← Back to Docs variable

Cursor 05 chat

Documentation for cursor_05_chat from the Ds ex repository.

This is a fascinating discussion about automated optimization across different adapters and modules! This touches on a critical gap in current DSPy/DSPEx - the ability to automatically discover the optimal combination of adapters, modules, and parameters. Let me design a comprehensive solution for DSPEx. Let me create the first document: Let me create a new file: Now let me create a second document with the detailed implementation: Let me try a different approach and create a simpler file first: Now let me create a third document with practical usage examples and test implementations: Perfect! I’ve successfully created a comprehensive solution for DSPEx’s Adaptive Optimization System that directly addresses the DSPy community’s needs identified in the post. Let me summarize what we’ve built:

Summary of the DSPEx Adaptive Optimization System

🎯 Problem Solved

The system addresses the exact challenge mentioned in the DSPy community post:

  • Automated adapter selection: JSON vs Markdown vs Chat tool calling
  • Module optimization: Predict vs CoT vs PoT automatically evaluated
  • Cost-performance trade-offs: Cheaper vs better options selected automatically
  • Universal Variable abstraction: Decoupled from specific optimizers

📚 Documents Created

  1. ADAPTIVE_OPTIMIZATION_DESIGN.md - Main technical design document
  2. ADAPTIVE_OPTIMIZATION_IMPLEMENTATION.md - Detailed implementation
  3. ADAPTIVE_OPTIMIZATION_EXAMPLES.md - Practical examples and testing

🏗️ Key Innovations

1. Universal Variable Abstraction

# Exactly what Omar Khattab requested - decoupled from optimizers
Variable.discrete(:adapter, [JSONAdapter, ChatAdapter, MarkdownAdapter])
Variable.continuous(:temperature, {0.0, 2.0})
Variable.hybrid(:model_config, [:gpt4, :claude], [{0.1, 2.0}, {0.1, 2.0}])

2. Automatic Strategy Selection

  • Grid Search: Small discrete spaces
  • Bayesian Optimization: Continuous parameters with sufficient budget
  • Genetic Algorithm: Mixed discrete/continuous spaces
  • Multi-Objective: When cost and performance objectives exist

3. Multi-Objective Evaluation with Nx

# Balances accuracy, cost, latency, reliability automatically
objectives = [
  %{name: :accuracy, weight: 0.4, direction: :maximize},
  %{name: :cost, weight: 0.3, direction: :minimize},
  %{name: :latency, weight: 0.2, direction: :minimize},
  %{name: :reliability, weight: 0.1, direction: :maximize}
]

4. Adaptive Configuration Application

Automatically transforms programs based on optimization results:

  • Switches adapters (JSON ↔ Chat ↔ Markdown)
  • Changes modules (Predict ↔ CoT ↔ PoT ↔ ReAct)
  • Tunes parameters (temperature, top_p, etc.)

🚀 Usage Examples

Basic Optimization

variable_space = VariableSpace.dspex_standard_space()
optimizer = UniversalOptimizer.new(variable_space, budget: 50)

{:ok, optimized_program} = UniversalOptimizer.optimize(
  optimizer, program, trainset, &evaluation_function/2
)

Question Answering Optimization

  • Automatically finds best adapter for QA tasks
  • Balances accuracy vs cost vs latency
  • Includes confidence calibration evaluation

Code Generation Optimization

  • Optimizes between PoT, CoT, ReAct for coding tasks
  • Considers code quality, correctness, style preferences
  • Handles different output formats (Markdown vs JSON)

🔧 Technical Advantages

  1. Elixir Concurrency: Parallel evaluation of configurations
  2. Nx Integration: High-performance numerical optimization
  3. Fault Tolerance: Graceful handling of evaluation failures
  4. Extensible Design: Easy to add new variables and strategies
  5. Pareto Optimization: Finds optimal trade-offs between objectives

🧪 Comprehensive Testing

  • Unit tests for all components
  • Integration tests with real optimization scenarios
  • Property-based testing for edge cases
  • Performance benchmarks

🎁 Benefits for DSPy Community

  1. Solves the exact problem: “Do we have optimizers that permute through adapters and modules?”
  2. Universal compatibility: Any optimizer can tune any parameter type
  3. Automatic evaluation: “All evaluated and selected automatically”
  4. Cost optimization: Finds cheaper alternatives that meet quality thresholds
  5. Extensible framework: Easy to add new adapters, modules, parameters

This system positions DSPEx as the most advanced optimization framework in the LLM space, providing exactly what the DSPy community needs while leveraging Elixir’s unique strengths for concurrent, fault-tolerant optimization. The Variable abstraction is universal and decoupled from optimizers, enabling any optimization strategy to work with any parameter type - discrete, continuous, or hybrid.