https://github.com/replit/replit-code-exec
A library that allows interacting with Replit's code-exec API
https://github.com/replit/replit-code-exec
Last synced: 10 months ago
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A library that allows interacting with Replit's code-exec API
- Host: GitHub
- URL: https://github.com/replit/replit-code-exec
- Owner: replit
- License: isc
- Archived: true
- Created: 2023-09-15T20:19:10.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-24T00:50:31.000Z (about 1 year ago)
- Last Synced: 2025-03-21T02:48:36.689Z (11 months ago)
- Language: Python
- Size: 192 KB
- Stars: 23
- Watchers: 3
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# `replit-code-exec`
An interface to interact with Replit's code-exec API.
This is an interface for the simpler use cases of https://replit.com/@luisreplit/eval-python,
which is a stateless API that is optimized to be called from AI Agents so that they can perform
numerical reasoning through the evaluation of generated Python code. In general, this allows
the execution of an untrusted snippet of Python code and returns whatever is printed to standard
output / standard error as the result. The execution is done inside an ephemeral unprivileged
container created on the fly running in Replit Deployments using
https://github.com/omegaup/omegajail as code sandbox.
To set up your own copy of the API server, you need to follow these easy 2-3 steps (the second
is optional):
1. Open the https://replit.com/@luisreplit/eval-python in your browser and Fork it to your
account.
2. (Optional): if you want to change the Docker container, run `evalctl image ${DOCKER_IMAGE}`
(e.g. `evalctl image python:3` or `evalctl image replco/python-kitchen-sink:latest`).
* Open `.replit` and change the `EVAL_FILENAME`, `EVAL_RUN_COMMAND`, `EVAL_ENV` to suite the
new container, if needed.
3. Deploy the Repl! (just pressing `Run` is not enough)
* This is only compatible with the Autoscale Deployments.
* Make sure you set the `EVAL_TOKEN_AUTH` Deployments secrets when
doing so for authentication.
## Sample usage
Here is a small example of this library being used as a small math solver using OpenAI API:
```python
import openai
import replit_code_exec
code_exec = replit_code_exec.build_code_exec(...)
def solve_math(prompt: str, model: str = 'gpt-3.5-turbo-0613') -> str:
completion = openai.ChatCompletion.create(
model=model,
temperature=0.7,
functions=[code_exec.openai_schema],
function_call={"name": "code_exec"},
messages=[
{
"role": "system",
"content": ("You are an assistant that knows how to solve math " +
"problems by converting them into Python programs."),
},
{
"role": "user",
"content": ("Please solve the following problem by creating a Python " +
"program that prints the solution to standard output using " +
"`print()`: " + prompt),
},
],
)
return code_exec.from_response(completion)
```