baserun 1.0.0b5

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

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
• annotate: 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"})

Further Documentation

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

License

MIT License