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fix: adapt OpenAI client for AI Foundry compatibility
Modify API parameter handling in OpenAILLM to properly support AI Foundry endpoints: - Add special case for AI Foundry endpoints - Remove unsupported parameters for o-series models - Update URL detection for Azure endpoints - Improve parameter cleaning for different API variants
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‎config.yaml‎ renamed to ‎examples/trading_volume_optimization_orchestrator/config.yaml‎

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# OpenEvolve Default Configuration
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# This file contains all available configuration options with sensible defaults
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# You can use this as a template for your own configuration
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# General settings
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max_iterations: 1000 # Maximum number of evolution iterations
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checkpoint_interval: 50 # Save checkpoints every N iterations
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log_level: "INFO" # Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
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log_dir: null # Custom directory for logs (default: output_dir/logs)
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random_seed: null # Random seed for reproducibility (null = random)
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# Evolution settings
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diff_based_evolution: true # Use diff-based evolution (true) or full rewrites (false)
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allow_full_rewrites: false # Allow occasional full rewrites even in diff-based mode
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max_code_length: 10000 # Maximum allowed code length in characters
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# LLM configuration
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llm:
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models:
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- name: "o1"
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weight: 1.0
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evaluator_models:
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- name: "o1"
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weight: 1.0
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# Azure endpoint *root* – no path, no query string
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api_base: "https://<YOUR_BASE>.openai.azure.com/openai/deployments/<YOUR_DEPLOYMENT_eg_o1"
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# Tell the SDK which API flavour and version to use
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# api_type: "azure"
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# api_version: "2025-01-01-preview"
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api_key: YOUR_API_KEY # Or provide it directly here
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temperature: 0.7
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top_p: 0.95
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max_tokens: 4096
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timeout: 60
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retries: 3
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retry_delay: 5
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# Prompt configuration
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prompt:
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template_dir: null # Custom directory for prompt templates
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system_message: "You are an expert coder helping to improve programs through evolution."
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evaluator_system_message: "You are an expert code reviewer."
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# Number of examples to include in the prompt
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num_top_programs: 3 # Number of top-performing programs to include
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num_diverse_programs: 2 # Number of diverse programs to include
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# Template stochasticity
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use_template_stochasticity: true # Use random variations in templates for diversity
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template_variations: # Different phrasings for parts of the template
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improvement_suggestion:
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- "Here's how we could improve this code:"
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- "I suggest the following improvements:"
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- "We can enhance this code by:"
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# Note: meta-prompting features are not yet implemented
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# Database configuration
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database:
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# General settings
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db_path: null # Path to persist database (null = in-memory only)
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in_memory: true # Keep database in memory for faster access
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# Evolutionary parameters
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population_size: 1000 # Maximum number of programs to keep in memory
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archive_size: 100 # Size of elite archive
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num_islands: 5 # Number of islands for island model (separate populations)
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# Island-based evolution parameters
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# Islands provide diversity by maintaining separate populations that evolve independently.
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# Migration periodically shares the best solutions between adjacent islands.
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migration_interval: 50 # Migrate between islands every N generations
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migration_rate: 0.1 # Fraction of top programs to migrate (0.1 = 10%)
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# Selection parameters
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elite_selection_ratio: 0.1 # Ratio of elite programs to select
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exploration_ratio: 0.2 # Ratio of exploration vs exploitation
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exploitation_ratio: 0.7 # Ratio of exploitation vs random selection
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# Note: diversity_metric is fixed to "edit_distance" (feature_based not implemented)
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# Feature map dimensions for MAP-Elites
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feature_dimensions: # Dimensions for MAP-Elites feature map
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- "score" # Performance score
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- "complexity" # Code complexity (length)
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feature_bins: 10 # Number of bins per dimension
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# Evaluator configuration
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evaluator:
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# General settings
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timeout: 300 # Maximum evaluation time in seconds
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max_retries: 3 # Maximum number of retries for evaluation
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# Note: resource limits (memory_limit_mb, cpu_limit) are not yet implemented
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# Evaluation strategies
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cascade_evaluation: true # Use cascade evaluation to filter bad solutions early
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cascade_thresholds: # Thresholds for advancing to next evaluation stage
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- 0.5 # First stage threshold
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- 0.75 # Second stage threshold
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- 0.9 # Third stage threshold
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# Parallel evaluation
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parallel_evaluations: 4 # Number of parallel evaluations
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# Note: distributed evaluation is not yet implemented
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# LLM-based feedback (experimental)
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use_llm_feedback: false # Use LLM to evaluate code quality
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llm_feedback_weight: 0.1 # Weight for LLM feedback in final score
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# OpenEvolve Default Configuration
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# This file contains all available configuration options with sensible defaults
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# You can use this as a template for your own configuration
4+
5+
# General settings
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max_iterations: 1000 # Maximum number of evolution iterations
7+
checkpoint_interval: 50 # Save checkpoints every N iterations
8+
log_level: "INFO" # Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
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log_dir: null # Custom directory for logs (default: output_dir/logs)
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random_seed: null # Random seed for reproducibility (null = random)
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# Evolution settings
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diff_based_evolution: true # Use diff-based evolution (true) or full rewrites (false)
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allow_full_rewrites: false # Allow occasional full rewrites even in diff-based mode
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max_code_length: 10000 # Maximum allowed code length in characters
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# LLM configuration
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llm:
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models:
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- name: "gpt-4"
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weight: 1.0
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evaluator_models:
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- name: "gpt-4"
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weight: 1.0
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# Azure endpoint *root* – no path, no query string
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api_base: "https://XXXXXX.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2025-01-01-preview"
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# Tell the SDK which API flavour and version to use
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# api_type: "azure"
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# api_version: "2025-01-01-preview"
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api_key: "XXXXXXXXXXXXXXXXXX" # Or provide it directly here
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temperature: 0.7
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top_p: 0.95
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max_tokens: 4096
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timeout: 60
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retries: 3
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retry_delay: 5
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# Prompt configuration
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prompt:
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template_dir: null # Custom directory for prompt templates
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system_message: "You are an expert coder helping to improve programs through evolution."
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evaluator_system_message: "You are an expert code reviewer."
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# Number of examples to include in the prompt
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num_top_programs: 3 # Number of top-performing programs to include
50+
num_diverse_programs: 2 # Number of diverse programs to include
51+
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# Template stochasticity
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use_template_stochasticity: true # Use random variations in templates for diversity
54+
template_variations: # Different phrasings for parts of the template
55+
improvement_suggestion:
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- "Here's how we could improve this code:"
57+
- "I suggest the following improvements:"
58+
- "We can enhance this code by:"
59+
60+
# Note: meta-prompting features are not yet implemented
61+
62+
# Database configuration
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database:
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# General settings
65+
db_path: null # Path to persist database (null = in-memory only)
66+
in_memory: true # Keep database in memory for faster access
67+
68+
# Evolutionary parameters
69+
population_size: 1000 # Maximum number of programs to keep in memory
70+
archive_size: 100 # Size of elite archive
71+
num_islands: 5 # Number of islands for island model (separate populations)
72+
73+
# Island-based evolution parameters
74+
# Islands provide diversity by maintaining separate populations that evolve independently.
75+
# Migration periodically shares the best solutions between adjacent islands.
76+
migration_interval: 50 # Migrate between islands every N generations
77+
migration_rate: 0.1 # Fraction of top programs to migrate (0.1 = 10%)
78+
79+
# Selection parameters
80+
elite_selection_ratio: 0.1 # Ratio of elite programs to select
81+
exploration_ratio: 0.2 # Ratio of exploration vs exploitation
82+
exploitation_ratio: 0.7 # Ratio of exploitation vs random selection
83+
# Note: diversity_metric is fixed to "edit_distance" (feature_based not implemented)
84+
85+
# Feature map dimensions for MAP-Elites
86+
feature_dimensions: # Dimensions for MAP-Elites feature map
87+
- "score" # Performance score
88+
- "complexity" # Code complexity (length)
89+
feature_bins: 10 # Number of bins per dimension
90+
91+
# Evaluator configuration
92+
evaluator:
93+
# General settings
94+
timeout: 300 # Maximum evaluation time in seconds
95+
max_retries: 3 # Maximum number of retries for evaluation
96+
97+
# Note: resource limits (memory_limit_mb, cpu_limit) are not yet implemented
98+
99+
# Evaluation strategies
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cascade_evaluation: true # Use cascade evaluation to filter bad solutions early
101+
cascade_thresholds: # Thresholds for advancing to next evaluation stage
102+
- 0.5 # First stage threshold
103+
- 0.75 # Second stage threshold
104+
- 0.9 # Third stage threshold
105+
106+
# Parallel evaluation
107+
parallel_evaluations: 4 # Number of parallel evaluations
108+
# Note: distributed evaluation is not yet implemented
109+
110+
# LLM-based feedback (experimental)
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use_llm_feedback: false # Use LLM to evaluate code quality
112+
llm_feedback_weight: 0.1 # Weight for LLM feedback in final score

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