Generate Executor Test Cases¶

This document provides comprehensive test cases for the generate_executor to demonstrate its intelligent task tree generation capabilities. These examples show how the executor understands business requirements and generates appropriate task trees.

Quick Start¶

All test cases can be run using the CLI command:

apflow generate task-tree "YOUR_REQUIREMENT_HERE"

Or save the output to a file:

apflow generate task-tree "YOUR_REQUIREMENT" --output tasks.json

With custom LLM parameters:

apflow generate task-tree "YOUR_REQUIREMENT" --temperature 0.9 --max-tokens 6000

Test Case 1: Complex Data Pipeline¶

Command:

apflow generate task-tree "Fetch data from two different APIs in parallel, then merge the results, validate the merged data, and finally save to database"

Expected Behavior: - Should generate parallel tasks for fetching from two APIs - Should create a merge/aggregate task that depends on both fetch tasks - Should create validation and save tasks in sequence - Should demonstrate understanding of parallel execution patterns

Test Case 2: ETL Workflow¶

Command:

apflow generate task-tree "Extract data from a REST API, transform it by filtering and aggregating, then load it into a database with proper error handling"

Expected Behavior: - Should create sequential pipeline: Extract → Transform → Load - Should use appropriate executors for each step - Should include proper dependencies for execution order

Test Case 3: Multi-Source Data Collection¶

Command:

apflow generate task-tree "Collect system information about CPU and memory in parallel, then run a command to analyze the collected data, and finally aggregate the results"

Expected Behavior: - Should use system_info_executor for parallel data collection - Should create command_executor for analysis - Should use aggregate_results_executor for final step - Should demonstrate parent_id for organization and dependencies for execution order

Test Case 4: API Integration with Processing¶

Command:

apflow generate task-tree "Call a REST API to get user data, process the response to extract key information using a Python script, validate the processed data, and save results to a file"

Expected Behavior: - Should create rest_executor for API call - Should create command_executor for processing - Should create validation and file operations - Should show proper dependency chain

Test Case 5: Complex Workflow with Conditional Steps¶

Command:

apflow generate task-tree "Fetch data from API, then process it in two different ways in parallel (filter and aggregate), merge both results, and finally save to database"

Expected Behavior: - Should demonstrate fan-out pattern (one task spawns multiple) - Should demonstrate fan-in pattern (multiple tasks converge) - Should show proper use of dependencies for parallel execution

Test Case 6: Real-World Business Scenario¶

Command:

apflow generate task-tree "Monitor system resources (CPU, memory, disk) in parallel, analyze the collected metrics, generate a report, and send notification if any metric exceeds threshold"

Expected Behavior: - Should use system_info_executor multiple times in parallel - Should create analysis and reporting tasks - Should demonstrate complex dependency relationships

Test Case 7: Data Processing Pipeline¶

Command:

apflow generate task-tree "Download data from multiple sources simultaneously, transform each dataset independently, then combine all transformed results into a single output file"

Expected Behavior: - Should show parallel download tasks - Should show parallel transformation tasks - Should create final aggregation task - Should demonstrate proper dependency management

Test Case 8: API Chain with Error Handling¶

Command:

apflow generate task-tree "Call API A to get authentication token, use token to call API B for data, process the data, and if processing fails, call a fallback API"

Expected Behavior: - Should create sequential API calls with token passing - Should demonstrate optional dependencies for fallback - Should show proper error handling pattern

Test Case 9: Hierarchical Data Processing¶

Command:

apflow generate task-tree "Fetch data from API, organize it into categories, process each category independently in parallel, then aggregate all category results"

Expected Behavior: - Should demonstrate hierarchical organization (parent_id) - Should show parallel processing within categories - Should create final aggregation step - Should show both organizational and execution dependencies

Test Case 10: Complete Business Workflow¶

Command:

apflow generate task-tree "Create a workflow that fetches customer data from API, validates customer information, processes orders in parallel for each customer, aggregates order results, calculates totals, and generates a final report"

Expected Behavior: - Should demonstrate complex multi-step workflow - Should show parallel processing pattern - Should create proper dependency chain - Should include all necessary executors

Usage Tips¶

1. Be Specific: More detailed requirements lead to better task trees
2. Mention Patterns: Use words like "parallel", "sequential", "merge", "aggregate" to guide generation
3. Specify Executors: Mention specific operations (API, database, file, command) for better executor selection
4. Describe Flow: Explain the data flow and execution order in your requirement

Expected Improvements¶

With intelligent prompt generation, the executor should: - ✅ Select relevant documentation sections based on requirement keywords - ✅ Understand business context and map to appropriate executors - ✅ Generate complete, realistic input parameters - ✅ Create proper dependency chains for execution order - ✅ Use parent_id appropriately for organization - ✅ Follow framework best practices and patterns