[Evals API][6/n] meta-reference llm as judge, registration for ScoringFnDefs (#330)

* wip scoring refactor

* llm as judge, move folders

* test full generation + eval

* extract score regex to llm context

* remove prints, cleanup braintrust in this branch

* change json -> class

* remove initialize

* address nits

* check identifier prefix

* udpate MANIFEST

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -26,6 +26,10 @@ class Parameter(BaseModel):
 class LLMAsJudgeContext(BaseModel):
     judge_model: str
     prompt_template: Optional[str] = None
+    judge_score_regex: Optional[List[str]] = Field(
+        description="Regex to extract the score from the judge response",
+        default=None,
+    )
 
 
 @json_schema_type

llama_stack/providers/impls/meta_reference/eval/eval.py

@@ -18,6 +18,7 @@ from .config import MetaReferenceEvalConfig
 
 
 class ColumnName(Enum):
+    input_query = "input_query"
     expected_answer = "expected_answer"
     chat_completion_input = "chat_completion_input"
     completion_input = "completion_input"
@@ -53,10 +54,12 @@ class MetaReferenceEvalImpl(Eval):
 
         expected_schemas = [
             {
+                ColumnName.input_query.value: StringType(),
                 ColumnName.expected_answer.value: StringType(),
                 ColumnName.chat_completion_input.value: ChatCompletionInputType(),
             },
             {
+                ColumnName.input_query.value: StringType(),
                 ColumnName.expected_answer.value: StringType(),
                 ColumnName.completion_input.value: CompletionInputType(),
             },

llama_stack/providers/impls/meta_reference/scoring/__init__.py

@@ -16,6 +16,8 @@ async def get_provider_impl(
 ):
     from .scoring import MetaReferenceScoringImpl
 
-    impl = MetaReferenceScoringImpl(config, deps[Api.datasetio], deps[Api.datasets])
+    impl = MetaReferenceScoringImpl(
+        config, deps[Api.datasetio], deps[Api.datasets], deps[Api.inference]
+    )
     await impl.initialize()
     return impl

llama_stack/providers/impls/meta_reference/scoring/scoring.py

@@ -11,24 +11,25 @@ from llama_stack.apis.scoring_functions import *  # noqa: F403
 from llama_stack.apis.common.type_system import *  # noqa: F403
 from llama_stack.apis.datasetio import *  # noqa: F403
 from llama_stack.apis.datasets import *  # noqa: F403
-
+from llama_stack.apis.inference.inference import Inference
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.equality_scoring_fn import (
     EqualityScoringFn,
 )
 
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.llm_as_judge_scoring_fn import (
+    LlmAsJudgeScoringFn,
+)
+
 from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.subset_of_scoring_fn import (
     SubsetOfScoringFn,
 )
 
 from .config import MetaReferenceScoringConfig
 
-SUPPORTED_SCORING_FNS = [
-    EqualityScoringFn,
-    SubsetOfScoringFn,
-]
+FIXED_FNS = [EqualityScoringFn, SubsetOfScoringFn]
 
-SCORER_REGISTRY = {x.scoring_function_def.identifier: x for x in SUPPORTED_SCORING_FNS}
+LLM_JUDGE_FNS = [LlmAsJudgeScoringFn]
 
 
 class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
@@ -37,22 +38,44 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
         config: MetaReferenceScoringConfig,
         datasetio_api: DatasetIO,
         datasets_api: Datasets,
+        inference_api: Inference,
     ) -> None:
         self.config = config
         self.datasetio_api = datasetio_api
         self.datasets_api = datasets_api
+        self.inference_api = inference_api
+        self.scoring_fn_id_impls = {}
 
-    async def initialize(self) -> None: ...
+    async def initialize(self) -> None:
+        for x in FIXED_FNS:
+            impl = x()
+            for fn_defs in impl.get_supported_scoring_fn_defs():
+                self.scoring_fn_id_impls[fn_defs.identifier] = impl
+        for x in LLM_JUDGE_FNS:
+            impl = x(inference_api=self.inference_api)
+            for fn_defs in impl.get_supported_scoring_fn_defs():
+                self.scoring_fn_id_impls[fn_defs.identifier] = impl
+                self.llm_as_judge_fn = impl
 
     async def shutdown(self) -> None: ...
 
     async def list_scoring_functions(self) -> List[ScoringFnDef]:
-        return [x.scoring_function_def for x in SUPPORTED_SCORING_FNS]
+        scoring_fn_defs_list = [
+            fn_def
+            for impl in self.scoring_fn_id_impls.values()
+            for fn_def in impl.get_supported_scoring_fn_defs()
+        ]
+
+        for f in scoring_fn_defs_list:
+            assert f.identifier.startswith(
+                "meta-reference"
+            ), "All meta-reference scoring fn must have identifier prefixed with 'meta-reference'! "
+
+        return scoring_fn_defs_list
 
     async def register_scoring_function(self, function_def: ScoringFnDef) -> None:
-        raise NotImplementedError(
-            "Dynamically registering scoring functions is not supported"
-        )
+        self.llm_as_judge_fn.register_scoring_fn_def(function_def)
+        self.scoring_fn_id_impls[function_def.identifier] = self.llm_as_judge_fn
 
     async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
         dataset_def = await self.datasets_api.get_dataset(dataset_identifier=dataset_id)
@@ -99,11 +122,11 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     ) -> ScoreResponse:
         res = {}
         for scoring_fn_id in scoring_functions:
-            if scoring_fn_id not in SCORER_REGISTRY:
+            if scoring_fn_id not in self.scoring_fn_id_impls:
                 raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
-            scoring_fn = SCORER_REGISTRY[scoring_fn_id]()
-            score_results = scoring_fn.score(input_rows)
-            agg_results = scoring_fn.aggregate(score_results)
+            scoring_fn = self.scoring_fn_id_impls[scoring_fn_id]
+            score_results = await scoring_fn.score(input_rows, scoring_fn_id)
+            agg_results = await scoring_fn.aggregate(score_results)
             res[scoring_fn_id] = ScoringResult(
                 score_rows=score_results,
                 aggregated_results=agg_results,

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/base_scoring_fn.py

@@ -17,21 +17,41 @@ class BaseScoringFn(ABC):
     - aggregate(self, scoring_fn_results)
     """
 
-    scoring_function_def: ScoringFnDef
-
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
+        self.supported_fn_defs_registry = {}
 
     def __str__(self) -> str:
         return self.__class__.__name__
 
+    def get_supported_scoring_fn_defs(self) -> List[ScoringFnDef]:
+        return [x for x in self.supported_fn_defs_registry.values()]
+
+    def register_scoring_fn_def(self, scoring_fn_def: ScoringFnDef) -> None:
+        if scoring_fn_def.identifier in self.supported_fn_defs_registry:
+            raise ValueError(
+                f"Scoring function def with identifier {scoring_fn_def.identifier} already exists."
+            )
+        self.supported_fn_defs_registry[scoring_fn_def.identifier] = scoring_fn_def
+
     @abstractmethod
-    def score_row(self, input_row: Dict[str, Any]) -> ScoringResultRow:
+    async def score_row(
+        self, input_row: Dict[str, Any], scoring_fn_identifier: Optional[str] = None
+    ) -> ScoringResultRow:
         raise NotImplementedError()
 
     @abstractmethod
-    def aggregate(self, scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
+    async def aggregate(
+        self, scoring_results: List[ScoringResultRow]
+    ) -> Dict[str, Any]:
         raise NotImplementedError()
 
-    def score(self, input_rows: List[Dict[str, Any]]) -> List[ScoringResultRow]:
-        return [self.score_row(input_row) for input_row in input_rows]
+    async def score(
+        self,
+        input_rows: List[Dict[str, Any]],
+        scoring_fn_identifier: Optional[str] = None,
+    ) -> List[ScoringResultRow]:
+        return [
+            await self.score_row(input_row, scoring_fn_identifier)
+            for input_row in input_rows
+        ]

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/common.py

@@ -3,10 +3,13 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+from pathlib import Path
 from typing import Any, Dict, List
 
 from llama_stack.apis.scoring import ScoringResultRow
 
+FN_DEFS_PATH = Path(__file__).parent / "fn_defs"
+
 
 def aggregate_accuracy(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
     num_correct = sum(result["score"] for result in scoring_results)
@@ -17,3 +20,12 @@ def aggregate_accuracy(scoring_results: List[ScoringResultRow]) -> Dict[str, Any
         "num_correct": num_correct,
         "num_total": len(scoring_results),
     }
+
+
+def aggregate_average(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
+    return {
+        "average": sum(
+            result["score"] for result in scoring_results if result["score"] is not None
+        )
+        / len([_ for _ in scoring_results if _["score"] is not None]),
+    }

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/equality_scoring_fn.py

@@ -10,24 +10,32 @@ from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_
 from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
 from llama_stack.apis.scoring import *  # noqa: F401, F403
 from llama_stack.apis.common.type_system import *  # noqa: F403
+
 from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
     aggregate_accuracy,
 )
 
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.fn_defs.equality import (
+    equality,
+)
+
 
 class EqualityScoringFn(BaseScoringFn):
     """
     A scoring_fn that assigns a score of 1.0 if the input string matches the target string, and 0.0 otherwise.
     """
 
-    scoring_function_def = ScoringFnDef(
-        identifier="equality",
-        description="Returns 1.0 if the input is equal to the target, 0.0 otherwise.",
-        parameters=[],
-        return_type=NumberType(),
-    )
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.supported_fn_defs_registry = {
+            equality.identifier: equality,
+        }
 
-    def score_row(self, input_row: Dict[str, Any]) -> ScoringResultRow:
+    async def score_row(
+        self,
+        input_row: Dict[str, Any],
+        scoring_fn_identifier: Optional[str] = "equality",
+    ) -> ScoringResultRow:
         assert "expected_answer" in input_row, "Expected answer not found in input row."
         assert (
             "generated_answer" in input_row
@@ -40,5 +48,7 @@ class EqualityScoringFn(BaseScoringFn):
             "score": score,
         }
 
-    def aggregate(self, scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
+    async def aggregate(
+        self, scoring_results: List[ScoringResultRow]
+    ) -> Dict[str, Any]:
         return aggregate_accuracy(scoring_results)

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/fn_defs/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/fn_defs/equality.py

@@ -0,0 +1,16 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.common.type_system import NumberType
+from llama_stack.apis.scoring_functions import ScoringFnDef
+
+
+equality = ScoringFnDef(
+    identifier="meta-reference::equality",
+    description="Returns 1.0 if the input is equal to the target, 0.0 otherwise.",
+    parameters=[],
+    return_type=NumberType(),
+)

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/fn_defs/llm_as_judge_8b_correctness.py

@@ -0,0 +1,36 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
+from llama_stack.apis.scoring import *  # noqa: F401, F403
+from llama_stack.apis.common.type_system import NumberType
+
+JUDGE_PROMPT = """
+You will be given a question, a expected_answer, and a system_answer.
+Your task is to provide a 'total rating' scoring how well the system_answer answers compared with ground truth in expected_answer in terms of factual correctness to the question.
+Give your answer as a integer on a scale of 0 to 5, where 0 means that the system_answer is not correct at all compared with expected_answer, and 5 means that the answer completely and correctly answers the question.
+Provide your feedback as follows:
+Feedback:::
+Total rating: (your rating, as a int between 0 and 5)
+Now here are the question, expected_answer, system_answer.
+Question: {input_query}
+Expected Answer: {expected_answer}
+System Answer: {generated_answer}
+Feedback:::
+Total rating:
+"""
+
+llm_as_judge_8b_correctness = ScoringFnDef(
+    identifier="meta-reference::llm_as_judge_8b_correctness",
+    description="Llm As Judge Scoring Function",
+    parameters=[],
+    return_type=NumberType(),
+    context=LLMAsJudgeContext(
+        prompt_template=JUDGE_PROMPT,
+        judge_model="Llama3.1-8B-Instruct",
+        judge_score_regex=[r"Total rating: (\d+)", r"rating: (\d+)", r"Rating: (\d+)"],
+    ),
+)

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/fn_defs/subset_of.py

@@ -0,0 +1,16 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.common.type_system import NumberType
+from llama_stack.apis.scoring_functions import ScoringFnDef
+
+
+subset_of = ScoringFnDef(
+    identifier="meta-reference::subset_of",
+    description="Returns 1.0 if the expected is included in generated, 0.0 otherwise.",
+    parameters=[],
+    return_type=NumberType(),
+)

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/llm_as_judge_scoring_fn.py

@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from llama_stack.apis.inference.inference import Inference
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_fn import (
+    BaseScoringFn,
+)
+from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
+from llama_stack.apis.scoring import *  # noqa: F401, F403
+from llama_stack.apis.common.type_system import *  # noqa: F403
+import re
+
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
+    aggregate_average,
+)
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.fn_defs.llm_as_judge_8b_correctness import (
+    llm_as_judge_8b_correctness,
+)
+
+
+class LlmAsJudgeScoringFn(BaseScoringFn):
+    """
+    A scoring_fn that assigns
+    """
+
+    def __init__(self, inference_api: Inference, *arg, **kwargs) -> None:
+        super().__init__(*arg, **kwargs)
+        self.inference_api = inference_api
+        self.supported_fn_defs_registry = {
+            llm_as_judge_8b_correctness.identifier: llm_as_judge_8b_correctness,
+        }
+
+    async def score_row(
+        self,
+        input_row: Dict[str, Any],
+        scoring_fn_identifier: Optional[str] = None,
+    ) -> ScoringResultRow:
+        assert (
+            scoring_fn_identifier is not None
+        ), "Scoring function identifier not found."
+        fn_def = self.supported_fn_defs_registry[scoring_fn_identifier]
+        assert fn_def.context is not None, f"LLMAsJudgeContext not found for {fn_def}."
+        assert (
+            fn_def.context.prompt_template is not None
+        ), "LLM Judge prompt_template not found."
+        assert (
+            fn_def.context.judge_score_regex is not None
+        ), "LLM Judge judge_score_regex not found."
+
+        input_query = input_row["input_query"]
+        expected_answer = input_row["expected_answer"]
+        generated_answer = input_row["generated_answer"]
+
+        judge_input_msg = fn_def.context.prompt_template.format(
+            input_query=input_query,
+            expected_answer=expected_answer,
+            generated_answer=generated_answer,
+        )
+
+        judge_response = await self.inference_api.chat_completion(
+            model=fn_def.context.judge_model,
+            messages=[
+                {
+                    "role": "user",
+                    "content": judge_input_msg,
+                }
+            ],
+        )
+        content = judge_response.completion_message.content
+        rating_regexs = fn_def.context.judge_score_regex
+
+        judge_rating = None
+        for regex in rating_regexs:
+            match = re.search(regex, content)
+            if match:
+                judge_rating = int(match.group(1))
+                break
+
+        return {
+            "score": judge_rating,
+            "judge_feedback": content,
+        }
+
+    async def aggregate(
+        self, scoring_results: List[ScoringResultRow]
+    ) -> Dict[str, Any]:
+        return aggregate_average(scoring_results)

llama_stack/providers/impls/meta_reference/scoring/scoring_fn/subset_of_scoring_fn.py

@@ -14,25 +14,27 @@ from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import
     aggregate_accuracy,
 )
 
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.fn_defs.subset_of import (
+    subset_of,
+)
+
 
 class SubsetOfScoringFn(BaseScoringFn):
     """
     A scoring_fn that assigns a score of 1.0 if the expected string is included in the generated string, and 0.0 otherwise.
     """
 
-    scoring_function_def = ScoringFnDef(
-        identifier="subset_of",
-        description="Returns 1.0 if the expected is included in generated, 0.0 otherwise.",
-        parameters=[],
-        return_type=NumberType(),
-    )
-
-    def score_row(self, input_row: Dict[str, Any]) -> ScoringResultRow:
-        assert "expected_answer" in input_row, "Expected answer not found in input row."
-        assert (
-            "generated_answer" in input_row
-        ), "Generated answer not found in input row."
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.supported_fn_defs_registry = {
+            subset_of.identifier: subset_of,
+        }
 
+    async def score_row(
+        self,
+        input_row: Dict[str, Any],
+        scoring_fn_identifier: Optional[str] = "subset_of",
+    ) -> ScoringResultRow:
         expected_answer = input_row["expected_answer"]
         generated_answer = input_row["generated_answer"]
         score = 1.0 if expected_answer in generated_answer else 0.0
@@ -40,5 +42,7 @@ class SubsetOfScoringFn(BaseScoringFn):
             "score": score,
         }
 
-    def aggregate(self, scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
+    async def aggregate(
+        self, scoring_results: List[ScoringResultRow]
+    ) -> Dict[str, Any]:
         return aggregate_accuracy(scoring_results)

llama_stack/providers/registry/scoring.py

@@ -20,6 +20,7 @@ def available_providers() -> List[ProviderSpec]:
             api_dependencies=[
                 Api.datasetio,
                 Api.datasets,
+                Api.inference,
             ],
         ),
     ]

llama_stack/providers/tests/datasetio/test_datasetio.py

@@ -70,6 +70,7 @@ async def register_dataset(
     if for_generation:
         dataset_schema = {
             "expected_answer": StringType(),
+            "input_query": StringType(),
             "chat_completion_input": ChatCompletionInputType(),
         }
     else:

llama_stack/providers/tests/eval/provider_config_example.yaml

@@ -16,3 +16,7 @@ providers:
       provider_type: remote::tgi
       config:
         url: http://127.0.0.1:5009
+    - provider_id: test-tgi-2
+      provider_type: remote::tgi
+      config:
+        url: http://127.0.0.1:5010

llama_stack/providers/tests/eval/test_eval.py

@@ -65,7 +65,10 @@ async def test_eval(eval_settings):
             model="Llama3.2-1B-Instruct",
             sampling_params=SamplingParams(),
         ),
-        scoring_functions=["subset_of"],
+        scoring_functions=[
+            "meta-reference::subset_of",
+            "meta-reference::llm_as_judge_8b_correctness",
+        ],
     )
     assert response.job_id == "0"
     job_status = await eval_impl.job_status(response.job_id)
@@ -76,4 +79,5 @@ async def test_eval(eval_settings):
 
     assert eval_response is not None
     assert len(eval_response.generations) == 5
-    assert "subset_of" in eval_response.scores
+    assert "meta-reference::subset_of" in eval_response.scores
+    assert "meta-reference::llm_as_judge_8b_correctness" in eval_response.scores

llama_stack/providers/tests/scoring/provider_config_example.yaml

@@ -7,3 +7,8 @@ providers:
     - provider_id: test-meta
       provider_type: meta-reference
       config: {}
+  inference:
+    - provider_id: tgi0
+      provider_type: remote::tgi
+      config:
+        url: http://127.0.0.1:5009

llama_stack/providers/tests/scoring/test_scoring.py

@@ -33,7 +33,9 @@ from llama_stack.providers.tests.resolver import resolve_impls_for_test
 
 @pytest_asyncio.fixture(scope="session")
 async def scoring_settings():
-    impls = await resolve_impls_for_test(Api.scoring, deps=[Api.datasetio])
+    impls = await resolve_impls_for_test(
+        Api.scoring, deps=[Api.datasetio, Api.inference]
+    )
     return {
         "scoring_impl": impls[Api.scoring],
         "scoring_functions_impl": impls[Api.scoring_functions],
@@ -48,7 +50,50 @@ async def test_scoring_functions_list(scoring_settings):
     assert isinstance(scoring_functions, list)
     assert len(scoring_functions) > 0
     function_ids = [f.identifier for f in scoring_functions]
-    assert "equality" in function_ids
+    assert "meta-reference::equality" in function_ids
+    assert "meta-reference::subset_of" in function_ids
+    assert "meta-reference::llm_as_judge_8b_correctness" in function_ids
+
+
+@pytest.mark.asyncio
+async def test_scoring_functions_register(scoring_settings):
+    scoring_impl = scoring_settings["scoring_impl"]
+    scoring_functions_impl = scoring_settings["scoring_functions_impl"]
+    datasets_impl = scoring_settings["datasets_impl"]
+    test_prompt = """Output a number between 0 to 10. Your answer must match the format \n Number: <answer>"""
+    # register the scoring function
+    await scoring_functions_impl.register_scoring_function(
+        ScoringFnDefWithProvider(
+            identifier="meta-reference::llm_as_judge_8b_random",
+            description="Llm As Judge Scoring Function",
+            parameters=[],
+            return_type=NumberType(),
+            context=LLMAsJudgeContext(
+                prompt_template=test_prompt,
+                judge_model="Llama3.1-8B-Instruct",
+                judge_score_regex=[r"Number: (\d+)"],
+            ),
+            provider_id="test-meta",
+        )
+    )
+
+    scoring_functions = await scoring_functions_impl.list_scoring_functions()
+    assert isinstance(scoring_functions, list)
+    assert len(scoring_functions) > 0
+    function_ids = [f.identifier for f in scoring_functions]
+    assert "meta-reference::llm_as_judge_8b_random" in function_ids
+
+    # test score using newly registered scoring function
+    await register_dataset(datasets_impl)
+    response = await datasets_impl.list_datasets()
+    assert len(response) == 1
+    response = await scoring_impl.score_batch(
+        dataset_id=response[0].identifier,
+        scoring_functions=[
+            "meta-reference::llm_as_judge_8b_random",
+        ],
+    )
+    assert "meta-reference::llm_as_judge_8b_random" in response.results
 
 
 @pytest.mark.asyncio
@@ -62,8 +107,14 @@ async def test_scoring_score(scoring_settings):
 
     response = await scoring_impl.score_batch(
         dataset_id=response[0].identifier,
-        scoring_functions=["equality"],
+        scoring_functions=[
+            "meta-reference::equality",
+            "meta-reference::subset_of",
+            "meta-reference::llm_as_judge_8b_correctness",
+        ],
     )
 
-    assert len(response.results) == 1
-    assert "equality" in response.results
+    assert len(response.results) == 3
+    assert "meta-reference::equality" in response.results
+    assert "meta-reference::subset_of" in response.results
+    assert "meta-reference::llm_as_judge_8b_correctness" in response.results

tests/examples/evals-tgi-run.yaml

@@ -33,6 +33,10 @@ providers:
     provider_type: remote::tgi
     config:
       url: http://127.0.0.1:5009
+  - provider_id: tgi1
+    provider_type: remote::tgi
+    config:
+      url: http://127.0.0.1:5010
   memory:
   - provider_id: meta-reference
     provider_type: meta-reference