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435f34b05e
i find `test_structured_output` to be flakey. it's both a functionality
and accuracy test -
```
answer = AnswerFormat.model_validate_json(response.completion_message.content)
assert answer.first_name == "Michael"
assert answer.last_name == "Jordan"
assert answer.year_of_birth == 1963
assert answer.num_seasons_in_nba == 15
```
it's an accuracy test because it checks the value of first/last name,
birth year, and num seasons.
i find that -
- llama-3.1-8b-instruct and llama-3.2-3b-instruct pass the functionality
portion
- llama-3.2-3b-instruct consistently fails the accuracy portion
(thinking MJ was in the NBA for 14 seasons)
- llama-3.1-8b-instruct occasionally fails the accuracy portion
suggestions (not mutually exclusive) -
1. turn the test into functionality only, skip the value checks
2. split the test into a functionality version and an xfail accuracy
version
3. add context to the prompt so the llm can answer without accessing
embedded memory
# What does this PR do?
implements option (3) by adding context to the system prompt.
## Test Plan
`pytest -s -v ... llama_stack/providers/tests/inference/ ... -k
structured_output`
## Before submitting
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [x] Updated relevant documentation.
- [x] Wrote necessary unit or integration tests.
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| datatypes.py | ||