baserun 2.0.9

tools for testing, debugging, and evaluating llm features.

Baserun

Baserun is the testing and observability platform for LLM apps.

Quick Start

1. Install Baserun

pip install baserun

2. Set the Baserun 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"

Usage

In order to have Baserun trace your LLM Requests, all you need to do is import OpenAI from baserun instead of openai. Creating an OpenAI client object automatically starts the trace, and all future LLM requests made with this client object will be captured.

from baserun import OpenAI


def example():
    client = OpenAI()
    completion = client.chat.completions.create(
        name="Paris Activities",
        model="gpt-4o",
        temperature=0.7,
        messages=[
            {
                "role": "user",
                "content": "What are three activities to do in Paris?"
            }
        ],
    )


if __name__ == "__main__":
    print(example())

Alternate init method

If, for some reason, you don't wish to use Baserun's OpenAI client, you can simply wrap your normal OpenAI client using init.

from baserun import init

client = init(OpenAI())

Configuring the trace

When you start a trace by initializing an OpenAI object, there are several optional parameters you can set for that trace:

• name: A customized name for the trace
• result: Some end result or output for the trace
• user: A username or user ID to associate with this trace.
• session: A session ID to associate with this trace.
• trace_id: A previously-generated or custom UUID (e.g. to continue a previous trace)

from baserun import OpenAI

def example():
    client = OpenAI(result="What are three activities to do in Paris?")
    client.name = "Example"
    client.user = "user123"
    client.session = "session123"

    completion = client.chat.completions.create(
        name="Paris Activities",
        model="gpt-4o",
        temperature=0.7,
        messages=[
            {
                "role": "user",
                "content": "What are three activities to do in Paris?"
            }
        ],
    )
    client.result = "Done"

Evals

Evaluating Completions

You can perform evals directly on a completion object. The includes eval is used here as an example, and checks if a string is included in the completion's output. The argument passed to eval() is a name or label used for your reference.

from baserun import OpenAI

def example():
    client = OpenAI()
    completion = client.chat.completions.create(
        model="gpt-4o",
        temperature=0.7,
        messages=[
            {
                "role": "user",
                "content": "What are three activities to do in Paris?"
            }
        ],
    )
    client.eval("include_eiffel_tower").includes("Eiffel Tower")

Tags

You can add tags either to the traced OpenAI object or to the completion. There are several different types of tags:

• log: Any arbitrary logs you want to attach to a trace or completion
• feedback: Any score-based feedback given from users (e.g. thumbs up/down, star rating)
• variable: Any variables used, e.g. while rendering a template
• custom: Any arbitrary attributes you want to attach to a trace or completion

Each tag type has functions on traced OpenAI objects and completions. Each tag function can accept a metadata parameter which is an arbitrary dictionary with any values you might want to capture.

from baserun import OpenAI

def example():
    client = OpenAI()
    client.log("Gathering user input")
    city = input()
    completion = client.chat.completions.create(
        model="gpt-4o",
        temperature=0.7,
        messages=[
            {
                "role": "user",
                "content": f"What are three activities to do in {city}?"
            }
        ],
    )
    completion.variable("city", city)
    user_score = input()
    client.feedback("User Score", score=user_score, metadata={"My key": "My value"})

Adding tags to a completed trace or completion

After a trace has been completed you may wish to add additional tags to a trace or completion. For example, you might have user feedback that is gathered well after the fact. To add these tags, you need to store the trace_id, and, if the tag is for a completion, the completion_id. You can then use the tag, log, or feedback functions to submit those tags.

from baserun import OpenAI, log, feedback

client = OpenAI(name="trace to be resumed")
completion = client.chat.completions.create(
    name="completion to be resumed",
    model="gpt-4o",
    messages=[{"role": "user", "content": "What are three activities to do in Paris?"}],
)

# Store these values
trace_id = client.trace_id
completion_id = completion.completion_id

# A few moments later...
log("Tagging resumed", trace_id=trace_id, completion_id=completion_id)
feedback("User satisfaction", 0.9, trace_id=trace_id, completion_id=completion_id)

Unsupported models

Baserun ships with support for OpenAI and Anthropic. If you use another provider or library, you can still use Baserun by manually creating "generic" objects. Notably, generic completions must be submitted explicitly using submit_to_baserun. Here's what that looks like:

question = "What is the capital of the US?"
response = call_my_custom_model(question)

client = GenericClient(name="My Traced Client")
completion = GenericCompletion(
    model="my custom model",
    name="My Completion",
    input_messages=[GenericInputMessage(content=question, role="user")],
    choices=[GenericChoice(message=GenericCompletionMessage(content=response))],
    client=client,
    trace_id=client.trace_id,
)
completion.submit_to_baserun()

Datasets

Baserun has built-in support for the datasets library by HuggingFace. You can use the Dataset class to submit datasets. See the HuggingFace documentation to learn more about the datasets library.

Once you have loaded your dataset, you can submit it to Baserun by using the submit_dataset function.

from datasets import Dataset
from baserun import submit_dataset


data_samples = {
    "question": ["When was the first super bowl?"],
    "answer": ["The first Super Bowl was held on January 15, 1967. It took place at the Los Angeles Memorial Coliseum in Los Angeles, California."],
    "contexts": [
        [
            "The First AFL–NFL World Championship Game was an American football game played on January 15, 1967, at the Los Angeles Memorial Coliseum in Los Angeles,"
        ],
    ],
    "ground_truth": [
        "The first Super Bowl was held on January 15, 1967",
    ],
}

dataset = Dataset.from_dict(data_samples)
submit_dataset(dataset, "questions")

Using Datasets for evals

Once you have submitted a dataset, you can use the get_dataset function to retrieve it. The retrieved dataset can automatically create scenarios from your data. From there, you can easily evaluate these scenarios by using the evaluate function:

from baserun import OpenAI, evaluate

dataset = await get_dataset(name="capital questions")
question = "What is the capital of {country}?"

client = OpenAI()
experiment = Experiment(dataset=dataset, client=client, name="Dataset online eval run")
for scenario in experiment.scenarios:
    evaluators = [Includes(scenario=scenario, expected="{city}"), Correctness(scenario=scenario, question=question)]

    completion = client.chat.completions.create(
        name=scenario.name,
        model="gpt-4o",
        messages=scenario.format_messages([{"role": "user", "content": question}]),
        variables=scenario.input,
    )
    output = completion.choices[0].message.content
    client.output = output
    scenario.actual = output

    evaluate(evaluators, scenario, completion=completion)

Further Documentation

For a deeper dive on all capabilities and more advanced usage, please refer to our Documentation.

License

MIT License