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[Evals API][4/n] evals with generation meta-reference impl (#303)

Browse source 
* wip

* dataset validation

* test_scoring

* cleanup

* clean up test

* comments

* error checking

* dataset client

* test client:

* datasetio client

* clean up

* basic scoring function works

* scorer wip

* equality scorer

* score batch impl

* score batch

* update scoring test

* refactor

* validate scorer input

* address comments

* evals with generation

* add all rows scores to ScoringResult

* minor typing

* bugfix

* scoring function def rename

* rebase name

* refactor

* address comments

* Update iOS inference instructions for new quantization

* Small updates to quantization config

* Fix score threshold in faiss

* Bump version to 0.0.45

* Handle both ipv6 and ipv4 interfaces together

* update manifest for build templates

* Update getting_started.md

* chatcompletion & completion input type validation

* inclusion->subsetof

* error checking

* scoring_function -> scoring_fn rename, scorer -> scoring_fn rename

* address comments

* [Evals API][5/n] fixes to generate openapi spec (#323)

* generate openapi

* typing comment, dataset -> dataset_id

* remove custom type

* sample eval run.yaml

---------

Co-authored-by: Dalton Flanagan <6599399+dltn@users.noreply.github.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
This commit is contained in:
Xi Yan 2024-10-25 13:12:39 -07:00 • committed by GitHub
parent 426d821e7f
commit abdf7cddf3
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31 changed files with 3371 additions and 1296 deletions
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25
llama_stack/providers/tests/datasetio/test_datasetio.py
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@ -61,20 +61,31 @@ def data_url_from_file(file_path: str) -> str:
return data_url
async def register_dataset(datasets_impl: Datasets):
async def register_dataset(
datasets_impl: Datasets, for_generation=False, dataset_id="test_dataset"
):
test_file = Path(os.path.abspath(__file__)).parent / "test_dataset.csv"
test_url = data_url_from_file(str(test_file))
if for_generation:
dataset_schema = {
"expected_answer": StringType(),
"chat_completion_input": ChatCompletionInputType(),
}
else:
dataset_schema = {
"expected_answer": StringType(),
"input_query": StringType(),
"generated_answer": StringType(),
}
dataset = DatasetDefWithProvider(
identifier="test_dataset",
identifier=dataset_id,
provider_id=os.environ["PROVIDER_ID"],
url=URL(
uri=test_url,
),
dataset_schema={
"generated_answer": StringType(),
"expected_answer": StringType(),
"input_query": StringType(),
},
dataset_schema=dataset_schema,
)
await datasets_impl.register_dataset(dataset)