15ad34928d
- Fixed parent-to-parent task moves where original task would remain as duplicate - Fixed moving tasks to become subtasks of empty parents (validation errors) - Fixed moving subtasks between different parent tasks - Improved comma-separated batch moves with proper error handling - Updated MCP tool to use core logic instead of custom implementation - Resolves task duplication issues and enables proper task hierarchy reorganization
62 lines
3.0 KiB
Plaintext
62 lines
3.0 KiB
Plaintext
# Task ID: 86
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# Title: Implement Separate Context Window and Output Token Limits
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# Status: pending
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# Dependencies: None
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# Priority: high
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# Description: Replace the ambiguous MAX_TOKENS configuration with separate contextWindowTokens and maxOutputTokens fields to properly handle model token limits and enable dynamic token allocation.
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# Details:
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Currently, the MAX_TOKENS configuration entry is ambiguous and doesn't properly differentiate between:
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1. Context window tokens (total input + output capacity)
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2. Maximum output tokens (generation limit)
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This causes issues where:
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- The system can't properly validate prompt lengths against model capabilities
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- Output token allocation is not optimized based on input length
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- Different models with different token architectures are handled inconsistently
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This epic will implement a comprehensive solution that:
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- Updates supported-models.json with accurate contextWindowTokens and maxOutputTokens for each model
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- Modifies config-manager.js to use separate maxInputTokens and maxOutputTokens in role configurations
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- Implements a token counting utility for accurate prompt measurement
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- Updates ai-services-unified.js to dynamically calculate available output tokens
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- Provides migration guidance and validation for existing configurations
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- Adds comprehensive error handling and validation throughout the system
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The end result will be more precise token management, better cost control, and reduced likelihood of hitting model context limits.
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# Test Strategy:
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1. Verify all models have accurate token limit data from official documentation
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2. Test dynamic token allocation with various prompt lengths
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3. Ensure backward compatibility with existing .taskmasterconfig files
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4. Validate error messages are clear and actionable
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5. Test with multiple AI providers to ensure consistent behavior
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6. Performance test token counting utility with large prompts
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# Subtasks:
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## 1. Update supported-models.json with token limit fields [pending]
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### Dependencies: None
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### Description: Modify the supported-models.json file to include contextWindowTokens and maxOutputTokens fields for each model, replacing the ambiguous max_tokens field.
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### Details:
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For each model entry in supported-models.json:
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1. Add `contextWindowTokens` field representing the total context window (input + output tokens)
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2. Add `maxOutputTokens` field representing the maximum tokens the model can generate
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3. Remove or deprecate the ambiguous `max_tokens` field if present
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Research and populate accurate values for each model from official documentation:
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- For OpenAI models (e.g., gpt-4o): contextWindowTokens=128000, maxOutputTokens=16384
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- For Anthropic models (e.g., Claude 3.7): contextWindowTokens=200000, maxOutputTokens=8192
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- For other providers, find official documentation or use reasonable defaults
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Example entry:
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```json
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{
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"id": "claude-3-7-sonnet-20250219",
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"swe_score": 0.623,
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"cost_per_1m_tokens": { "input": 3.0, "output": 15.0 },
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"allowed_roles": ["main", "fallback"],
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"contextWindowTokens": 200000,
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"maxOutputTokens": 8192
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}
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```
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