Generate Synthetic Reasoning Data Agents

Learn how to generate chain-of-thought reasoning data using PraisonAI Agents.

from praisonaiagents import Agent

coordinator = Agent(name="CoT Coordinator", instructions="Generate and evaluate reasoning datasets.")
coordinator.start("Generate chain-of-thought examples for maths word problems")

The user starts the workflow; agents generate, evaluate, and upload chain-of-thought training data.

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.

How It Works

The user starts the workflow; the generator creates Q&A pairs, the CoT agent expands each into reasoning steps, and the uploader publishes the dataset.

Quick Start

1. Install Package

   First, install the PraisonAI Agents package:

   pip install "praisonaiagents[llm]" datasets huggingface-hub pandas

2. Set API Key

   Set your OpenAI API key as an environment variable in your terminal:

   export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
   export HF_TOKEN=your_huggingface_token_here

3. Create a file

   Create a new file app.py with the basic setup:

   from praisonaiagents import Agent, Task, AgentTeam
   from praisonaiagents import cot_save, cot_upload_to_huggingface
   from pydantic import BaseModel
   import os
   
   # Define Pydantic model for structured output
   class DecisionModel(BaseModel):
       response: str
       decision: str
   
   def write_csv(file_path, data):
       """Write data to CSV file."""
       if not os.path.exists(file_path):
           with open(file_path, 'w') as file:
               file.write(data + '\n')
       else:
           with open(file_path, 'a') as file:
               file.write(data + '\n')
       return f"Data appended to {file_path}"
   
   def count_questions(file_path):
       """Count lines in file."""
       with open(file_path, 'r') as file:
           return sum(1 for _ in file)
   
   # Create specialized agents
   qa_generator = Agent(
       name="Generator",
       role="Question Creator",
       goal="Create challenging math and logic questions",
       backstory="Expert in educational content creation",
       llm="gpt-4o-mini",
       tools=[write_csv, count_questions]
   )
   
   total_questions_evaluator = Agent(
       name="TotalQuestionsEvaluator",
       role="Total Questions Evaluator",
       goal="Evaluate the total number of questions in qa_pairs.csv file",
       backstory="Expert in evaluating the total number of questions in a file",
       llm="gpt-4o-mini",
       tools=[count_questions],
       output="silent"
   )
   
   cot_generator = Agent(
       name="COTGenerator",
       role="Chain of Thought Specialist",
       goal="Generate and manage chain of thought solutions for Q&A pairs",
       backstory="Expert in breaking down problems and generating detailed solution steps",
       tools=[cot_save],
       llm="gpt-4o-mini",
       output="silent"
   )
   
   upload_to_huggingface = Agent(
       name="UploadToHuggingface",
       role="Upload to Huggingface",
       goal="Upload the generated chain of thought solutions to a Huggingface dataset",
       backstory="Expert in saving data to Huggingface",
       tools=[cot_upload_to_huggingface],
       llm="gpt-4o-mini",
       output="silent"
   )
   
   # Create workflow with repeat pattern for generation
   from praisonaiagents import AgentFlow, Task, WorkflowContext, StepResult
   from praisonaiagents import repeat, loop
   
   # Step handlers using agents
   def generate_qa(ctx: WorkflowContext) -> StepResult:
       result = qa_generator.chat("""Generate question and answer in csv format: question, answer
       Generate 10 unique questions and answers. Example:
       What is the sum of numbers from 1 to 10?, 55
       Number of r's in the word strawberry, 3""")
       write_csv("qa_pairs.csv", result)
       return StepResult(output=result)
   
   def evaluate_count(ctx: WorkflowContext) -> StepResult:
       count = count_questions("qa_pairs.csv")
       return StepResult(
           output=f"count: {count}",
           variables={"question_count": count}
       )
   
   def generate_cot(ctx: WorkflowContext) -> StepResult:
       result = cot_generator.chat(f"Generate chain of thought for: {ctx.variables.get('current_item')}")
       cot_save(result)
       return StepResult(output=result)
   
   def upload_dataset(ctx: WorkflowContext) -> StepResult:
       result = upload_to_huggingface.chat("Upload cot_solutions.csv to mervinpraison/cot-dataset")
       return StepResult(output=result)
   
   # Create workflow
   workflow = AgentFlow(
       steps=[
           generate_qa,
           evaluate_count,
           loop(generate_cot, over="qa_pairs", from_csv="qa_pairs.csv"),
           upload_dataset
       ]
   )
   
   result = workflow.start("Generate reasoning data")

4. Run the application

   Execute the Python script to start generating chain-of-thought data:

   python app.py

Features

Understanding the Workflow

generate_task = Task(
    task_type="decision",
    condition={
        "more": "generate_task",
        "done": "evaluate_total_questions"
    }
)

generate_cot_task = Task(
    task_type="loop",
    input_file="qa_pairs.csv",
    output_pydantic=DecisionModel
)

Next Steps

Best Practices

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