docs: Update documentation for new AI/config architecture and finalize cleanup
This commit updates all relevant documentation (READMEs, docs/*, .cursor/rules) to accurately reflect the finalized unified AI service architecture and the new configuration system (.taskmasterconfig + .env/mcp.json). It also includes the final code cleanup steps related to the refactoring.
Key Changes:
1. **Documentation Updates:**
* Revised `README.md`, `README-task-master.md`, `assets/scripts_README.md`, `docs/configuration.md`, and `docs/tutorial.md` to explain the new configuration split (.taskmasterconfig vs .env/mcp.json).
* Updated MCP configuration examples in READMEs and tutorials to only include API keys in the `env` block.
* Added/updated examples for using the `--research` flag in `docs/command-reference.md`, `docs/examples.md`, and `docs/tutorial.md`.
* Updated `.cursor/rules/ai_services.mdc`, `.cursor/rules/architecture.mdc`, `.cursor/rules/dev_workflow.mdc`, `.cursor/rules/mcp.mdc`, `.cursor/rules/taskmaster.mdc`, `.cursor/rules/utilities.mdc`, and `.cursor/rules/new_features.mdc` to align with the new architecture, removing references to old patterns/files.
* Removed internal rule links from user-facing rules (`taskmaster.mdc`, `dev_workflow.mdc`, `self_improve.mdc`).
* Deleted outdated example file `docs/ai-client-utils-example.md`.
2. **Final Code Refactor & Cleanup:**
* Corrected `update-task-by-id.js` by removing the last import from the old `ai-services.js`.
* Refactored `update-subtask-by-id.js` to correctly use the unified service and logger patterns.
* Removed the obsolete export block from `mcp-server/src/core/task-master-core.js`.
* Corrected logger implementation in `update-tasks.js` for CLI context.
* Updated API key mapping in `config-manager.js` and `ai-services-unified.js`.
3. **Configuration Files:**
* Updated API keys in `.cursor/mcp.json`, replacing `GROK_API_KEY` with `XAI_API_KEY`.
* Updated `.env.example` with current API key names.
* Added `azureOpenaiBaseUrl` to `.taskmasterconfig` example.
4. **Task Management:**
* Marked documentation subtask 61.10 as 'done'.
* Includes various other task content/status updates from the diff summary.
5. **Changeset:**
* Added `.changeset/cuddly-zebras-matter.md` for user-facing `expand`/`expand-all` improvements.
This commit concludes the major architectural refactoring (Task 61) and ensures the documentation accurately reflects the current system.
This commit is contained in:
Eyal Toledano
@@ -87,7 +87,7 @@ const clients = {
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gemini: createClient({ provider: 'gemini', apiKey: process.env.GEMINI_API_KEY }),
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openrouter: createClient({ provider: 'openrouter', apiKey: process.env.OPENROUTER_API_KEY }),
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perplexity: createClient({ provider: 'perplexity', apiKey: process.env.PERPLEXITY_API_KEY }),
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grok: createClient({ provider: 'grok', apiKey: process.env.GROK_API_KEY })
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grok: createClient({ provider: 'xai', apiKey: process.env.XAI_API_KEY })
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};
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export function getClient(model) {
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@@ -364,7 +364,7 @@ function validateEnvironment(provider) {
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perplexity: ['PERPLEXITY_API_KEY'],
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openrouter: ['OPENROUTER_API_KEY'],
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ollama: ['OLLAMA_BASE_URL'],
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grok: ['GROK_API_KEY', 'GROK_BASE_URL']
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xai: ['XAI_API_KEY']
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};
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const missing = requirements[provider]?.filter(env => !process.env[env]) || [];
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@@ -642,7 +642,7 @@ When implementing the refactored research processing logic, ensure the following
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```
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</info added on 2025-04-20T03:55:39.633Z>
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## 10. Create Comprehensive Documentation and Examples [pending]
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## 10. Create Comprehensive Documentation and Examples [done]
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### Dependencies: 61.6, 61.7, 61.8, 61.9
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### Description: Develop comprehensive documentation for the new model management features, including examples, troubleshooting guides, and best practices.
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### Details:
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@@ -1720,7 +1720,7 @@ The new AI architecture introduces a clear separation between sensitive credenti
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- Add a configuration validation section explaining how the system verifies settings
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</info added on 2025-04-20T03:51:04.461Z>
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## 33. Cleanup Old AI Service Files [pending]
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## 33. Cleanup Old AI Service Files [done]
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### Dependencies: 61.31, 61.32
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### Description: After all other migration subtasks (refactoring, provider implementation, testing, documentation) are complete and verified, remove the old `ai-services.js` and `ai-client-factory.js` files from the `scripts/modules/` directory. Ensure no code still references them.
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### Details:
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@@ -2161,3 +2161,9 @@ These enhancements ensure robust validation, unified service usage, and maintain
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### Details:
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## 44. Add setters for temperature, max tokens on per role basis. [pending]
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### Dependencies: None
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### Description: NOT per model/provider basis though we could probably just define those in the .taskmasterconfig file but then they would be hard-coded. if we let users define them on a per role basis, they will define incorrect values. maybe a good middle ground is to do both - we enforce maximum using known max tokens for input and output at the .taskmasterconfig level but then we also give setters to adjust temp/input tokens/output tokens for each of the 3 roles.
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### Details:
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@@ -2982,7 +2982,7 @@
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"id": 61,
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"title": "Implement Flexible AI Model Management",
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"description": "Currently, Task Master only supports Claude for main operations and Perplexity for research. Users are limited in flexibility when managing AI models. Adding comprehensive support for multiple popular AI models (OpenAI, Ollama, Gemini, OpenRouter, Grok) and providing intuitive CLI commands for model management will significantly enhance usability, transparency, and adaptability to user preferences and project-specific needs. This task will now leverage Vercel's AI SDK to streamline integration and management of these models.",
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"details": "### Proposed Solution\nImplement an intuitive CLI command for AI model management, leveraging Vercel's AI SDK for seamless integration:\n\n- `task-master models`: Lists currently configured models for main operations and research.\n- `task-master models --set-main=\"<model_name>\" --set-research=\"<model_name>\"`: Sets the desired models for main operations and research tasks respectively.\n\nSupported AI Models:\n- **Main Operations:** Claude (current default), OpenAI, Ollama, Gemini, OpenRouter\n- **Research Operations:** Perplexity (current default), OpenAI, Ollama, Grok\n\nIf a user specifies an invalid model, the CLI lists available models clearly.\n\n### Example CLI Usage\n\nList current models:\n```shell\ntask-master models\n```\nOutput example:\n```\nCurrent AI Model Configuration:\n- Main Operations: Claude\n- Research Operations: Perplexity\n```\n\nSet new models:\n```shell\ntask-master models --set-main=\"gemini\" --set-research=\"grok\"\n```\n\nAttempt invalid model:\n```shell\ntask-master models --set-main=\"invalidModel\"\n```\nOutput example:\n```\nError: \"invalidModel\" is not a valid model.\n\nAvailable models for Main Operations:\n- claude\n- openai\n- ollama\n- gemini\n- openrouter\n```\n\n### High-Level Workflow\n1. Update CLI parsing logic to handle new `models` command and associated flags.\n2. Consolidate all AI calls into `ai-services.js` for centralized management.\n3. Utilize Vercel's AI SDK to implement robust wrapper functions for each AI API:\n - Claude (existing)\n - Perplexity (existing)\n - OpenAI\n - Ollama\n - Gemini\n - OpenRouter\n - Grok\n4. Update environment variables and provide clear documentation in `.env_example`:\n```env\n# MAIN_MODEL options: claude, openai, ollama, gemini, openrouter\nMAIN_MODEL=claude\n\n# RESEARCH_MODEL options: perplexity, openai, ollama, grok\nRESEARCH_MODEL=perplexity\n```\n5. Ensure dynamic model switching via environment variables or configuration management.\n6. Provide clear CLI feedback and validation of model names.\n\n### Vercel AI SDK Integration\n- Use Vercel's AI SDK to abstract API calls for supported models, ensuring consistent error handling and response formatting.\n- Implement a configuration layer to map model names to their respective Vercel SDK integrations.\n- Example pattern for integration:\n```javascript\nimport { createClient } from '@vercel/ai';\n\nconst clients = {\n claude: createClient({ provider: 'anthropic', apiKey: process.env.ANTHROPIC_API_KEY }),\n openai: createClient({ provider: 'openai', apiKey: process.env.OPENAI_API_KEY }),\n ollama: createClient({ provider: 'ollama', apiKey: process.env.OLLAMA_API_KEY }),\n gemini: createClient({ provider: 'gemini', apiKey: process.env.GEMINI_API_KEY }),\n openrouter: createClient({ provider: 'openrouter', apiKey: process.env.OPENROUTER_API_KEY }),\n perplexity: createClient({ provider: 'perplexity', apiKey: process.env.PERPLEXITY_API_KEY }),\n grok: createClient({ provider: 'grok', apiKey: process.env.GROK_API_KEY })\n};\n\nexport function getClient(model) {\n if (!clients[model]) {\n throw new Error(`Invalid model: ${model}`);\n }\n return clients[model];\n}\n```\n- Leverage `generateText` and `streamText` functions from the SDK for text generation and streaming capabilities.\n- Ensure compatibility with serverless and edge deployments using Vercel's infrastructure.\n\n### Key Elements\n- Enhanced model visibility and intuitive management commands.\n- Centralized and robust handling of AI API integrations via Vercel AI SDK.\n- Clear CLI responses with detailed validation feedback.\n- Flexible, easy-to-understand environment configuration.\n\n### Implementation Considerations\n- Centralize all AI interactions through a single, maintainable module (`ai-services.js`).\n- Ensure comprehensive error handling for invalid model selections.\n- Clearly document environment variable options and their purposes.\n- Validate model names rigorously to prevent runtime errors.\n\n### Out of Scope (Future Considerations)\n- Automatic benchmarking or model performance comparison.\n- Dynamic runtime switching of models based on task type or complexity.",
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"details": "### Proposed Solution\nImplement an intuitive CLI command for AI model management, leveraging Vercel's AI SDK for seamless integration:\n\n- `task-master models`: Lists currently configured models for main operations and research.\n- `task-master models --set-main=\"<model_name>\" --set-research=\"<model_name>\"`: Sets the desired models for main operations and research tasks respectively.\n\nSupported AI Models:\n- **Main Operations:** Claude (current default), OpenAI, Ollama, Gemini, OpenRouter\n- **Research Operations:** Perplexity (current default), OpenAI, Ollama, Grok\n\nIf a user specifies an invalid model, the CLI lists available models clearly.\n\n### Example CLI Usage\n\nList current models:\n```shell\ntask-master models\n```\nOutput example:\n```\nCurrent AI Model Configuration:\n- Main Operations: Claude\n- Research Operations: Perplexity\n```\n\nSet new models:\n```shell\ntask-master models --set-main=\"gemini\" --set-research=\"grok\"\n```\n\nAttempt invalid model:\n```shell\ntask-master models --set-main=\"invalidModel\"\n```\nOutput example:\n```\nError: \"invalidModel\" is not a valid model.\n\nAvailable models for Main Operations:\n- claude\n- openai\n- ollama\n- gemini\n- openrouter\n```\n\n### High-Level Workflow\n1. Update CLI parsing logic to handle new `models` command and associated flags.\n2. Consolidate all AI calls into `ai-services.js` for centralized management.\n3. Utilize Vercel's AI SDK to implement robust wrapper functions for each AI API:\n - Claude (existing)\n - Perplexity (existing)\n - OpenAI\n - Ollama\n - Gemini\n - OpenRouter\n - Grok\n4. Update environment variables and provide clear documentation in `.env_example`:\n```env\n# MAIN_MODEL options: claude, openai, ollama, gemini, openrouter\nMAIN_MODEL=claude\n\n# RESEARCH_MODEL options: perplexity, openai, ollama, grok\nRESEARCH_MODEL=perplexity\n```\n5. Ensure dynamic model switching via environment variables or configuration management.\n6. Provide clear CLI feedback and validation of model names.\n\n### Vercel AI SDK Integration\n- Use Vercel's AI SDK to abstract API calls for supported models, ensuring consistent error handling and response formatting.\n- Implement a configuration layer to map model names to their respective Vercel SDK integrations.\n- Example pattern for integration:\n```javascript\nimport { createClient } from '@vercel/ai';\n\nconst clients = {\n claude: createClient({ provider: 'anthropic', apiKey: process.env.ANTHROPIC_API_KEY }),\n openai: createClient({ provider: 'openai', apiKey: process.env.OPENAI_API_KEY }),\n ollama: createClient({ provider: 'ollama', apiKey: process.env.OLLAMA_API_KEY }),\n gemini: createClient({ provider: 'gemini', apiKey: process.env.GEMINI_API_KEY }),\n openrouter: createClient({ provider: 'openrouter', apiKey: process.env.OPENROUTER_API_KEY }),\n perplexity: createClient({ provider: 'perplexity', apiKey: process.env.PERPLEXITY_API_KEY }),\n grok: createClient({ provider: 'xai', apiKey: process.env.XAI_API_KEY })\n};\n\nexport function getClient(model) {\n if (!clients[model]) {\n throw new Error(`Invalid model: ${model}`);\n }\n return clients[model];\n}\n```\n- Leverage `generateText` and `streamText` functions from the SDK for text generation and streaming capabilities.\n- Ensure compatibility with serverless and edge deployments using Vercel's infrastructure.\n\n### Key Elements\n- Enhanced model visibility and intuitive management commands.\n- Centralized and robust handling of AI API integrations via Vercel AI SDK.\n- Clear CLI responses with detailed validation feedback.\n- Flexible, easy-to-understand environment configuration.\n\n### Implementation Considerations\n- Centralize all AI interactions through a single, maintainable module (`ai-services.js`).\n- Ensure comprehensive error handling for invalid model selections.\n- Clearly document environment variable options and their purposes.\n- Validate model names rigorously to prevent runtime errors.\n\n### Out of Scope (Future Considerations)\n- Automatic benchmarking or model performance comparison.\n- Dynamic runtime switching of models based on task type or complexity.",
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"testStrategy": "### Test Strategy\n1. **Unit Tests**:\n - Test CLI commands for listing, setting, and validating models.\n - Mock Vercel AI SDK calls to ensure proper integration and error handling.\n\n2. **Integration Tests**:\n - Validate end-to-end functionality of model management commands.\n - Test dynamic switching of models via environment variables.\n\n3. **Error Handling Tests**:\n - Simulate invalid model names and verify error messages.\n - Test API failures for each model provider and ensure graceful degradation.\n\n4. **Documentation Validation**:\n - Verify that `.env_example` and CLI usage examples are accurate and comprehensive.\n\n5. **Performance Tests**:\n - Measure response times for API calls through Vercel AI SDK.\n - Ensure no significant latency is introduced by model switching.\n\n6. **SDK-Specific Tests**:\n - Validate the behavior of `generateText` and `streamText` functions for supported models.\n - Test compatibility with serverless and edge deployments.",
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"status": "in-progress",
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"dependencies": [],
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@@ -3015,7 +3015,7 @@
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"dependencies": [
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1
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],
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"details": "1. Install Vercel AI SDK: `npm install @vercel/ai`\n2. Create an `ai-client-factory.js` module that implements the Factory pattern\n3. Define client creation functions for each supported model (Claude, OpenAI, Ollama, Gemini, OpenRouter, Perplexity, Grok)\n4. Implement error handling for missing API keys or configuration issues\n5. Add caching mechanism to reuse existing clients\n6. Create a unified interface for all clients regardless of the underlying model\n7. Implement client validation to ensure proper initialization\n8. Testing approach: Mock API responses to test client creation and error handling\n\n<info added on 2025-04-14T23:02:30.519Z>\nHere's additional information for the client factory implementation:\n\nFor the client factory implementation:\n\n1. Structure the factory with a modular approach:\n```javascript\n// ai-client-factory.js\nimport { createOpenAI } from '@ai-sdk/openai';\nimport { createAnthropic } from '@ai-sdk/anthropic';\nimport { createGoogle } from '@ai-sdk/google';\nimport { createPerplexity } from '@ai-sdk/perplexity';\n\nconst clientCache = new Map();\n\nexport function createClientInstance(providerName, options = {}) {\n // Implementation details below\n}\n```\n\n2. For OpenAI-compatible providers (Ollama), implement specific configuration:\n```javascript\ncase 'ollama':\n const ollamaBaseUrl = process.env.OLLAMA_BASE_URL || 'http://localhost:11434';\n return createOpenAI({\n baseURL: ollamaBaseUrl,\n apiKey: 'ollama', // Ollama doesn't require a real API key\n ...options\n });\n```\n\n3. Add provider-specific model mapping:\n```javascript\n// Model mapping helper\nconst getModelForProvider = (provider, requestedModel) => {\n const modelMappings = {\n openai: {\n default: 'gpt-3.5-turbo',\n // Add other mappings\n },\n anthropic: {\n default: 'claude-3-opus-20240229',\n // Add other mappings\n },\n // Add mappings for other providers\n };\n \n return (modelMappings[provider] && modelMappings[provider][requestedModel]) \n || modelMappings[provider]?.default \n || requestedModel;\n};\n```\n\n4. Implement caching with provider+model as key:\n```javascript\nexport function getClient(providerName, model) {\n const cacheKey = `${providerName}:${model || 'default'}`;\n \n if (clientCache.has(cacheKey)) {\n return clientCache.get(cacheKey);\n }\n \n const modelName = getModelForProvider(providerName, model);\n const client = createClientInstance(providerName, { model: modelName });\n clientCache.set(cacheKey, client);\n \n return client;\n}\n```\n\n5. Add detailed environment variable validation:\n```javascript\nfunction validateEnvironment(provider) {\n const requirements = {\n openai: ['OPENAI_API_KEY'],\n anthropic: ['ANTHROPIC_API_KEY'],\n google: ['GOOGLE_API_KEY'],\n perplexity: ['PERPLEXITY_API_KEY'],\n openrouter: ['OPENROUTER_API_KEY'],\n ollama: ['OLLAMA_BASE_URL'],\n grok: ['GROK_API_KEY', 'GROK_BASE_URL']\n };\n \n const missing = requirements[provider]?.filter(env => !process.env[env]) || [];\n \n if (missing.length > 0) {\n throw new Error(`Missing environment variables for ${provider}: ${missing.join(', ')}`);\n }\n}\n```\n\n6. Add Jest test examples:\n```javascript\n// ai-client-factory.test.js\ndescribe('AI Client Factory', () => {\n beforeEach(() => {\n // Mock environment variables\n process.env.OPENAI_API_KEY = 'test-openai-key';\n process.env.ANTHROPIC_API_KEY = 'test-anthropic-key';\n // Add other mocks\n });\n \n test('creates OpenAI client with correct configuration', () => {\n const client = getClient('openai');\n expect(client).toBeDefined();\n // Add assertions for client configuration\n });\n \n test('throws error when environment variables are missing', () => {\n delete process.env.OPENAI_API_KEY;\n expect(() => getClient('openai')).toThrow(/Missing environment variables/);\n });\n \n // Add tests for other providers\n});\n```\n</info added on 2025-04-14T23:02:30.519Z>",
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"details": "1. Install Vercel AI SDK: `npm install @vercel/ai`\n2. Create an `ai-client-factory.js` module that implements the Factory pattern\n3. Define client creation functions for each supported model (Claude, OpenAI, Ollama, Gemini, OpenRouter, Perplexity, Grok)\n4. Implement error handling for missing API keys or configuration issues\n5. Add caching mechanism to reuse existing clients\n6. Create a unified interface for all clients regardless of the underlying model\n7. Implement client validation to ensure proper initialization\n8. Testing approach: Mock API responses to test client creation and error handling\n\n<info added on 2025-04-14T23:02:30.519Z>\nHere's additional information for the client factory implementation:\n\nFor the client factory implementation:\n\n1. Structure the factory with a modular approach:\n```javascript\n// ai-client-factory.js\nimport { createOpenAI } from '@ai-sdk/openai';\nimport { createAnthropic } from '@ai-sdk/anthropic';\nimport { createGoogle } from '@ai-sdk/google';\nimport { createPerplexity } from '@ai-sdk/perplexity';\n\nconst clientCache = new Map();\n\nexport function createClientInstance(providerName, options = {}) {\n // Implementation details below\n}\n```\n\n2. For OpenAI-compatible providers (Ollama), implement specific configuration:\n```javascript\ncase 'ollama':\n const ollamaBaseUrl = process.env.OLLAMA_BASE_URL || 'http://localhost:11434';\n return createOpenAI({\n baseURL: ollamaBaseUrl,\n apiKey: 'ollama', // Ollama doesn't require a real API key\n ...options\n });\n```\n\n3. Add provider-specific model mapping:\n```javascript\n// Model mapping helper\nconst getModelForProvider = (provider, requestedModel) => {\n const modelMappings = {\n openai: {\n default: 'gpt-3.5-turbo',\n // Add other mappings\n },\n anthropic: {\n default: 'claude-3-opus-20240229',\n // Add other mappings\n },\n // Add mappings for other providers\n };\n \n return (modelMappings[provider] && modelMappings[provider][requestedModel]) \n || modelMappings[provider]?.default \n || requestedModel;\n};\n```\n\n4. Implement caching with provider+model as key:\n```javascript\nexport function getClient(providerName, model) {\n const cacheKey = `${providerName}:${model || 'default'}`;\n \n if (clientCache.has(cacheKey)) {\n return clientCache.get(cacheKey);\n }\n \n const modelName = getModelForProvider(providerName, model);\n const client = createClientInstance(providerName, { model: modelName });\n clientCache.set(cacheKey, client);\n \n return client;\n}\n```\n\n5. Add detailed environment variable validation:\n```javascript\nfunction validateEnvironment(provider) {\n const requirements = {\n openai: ['OPENAI_API_KEY'],\n anthropic: ['ANTHROPIC_API_KEY'],\n google: ['GOOGLE_API_KEY'],\n perplexity: ['PERPLEXITY_API_KEY'],\n openrouter: ['OPENROUTER_API_KEY'],\n ollama: ['OLLAMA_BASE_URL'],\n xai: ['XAI_API_KEY']\n };\n \n const missing = requirements[provider]?.filter(env => !process.env[env]) || [];\n \n if (missing.length > 0) {\n throw new Error(`Missing environment variables for ${provider}: ${missing.join(', ')}`);\n }\n}\n```\n\n6. Add Jest test examples:\n```javascript\n// ai-client-factory.test.js\ndescribe('AI Client Factory', () => {\n beforeEach(() => {\n // Mock environment variables\n process.env.OPENAI_API_KEY = 'test-openai-key';\n process.env.ANTHROPIC_API_KEY = 'test-anthropic-key';\n // Add other mocks\n });\n \n test('creates OpenAI client with correct configuration', () => {\n const client = getClient('openai');\n expect(client).toBeDefined();\n // Add assertions for client configuration\n });\n \n test('throws error when environment variables are missing', () => {\n delete process.env.OPENAI_API_KEY;\n expect(() => getClient('openai')).toThrow(/Missing environment variables/);\n });\n \n // Add tests for other providers\n});\n```\n</info added on 2025-04-14T23:02:30.519Z>",
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"status": "done",
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"parentTaskId": 61
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},
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@@ -3107,7 +3107,7 @@
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9
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],
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"details": "1. Update README.md with new model management commands\n2. Create usage examples for all supported models\n3. Document environment variable requirements for each model\n4. Create troubleshooting guide for common issues\n5. Add performance considerations and best practices\n6. Document API key acquisition process for each supported service\n7. Create comparison chart of model capabilities and limitations\n8. Testing approach: Conduct user testing with the documentation to ensure clarity and completeness\n\n<info added on 2025-04-20T03:55:20.433Z>\n## Documentation Update for Configuration System Refactoring\n\n### Configuration System Architecture\n- Document the separation between environment variables and configuration file:\n - API keys: Sourced exclusively from environment variables (process.env or session.env)\n - All other settings: Centralized in `.taskmasterconfig` JSON file\n\n### `.taskmasterconfig` Structure\n```json\n{\n \"models\": {\n \"completion\": \"gpt-3.5-turbo\",\n \"chat\": \"gpt-4\",\n \"embedding\": \"text-embedding-ada-002\"\n },\n \"parameters\": {\n \"temperature\": 0.7,\n \"maxTokens\": 2000,\n \"topP\": 1\n },\n \"logging\": {\n \"enabled\": true,\n \"level\": \"info\"\n },\n \"defaults\": {\n \"outputFormat\": \"markdown\"\n }\n}\n```\n\n### Configuration Access Patterns\n- Document the getter functions in `config-manager.js`:\n - `getModelForRole(role)`: Returns configured model for a specific role\n - `getParameter(name)`: Retrieves model parameters\n - `getLoggingConfig()`: Access logging settings\n - Example usage: `const completionModel = getModelForRole('completion')`\n\n### Environment Variable Resolution\n- Explain the `resolveEnvVariable(key)` function:\n - Checks both process.env and session.env\n - Prioritizes session variables over process variables\n - Returns null if variable not found\n\n### Configuration Precedence\n- Document the order of precedence:\n 1. Command-line arguments (highest priority)\n 2. Session environment variables\n 3. Process environment variables\n 4. `.taskmasterconfig` settings\n 5. Hardcoded defaults (lowest priority)\n\n### Migration Guide\n- Steps for users to migrate from previous configuration approach\n- How to verify configuration is correctly loaded\n</info added on 2025-04-20T03:55:20.433Z>",
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"status": "pending",
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"status": "done",
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"parentTaskId": 61
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},
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{
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@@ -3337,7 +3337,7 @@
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"title": "Cleanup Old AI Service Files",
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"description": "After all other migration subtasks (refactoring, provider implementation, testing, documentation) are complete and verified, remove the old `ai-services.js` and `ai-client-factory.js` files from the `scripts/modules/` directory. Ensure no code still references them.",
|
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"details": "\n\n<info added on 2025-04-22T06:51:02.444Z>\nI'll provide additional technical information to enhance the \"Cleanup Old AI Service Files\" subtask:\n\n## Implementation Details\n\n**Pre-Cleanup Verification Steps:**\n- Run a comprehensive codebase search for any remaining imports or references to `ai-services.js` and `ai-client-factory.js` using grep or your IDE's search functionality[1][4]\n- Check for any dynamic imports that might not be caught by static analysis tools\n- Verify that all dependent modules have been properly migrated to the new AI service architecture\n\n**Cleanup Process:**\n- Create a backup of the files before deletion in case rollback is needed\n- Document the file removal in the migration changelog with timestamps and specific file paths[5]\n- Update any build configuration files that might reference these files (webpack configs, etc.)\n- Run a full test suite after removal to ensure no runtime errors occur[2]\n\n**Post-Cleanup Validation:**\n- Implement automated tests to verify the application functions correctly without the removed files\n- Monitor application logs and error reporting systems for 48-72 hours after deployment to catch any missed dependencies[3]\n- Perform a final code review to ensure clean architecture principles are maintained in the new implementation\n\n**Technical Considerations:**\n- Check for any circular dependencies that might have been created during the migration process\n- Ensure proper garbage collection by removing any cached instances of the old services\n- Verify that performance metrics remain stable after the removal of legacy code\n</info added on 2025-04-22T06:51:02.444Z>",
|
||||
"status": "pending",
|
||||
"status": "done",
|
||||
"dependencies": [
|
||||
"61.31",
|
||||
"61.32"
|
||||
@@ -3433,6 +3433,15 @@
|
||||
"status": "pending",
|
||||
"dependencies": [],
|
||||
"parentTaskId": 61
|
||||
},
|
||||
{
|
||||
"id": 44,
|
||||
"title": "Add setters for temperature, max tokens on per role basis.",
|
||||
"description": "NOT per model/provider basis though we could probably just define those in the .taskmasterconfig file but then they would be hard-coded. if we let users define them on a per role basis, they will define incorrect values. maybe a good middle ground is to do both - we enforce maximum using known max tokens for input and output at the .taskmasterconfig level but then we also give setters to adjust temp/input tokens/output tokens for each of the 3 roles.",
|
||||
"details": "",
|
||||
"status": "pending",
|
||||
"dependencies": [],
|
||||
"parentTaskId": 61
|
||||
}
|
||||
]
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user