Code Generation Pipeline Example

This example demonstrates a complete code generation and evaluation pipeline using multiple framework components.

Enterprise Evaluation System

Uses the 12-tier evaluation system from our 237 enterprise modules. See Enterprise Documentation for advanced evaluation patterns.

Overview

This pipeline combines LLMs, guardrails, monitoring, and evaluation to create a robust code generation system.

Code

import logging

logger = logging.getLogger(__name__)

from agenticaiframework.agents import Agent
from agenticaiframework.tasks import Task
from agenticaiframework.llms import LLMManager
from agenticaiframework.guardrails import Guardrail
from agenticaiframework.monitoring import Monitor
from agenticaiframework.evaluation import Evaluator

# Example: Code Generation and Evaluation Pipeline
if __name__ == "__main__":
    # Initialize components
    llm = LLMManager()
    llm.register_model("gpt-4", lambda prompt, kwargs: f"[Simulated GPT-4 Code Generation for: {prompt}]")
    llm.set_active_model("gpt-4")
    guardrail = Guardrail(rules=["Generate syntactically correct code", "Avoid insecure code patterns"])
    monitor = Monitor()
    evaluator = Evaluator(metrics=["correctness", "efficiency", "readability"])

    # Create agent
    code_agent = Agent(
        name="CodeGenAgent",
        role="Code Generator",
        capabilities=["generate_code", "evaluate_code"],
        config={"llm": llm, "guardrail": guardrail, "monitor": monitor}
    )

    # Define task
    code_task = Task(
        name="FibonacciCodeGen",
        objective="Generate a Python function that calculates the nth Fibonacci number using memoization.",
        executor=lambda: llm.generate("Write a Python function for nth Fibonacci number using memoization.")
    )

    # Execute and evaluate
    result = code_agent.execute_task(code_task)
    evaluation = evaluator.evaluate(result)

    logger.info(f"Generated Code: {result}")
    logger.info(f"Evaluation: {evaluation}")

Key Components

• LLMManager: Manages language model integration
• Guardrails: Ensures code quality and security
• Monitor: Tracks performance metrics
• Evaluator: Assesses code quality

Expected Output

The pipeline will: 1. Generate code using the LLM 2. Apply guardrails for validation 3. Monitor execution 4. Evaluate the generated code 5. Return results with metrics )

# Run task
generated_code = code_task.run()

# Evaluate code
evaluation_result = evaluator.evaluate(generated_code)

# Output results
logger.info("=== Generated Code ===")
logger.info(generated_code)
logger.info("\n=== Evaluation Result ===")
logger.info(evaluation_result)