At this moment, Llama 3 is one of the most capable open-source models. In this guide, we give Llama 3 code interpreter capabilities and test it on data analysis and data visualization task.

🔗 Full code on GitHub

Code Interpreter SDK

We will show how to build a code interpreter with Llama 3 on Groq, and powered by open-source Code Interpreter SDK by E2B. The E2B Code Interpreter SDK quickly creates a secure cloud sandbox powered by Firecracker. Inside this sandbox is a running Jupyter server that the LLM can use.

Key links

Overview

Setup
Configuration and API keys
Creating code interpreter
Calling Llama 3
Connecting Llama 3 and code interpreter

1. Setup

We will be working in Jupyter notebook. First, we install the E2B code interpreter SDK and Groq's Python SDK.

%pip install groq e2b_code_interpreter

Collecting groq
  Downloading groq-0.5.0-py3-none-any.whl (75 kB)
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Collecting websockets>=11.0.3 (from e2b>=0.14.11->e2b_code_interpreter)
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Installing collected packages: aenum, websockets, jsonrpcclient, h11, httpcore, httpx, e2b, groq, e2b_code_interpreter
Successfully installed aenum-3.1.15 e2b-0.14.14 e2b_code_interpreter-0.0.3 groq-0.5.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 jsonrpcclient-4.0.3 websockets-12.0

2. Configuration and API keys

Then we store the Groq and E2B API keys and set the model name for the Llama 3 instance you will use. In the system prompt we define sets the rules for the interaction with Llama.

# TODO: Get your Groq AI API key from https://console.groq.com/
GROQ_API_KEY = ""

# TODO: Get your E2B API key from https://docs.e2b.dev
E2B_API_KEY = ""

# Or use 8b version
# MODEL_NAME = "llama3-8b-8192"
MODEL_NAME = "llama3-70b-8192"

SYSTEM_PROMPT = """you are a python data scientist. you are given tasks to complete and you run python code to solve them.
- the python code runs in jupyter notebook.
- every time you call `execute_python` tool, the python code is executed in a separate cell. it's okay to multiple calls to `execute_python`.
- display visualizations using matplotlib or any other visualization library directly in the notebook. don't worry about saving the visualizations to a file.
- you have access to the internet and can make api requests.
- you also have access to the filesystem and can read/write files.
- you can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled.
- you can run any python code you want, everything is running in a secure sandbox environment"""


tools = [
  {
    "type": "function",
      "function": {
        "name": "execute_python",
        "description": "Execute python code in a Jupyter notebook cell and returns any result, stdout, stderr, display_data, and error.",
        "parameters": {
          "type": "object",
          "properties": {
            "code": {
              "type": "string",
              "description": "The python code to execute in a single cell.",
            }
          },
          "required": ["code"],
        },
      },
  }
]

3. Creating code interpreter

We define the main function that uses the E2B code interpreter to execute code in a Jupyter Notebook cell. We'll be calling this function a little bit further when we're parsing the Llama's response with tool calls.

def code_interpret(e2b_code_interpreter, code):
  print("Running code interpreter...")
  exec = e2b_code_interpreter.notebook.exec_cell(
    code,
    on_stderr=lambda stderr: print("[Code Interpreter]", stderr),
    on_stdout=lambda stdout: print("[Code Interpreter]", stdout),
    # You can also stream code execution results
    # on_result=...
  )

  if exec.error:
    print("[Code Interpreter ERROR]", exec.error)
  else:
    return exec.results

4. Calling Llama 3

Now we're going to define and implement chat_with_llama method. In this method, we'll call the LLM with our tools dictionary, parse the output, and call our code_interpret method we defined above.

See the Groq documentation to get started.

import os
import json
import re
from groq import Groq

client = Groq(api_key=GROQ_API_KEY)

def chat_with_llama(e2b_code_interpreter, user_message):
  print(f"\n{'='*50}\nUser message: {user_message}\n{'='*50}")

  messages = [
      {"role": "system", "content": SYSTEM_PROMPT},
      {"role": "user", "content": user_message}
  ]

  response = client.chat.completions.create(
      model=MODEL_NAME,
      messages=messages,
      tools=tools,
      tool_choice="auto",
      max_tokens=4096,
  )

  response_message = response.choices[0].message
  tool_calls = response_message.tool_calls

  if tool_calls:
    for tool_call in tool_calls:
      function_name = tool_call.function.name
      function_args = json.loads(tool_call.function.arguments)
      if function_name == "execute_python":
        code = function_args["code"]
        code_interpreter_results = code_interpret(e2b_code_interpreter, code)
        return code_interpreter_results
      else:
        raise Exception(f"Unknown tool {function_name}")
  else:
    print(f"(No tool call in model's response) {response_message}")
    return []

5. Connecting Llama 3 and code interpreter

Finally, we can instantiate the code interpreter and pass the E2B API key. Then we call the chat_with_llama method with our user message and the code_interpreter instance.

from e2b_code_interpreter import CodeInterpreter

with CodeInterpreter(api_key=E2B_API_KEY) as code_interpreter:
  code_results = chat_with_llama(
    code_interpreter,
    "Visualize a distribution of height of men based on the latest data you know"
  )
  if code_results:
    first_result = code_results[0]
  else:
    print("No code results")
    exit(0)


# This will render the image
# You can also access the data directly
# first_result.png
# first_result.jpg
# first_result.pdf
# ...
first_result

==================================================
User message: Visualize a distribution of height of men based on the latest data you know
==================================================
Running code interpreter...

🔗 Full code on GitHub

How to add code interpreter to Llama 3

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