Tools for testing, debugging, and evaluating LLM features.
Project description
Baserun
Baserun is the testing and observability platform for LLM apps.
Quick Start
1. Install Baserun
pip install baserun
2. Generate an API key
Create an account at https://baserun.ai. Then generate an API key for your project in the settings tab. Set it as an environment variable:
export BASERUN_API_KEY="your_api_key_here"
Or set baserun.api_key
to its value:
baserun.api_key = "br-..."
3. Start testing
Use our pytest plugin and start immediately testing with Baserun. By default all OpenAI and Anthropic requests will be automatically logged.
# test_module.py
import openai
def test_paris_trip():
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
temperature=0.7,
messages=[
{
"role": "user",
"content": "What are three activities to do in Paris?"
}
],
)
assert "Eiffel Tower" in response['choices'][0]['message']['content']
To run the test and log to baserun:
pytest --baserun test_module.py
...
========================Baserun========================
Test results available at: https://baserun.ai/runs/<id>
=======================================================
Production usage
You can use Baserun for production observability as well. To do so, simply call baserun.init()
somewhere during your application's startup, and add the @baserun.trace
decorator to the function you want to observe (e.g. a request/response handler). For example,
import sys
import openai
import baserun
@baserun.trace
def answer_question(question: str) -> str:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": question}],
)
return response["choices"][0]["message"]["content"]
if __name__ == "__main__":
baserun.init()
print(answer_question(sys.argv[-1]))
Documentation
For a deeper dive on all capabilities and more advanced usage, please refer to our Documentation.
Contributing
Contributions to baserun-py are welcome! Below are some guidelines to help you get started.
Dependencies
Install the dependencies:
pip install -r requirements.txt
Install the dev dependencies with:
pip install -r requirements-dev.txt
Tests
You can run tests using pytest
. Note is that in pytest the remote server is mocked, so network requests are not actually made to Baserun's backend.
If you want to emulate production tracing, we have a utility for that:
python tests/testing_functions.py {function_to_test}
Take a look at the list of functions in that file: any function with the @baserun.trace
decorator can be used.
gRPC and Protobuf
If you're making changes to baserun.proto
, you'll need to compile those changes. Run the following command:
python -m grpc_tools.protoc -Ibaserun --python_out=baserun --pyi_out=baserun --grpc_python_out=baserun baserun/v1/baserun.proto
A Note on Breaking Changes
Be cautious when making breaking changes to protobuf definitions. These could impact backward compatibility and require corresponding server-side changes, so be sure to discuss it with our maintainers.
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