Generate Synthetic Reasoning Data Agents
Learn how to generate chain-of-thought reasoning data using PraisonAI Agents.
What is Chain-of-Thought Generation?
Chain-of-Thought (CoT) Generation is a process where AI agents create detailed, step-by-step reasoning paths for solving problems. This involves generating questions, evaluating them, producing detailed solution steps, and making the data available for training and analysis.
Quick Start
Install Package
First, install the PraisonAI Agents package:
Set API Key
Set your OpenAI API key as an environment variable in your terminal:
Create a file
Create a new file app.py
with the basic setup:
Run the application
Execute the Python script to start generating chain-of-thought data:
Features
Question Generation
Create challenging math and logic questions with answers.
Question Evaluation
Evaluate and validate generated questions for quality.
CoT Solutions
Generate detailed chain-of-thought solutions for each question.
Data Management
Save and manage generated data in structured formats.
HuggingFace Integration
Upload datasets directly to HuggingFace for sharing.
Understanding the Workflow
Key Components
Key Components
Question Generator
Creates unique math and logic questions with answers. Uses write_csv
and count_questions
tools.
Questions Evaluator
Validates the total number of generated questions. Uses count_questions
tool.
CoT Generator
Produces detailed step-by-step solutions. Uses cot_save
tool for solution management.
HuggingFace Uploader
Publishes datasets to HuggingFace. Uses cot_upload_to_huggingface
tool.
Task Types and Flow Control
Task Types and Flow Control
Used in question generation and evaluation phases.
Conditions determine whether to continue generating or move forward. The task can loop back to itself or proceed to the next task.
Used in question generation and evaluation phases.
Conditions determine whether to continue generating or move forward. The task can loop back to itself or proceed to the next task.
Used in Chain-of-Thought generation phase.
Always use Pydantic models for output validation in loop tasks to ensure data consistency.
Each task type serves a specific purpose in the workflow:
- Decision Tasks: Control flow and branching logic
- Loop Tasks: Process data iteratively with validation