Merge branch 'eval_task_register' into mmlu_benchmark

llama_stack/apis/eval/eval.py

@@ -38,14 +38,16 @@ EvalCandidate = Annotated[
 
 @json_schema_type
 class BenchmarkEvalTaskConfig(BaseModel):
-    eval_candidate: EvalCandidate  # type: ignore
+    type: Literal["benchmark"] = "benchmark"
+    eval_candidate: EvalCandidate
 
 
 @json_schema_type
 class AppEvalTaskConfig(BaseModel):
-    eval_candidate: EvalCandidate  # type: ignore
-    scoring_params: Dict[str, ScoringFnParams] = Field(  # type: ignore
-        description="Map between scoring function id and parameters",
+    type: Literal["app"] = "app"
+    eval_candidate: EvalCandidate
+    scoring_params: Dict[str, ScoringFnParams] = Field(
+        description="Map between scoring function id and parameters for each scoring function you want to run",
         default_factory=dict,
     )
     # we could optinally add any specific dataset config here
@@ -64,18 +66,18 @@ class EvaluateResponse(BaseModel):
 
 
 class Eval(Protocol):
-    @webmethod(route="/eval/run_benchmark_eval", method="POST")
-    async def run_benchmark_eval(
+    @webmethod(route="/eval/run_benchmark", method="POST")
+    async def run_benchmark(
         self,
         benchmark_id: str,
-        eval_task_config: BenchmarkEvalTaskConfig,
+        benchmark_config: BenchmarkEvalTaskConfig,
     ) -> Job: ...
 
     @webmethod(route="/eval/run_eval", method="POST")
     async def run_eval(
         self,
-        eval_task_def: EvalTaskDef,
-        eval_task_config: EvalTaskConfig,
+        task: EvalTaskDef,
+        task_config: AppEvalTaskConfig,
     ) -> Job: ...
 
     @webmethod(route="/eval/evaluate_rows", method="POST")
@@ -83,14 +85,17 @@ class Eval(Protocol):
         self,
         input_rows: List[Dict[str, Any]],
         scoring_functions: List[str],
-        eval_task_config: EvalTaskConfig,  # type: ignore
+        task_config: EvalTaskConfig,
+        eval_task_id: Optional[str] = None,
     ) -> EvaluateResponse: ...
 
     @webmethod(route="/eval/job/status", method="GET")
-    async def job_status(self, job_id: str) -> Optional[JobStatus]: ...
+    async def job_status(
+        self, job_id: str, eval_task_id: str
+    ) -> Optional[JobStatus]: ...
 
     @webmethod(route="/eval/job/cancel", method="POST")
-    async def job_cancel(self, job_id: str) -> None: ...
+    async def job_cancel(self, job_id: str, eval_task_id: str) -> None: ...
 
     @webmethod(route="/eval/job/result", method="GET")
-    async def job_result(self, job_id: str) -> EvaluateResponse: ...
+    async def job_result(self, job_id: str, eval_task_id: str) -> EvaluateResponse: ...

llama_stack/apis/scoring/scoring.py

@@ -48,8 +48,7 @@ class Scoring(Protocol):
     async def score_batch(
         self,
         dataset_id: str,
-        scoring_functions: List[str],
-        scoring_params: Optional[Dict[str, ScoringFnParams]] = None,
+        scoring_functions: Optional[Dict[str, ScoringFnParams]] = None,
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse: ...
 
@@ -57,6 +56,5 @@ class Scoring(Protocol):
     async def score(
         self,
         input_rows: List[Dict[str, Any]],
-        scoring_functions: List[str],
-        scoring_params: Optional[Dict[str, ScoringFnParams]] = None,
+        scoring_functions: Optional[Dict[str, ScoringFnParams]] = None,
     ) -> ScoreResponse: ...

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -33,7 +33,7 @@ class ScoringConfigType(Enum):
 
 @json_schema_type
 class LLMAsJudgeScoringFnParams(BaseModel):
-    type: Literal[ScoringConfigType.llm_as_judge.value] = (  # type: ignore
+    type: Literal[ScoringConfigType.llm_as_judge.value] = (
         ScoringConfigType.llm_as_judge.value
     )
     judge_model: str
@@ -46,7 +46,7 @@ class LLMAsJudgeScoringFnParams(BaseModel):
 
 @json_schema_type
 class RegexParserScoringFnParams(BaseModel):
-    type: Literal[ScoringConfigType.regex_parser.value] = (  # type: ignore
+    type: Literal[ScoringConfigType.regex_parser.value] = (
         ScoringConfigType.regex_parser.value
     )
     parsing_regexes: Optional[List[str]] = Field(
@@ -75,8 +75,8 @@ class ScoringFnDef(BaseModel):
     return_type: ParamType = Field(
         description="The return type of the deterministic function",
     )
-    params: Optional[ScoringFnParams] = Field(  # type: ignore
-        description="The parameters for the scoring function for benchmark eval, we could override this for app eval",
+    params: Optional[ScoringFnParams] = Field(
+        description="The parameters for the scoring function for benchmark eval, these can be overridden for app eval",
         default=None,
     )
     # We can optionally add information here to support packaging of code, etc.

llama_stack/distribution/resolver.py

@@ -8,6 +8,8 @@ import inspect
 
 from typing import Any, Dict, List, Set
 
+from termcolor import cprint
+
 from llama_stack.providers.datatypes import *  # noqa: F403
 from llama_stack.distribution.datatypes import *  # noqa: F403
 
@@ -100,6 +102,12 @@ async def resolve_impls(
                 )
 
             p = provider_registry[api][provider.provider_type]
+            if p.deprecation_warning:
+                cprint(
+                    f"Provider `{provider.provider_type}` for API `{api}` is deprecated and will be removed in a future release: {p.deprecation_warning}",
+                    "red",
+                    attrs=["bold"],
+                )
             p.deps__ = [a.value for a in p.api_dependencies]
             spec = ProviderWithSpec(
                 spec=p,

llama_stack/distribution/routers/routers.py

@@ -216,18 +216,16 @@ class ScoringRouter(Scoring):
     async def score_batch(
         self,
         dataset_id: str,
-        scoring_functions: List[str],
-        scoring_params: Optional[Dict[str, ScoringFnParams]] = None,
+        scoring_functions: Optional[Dict[str, ScoringFnParams]] = None,
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
         res = {}
-        for fn_identifier in scoring_functions:
+        for fn_identifier in scoring_functions.keys():
             score_response = await self.routing_table.get_provider_impl(
                 fn_identifier
             ).score_batch(
                 dataset_id=dataset_id,
-                scoring_functions=[fn_identifier],
-                scoring_params=scoring_params,
+                scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
             )
             res.update(score_response.results)
 
@@ -241,18 +239,16 @@ class ScoringRouter(Scoring):
     async def score(
         self,
         input_rows: List[Dict[str, Any]],
-        scoring_functions: List[str],
-        scoring_params: Optional[Dict[str, ScoringFnParams]] = None,
+        scoring_functions: Optional[Dict[str, ScoringFnParams]] = None,
     ) -> ScoreResponse:
         res = {}
         # look up and map each scoring function to its provider impl
-        for fn_identifier in scoring_functions:
+        for fn_identifier in scoring_functions.keys():
             score_response = await self.routing_table.get_provider_impl(
                 fn_identifier
             ).score(
                 input_rows=input_rows,
-                scoring_functions=[fn_identifier],
-                scoring_params=scoring_params,
+                scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
             )
             res.update(score_response.results)
 
@@ -272,24 +268,21 @@ class EvalRouter(Eval):
     async def shutdown(self) -> None:
         pass
 
-    async def run_benchmark_eval(
+    async def run_benchmark(
         self,
         benchmark_id: str,
-        eval_task_config: BenchmarkEvalTaskConfig,
+        benchmark_config: BenchmarkEvalTaskConfig,
     ) -> Job:
         pass
 
     async def run_eval(
         self,
-        eval_task_def: EvalTaskDef,
-        eval_task_config: EvalTaskConfig,
+        task: EvalTaskDef,
+        task_config: AppEvalTaskConfig,
     ) -> Job:
-        # NOTE: We need to use DEFAULT_EVAL_TASK_IDENTIFIER to make the router work for all app evals
-        return await self.routing_table.get_provider_impl(
-            DEFAULT_EVAL_TASK_IDENTIFIER
-        ).run_eval(
-            eval_task_def=eval_task_def,
-            eval_task_config=eval_task_config,
+        return await self.routing_table.get_provider_impl(task.identifier).run_eval(
+            task=task,
+            task_config=task_config,
         )
 
     @webmethod(route="/eval/evaluate_rows", method="POST")
@@ -297,29 +290,42 @@ class EvalRouter(Eval):
         self,
         input_rows: List[Dict[str, Any]],
         scoring_functions: List[str],
-        eval_task_config: EvalTaskConfig,  # type: ignore
+        task_config: EvalTaskConfig,
+        eval_task_id: Optional[str] = None,
     ) -> EvaluateResponse:
         # NOTE: This is to deal with the case where we do not pre-register an eval benchmark_task
         # We use default DEFAULT_EVAL_TASK_IDENTIFIER as identifier
-        return await self.routing_table.get_provider_impl(
-            DEFAULT_EVAL_TASK_IDENTIFIER
-        ).evaluate_rows(
+        if eval_task_id is None:
+            eval_task_id = DEFAULT_EVAL_TASK_IDENTIFIER
+        return await self.routing_table.get_provider_impl(eval_task_id).evaluate_rows(
             input_rows=input_rows,
             scoring_functions=scoring_functions,
-            eval_task_config=eval_task_config,
+            task_config=task_config,
         )
 
-    async def job_status(self, job_id: str) -> Optional[JobStatus]:
-        return await self.routing_table.get_provider_impl(
-            DEFAULT_EVAL_TASK_IDENTIFIER
-        ).job_status(job_id)
+    async def job_status(
+        self,
+        job_id: str,
+        eval_task_id: str,
+    ) -> Optional[JobStatus]:
+        return await self.routing_table.get_provider_impl(eval_task_id).job_status(
+            job_id, eval_task_id
+        )
 
-    async def job_cancel(self, job_id: str) -> None:
-        await self.routing_table.get_provider_impl(
-            DEFAULT_EVAL_TASK_IDENTIFIER
-        ).job_cancel(job_id)
+    async def job_cancel(
+        self,
+        job_id: str,
+        eval_task_id: str,
+    ) -> None:
+        await self.routing_table.get_provider_impl(eval_task_id).job_cancel(
+            job_id, eval_task_id
+        )
 
-    async def job_result(self, job_id: str) -> EvaluateResponse:
-        return await self.routing_table.get_provider_impl(
-            DEFAULT_EVAL_TASK_IDENTIFIER
-        ).job_result(job_id)
+    async def job_result(
+        self,
+        job_id: str,
+        eval_task_id: str,
+    ) -> EvaluateResponse:
+        return await self.routing_table.get_provider_impl(eval_task_id).job_result(
+            job_id, eval_task_id
+        )

llama_stack/distribution/server/server.py

@@ -31,7 +31,7 @@ from llama_stack.distribution.distribution import (
     get_provider_registry,
 )
 
-from llama_stack.distribution.utils.config_dirs import DISTRIBS_BASE_DIR
+from llama_stack.distribution.store.registry import create_dist_registry
 
 from llama_stack.providers.utils.telemetry.tracing import (
     end_trace,
@@ -42,8 +42,6 @@ from llama_stack.providers.utils.telemetry.tracing import (
 from llama_stack.distribution.datatypes import *  # noqa: F403
 from llama_stack.distribution.request_headers import set_request_provider_data
 from llama_stack.distribution.resolver import resolve_impls
-from llama_stack.distribution.store import CachedDiskDistributionRegistry
-from llama_stack.providers.utils.kvstore import kvstore_impl, SqliteKVStoreConfig
 
 from .endpoints import get_all_api_endpoints
 
@@ -281,21 +279,8 @@ def main(
         config = StackRunConfig(**yaml.safe_load(fp))
 
     app = FastAPI()
-    # instantiate kvstore for storing and retrieving distribution metadata
-    if config.metadata_store:
-        dist_kvstore = asyncio.run(kvstore_impl(config.metadata_store))
-    else:
-        dist_kvstore = asyncio.run(
-            kvstore_impl(
-                SqliteKVStoreConfig(
-                    db_path=(
-                        DISTRIBS_BASE_DIR / config.image_name / "kvstore.db"
-                    ).as_posix()
-                )
-            )
-        )
 
-    dist_registry = CachedDiskDistributionRegistry(dist_kvstore)
+    dist_registry, dist_kvstore = asyncio.run(create_dist_registry(config))
 
     impls = asyncio.run(resolve_impls(config, get_provider_registry(), dist_registry))
     if Api.telemetry in impls:

llama_stack/distribution/store/registry.py

@@ -9,9 +9,17 @@ from typing import Dict, List, Protocol
 
 import pydantic
 
-from llama_stack.distribution.datatypes import RoutableObjectWithProvider
+from llama_stack.distribution.datatypes import (
+    RoutableObjectWithProvider,
+    StackRunConfig,
+)
+from llama_stack.distribution.utils.config_dirs import DISTRIBS_BASE_DIR
 
-from llama_stack.providers.utils.kvstore import KVStore
+from llama_stack.providers.utils.kvstore import (
+    KVStore,
+    kvstore_impl,
+    SqliteKVStoreConfig,
+)
 
 
 class DistributionRegistry(Protocol):
@@ -133,3 +141,21 @@ class CachedDiskDistributionRegistry(DiskDistributionRegistry):
             self.cache[obj.identifier].append(obj)
 
         return success
+
+
+async def create_dist_registry(
+    config: StackRunConfig,
+) -> tuple[CachedDiskDistributionRegistry, KVStore]:
+    # instantiate kvstore for storing and retrieving distribution metadata
+    if config.metadata_store:
+        dist_kvstore = await kvstore_impl(config.metadata_store)
+    else:
+        dist_kvstore = await kvstore_impl(
+            SqliteKVStoreConfig(
+                db_path=(
+                    DISTRIBS_BASE_DIR / config.image_name / "kvstore.db"
+                ).as_posix()
+            )
+        )
+
+    return CachedDiskDistributionRegistry(dist_kvstore), dist_kvstore

llama_stack/providers/datatypes.py

@@ -90,6 +90,10 @@ class ProviderSpec(BaseModel):
         default_factory=list,
         description="Higher-level API surfaces may depend on other providers to provide their functionality",
     )
+    deprecation_warning: Optional[str] = Field(
+        default=None,
+        description="If this provider is deprecated, specify the warning message here",
+    )
 
     # used internally by the resolver; this is a hack for now
     deps__: List[str] = Field(default_factory=list)

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

@@ -4,10 +4,9 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from pydantic import BaseModel, Field
-
 from llama_stack.providers.utils.kvstore import KVStoreConfig
 from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
+from pydantic import BaseModel, Field
 
 
 class MetaReferenceAgentsImplConfig(BaseModel):

llama_stack/providers/inline/agents/meta_reference/persistence.py

@@ -11,9 +11,8 @@ from datetime import datetime
 
 from typing import List, Optional
 from llama_stack.apis.agents import *  # noqa: F403
-from pydantic import BaseModel
-
 from llama_stack.providers.utils.kvstore import KVStore
+from pydantic import BaseModel
 
 
 class AgentSessionInfo(BaseModel):

llama_stack/providers/inline/agents/meta_reference/rag/context_retriever.py

@@ -10,14 +10,13 @@ from jinja2 import Template
 from llama_models.llama3.api import *  # noqa: F403
 
 
-from termcolor import cprint  # noqa: F401
-
 from llama_stack.apis.agents import (
     DefaultMemoryQueryGeneratorConfig,
     LLMMemoryQueryGeneratorConfig,
     MemoryQueryGenerator,
     MemoryQueryGeneratorConfig,
 )
+from termcolor import cprint  # noqa: F401
 from llama_stack.apis.inference import *  # noqa: F403
 
 

llama_stack/providers/inline/agents/meta_reference/tools/safety.py

@@ -9,8 +9,7 @@ from typing import List
 from llama_stack.apis.inference import Message
 from llama_stack.apis.safety import *  # noqa: F403
 
-from llama_stack.providers.inline.meta_reference.agents.safety import ShieldRunnerMixin
-
+from ..safety import ShieldRunnerMixin
 from .builtin import BaseTool
 
 

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

@@ -10,9 +10,8 @@ from llama_models.datatypes import *  # noqa: F403
 from llama_models.sku_list import resolve_model
 
 from llama_stack.apis.inference import *  # noqa: F401, F403
-from pydantic import BaseModel, Field, field_validator
-
 from llama_stack.providers.utils.inference import supported_inference_models
+from pydantic import BaseModel, Field, field_validator
 
 
 class MetaReferenceInferenceConfig(BaseModel):

llama_stack/providers/inline/inference/meta_reference/generation.py

@@ -35,13 +35,12 @@ from termcolor import cprint
 
 from llama_stack.apis.inference import *  # noqa: F403
 
-from lmformatenforcer import JsonSchemaParser, TokenEnforcer, TokenEnforcerTokenizerData
-
 from llama_stack.distribution.utils.model_utils import model_local_dir
 from llama_stack.providers.utils.inference.prompt_adapter import (
     augment_content_with_response_format_prompt,
     chat_completion_request_to_messages,
 )
+from lmformatenforcer import JsonSchemaParser, TokenEnforcer, TokenEnforcerTokenizerData
 
 from .config import (
     Fp8QuantizationConfig,

llama_stack/providers/inline/inference/meta_reference/parallel_utils.py

@@ -28,13 +28,13 @@ from fairscale.nn.model_parallel.initialize import (
     get_model_parallel_src_rank,
 )
 
+from llama_stack.apis.inference import ChatCompletionRequest, CompletionRequest
+
 from pydantic import BaseModel, Field
 
 from torch.distributed.launcher.api import elastic_launch, LaunchConfig
 from typing_extensions import Annotated
 
-from llama_stack.apis.inference import ChatCompletionRequest, CompletionRequest
-
 from .generation import TokenResult
 
 

llama_stack/providers/inline/inference/meta_reference/quantization/loader.py

@@ -20,16 +20,15 @@ from llama_models.datatypes import CheckpointQuantizationFormat
 from llama_models.llama3.api.args import ModelArgs
 from llama_models.llama3.reference_impl.model import Transformer, TransformerBlock
 from llama_models.sku_list import resolve_model
+
+from llama_stack.apis.inference import QuantizationType
+
 from termcolor import cprint
 from torch import nn, Tensor
 
 from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
 
-from llama_stack.apis.inference import QuantizationType
-
-from llama_stack.providers.inline.meta_reference.inference.config import (
-    MetaReferenceQuantizedInferenceConfig,
-)
+from ..config import MetaReferenceQuantizedInferenceConfig
 
 
 def swiglu_wrapper(

llama_stack/providers/inline/inference/vllm/config.py

@@ -5,9 +5,9 @@
 # the root directory of this source tree.
 
 from llama_models.schema_utils import json_schema_type
-from pydantic import BaseModel, Field, field_validator
 
 from llama_stack.providers.utils.inference import supported_inference_models
+from pydantic import BaseModel, Field, field_validator
 
 
 @json_schema_type

llama_stack/providers/inline/memory/faiss/config.py

@@ -5,13 +5,13 @@
 # the root directory of this source tree.
 
 from llama_models.schema_utils import json_schema_type
-from pydantic import BaseModel
 
 from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
 from llama_stack.providers.utils.kvstore.config import (
     KVStoreConfig,
     SqliteKVStoreConfig,
 )
+from pydantic import BaseModel
 
 
 @json_schema_type

llama_stack/providers/inline/memory/faiss/faiss.py

@@ -8,10 +8,11 @@ import logging
 
 from typing import Any, Dict, List, Optional
 
-import faiss
 import numpy as np
 from numpy.typing import NDArray
 
+import faiss
+
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 
 from llama_stack.apis.memory import *  # noqa: F403

llama_stack/providers/inline/meta_reference/eval/eval.py

@@ -7,11 +7,17 @@ from enum import Enum
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 
 from .....apis.common.job_types import Job
-from .....apis.eval.eval import BenchmarkEvalTaskConfig
+from .....apis.eval.eval import (
+    AppEvalTaskConfig,
+    BenchmarkEvalTaskConfig,
+    Eval,
+    EvalTaskConfig,
+    EvaluateResponse,
+    JobStatus,
+)
 from llama_stack.apis.common.type_system import *  # noqa: F403
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
-from llama_stack.apis.eval import Eval, EvalTaskConfig, EvaluateResponse, JobStatus
 from llama_stack.apis.eval_tasks import EvalTaskDef
 from llama_stack.apis.inference import Inference
 from llama_stack.apis.scoring import Scoring
@@ -19,6 +25,10 @@ from llama_stack.providers.datatypes import EvalTasksProtocolPrivate
 
 from .config import MetaReferenceEvalConfig
 
+
+# NOTE: this is the default eval task identifier for app eval
+# it is used to make the router work for all app evals
+# For app eval using other eval providers, the eval task identifier will be different
 DEFAULT_EVAL_TASK_IDENTIFIER = "meta-reference::app_eval"
 
 
@@ -88,21 +98,21 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
                 f"Dataset {dataset_id} does not have a correct input schema in {expected_schemas}"
             )
 
-    async def run_benchmark_eval(
+    async def run_benchmark(
         self,
         benchmark_id: str,
-        eval_task_config: BenchmarkEvalTaskConfig,
+        benchmark_config: BenchmarkEvalTaskConfig,
     ) -> Job:
         raise NotImplementedError("Benchmark eval is not implemented yet")
 
     async def run_eval(
         self,
-        eval_task_def: EvalTaskDef,
-        eval_task_config: EvalTaskConfig,
+        task: EvalTaskDef,
+        task_config: AppEvalTaskConfig,
     ) -> Job:
-        dataset_id = eval_task_def.dataset_id
-        candidate = eval_task_config.eval_candidate
-        scoring_functions = eval_task_def.scoring_functions
+        dataset_id = task.dataset_id
+        candidate = task_config.eval_candidate
+        scoring_functions = task.scoring_functions
 
         await self.validate_eval_input_dataset_schema(dataset_id=dataset_id)
         all_rows = await self.datasetio_api.get_rows_paginated(
@@ -112,7 +122,7 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
         res = await self.evaluate_rows(
             input_rows=all_rows.rows,
             scoring_functions=scoring_functions,
-            eval_task_config=eval_task_config,
+            task_config=task_config,
         )
 
         # TODO: currently needs to wait for generation before returning
@@ -125,9 +135,10 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
         self,
         input_rows: List[Dict[str, Any]],
         scoring_functions: List[str],
-        eval_task_config: EvalTaskConfig,
+        task_config: EvalTaskConfig,
+        eval_task_id: Optional[str] = None,
     ) -> EvaluateResponse:
-        candidate = eval_task_config.eval_candidate
+        candidate = task_config.eval_candidate
         if candidate.type == "agent":
             raise NotImplementedError(
                 "Evaluation with generation has not been implemented for agents"
@@ -179,23 +190,33 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
             for input_r, generated_r in zip(input_rows, generations)
         ]
 
+        if task_config.type == "app" and task_config.scoring_params is not None:
+            scoring_functions_dict = {
+                scoring_fn_id: task_config.scoring_params.get(scoring_fn_id, None)
+                for scoring_fn_id in scoring_functions
+            }
+        else:
+            scoring_functions_dict = {
+                scoring_fn_id: None for scoring_fn_id in scoring_functions
+            }
+
         score_response = await self.scoring_api.score(
-            input_rows=score_input_rows, scoring_functions=scoring_functions
+            input_rows=score_input_rows, scoring_functions=scoring_functions_dict
         )
 
         return EvaluateResponse(generations=generations, scores=score_response.results)
 
-    async def job_status(self, job_id: str) -> Optional[JobStatus]:
+    async def job_status(self, job_id: str, eval_task_id: str) -> Optional[JobStatus]:
         if job_id in self.jobs:
             return JobStatus.completed
 
         return None
 
-    async def job_cancel(self, job_id: str) -> None:
+    async def job_cancel(self, job_id: str, eval_task_id: str) -> None:
         raise NotImplementedError("Job cancel is not implemented yet")
 
-    async def job_result(self, job_id: str) -> EvaluateResponse:
-        status = await self.job_status(job_id)
+    async def job_result(self, job_id: str, eval_task_id: str) -> EvaluateResponse:
+        status = await self.job_status(job_id, eval_task_id)
         if not status or status != JobStatus.completed:
             raise ValueError(f"Job is not completed, Status: {status.value}")
 

llama_stack/providers/inline/meta_reference/memory/tests/test_faiss.py

@@ -1,73 +0,0 @@
-# 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 tempfile
-
-import pytest
-from llama_stack.apis.memory import MemoryBankType, VectorMemoryBankDef
-from llama_stack.providers.inline.meta_reference.memory.config import FaissImplConfig
-
-from llama_stack.providers.inline.meta_reference.memory.faiss import FaissMemoryImpl
-from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
-
-
-class TestFaissMemoryImpl:
-    @pytest.fixture
-    def faiss_impl(self):
-        # Create a temporary SQLite database file
-        temp_db = tempfile.NamedTemporaryFile(suffix=".db", delete=False)
-        config = FaissImplConfig(kvstore=SqliteKVStoreConfig(db_path=temp_db.name))
-        return FaissMemoryImpl(config)
-
-    @pytest.mark.asyncio
-    async def test_initialize(self, faiss_impl):
-        # Test empty initialization
-        await faiss_impl.initialize()
-        assert len(faiss_impl.cache) == 0
-
-        # Test initialization with existing banks
-        bank = VectorMemoryBankDef(
-            identifier="test_bank",
-            type=MemoryBankType.vector.value,
-            embedding_model="all-MiniLM-L6-v2",
-            chunk_size_in_tokens=512,
-            overlap_size_in_tokens=64,
-        )
-
-        # Register a bank and reinitialize to test loading
-        await faiss_impl.register_memory_bank(bank)
-
-        # Create new instance to test initialization with existing data
-        new_impl = FaissMemoryImpl(faiss_impl.config)
-        await new_impl.initialize()
-
-        assert len(new_impl.cache) == 1
-        assert "test_bank" in new_impl.cache
-
-    @pytest.mark.asyncio
-    async def test_register_memory_bank(self, faiss_impl):
-        bank = VectorMemoryBankDef(
-            identifier="test_bank",
-            type=MemoryBankType.vector.value,
-            embedding_model="all-MiniLM-L6-v2",
-            chunk_size_in_tokens=512,
-            overlap_size_in_tokens=64,
-        )
-
-        await faiss_impl.initialize()
-        await faiss_impl.register_memory_bank(bank)
-
-        assert "test_bank" in faiss_impl.cache
-        assert faiss_impl.cache["test_bank"].bank == bank
-
-        # Verify persistence
-        new_impl = FaissMemoryImpl(faiss_impl.config)
-        await new_impl.initialize()
-        assert "test_bank" in new_impl.cache
-
-
-if __name__ == "__main__":
-    pytest.main([__file__])

llama_stack/providers/inline/meta_reference/scoring/scoring.py

@@ -89,8 +89,7 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     async def score_batch(
         self,
         dataset_id: str,
-        scoring_functions: List[str],
-        scoring_params: Optional[Dict[str, ScoringFnParams]] = None,
+        scoring_functions: Optional[Dict[str, ScoringFnParams]] = None,
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
         await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id)
@@ -101,7 +100,6 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
         res = await self.score(
             input_rows=all_rows.rows,
             scoring_functions=scoring_functions,
-            scoring_params=scoring_params,
         )
         if save_results_dataset:
             # TODO: persist and register dataset on to server for reading
@@ -115,17 +113,14 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     async def score(
         self,
         input_rows: List[Dict[str, Any]],
-        scoring_functions: List[str],
-        scoring_params: Optional[Dict[str, ScoringFnParams]] = None,
+        scoring_functions: Optional[Dict[str, ScoringFnParams]] = None,
     ) -> ScoreResponse:
         res = {}
-        for scoring_fn_id in scoring_functions:
+        for scoring_fn_id in scoring_functions.keys():
             if scoring_fn_id not in self.scoring_fn_id_impls:
                 raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
             scoring_fn = self.scoring_fn_id_impls[scoring_fn_id]
-            scoring_fn_params = None
-            if scoring_params is not None:
-                scoring_fn_params = scoring_params.get(scoring_fn_id, None)
+            scoring_fn_params = scoring_functions.get(scoring_fn_id, None)
             score_results = await scoring_fn.score(
                 input_rows, scoring_fn_id, scoring_fn_params
             )

llama_stack/providers/registry/agents.py

@@ -22,8 +22,8 @@ def available_providers() -> List[ProviderSpec]:
                 "scikit-learn",
             ]
             + kvstore_dependencies(),
-            module="llama_stack.providers.inline.meta_reference.agents",
-            config_class="llama_stack.providers.inline.meta_reference.agents.MetaReferenceAgentsImplConfig",
+            module="llama_stack.providers.inline.agents.meta_reference",
+            config_class="llama_stack.providers.inline.agents.meta_reference.MetaReferenceAgentsImplConfig",
             api_dependencies=[
                 Api.inference,
                 Api.safety,

llama_stack/providers/registry/inference.py

@@ -27,8 +27,8 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.inference,
             provider_type="meta-reference",
             pip_packages=META_REFERENCE_DEPS,
-            module="llama_stack.providers.inline.meta_reference.inference",
-            config_class="llama_stack.providers.inline.meta_reference.inference.MetaReferenceInferenceConfig",
+            module="llama_stack.providers.inline.inference.meta_reference",
+            config_class="llama_stack.providers.inline.inference.meta_reference.MetaReferenceInferenceConfig",
         ),
         InlineProviderSpec(
             api=Api.inference,
@@ -40,8 +40,17 @@ def available_providers() -> List[ProviderSpec]:
                     "torchao==0.5.0",
                 ]
             ),
-            module="llama_stack.providers.inline.meta_reference.inference",
-            config_class="llama_stack.providers.inline.meta_reference.inference.MetaReferenceQuantizedInferenceConfig",
+            module="llama_stack.providers.inline.inference.meta_reference",
+            config_class="llama_stack.providers.inline.inference.meta_reference.MetaReferenceQuantizedInferenceConfig",
+        ),
+        InlineProviderSpec(
+            api=Api.inference,
+            provider_type="vllm",
+            pip_packages=[
+                "vllm",
+            ],
+            module="llama_stack.providers.inline.inference.vllm",
+            config_class="llama_stack.providers.inline.inference.vllm.VLLMConfig",
         ),
         remote_provider_spec(
             api=Api.inference,
@@ -117,7 +126,7 @@ def available_providers() -> List[ProviderSpec]:
                 ],
                 module="llama_stack.providers.remote.inference.together",
                 config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig",
-                provider_data_validator="llama_stack.providers.remote.safety.together.TogetherProviderDataValidator",
+                provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator",
             ),
         ),
         remote_provider_spec(
@@ -140,13 +149,4 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
             ),
         ),
-        InlineProviderSpec(
-            api=Api.inference,
-            provider_type="vllm",
-            pip_packages=[
-                "vllm",
-            ],
-            module="llama_stack.providers.inline.vllm",
-            config_class="llama_stack.providers.inline.vllm.VLLMConfig",
-        ),
     ]

llama_stack/providers/registry/memory.py

@@ -36,8 +36,16 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.memory,
             provider_type="meta-reference",
             pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
-            module="llama_stack.providers.inline.meta_reference.memory",
-            config_class="llama_stack.providers.inline.meta_reference.memory.FaissImplConfig",
+            module="llama_stack.providers.inline.memory.faiss",
+            config_class="llama_stack.providers.inline.memory.faiss.FaissImplConfig",
+            deprecation_warning="Please use the `faiss` provider instead.",
+        ),
+        InlineProviderSpec(
+            api=Api.memory,
+            provider_type="faiss",
+            pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
+            module="llama_stack.providers.inline.memory.faiss",
+            config_class="llama_stack.providers.inline.memory.faiss.FaissImplConfig",
         ),
         remote_provider_spec(
             Api.memory,

llama_stack/providers/registry/safety.py

@@ -24,8 +24,8 @@ def available_providers() -> List[ProviderSpec]:
                 "transformers",
                 "torch --index-url https://download.pytorch.org/whl/cpu",
             ],
-            module="llama_stack.providers.inline.meta_reference.safety",
-            config_class="llama_stack.providers.inline.meta_reference.safety.SafetyConfig",
+            module="llama_stack.providers.inline.safety.meta_reference",
+            config_class="llama_stack.providers.inline.safety.meta_reference.SafetyConfig",
             api_dependencies=[
                 Api.inference,
             ],
@@ -54,8 +54,8 @@ def available_providers() -> List[ProviderSpec]:
             pip_packages=[
                 "codeshield",
             ],
-            module="llama_stack.providers.inline.meta_reference.codeshield",
-            config_class="llama_stack.providers.inline.meta_reference.codeshield.CodeShieldConfig",
+            module="llama_stack.providers.inline.safety.meta_reference",
+            config_class="llama_stack.providers.inline.safety.meta_reference.CodeShieldConfig",
             api_dependencies=[],
         ),
     ]

llama_stack/providers/remote/inference/fireworks/config.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from typing import Optional
+
 from llama_models.schema_utils import json_schema_type
 from pydantic import BaseModel, Field
 
@@ -14,7 +16,7 @@ class FireworksImplConfig(BaseModel):
         default="https://api.fireworks.ai/inference",
         description="The URL for the Fireworks server",
     )
-    api_key: str = Field(
-        default="",
+    api_key: Optional[str] = Field(
+        default=None,
         description="The Fireworks.ai API Key",
     )

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -9,12 +9,11 @@ from typing import AsyncGenerator
 from fireworks.client import Fireworks
 
 from llama_models.llama3.api.chat_format import ChatFormat
-
 from llama_models.llama3.api.datatypes import Message
 from llama_models.llama3.api.tokenizer import Tokenizer
 
 from llama_stack.apis.inference import *  # noqa: F403
-
+from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_compat import (
     get_sampling_options,
@@ -32,7 +31,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import FireworksImplConfig
 
-
 FIREWORKS_SUPPORTED_MODELS = {
     "Llama3.1-8B-Instruct": "fireworks/llama-v3p1-8b-instruct",
     "Llama3.1-70B-Instruct": "fireworks/llama-v3p1-70b-instruct",
@@ -41,10 +39,13 @@ FIREWORKS_SUPPORTED_MODELS = {
     "Llama3.2-3B-Instruct": "fireworks/llama-v3p2-3b-instruct",
     "Llama3.2-11B-Vision-Instruct": "fireworks/llama-v3p2-11b-vision-instruct",
     "Llama3.2-90B-Vision-Instruct": "fireworks/llama-v3p2-90b-vision-instruct",
+    "Llama-Guard-3-8B": "fireworks/llama-guard-3-8b",
 }
 
 
-class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
+class FireworksInferenceAdapter(
+    ModelRegistryHelper, Inference, NeedsRequestProviderData
+):
     def __init__(self, config: FireworksImplConfig) -> None:
         ModelRegistryHelper.__init__(
             self, stack_to_provider_models_map=FIREWORKS_SUPPORTED_MODELS
@@ -53,11 +54,24 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
         self.formatter = ChatFormat(Tokenizer.get_instance())
 
     async def initialize(self) -> None:
-        return
+        pass
 
     async def shutdown(self) -> None:
         pass
 
+    def _get_client(self) -> Fireworks:
+        fireworks_api_key = None
+        if self.config.api_key is not None:
+            fireworks_api_key = self.config.api_key
+        else:
+            provider_data = self.get_request_provider_data()
+            if provider_data is None or not provider_data.fireworks_api_key:
+                raise ValueError(
+                    'Pass Fireworks API Key in the header X-LlamaStack-ProviderData as { "fireworks_api_key": <your api key>}'
+                )
+            fireworks_api_key = provider_data.fireworks_api_key
+        return Fireworks(api_key=fireworks_api_key)
+
     async def completion(
         self,
         model: str,
@@ -75,28 +89,53 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
             stream=stream,
             logprobs=logprobs,
         )
-        client = Fireworks(api_key=self.config.api_key)
         if stream:
-            return self._stream_completion(request, client)
+            return self._stream_completion(request)
         else:
-            return await self._nonstream_completion(request, client)
+            return await self._nonstream_completion(request)
 
     async def _nonstream_completion(
-        self, request: CompletionRequest, client: Fireworks
+        self, request: CompletionRequest
     ) -> CompletionResponse:
         params = await self._get_params(request)
-        r = await client.completion.acreate(**params)
+        r = await self._get_client().completion.acreate(**params)
         return process_completion_response(r, self.formatter)
 
-    async def _stream_completion(
-        self, request: CompletionRequest, client: Fireworks
-    ) -> AsyncGenerator:
+    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
         params = await self._get_params(request)
 
-        stream = client.completion.acreate(**params)
+        # Wrapper for async generator similar
+        async def _to_async_generator():
+            stream = self._get_client().completion.create(**params)
+            for chunk in stream:
+                yield chunk
+
+        stream = _to_async_generator()
         async for chunk in process_completion_stream_response(stream, self.formatter):
             yield chunk
 
+    def _build_options(
+        self, sampling_params: Optional[SamplingParams], fmt: ResponseFormat
+    ) -> dict:
+        options = get_sampling_options(sampling_params)
+        options.setdefault("max_tokens", 512)
+
+        if fmt:
+            if fmt.type == ResponseFormatType.json_schema.value:
+                options["response_format"] = {
+                    "type": "json_object",
+                    "schema": fmt.json_schema,
+                }
+            elif fmt.type == ResponseFormatType.grammar.value:
+                options["response_format"] = {
+                    "type": "grammar",
+                    "grammar": fmt.bnf,
+                }
+            else:
+                raise ValueError(f"Unknown response format {fmt.type}")
+
+        return options
+
     async def chat_completion(
         self,
         model: str,
@@ -121,32 +160,35 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
             logprobs=logprobs,
         )
 
-        client = Fireworks(api_key=self.config.api_key)
         if stream:
-            return self._stream_chat_completion(request, client)
+            return self._stream_chat_completion(request)
         else:
-            return await self._nonstream_chat_completion(request, client)
+            return await self._nonstream_chat_completion(request)
 
     async def _nonstream_chat_completion(
-        self, request: ChatCompletionRequest, client: Fireworks
+        self, request: ChatCompletionRequest
     ) -> ChatCompletionResponse:
         params = await self._get_params(request)
         if "messages" in params:
-            r = await client.chat.completions.acreate(**params)
+            r = await self._get_client().chat.completions.acreate(**params)
         else:
-            r = await client.completion.acreate(**params)
+            r = await self._get_client().completion.acreate(**params)
         return process_chat_completion_response(r, self.formatter)
 
     async def _stream_chat_completion(
-        self, request: ChatCompletionRequest, client: Fireworks
+        self, request: ChatCompletionRequest
     ) -> AsyncGenerator:
         params = await self._get_params(request)
 
-        if "messages" in params:
-            stream = client.chat.completions.acreate(**params)
-        else:
-            stream = client.completion.acreate(**params)
+        async def _to_async_generator():
+            if "messages" in params:
+                stream = await self._get_client().chat.completions.acreate(**params)
+            else:
+                stream = self._get_client().completion.create(**params)
+            for chunk in stream:
+                yield chunk
 
+        stream = _to_async_generator()
         async for chunk in process_chat_completion_stream_response(
             stream, self.formatter
         ):
@@ -167,41 +209,22 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
                 input_dict["prompt"] = chat_completion_request_to_prompt(
                     request, self.formatter
                 )
-        elif isinstance(request, CompletionRequest):
+        else:
             assert (
                 not media_present
             ), "Fireworks does not support media for Completion requests"
             input_dict["prompt"] = completion_request_to_prompt(request, self.formatter)
-        else:
-            raise ValueError(f"Unknown request type {type(request)}")
 
         # Fireworks always prepends with BOS
         if "prompt" in input_dict:
             if input_dict["prompt"].startswith("<|begin_of_text|>"):
                 input_dict["prompt"] = input_dict["prompt"][len("<|begin_of_text|>") :]
 
-        options = get_sampling_options(request.sampling_params)
-        options.setdefault("max_tokens", 512)
-
-        if fmt := request.response_format:
-            if fmt.type == ResponseFormatType.json_schema.value:
-                options["response_format"] = {
-                    "type": "json_object",
-                    "schema": fmt.json_schema,
-                }
-            elif fmt.type == ResponseFormatType.grammar.value:
-                options["response_format"] = {
-                    "type": "grammar",
-                    "grammar": fmt.bnf,
-                }
-            else:
-                raise ValueError(f"Unknown response format {fmt.type}")
-
         return {
             "model": self.map_to_provider_model(request.model),
             **input_dict,
             "stream": request.stream,
-            **options,
+            **self._build_options(request.sampling_params, request.response_format),
         }
 
     async def embeddings(

llama_stack/providers/remote/inference/together/__init__.py

@@ -4,9 +4,15 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from pydantic import BaseModel
+
 from .config import TogetherImplConfig
 
 
+class TogetherProviderDataValidator(BaseModel):
+    together_api_key: str
+
+
 async def get_adapter_impl(config: TogetherImplConfig, _deps):
     from .together import TogetherInferenceAdapter
 

llama_stack/providers/tests/agents/fixtures.py

@@ -11,7 +11,7 @@ import pytest_asyncio
 
 from llama_stack.distribution.datatypes import Api, Provider
 
-from llama_stack.providers.inline.meta_reference.agents import (
+from llama_stack.providers.inline.agents.meta_reference import (
     MetaReferenceAgentsImplConfig,
 )
 

llama_stack/providers/tests/eval/test_eval.py

@@ -52,7 +52,7 @@ class Testeval:
         response = await eval_impl.evaluate_rows(
             input_rows=rows.rows,
             scoring_functions=scoring_functions,
-            eval_task_config=AppEvalTaskConfig(
+            task_config=AppEvalTaskConfig(
                 eval_candidate=ModelCandidate(
                     model="Llama3.2-3B-Instruct",
                     sampling_params=SamplingParams(),
@@ -76,13 +76,13 @@ class Testeval:
         ]
 
         response = await eval_impl.run_eval(
-            eval_task_def=EvalTaskDef(
+            task=EvalTaskDef(
                 # NOTE: this is needed to make the router work for all app evals
                 identifier="meta-reference::app_eval",
                 dataset_id="test_dataset_for_eval",
                 scoring_functions=scoring_functions,
             ),
-            eval_task_config=AppEvalTaskConfig(
+            task_config=AppEvalTaskConfig(
                 eval_candidate=ModelCandidate(
                     model="Llama3.2-3B-Instruct",
                     sampling_params=SamplingParams(),
@@ -90,9 +90,13 @@ class Testeval:
             ),
         )
         assert response.job_id == "0"
-        job_status = await eval_impl.job_status(response.job_id)
+        job_status = await eval_impl.job_status(
+            response.job_id, "meta-reference::app_eval"
+        )
         assert job_status and job_status.value == "completed"
-        eval_response = await eval_impl.job_result(response.job_id)
+        eval_response = await eval_impl.job_result(
+            response.job_id, "meta-reference::app_eval"
+        )
 
         assert eval_response is not None
         assert len(eval_response.generations) == 5

llama_stack/providers/tests/inference/fixtures.py

@@ -10,7 +10,7 @@ import pytest
 import pytest_asyncio
 
 from llama_stack.distribution.datatypes import Api, Provider
-from llama_stack.providers.inline.meta_reference.inference import (
+from llama_stack.providers.inline.inference.meta_reference import (
     MetaReferenceInferenceConfig,
 )
 

llama_stack/providers/tests/memory/fixtures.py

@@ -11,7 +11,7 @@ import pytest
 import pytest_asyncio
 
 from llama_stack.distribution.datatypes import Api, Provider
-from llama_stack.providers.inline.meta_reference.memory import FaissImplConfig
+from llama_stack.providers.inline.memory.faiss import FaissImplConfig
 from llama_stack.providers.remote.memory.pgvector import PGVectorConfig
 from llama_stack.providers.remote.memory.weaviate import WeaviateConfig
 

llama_stack/providers/tests/safety/fixtures.py

@@ -8,7 +8,7 @@ import pytest
 import pytest_asyncio
 
 from llama_stack.distribution.datatypes import Api, Provider
-from llama_stack.providers.inline.meta_reference.safety import (
+from llama_stack.providers.inline.safety.meta_reference import (
     LlamaGuardShieldConfig,
     SafetyConfig,
 )

llama_stack/providers/tests/scoring/test_scoring.py

@@ -44,10 +44,10 @@ class TestScoring:
         )
         assert len(rows.rows) == 3
 
-        scoring_functions = [
-            "meta-reference::llm_as_judge_8b_correctness",
-            "meta-reference::equality",
-        ]
+        scoring_functions = {
+            "meta-reference::llm_as_judge_8b_correctness": None,
+            "meta-reference::equality": None,
+        }
         response = await scoring_impl.score(
             input_rows=rows.rows,
             scoring_functions=scoring_functions,
@@ -83,7 +83,7 @@ class TestScoring:
         )
         assert len(rows.rows) == 3
 
-        params = {
+        scoring_functions = {
             "meta-reference::llm_as_judge_8b_correctness": LLMAsJudgeScoringFnParams(
                 judge_model="Llama3.1-405B-Instruct",
                 prompt_template="Output a number response in the following format: Score: <number>, where <number> is the number between 0 and 9.",
@@ -91,13 +91,9 @@ class TestScoring:
             )
         }
 
-        scoring_functions = [
-            "meta-reference::llm_as_judge_8b_correctness",
-        ]
         response = await scoring_impl.score(
             input_rows=rows.rows,
             scoring_functions=scoring_functions,
-            scoring_params=params,
         )
         assert len(response.results) == len(scoring_functions)
         for x in scoring_functions:
@@ -108,7 +104,6 @@ class TestScoring:
         response = await scoring_impl.score_batch(
             dataset_id="test_dataset",
             scoring_functions=scoring_functions,
-            scoring_params=params,
         )
         assert len(response.results) == len(scoring_functions)
         for x in scoring_functions: