docs: update changeset with model config while preserving existing changes
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Eyal Toledano
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tasks/task_051.txt
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tasks/task_051.txt
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# Task ID: 51
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# Title: Implement Perplexity Research Command
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# Status: pending
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# Dependencies: None
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# Priority: medium
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# Description: Create a command that allows users to quickly research topics using Perplexity AI, with options to include task context or custom prompts.
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# Details:
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Develop a new command called 'research' that integrates with Perplexity AI's API to fetch information on specified topics. The command should:
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1. Accept the following parameters:
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- A search query string (required)
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- A task or subtask ID for context (optional)
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- A custom prompt to guide the research (optional)
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2. When a task/subtask ID is provided, extract relevant information from it to enrich the research query with context.
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3. Implement proper API integration with Perplexity, including authentication and rate limiting handling.
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4. Format and display the research results in a readable format in the terminal, with options to:
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- Save the results to a file
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- Copy results to clipboard
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- Generate a summary of key points
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5. Cache research results to avoid redundant API calls for the same queries.
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6. Provide a configuration option to set the depth/detail level of research (quick overview vs. comprehensive).
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7. Handle errors gracefully, especially network issues or API limitations.
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The command should follow the existing CLI structure and maintain consistency with other commands in the system.
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# Test Strategy:
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1. Unit tests:
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- Test the command with various combinations of parameters (query only, query+task, query+custom prompt, all parameters)
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- Mock the Perplexity API responses to test different scenarios (successful response, error response, rate limiting)
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- Verify that task context is correctly extracted and incorporated into the research query
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2. Integration tests:
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- Test actual API calls to Perplexity with valid credentials (using a test account)
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- Verify the caching mechanism works correctly for repeated queries
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- Test error handling with intentionally invalid requests
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3. User acceptance testing:
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- Have team members use the command for real research needs and provide feedback
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- Verify the command works in different network environments
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- Test the command with very long queries and responses
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4. Performance testing:
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- Measure and optimize response time for queries
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- Test behavior under poor network conditions
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Validate that the research results are properly formatted, readable, and that all output options (save, copy) function correctly.
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# Subtasks:
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## 1. Create Perplexity API Client Service [pending]
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### Dependencies: None
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### Description: Develop a service module that handles all interactions with the Perplexity AI API, including authentication, request formatting, and response handling.
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### Details:
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Implementation details:
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1. Create a new service file `services/perplexityService.js`
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2. Implement authentication using the PERPLEXITY_API_KEY from environment variables
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3. Create functions for making API requests to Perplexity with proper error handling:
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- `queryPerplexity(searchQuery, options)` - Main function to query the API
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- `handleRateLimiting(response)` - Logic to handle rate limits with exponential backoff
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4. Implement response parsing and formatting functions
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5. Add proper error handling for network issues, authentication problems, and API limitations
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6. Create a simple caching mechanism using a Map or object to store recent query results
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7. Add configuration options for different detail levels (quick vs comprehensive)
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Testing approach:
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- Write unit tests using Jest to verify API client functionality with mocked responses
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- Test error handling with simulated network failures
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- Verify caching mechanism works correctly
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- Test with various query types and options
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## 2. Implement Task Context Extraction Logic [pending]
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### Dependencies: None
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### Description: Create utility functions to extract relevant context from tasks and subtasks to enhance research queries with project-specific information.
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### Details:
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Implementation details:
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1. Create a new utility file `utils/contextExtractor.js`
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2. Implement a function `extractTaskContext(taskId)` that:
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- Loads the task/subtask data from tasks.json
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- Extracts relevant information (title, description, details)
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- Formats the extracted information into a context string for research
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3. Add logic to handle both task and subtask IDs
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4. Implement a function to combine extracted context with the user's search query
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5. Create a function to identify and extract key terminology from tasks
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6. Add functionality to include parent task context when a subtask ID is provided
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7. Implement proper error handling for invalid task IDs
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Testing approach:
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- Write unit tests to verify context extraction from sample tasks
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- Test with various task structures and content types
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- Verify error handling for missing or invalid tasks
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- Test the quality of extracted context with sample queries
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## 3. Build Research Command CLI Interface [pending]
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### Dependencies: 51.1, 51.2
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### Description: Implement the Commander.js command structure for the 'research' command with all required options and parameters.
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### Details:
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Implementation details:
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1. Create a new command file `commands/research.js`
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2. Set up the Commander.js command structure with the following options:
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- Required search query parameter
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- `--task` or `-t` option for task/subtask ID
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- `--prompt` or `-p` option for custom research prompt
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- `--save` or `-s` option to save results to a file
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- `--copy` or `-c` option to copy results to clipboard
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- `--summary` or `-m` option to generate a summary
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- `--detail` or `-d` option to set research depth (default: medium)
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3. Implement command validation logic
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4. Connect the command to the Perplexity service created in subtask 1
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5. Integrate the context extraction logic from subtask 2
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6. Register the command in the main CLI application
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7. Add help text and examples
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Testing approach:
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- Test command registration and option parsing
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- Verify command validation logic works correctly
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- Test with various combinations of options
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- Ensure proper error messages for invalid inputs
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## 4. Implement Results Processing and Output Formatting [pending]
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### Dependencies: 51.1, 51.3
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### Description: Create functionality to process, format, and display research results in the terminal with options for saving, copying, and summarizing.
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### Details:
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Implementation details:
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1. Create a new module `utils/researchFormatter.js`
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2. Implement terminal output formatting with:
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- Color-coded sections for better readability
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- Proper text wrapping for terminal width
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- Highlighting of key points
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3. Add functionality to save results to a file:
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- Create a `research-results` directory if it doesn't exist
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- Save results with timestamp and query in filename
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- Support multiple formats (text, markdown, JSON)
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4. Implement clipboard copying using a library like `clipboardy`
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5. Create a summarization function that extracts key points from research results
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6. Add progress indicators during API calls
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7. Implement pagination for long results
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Testing approach:
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- Test output formatting with various result lengths and content types
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- Verify file saving functionality creates proper files with correct content
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- Test clipboard functionality
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- Verify summarization produces useful results
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## 5. Implement Caching and Results Management System [pending]
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### Dependencies: 51.1, 51.4
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### Description: Create a persistent caching system for research results and implement functionality to manage, retrieve, and reference previous research.
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### Details:
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Implementation details:
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1. Create a research results database using a simple JSON file or SQLite:
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- Store queries, timestamps, and results
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- Index by query and related task IDs
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2. Implement cache retrieval and validation:
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- Check for cached results before making API calls
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- Validate cache freshness with configurable TTL
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3. Add commands to manage research history:
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- List recent research queries
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- Retrieve past research by ID or search term
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- Clear cache or delete specific entries
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4. Create functionality to associate research results with tasks:
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- Add metadata linking research to specific tasks
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- Implement command to show all research related to a task
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5. Add configuration options for cache behavior in user settings
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6. Implement export/import functionality for research data
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Testing approach:
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- Test cache storage and retrieval with various queries
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- Verify cache invalidation works correctly
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- Test history management commands
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- Verify task association functionality
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- Test with large cache sizes to ensure performance
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