8b46e4f7f9
# PR Description
This PR creates a new toolkit called CodeSandbox. This toolkit has two
tools:
1. `RunCode`: Creates an E2B sandbox and runs the provided code in that
sandbox. Returns the execution logs, result, and errors. Supports
Python, JavaScript, R, Java, and Bash code.
2. `CreateStaticMatplotlibChart`: Creates a sandbox, runs the provided
python code that uses matplotlib, and returns the base64 encoded image
of the chart along with any logs or errors.
- I recommend not using `tool_choice="generate"` since the return object
contains a base64 image can be a lot of tokens that will not provide
much value to a generate's response.
Example of creating a pie chart:
```python
import base64
import json
import os
from openai import OpenAI
def call_tool_with_openai(client: OpenAI) -> dict:
response = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "There are 17 red apples, 4 green apples, and 10 yellow apples. Create a pie chart for this data.",
},
],
model="gpt-4o-mini",
user="you@example.com",
tools=["CodeSandbox.CreateStaticMatplotlibChart"],
tool_choice="execute",
)
return response
arcade_api_key = os.environ.get("ARCADE_API_KEY")
cloud_host = "http://localhost:9099/v1"
openai_client = OpenAI(
api_key=arcade_api_key,
base_url=cloud_host,
)
chat_result = call_tool_with_openai(openai_client)
tool_call_id = chat_result.choices[0].message.tool_calls[0].id
content = json.loads(chat_result.choices[0].message.content)
base64_image = content[tool_call_id]["value"]["base64_image"]
image_data = base64.b64decode(base64_image)
with open("output_image.png", "wb") as image_file:
image_file.write(image_data)
```
117 lines
2.8 KiB
Python
117 lines
2.8 KiB
Python
import arcade_code_sandbox
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from arcade_code_sandbox.tools.e2b import create_static_matplotlib_chart, run_code
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from arcade_code_sandbox.tools.models import E2BSupportedLanguage
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from arcade.core.catalog import ToolCatalog
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from arcade.sdk.eval import (
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EvalRubric,
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EvalSuite,
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tool_eval,
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)
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from arcade.sdk.eval.critic import BinaryCritic, SimilarityCritic
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merge_sort_code = """
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def merge_sort(arr):
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if len(arr) <= 1:
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return arr
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mid = len(arr) // 2
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left = merge_sort(arr[:mid])
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right = merge_sort(arr[mid:])
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return merge(left, right)
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def merge(left, right):
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result = []
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i, j = 0, 0
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while i < len(left) and j < len(right):
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if left[i] < right[j]:
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result.append(left[i])
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i += 1
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else:
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result.append(right[j])
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j += 1
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result.extend(left[i:])
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result.extend(right[j:])
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return result
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sample_list = ["banana", "apple", "cherry", "date", "elderberry"]
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sorted_list = merge_sort(sample_list)
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print("Sorted list:", sorted_list)
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"""
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matplotlib_chart_code = """
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import matplotlib.pyplot as plt
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labels = ['Apples', 'Bananas', 'Cherries', 'Dates']
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sizes = [30, 25, 20, 25]
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colors = ['red', 'yellow', 'purple', 'brown']
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plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
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plt.axis('equal')
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plt.title('Fruit Distribution')
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plt.savefig('fruit_pie_chart.png')
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"""
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# Evaluation rubric
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rubric = EvalRubric(
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fail_threshold=0.85,
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warn_threshold=0.95,
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)
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catalog = ToolCatalog()
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catalog.add_module(arcade_code_sandbox)
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@tool_eval()
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def code_sandbox_eval_suite():
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suite = EvalSuite(
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name="code_sandbox Tools Evaluation",
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system_message="You are an AI assistant with access to code_sandbox tools. Use them to help the user with their tasks.",
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catalog=catalog,
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rubric=rubric,
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)
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suite.add_case(
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name="Run code",
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user_message=f"Can you please run my merge sort algo?\n\n{merge_sort_code}",
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expected_tool_calls=[
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(
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run_code,
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{
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"code": merge_sort_code,
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"language": E2BSupportedLanguage.PYTHON,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="code", weight=0.8),
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BinaryCritic(critic_field="language", weight=0.2),
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],
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)
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suite.add_case(
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name="Create static matplotlib chart",
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user_message=f"Run this code:\n\n{matplotlib_chart_code}",
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expected_tool_calls=[
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(
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create_static_matplotlib_chart,
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{
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"code": matplotlib_chart_code,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="code", weight=1.0),
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],
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)
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return suite
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