skeleton evaluation implementation

llama_stack/providers/inline/evaluation/meta_reference/__init__.py

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+# 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 typing import Any, Dict
+
+from llama_stack.distribution.datatypes import Api
+
+from .config import MetaReferenceEvaluationConfig
+
+
+async def get_provider_impl(
+    config: MetaReferenceEvaluationConfig,
+    deps: Dict[Api, Any],
+):
+    from .evaluation import MetaReferenceEvaluationImpl
+
+    impl = MetaReferenceEvaluationImpl(
+        config,
+        deps[Api.datasetio],
+        deps[Api.datasets],
+        deps[Api.inference],
+        deps[Api.agents],
+    )
+    await impl.initialize()
+    return impl

llama_stack/providers/inline/evaluation/meta_reference/config.py

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+# 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 typing import Any, Dict
+
+from llama_stack.providers.utils.kvstore.config import (
+    KVStoreConfig,
+    SqliteKVStoreConfig,
+)
+
+from pydantic import BaseModel
+
+
+class MetaReferenceEvaluationConfig(BaseModel):
+    kvstore: KVStoreConfig
+
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]:
+        return {
+            "kvstore": SqliteKVStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="meta_reference_evaluation.db",
+            )
+        }

llama_stack/providers/inline/evaluation/meta_reference/evaluation.py

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+# 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.
+import json
+from typing import Any, Dict, List, Optional
+
+from llama_stack.apis.agents import Agents
+from llama_stack.apis.datasetio import DatasetIO
+from llama_stack.apis.datasets import Datasets
+from llama_stack.apis.inference import Inference
+from llama_stack.providers.datatypes import BenchmarksProtocolPrivate
+
+from tqdm import tqdm
+
+from .....apis.benchmarks import Benchmark
+
+from .....apis.common.job_types import Job
+from .....apis.evaluation.evaluation import (
+    Evaluation,
+    EvaluationCandidate,
+    EvaluationJob,
+    EvaluationResponse,
+    EvaluationTask,
+)
+from .config import MetaReferenceEvaluationConfig
+
+EVAL_TASKS_PREFIX = "benchmarks:"
+
+
+class MetaReferenceEvaluationImpl(
+    Evaluation,
+    BenchmarksProtocolPrivate,
+):
+    def __init__(
+        self,
+        config: MetaReferenceEvaluationConfig,
+        datasetio_api: DatasetIO,
+        datasets_api: Datasets,
+        inference_api: Inference,
+        agents_api: Agents,
+    ) -> None:
+        self.config = config
+        self.datasetio_api = datasetio_api
+        self.datasets_api = datasets_api
+        self.inference_api = inference_api
+        self.agents_api = agents_api
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def register_benchmark(self, benchmark: Benchmark) -> None:
+        pass
+
+    async def run(
+        self,
+        task: EvaluationTask,
+        candidate: EvaluationCandidate,
+    ) -> EvaluationJob:
+        raise NotImplementedError("Run is not implemented yet")
+
+    async def run_sync(
+        self,
+        task: EvaluationTask,
+        candidate: EvaluationCandidate,
+    ) -> EvaluationResponse:
+        raise NotImplementedError("Run sync is not implemented yet")
+
+    async def grade(self, task: EvaluationTask) -> EvaluationJob:
+        raise NotImplementedError("Grade is not implemented yet")
+
+    async def grade_sync(self, task: EvaluationTask) -> EvaluationResponse:
+        raise NotImplementedError("Grade sync is not implemented yet")