📓 Ground Truth Evaluations

In this quickstart you will create a evaluate an app using ground truth. Ground truth evaluation can be especially useful during early LLM experiments when you have a small set of example queries that are critical to get right.

Ground truth evaluation works by comparing the similarity of an LLM response compared to its matching verified response.

Add API keys

For this quickstart, you will need Open AI keys.

# !pip install trulens trulens-providers-openai openai

import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = "sk-proj-..."

from trulens.core import TruSession

session = TruSession()

Create Simple LLM Application

from openai import OpenAI
from trulens.core.otel.instrument import instrument

oai_client = OpenAI()

class APP:
    @instrument()
    def completion(self, prompt):
        completion = (
            oai_client.chat.completions.create(
                model="gpt-3.5-turbo",
                temperature=0,
                messages=[
                    {
                        "role": "user",
                        "content": f"Please answer the question: {prompt}",
                    }
                ],
            )
            .choices[0]
            .message.content
        )
        return completion

llm_app = APP()

Initialize Feedback Function(s)

from trulens.core import Metric
from trulens.core import Selector
from trulens.feedback import GroundTruthAgreement
from trulens.providers.openai import OpenAI as fOpenAI

golden_set = [
    {
        "query": "who invented the lightbulb?",
        "expected_response": "Thomas Edison",
    },
    {
        "query": "¿quien invento la bombilla?",
        "expected_response": "Thomas Edison",
    },
]

f_groundtruth = Metric(
    implementation=GroundTruthAgreement(
        golden_set, provider=fOpenAI()
    ).agreement_measure,
    name="Ground Truth Semantic Agreement",
    selectors={
        "prompt": Selector.select_record_input(),
        "response": Selector.select_record_output(),
    },
)

Instrument chain for logging with TruLens

# add trulens as a context manager for llm_app
from trulens.apps.app import TruApp

tru_app = TruApp(
    llm_app, app_name="LLM App", app_version="v1", feedbacks=[f_groundtruth]
)

# Instrumented query engine can operate as a context manager:
with tru_app as recording:
    llm_app.completion("¿quien invento la bombilla?")
    llm_app.completion("who invented the lightbulb?")

See results

session.get_leaderboard(app_ids=[tru_app.app_id])

from trulens.dashboard import run_dashboard

run_dashboard(session=session)