Execute Python Code Securely on Azure
Azure Code Interpreter Tool enables AI agents to execute Python code securely in Azure Dynamic Sessions with low latency, returning structured results.
Why it matters
Empower your LLM agent to securely execute generated Python code within a low-latency Azure environment. This tool enables dynamic code interpretation and file management for complex tasks.
Outcomes
What it gets done
Run Python code generated by an LLM agent in a secure Azure sandbox.
Manage files within the execution environment, including listing, uploading, and downloading.
Integrate code execution capabilities into your existing LLM agent workflows.
Achieve low-latency code interpretation for faster agent responses.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/li-tool-tools-azure-code-interpreter | bash Capabilities
What this skill does
Writes source code or scripts from a description.
Traces errors to their root cause and suggests fixes.
Analyzes code for bugs, style issues, and improvements.
Pulls structured data fields from unstructured text.
Writes and executes SQL or NoSQL queries on databases.
Overview
Azure Code Interpreter Tool
What it does
A LlamaIndex tool specification that wraps Azure Container Apps Dynamic Sessions, allowing AI agents to execute Python code in isolated, low-latency environments and return structured JSON results.
How it connects
Use this tool when your agent needs to perform calculations, data transformations, or execute dynamic Python logic that cannot be handled through static APIs or pre-built functions alone.
Source README
Azure Code Interpreter Tool
This tool leverages Azure Dynamic Sessions Pool to enable an Agent to run generated Python code in a secure environment with very low latency.
In order to utilize the tool, you will need to have the Session Pool management endpoint first. Learn more
Prerequisites
Make sure to create a Session Pool and note down the
poolManagementEndpoint.In order to have the code execution right, the correct role needs to be assigned to the current user agent. Be sure to assign
Session Pool Executorrole to the correct user agent's identity (e.g. User Email, Service Principal, Managed Identity, etc.) in Session Pool's access control panel through the Portal or CLI. Learn more
Usage
A more detailed sample is located in a Jupyter notebook here
Here's an example usage of the AzureCodeInterpreterToolSpec.
- First, install the Azure Dynamic Sessions package using
pip:
pip install llama-index-tools-azure-code-interpreter
- Create a file named
.envin the same directory as your script with the following content:
AZURE_POOL_MANAGEMENT_ENDPOINT=<poolManagementEndpoint>
- Next, set up the Dynamic Sessions tool and a LLM agent:
from llama_index.tools.azure_code_interpreter import (
AzureCodeInterpreterToolSpec,
)
from llama_index.core.agent import ReActAgent
from llama_index.llms.azure_openai import AzureOpenAI
llm = AzureOpenAI(
model="gpt-35-turbo",
deployment_name="gpt-35-deploy",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
code_interpreter_spec = AzureCodeInterpreterToolSpec(
pool_management_endpoint=os.getenv("AZURE_POOL_MANAGEMENT_ENDPOINT")
)
agent = ReActAgent.from_tools(
code_interpreter_spec.to_tool_list(), llm=llm, verbose=True
)
- Use the tool as you need:
print(agent.chat("Tell me the current time in Seattle."))
"""
Sample Return:
Thought: To provide the current time in Seattle, I need to calculate it based on the current UTC time and adjust for Seattle's time zone, which is Pacific Daylight Time (PDT) during daylight saving time and Pacific Standard Time (PST) outside of daylight saving time. PDT is UTC-7, and PST is UTC-8. I can use the code interpreter tool to get the current UTC time and adjust it accordingly.
Action: code_interpreter
Action Input: {'python_code': "from datetime import datetime, timedelta; import pytz; utc_now = datetime.now(pytz.utc); seattle_time = utc_now.astimezone(pytz.timezone('America/Los_Angeles')); seattle_time.strftime('%Y-%m-%d %H:%M:%S %Z%z')"}
Observation: {'$id': '1', 'status': 'Success', 'stdout': '', 'stderr': '', 'result': '2024-05-04 13:54:09 PDT-0700', 'executionTimeInMilliseconds': 120}
Thought: I can answer without using any more tools. I'll use the user's language to answer.
Answer: The current time in Seattle is 2024-05-04 13:54:09 PDT.
The current time in Seattle is 2024-05-04 13:54:09 PDT.
"""
print(dynamic_session_tool.code_interpreter("1+1"))
"""
Sample Return:
{'$id': '1', 'status': 'Success', 'stdout': '', 'stderr': '', 'result': 2, 'executionTimeInMilliseconds': 11}
"""
Included Tools
The AzureCodeInterpreterToolSpec provides the following tools to the agent:
code_interpreter: (Available to developer and LLM Agent in tool spec) Send a Python code to be executed in Azure Container Apps Dynamic Sessions and return the output in a JSON format.
list_files: (Available to developer and LLM Agent in tool spec) List the files available in a Session under the path /mnt/data.
upload_file: (Available to developer) Upload a file or a stream of data into a Session under the path /mnt/data.
download_file: (Available to developer) Download a file by its path relative to the path /mnt/data to the tool's hosting agent.
Discussion
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