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Automated Code Generation

Learn how to use QuantaLogic's agent for intelligent code generation and manipulation across your project.

Overview

Code generation capabilities enable your agent to: - Understand existing codebase structure - Generate new code snippets - Modify existing code - Perform complex refactoring tasks

Prerequisites

  • Python 3.12 or later
  • QuantaLogic library installed
  • DeepSeek API key (or alternative LLM provider)

Setting Up Code Generation Tools

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import os
from quantalogic import Agent
from quantalogic.tools import (
    SearchDefinitionNames,
    RipgrepTool,
    WriteFileTool,
    ReadFileTool,
    ReplaceInFileTool,
    ReadFileBlockTool,
    ListDirectoryTool
)

# Set API key
os.environ["DEEPSEEK_API_KEY"] = "your-deepseek-key"

# Define model and tools
MODEL_NAME = "deepseek/deepseek-chat"
agent = Agent(
    model_name=MODEL_NAME,
    tools=[
        SearchDefinitionNames(),
        RipgrepTool(),
        WriteFileTool(),
        ReadFileTool(),
        ReplaceInFileTool(),
        ReadFileBlockTool(),
        ListDirectoryTool(),
    ]
)

Available Code Generation Tools

Tool Description
SearchDefinitionNames Find function and class definitions
RipgrepTool Search across files using regex patterns
WriteFileTool Create and write new files
ReadFileTool Read file contents
ReplaceInFileTool Modify existing files
ReadFileBlockTool Read specific code blocks
ListDirectoryTool List directory contents

Example Tasks

1. Generate New Code

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# Generate a utility function
result = agent.solve_task(
    "Create a Python function to validate email addresses"
)
print(result)

2. Refactor Existing Code

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# Refactor a specific function or module
result = agent.solve_task(
    "Refactor the authentication module to improve security"
)
print(result)

3. Add Documentation

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# Add docstrings and type hints
result = agent.solve_task(
    "Add comprehensive docstrings to the user management module"
)
print(result)

Best Practices

  • Provide clear, specific task descriptions
  • Break complex tasks into smaller steps
  • Review generated code carefully
  • Use type hints and docstrings
  • Maintain consistent code style

Intelligent Generation

The agent understands context and can generate contextually relevant code.

Event Monitoring

Enable event monitoring to track code generation process:

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from quantalogic.console_print_events import console_print_events

agent.event_emitter.on(
    event=[
        "task_complete",
        "task_think_start",
        "task_think_end",
        "tool_execution_start",
        "tool_execution_end",
        "error_max_iterations_reached"
    ],
    listener=console_print_events
)

Complete Code Example

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import os
from quantalogic import Agent
from quantalogic.tools import (
    SearchDefinitionNames,
    RipgrepTool,
    WriteFileTool,
    ReadFileTool,
    ReplaceInFileTool,
    ReadFileBlockTool,
    ListDirectoryTool
)

# Set API key
os.environ["DEEPSEEK_API_KEY"] = "your-deepseek-key"

# Initialize agent with code generation tools
MODEL_NAME = "deepseek/deepseek-chat"
agent = Agent(
    model_name=MODEL_NAME,
    tools=[
        SearchDefinitionNames(),
        RipgrepTool(),
        WriteFileTool(),
        ReadFileTool(),
        ReplaceInFileTool(),
        ReadFileBlockTool(),
        ListDirectoryTool(),
    ]
)

# Generate a new utility function
result = agent.solve_task(
    "Create a Python function to validate email addresses"
)
print(result)

Code Review

Always review and test generated code before integration.