This guide helps you run experiments to test and validate changes in your LLM applications against a curated dataset. Learn more about the concepts of experiments here.
Grab your Space ID, Developer Key, and API key from the UI
from arize.experimental.datasets import ArizeDatasetsClient
client = ArizeDatasetsClient(api_key="*****", developer_key="*****")
Create your dataset
Datasets are useful groupings of data points you want to use for test cases. You can create datasets through code, using LLMs to auto-generate them, or by importing spans using the Arize UI.
Download this sample CSV and upload it into the UI. The CSV must have an id column. See example below:
id,topic
1,zebras
2,clouds
# Setup Datasets client
import pandas as pd
from arize.experimental.datasets.utils.constants import GENERATIVE
# Create dataframe to upload
data = [{"topic": "Zebras"}]
df = pd.DataFrame(data)
# Create dataset in Arize
dataset_id = arize_client.create_dataset(
dataset_name="haiku-topics"+ str(uuid1())[:5],
data=df,
space_id=space_id,
dataset_type=GENERATIVE
)
print(f"Dataset created with ID: {dataset_id}")
If you have added tracing to your application, you can create datasets by adding spans from your application with Arize. Go to the traces page and filter for the examples you care about.
In the example below, we are filtering for spans with a hallucination label, and adding them to a dataset.
Define your LLM task
Here is where you define the LLM task you are trying to test for its effectiveness. This could be structured data extraction, SQL generation, chatbot response generation, search, or any task of your choosing.
The input is the dataset_row so you can access the variables in your dataset, and the output is a string.
import os
import openai
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass("🔑 Enter your OpenAI API key: ")
# Create task which creates a haiku based on a topic
def create_haiku(dataset_row) -> str:
topic = dataset_row.get("topic")
openai_client = openai.OpenAI()
response = openai_client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": f"Write a haiku about {topic}"}],
max_tokens=20
)
return response.choices[0].message.content
Define your evaluation criteria
An evaluator is any function that (1) takes the task output and (2) returns an assessment. This gives you tremendous flexibility to write your own LLM judge using a custom template, or use code-based evaluators.
We use our OSS package Arize Phoenix to run LLM-based evaluations, using llm_classify. See here for the API reference.
from phoenix.evals import (
OpenAIModel,
llm_classify,
)
from arize.experimental.datasets.experiments.evaluators.base import (
EvaluationResult,
)
CUSTOM_TEMPLATE = """
You are evaluating whether tone is positive, neutral, or negative
[Message]: {output}
Respond with either "positive", "neutral", or "negative"
"""
def tone_eval(output):
df_in = pd.DataFrame({"output": output}, index=[0])
eval_df = llm_classify(
dataframe=df_in,
template=CUSTOM_TEMPLATE,
model=OpenAIModel(model="gpt-4o"),
rails=["positive", "neutral", "negative"],
provide_explanation=True,
)
# return score, label, explanation
return EvaluationResult(
score=1,
label=eval_df["label"][0],
explanation=eval_df["explanation"][0],
)
Run the experiment
To run an experiment, you need to specify the space you are logging it to, the dataset_id, the task you would like to run, and the list of evaluators defined on the output. This also logs the traces to Arize so you can debug each run.
See the run_experiment SDK definition for more info. You can specify dry_run=True , which does not log the result to Arize. You can also specify exit_on_error=True , which makes it easier to debug when an experiment doesn't run correctly.
