Opik¶

Main features¶

The Comet Opik platform is a suite of tools that allow you to evaluate the output of an LLM powered application.

In includes the following features:

Tracing: Ability to log LLM calls and traces to the Opik platform.
LLM evaluation metrics: A set of functions that evaluate the output of an LLM, these are both heuristic metrics and LLM as a Judge.
Evaluation: Ability to log test datasets in Opik and evaluate using some of our LLM evaluation metrics.

For a more detailed overview of the platform, you can refer to the Comet Opik documentation.

Installation¶

To get start with the package, you can install it using pip:

pip install opik

To finish configuring the Opik Python SDK, we recommend running the opik configure command from the command line:

opik configure

You can also call the configure function from the Python SDK:

import opik

opik.configure(use_local=False)

Using the SDK¶

Logging LLM calls¶

To log your first trace, you can use the track decorator:

from opik import track

@track
def llm_function(input: str) -> str:
   # Your LLM call
   # ...

   return "Hello, world!"

llm_function("Hello")

Note: The track decorator supports nested functions, if you track multiple functions, each functionc call will be associated with the parent trace.

Integrations: If you are using LangChain or OpenAI, Comet Opik as built-in integrations for these libraries.

Using LLM evaluation metrics¶

The opik package includes a number of LLM evaluation metrics, these are both heuristic metrics and LLM as a Judge.

All available metrics are listed in the metrics section.

These evaluation metrics can be used as:

from opik.evaluation.metrics import Hallucination

metric = Hallucination()

input = "What is the capital of France?"
output = "The capital of France is Paris, a city known for its iconic Eiffel Tower."
context = "Paris is the capital and most populous city of France."

score = metric.score(input, output, context)
print(f"Hallucination score: {score}")

Running evaluations¶

Evaluations are run using the evaluate function, this function takes a dataset, a task and a list of metrics and returns a dictionary of scores:

from opik import Opik, track
from opik.evaluation import evaluate
from opik.evaluation.metrics import EqualsMetric, HallucinationMetric
from opik.integrations.openai import track_openai
from typing import Dict

from typing import Dict

# Define the task to evaluate
openai_client = track_openai(openai.OpenAI())

@track()
def your_llm_application(input: str) -> str:
   response = openai_client.chat.completions.create(
      model="gpt-3.5-turbo",
      messages=[{"role": "user", "content": input}],
   )

   return response.choices[0].message.content

@track()
def your_context_retriever(input: str) -> str:
   return ["..."]

# Fetch the dataset
client = Opik()
dataset = client.get_dataset(name="your-dataset-name")

# Define the metrics
equals_metric = EqualsMetric()
hallucination_metric = HallucinationMetric()

# Define and run the evaluation
def evaluation_task(x: Dict):
   return {
      "input": x.input['user_question'],
      "output": your_llm_application(x.input['user_question']),
      "context": your_context_retriever(x.input['user_question'])
   }

evaluation = evaluate(
   dataset=dataset,
   task=evaluation_task,
   metrics=[equals_metric, hallucination_metric],
)

Storing prompts¶

You can store prompts in the Opik library using the Prompt object:

import opik

prompt = opik.Prompt(name="my-prompt", prompt="Write a summary of the following text: {{text}}")

Reference¶

You can learn more about the opik python SDK in the following sections:

Integrations

Evaluation

Prompt management

Prompt

Testing

llm_unit

Objects

Command Line Interface

CLI Reference

Documentation Guides

Opik Documentation