Building a Custom Code Interpreter for LLM Agents

In the realm of AI and machine learning, the ability to dynamically generate and execute code based on user prompts is a game-changer. This article delves into the process of building a custom code interpreter for Large Language Model (LLM) agents, a solution that offers unparalleled flexibility and security compared to built-in interpreters provided by API vendors.

Why Build a Custom Code Interpreter?

Built-in code interpreters, such as those offered by OpenAI's Assistants API, are powerful but come with limitations. These include restricted language or library support, task incompatibility, model constraints, cost considerations, and file size limitations. A custom code interpreter addresses these issues by allowing developers to tailor the environment to their specific needs.

The Core Concept: Dynamic Tool Calling

At the heart of this approach is the concept of "dynamic tool calling," where an LLM can generate and execute code blocks on the fly. This method is particularly useful for tasks like data analysis, machine learning workflow generation, and process automation.

Step-by-Step Guide

Step 1: Set Up an Isolated Code Execution Environment

To ensure security, it's crucial to execute LLM-generated code in an isolated environment. Docker containers are ideal for this purpose, as they restrict access to the host machine's resources.

# Build the Docker image
docker build -t python_sandbox:latest ./resources/docker
 
# Run the container in restricted mode
docker run -d --name sandbox --network none --cap-drop all --pids-limit 64 --tmpfs /tmp:rw,size=64M python_sandbox:latest sleep infinity

Step 2: Define and Test the Agents

Two agents are defined for this application:

1. FileAccessAgent: Reads files from the host machine and provides context to the PythonCodeExecAgent.
2. PythonCodeExecAgent: Generates Python code based on user prompts and executes it within the Docker container.

from resources.registry.agents.file_access_agent import FileAccessAgent
from resources.registry.agents.python_code_exec_agent import PythonExecAgent
 
# Instantiate the agents
file_ingestion_agent = FileAccessAgent()
data_analysis_agent = PythonExecAgent()
 
# Task execution
file_ingestion_agent_output = file_ingestion_agent.task(prompt)
data_analysis_agent.add_context(prompt)
data_analysis_agent.add_context(file_ingestion_agent_output)
 
# User interaction loop
while True:
    user_input = input("Type your question related to the data in the file. Type 'exit' to exit.")
    if user_input == "exit":
        break
    data_analysis_agent_output = data_analysis_agent.task(user_input)
    print(data_analysis_agent_output)

Step 3: Set Up Agentic Orchestration

The orchestration loop prompts the user for tasks, calls the appropriate agents, and displays the output. This setup ensures a seamless interaction between the user and the LLM agents.

Conclusion

Building a custom code interpreter for LLM agents offers a flexible and secure solution for executing dynamically generated code. By following the steps outlined in this article, developers can create powerful AI applications capable of tackling a wide range of tasks.

Reference: OpenAI Cookbook by Mmsingh-openai