM Advantages: The proposed concurrent evaluation engine using Task.async_stream will significantly outperform Python’s threading model for I/O-bound LLM calls.

  3. Type Safety: The macro-based approach provides compile-time guarantees that the Python version lacks, while maintaining the same developer ergonomics.

Why Start with DSPEx.Signature:

  1. Zero Dependencies: Can be implemented and tested in complete isolation
  2. Foundation: All other modules depend on this working correctly
  3. Compile-Time Safety: Catches errors early in the development cycle
  4. Clear Mapping: Direct correspondence to Python DSPy’s signature system

Next Steps:

After implementing DSPEx.Signature, the natural progression would be:

  1. DSPEx.Client (HTTP layer with mocks)
  2. DSPEx.Adapter (depends on Signature)
  3. DSPEx.Program/DSPEx.Predict (orchestration layer)
  4. DSPEx.Evaluate (concurrent evaluation engine)
  5. DSPEx.Teleprompter (optimization algorithms)

The implementation plan provides line-by-line mappings to the original Python code, ensuring we maintain feature parity while leveraging BEAM’s strengths in concurrency and fault tolerance.