EvaluationMetrics

Custom Metric

Opik allows you to define your own custom metrics, which is especially important when the metrics you need are not already available out of the box.

When to Create Custom Metrics

It is specially relevant to define your own metrics when:

  • You have domain-specific goals
  • Standard metrics don’t capture the nuance you need
  • You want to align with business KPIs
  • You’re experimenting with new evaluation approaches

If you want to write an LLM as a Judge metric, you can use either the G-Eval metric or create your own from scratch.

Writing your own custom metrics

To define a custom metric, you need to subclass the BaseMetric class and implement the score method and an optional ascore method:

1from typing import Any
2
3from opik.evaluation.metrics import base_metric, score_result
4
5
6class MyCustomMetric(base_metric.BaseMetric):
7    def __init__(self, name: str):
8        super().__init__(name)
9
10    def score(self, input: str, output: str, **ignored_kwargs: Any) -> score_result.ScoreResult:
11        # Add your logic here
12
13        return score_result.ScoreResult(
14            value=0,
15            name=self.name,
16            reason="Optional reason for the score"
17        )

The score method should return a ScoreResult object. The ascore method is optional and can be used to compute asynchronously if needed.

You can also return a list of ScoreResult objects as part of your custom metric. This is useful if you want to return multiple scores for a given input and output pair.

Now you can use the custom metric to score LLM outputs:

1metric = MyCustomMetric()
2
3metric.score(input="What is the capital of France?", output="Paris")

Also, this metric can now be used in the evaluate function as explained here: Evaluating LLMs.

Example: Creating a metric with OpenAI model

You can implement your own custom metric by creating a class that subclasses the BaseMetric class and implements the score method.

1import json
2from typing import Any
3
4from openai import OpenAI
5from opik.evaluation.metrics import base_metric, score_result
6
7
8class LLMJudgeMetric(base_metric.BaseMetric):
9    def __init__(self, name: str = "Factuality check", model_name: str = "gpt-4o"):
10        super().__init__(name)
11        self.llm_client = OpenAI()
12        self.model_name = model_name
13        self.prompt_template = """
14        You are an impartial judge evaluating the following claim for factual accuracy.
15        Analyze it carefully and provide a binary score: true if the claim is accurate,
16        false if it is inaccurate or contains errors. The format of your response
17        should be a JSON object with no additional text or backticks.
18
19        The format of your response should be a JSON object with no additional text or backticks that follows the format:
20        {{
21            "score": <true or false>
22        }}
23
24        Claim to evaluate: {output}
25
26        Response:
27        """
28
29    def score(self, output: str, **ignored_kwargs: Any) -> score_result.ScoreResult:
30        """
31        Score the output of an LLM.
32
33        Args:
34            output: The output of an LLM to score.
35            **ignored_kwargs: Any additional keyword arguments. This is important so that the metric can be used in the `evaluate` function.
36        """
37        # Construct the prompt based on the output of the LLM
38        prompt = self.prompt_template.format(output=output)
39
40        # Generate and parse the response from the LLM
41        response = self.llm_client.chat.completions.create(
42            model=self.model_name,
43            messages=[{"role": "user", "content": prompt}]
44        )
45        response_dict = json.loads(response.choices[0].message.content)
46
47        # Parse the response and convert to integer for logging
48        response_score = (
49            response_dict["score"]
50            if isinstance(response_dict["score"], bool)
51            else str(response_dict["score"]).strip().lower() == "true"
52        )
53
54        return score_result.ScoreResult(
55            name=self.name,
56            value=response_score
57        )

You can then use this metric to score your LLM outputs:

1metric = LLMJudgeMetric()
2
3metric.score(output="Paris is the capital of France")

In this example, we used the OpenAI Python client to call the LLM. You don’t have to use the OpenAI Python client, you can update the code example above to use any LLM client you have access to.

Example: Adding support for many LLM providers

In order to support a wide range of LLM providers, we recommend using the litellm library to call your LLM. This allows you to support hundreds of models without having to maintain a custom LLM client.

Opik providers a LitellmChatModel class that wraps the litellm library and can be used in your custom metric:

1import json
2from typing import Any
3
4from opik.evaluation.metrics import base_metric, score_result
5from opik.evaluation import models
6
7
8class LLMJudgeMetric(base_metric.BaseMetric):
9    def __init__(self, name: str = "Factuality check", model_name: str = "gpt-4o"):
10        super().__init__(name)
11
12        self.llm_client = models.LiteLLMChatModel(model_name=model_name)
13
14        self.prompt_template = """
15        You are an impartial judge evaluating the following claim for factual accuracy. Analyze it carefully
16        and respond with a number between 0 and 1: 1 if completely accurate, 0.5 if mixed accuracy, or 0 if inaccurate.
17        Then provide one brief sentence explaining your ruling.
18
19        The format of the your response should be a JSON object with no additional text or backticks that follows the format:
20        {{
21            "score": <score between 0 and 1>,
22            "reason": "<reason for the score>"
23        }}
24
25        Claim to evaluate: {output}
26
27        Response:
28        """
29
30    def score(self, output: str, **ignored_kwargs: Any) -> score_result.ScoreResult:
31        """
32        Score the output of an LLM.
33
34        Args:
35            output: The output of an LLM to score.
36            **ignored_kwargs: Any additional keyword arguments. This is important so that the metric can be used in the `evaluate` function.
37        """
38        # Construct the prompt based on the output of the LLM
39        prompt = self.prompt_template.format(output=output)
40
41        # Generate and parse the response from the LLM
42        response = self.llm_client.generate_string(input=prompt)
43
44        response_dict = json.loads(response)
45
46        return score_result.ScoreResult(
47            name=self.name,
48            value=response_dict["score"],
49            reason=response_dict["reason"]
50        )

You can then use this metric to score your LLM outputs:

1metric = LLMJudgeMetric()
2
3metric.score(output="Paris is the capital of France")

Example: Creating a metric with multiple scores

You can implement a metric that returns multiple scores, which will display as separate columns in the UI when using it in an evaluation.

To do so, setup your score method to return a list of ScoreResult objects.

1from typing import Any, List
2
3from opik.evaluation.metrics import base_metric, score_result
4
5class MultiScoreCustomMetric(base_metric.BaseMetric):
6    def __init__(self, name: str):
7        super().__init__(name)
8
9    def score(self, input: str, output: str, **ignored_kwargs: Any) -> List[score_result.ScoreResult]:
10        # Add your logic here
11
12        return [score_result.ScoreResult(
13            value=0,
14            name=self.name,
15            reason="Optional reason for the score"
16        ),
17        score_result.ScoreResult(
18            value=1,
19            name=f"{self.name}-2",
20            reason="Optional reason for the score"
21        )]

Example: Enforcing structured outputs

In the examples above, we ask the LLM to respond with a JSON object. However as this is not enforced, it is possible that the LLM returns a non-structured response. In order to avoid this, you can use the litellm library to enforce a structured output. This will make our custom metric more robust and less prone to failure.

For this we define the format of the response we expect from the LLM in the LLMJudgeBinaryResult class and pass it to the LiteLLM client:

1import json
2from pydantic import BaseModel
3from typing import Any
4
5from opik.evaluation.metrics import base_metric, score_result
6from opik.evaluation import models
7
8class LLMJudgeBinaryResult(BaseModel):
9    score: bool
10    reason: str
11
12class LLMJudgeMetric(base_metric.BaseMetric):
13    def __init__(self, name: str = "Factuality check", model_name: str = "gpt-4o"):
14        super().__init__(name)
15        self.llm_client = models.LiteLLMChatModel(model_name=model_name)
16        self.prompt_template = """
17        You are an impartial judge evaluating the following claim for factual accuracy. Analyze it carefully and provide a binary score: true if the claim is accurate, false if it is inaccurate or contains errors. Then provide one brief sentence explaining your ruling.
18
19         The format of the your response should be a json with no backticks that returns:
20
21        {{
22            "score": <true or false>,
23            "reason": "<reason for the score>"
24        }}
25
26        Claim to evaluate: {output}
27
28        Response:
29        """
30
31    def score(self, output: str, **ignored_kwargs: Any) -> score_result.ScoreResult:
32        """
33        Score the output of an LLM.
34
35        Args:
36            output: The output of an LLM to score.
37            **ignored_kwargs: Any additional keyword arguments. This is important so that the metric can be used in the `evaluate` function.
38        """
39        # Construct the prompt based on the output of the LLM
40        prompt = self.prompt_template.format(output=output)
41
42        # Generate and parse the response from the LLM
43        response = self.llm_client.generate_string(input=prompt, response_format=LLMJudgeBinaryResult)
44        response_dict = json.loads(response)
45
46        return score_result.ScoreResult(
47            name=self.name,
48            value=response_dict["score"],
49            reason=response_dict["reason"]
50        )

Similarly to the previous example, you can then use this metric to score your LLM outputs:

1metric = LLMJudgeMetric()
2
3metric.score(output="Paris is the capital of France")

Creating a custom metric using G-Eval

G-eval allows you to specify a set of criteria for your metric and it will use a Chain of Thought prompting technique to create some evaluation steps and return a score. You can read more about this advanced metric here.

To use G-Eval, you will need to specify a task introduction and evaluation criteria:

1from opik.evaluation.metrics import GEval
2
3metric = GEval(
4    task_introduction="You are an expert judge tasked with evaluating the faithfulness of an AI-generated answer to the given context.",
5    evaluation_criteria="""
6        The OUTPUT must not introduce new information beyond what's provided in the CONTEXT.
7        The OUTPUT must not contradict any information given in the CONTEXT.
8
9        Return only a score between 0 and 1.
10    """,
11)

Custom Conversation Metrics

For evaluating multi-turn conversations and dialogue systems, you’ll need specialized conversation metrics. These metrics evaluate entire conversation threads rather than single input-output pairs.

Learn how to create custom conversation metrics in the Custom Conversation Metrics guide.

What’s next

Creating custom metrics is just the beginning of building a comprehensive evaluation system for your LLM applications. In this guide, you’ve learned how to create custom metrics using different approaches, from simple metrics to sophisticated LLM-as-a-judge implementations, including specialized conversation thread metrics for multi-turn dialogue evaluation.

From here, you might want to: