chore(llmobs): implement answer relevancy ragas metric

[lievan]

Implements answer relevancy metric for ragas integration.

About Answer Relevancy

Answer relevancy metric focuses on assessing how pertinent the generated answer is to the given prompt. A lower score is assigned to answers that are incomplete or contain redundant information and higher scores indicate better relevancy. This metric is computed using the question, the retrieved contexts and the answer.

The Answer Relevancy is defined as the mean cosine similarity of the original question to a number of artificial questions, which where generated (reverse engineered) based on the response.

Example trace

Checklist

• PR author has checked that all the criteria below are met
• The PR description includes an overview of the change
• The PR description articulates the motivation for the change
• The change includes tests OR the PR description describes a testing strategy
• The PR description notes risks associated with the change, if any
• Newly-added code is easy to change
• The change follows the library release note guidelines
• The change includes or references documentation updates if necessary
• Backport labels are set (if applicable)

Reviewer Checklist

• Reviewer has checked that all the criteria below are met
• Title is accurate
• All changes are related to the pull request's stated goal
• Avoids breaking API changes
• Testing strategy adequately addresses listed risks
• Newly-added code is easy to change
• Release note makes sense to a user of the library
• If necessary, author has acknowledged and discussed the performance implications of this PR as reported in the benchmarks PR comment
• Backport labels are set in a manner that is consistent with the release branch maintenance policy

[github-actions]

CODEOWNERS have been resolved as:

ddtrace/llmobs/_evaluators/ragas/answer_relevancy.py                    @DataDog/ml-observability
tests/llmobs/llmobs_cassettes/tests.llmobs.test_llmobs_ragas_evaluators.answer_relevancy_inference.yaml  @DataDog/ml-observability
ddtrace/llmobs/_evaluators/ragas/base.py                                @DataDog/ml-observability
ddtrace/llmobs/_evaluators/ragas/models.py                              @DataDog/ml-observability
ddtrace/llmobs/_evaluators/runner.py                                    @DataDog/ml-observability
tests/llmobs/_utils.py                                                  @DataDog/ml-observability
tests/llmobs/conftest.py                                                @DataDog/ml-observability
tests/llmobs/test_llmobs_ragas_evaluators.py                            @DataDog/ml-observability

[pr-commenter]

Benchmarks

Benchmark execution time: 2025-01-15 20:24:49

Comparing candidate commit 640d2ad in PR branch evan.li/ragas-answer-rel with baseline commit bbc1bd8 in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 394 metrics, 2 unstable metrics.

[Yun-Kim]

As a next step/PR I think I'd like llmobs eval tests to avoid mocking the submit_evaluation function and instead create a dummy eval writer and use eval events to assert finished evals (like here for spans). WYDT?

[lievan]

As a next step/PR I think I'd like llmobs eval tests to avoid mocking the submit_evaluation function and instead create a dummy eval writer and use eval events to assert finished evals (like here for spans). WYDT?

@Yun-Kim sounds good, will add this as a todo for leaning up tests/ragas integration!