Merge remote-tracking branch 'origin/main' into support_3pt1_8b

docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb

@@ -544,7 +544,7 @@
               "    provider_type: inline::meta-reference\n",
               "  inference:\n",
               "  - config:\n",
-              "      api_key: 4985b03e627419b2964d34b8519ac6c4319f094d1ffb4f45514b4eb87e5427a2\n",
+              "      api_key: <...>\n",
               "      url: <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://api.together.xyz/v1</span>\n",
               "    provider_id: together\n",
               "    provider_type: remote::together\n",
@@ -663,7 +663,7 @@
               "    provider_type: inline::meta-reference\n",
               "  inference:\n",
               "  - config:\n",
-              "      api_key: 4985b03e627419b2964d34b8519ac6c4319f094d1ffb4f45514b4eb87e5427a2\n",
+              "      api_key: <...>\n",
               "      url: \u001b[4;94mhttps://api.together.xyz/v1\u001b[0m\n",
               "    provider_id: together\n",
               "    provider_type: remote::together\n",

docs/source/distributions/building_distro.md

@@ -338,8 +338,8 @@ distribution_spec:
     inference: remote::ollama
     memory: inline::faiss
     safety: inline::llama-guard
-    agents: meta-reference
+    agents: inline::meta-reference
-    telemetry: meta-reference
+    telemetry: inline::meta-reference
 image_type: conda
 ```
 

docs/zero_to_hero_guide/00_Inference101.ipynb

@@ -358,7 +358,7 @@
     "    if not stream:\n",
     "        cprint(f'> Response: {response.completion_message.content}', 'cyan')\n",
     "    else:\n",
-    "        async for log in EventLogger().log(response):\n",
+    "        for log in EventLogger().log(response):\n",
     "            log.print()\n",
     "\n",
     "# In a Jupyter Notebook cell, use `await` to call the function\n",
@@ -366,16 +366,6 @@
     "# To run it in a python file, use this line instead\n",
     "# asyncio.run(run_main())\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "9399aecc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#fin"
-   ]
   }
  ],
  "metadata": {

docs/zero_to_hero_guide/README.md

@@ -45,7 +45,7 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
 
 ---
 
-## Install Dependencies and Set Up Environment
+## Install Dependencies and Set Up Environmen
 
 1. **Create a Conda Environment**:
    Create a new Conda environment with Python 3.10:
@@ -73,7 +73,7 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
    Open a new terminal and install `llama-stack`:
    ```bash
    conda activate ollama
-   pip install llama-stack==0.0.55
+   pip install llama-stack==0.0.61
    ```
 
 ---
@@ -96,7 +96,7 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
 3. **Set the ENV variables by exporting them to the terminal**:
    ```bash
    export OLLAMA_URL="http://localhost:11434"
-   export LLAMA_STACK_PORT=5051
+   export LLAMA_STACK_PORT=5001
    export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
    export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B"
    ```
@@ -104,34 +104,29 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
 3. **Run the Llama Stack**:
    Run the stack with command shared by the API from earlier:
    ```bash
-   llama stack run ollama  \
+   llama stack run ollama
-      --port $LLAMA_STACK_PORT \
+      --port $LLAMA_STACK_PORT
-      --env INFERENCE_MODEL=$INFERENCE_MODEL \
+      --env INFERENCE_MODEL=$INFERENCE_MODEL
-      --env SAFETY_MODEL=$SAFETY_MODEL \
+      --env SAFETY_MODEL=$SAFETY_MODEL
       --env OLLAMA_URL=$OLLAMA_URL
    ```
    Note: Everytime you run a new model with `ollama run`, you will need to restart the llama stack. Otherwise it won't see the new model.
 
-The server will start and listen on `http://localhost:5051`.
+The server will start and listen on `http://localhost:5001`.
 
 ---
 ## Test with `llama-stack-client` CLI
-After setting up the server, open a new terminal window and install the llama-stack-client package.
+After setting up the server, open a new terminal window and configure the llama-stack-client.
 
-1. Install the llama-stack-client package
+1. Configure the CLI to point to the llama-stack server.
    ```bash
-   conda activate ollama
+   llama-stack-client configure --endpoint http://localhost:5001
-   pip install llama-stack-client
-   ```
-2. Configure the CLI to point to the llama-stack server.
-   ```bash
-   llama-stack-client configure --endpoint http://localhost:5051
    ```
    **Expected Output:**
    ```bash
-   Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5051
+   Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5001
    ```
-3. Test the CLI by running inference:
+2. Test the CLI by running inference:
    ```bash
    llama-stack-client inference chat-completion --message "Write me a 2-sentence poem about the moon"
    ```
@@ -153,16 +148,18 @@ After setting up the server, open a new terminal window and install the llama-st
 After setting up the server, open a new terminal window and verify it's working by sending a `POST` request using `curl`:
 
 ```bash
-curl http://localhost:$LLAMA_STACK_PORT/inference/chat_completion \
+curl http://localhost:$LLAMA_STACK_PORT/alpha/inference/chat-completion
--H "Content-Type: application/json" \
+-H "Content-Type: application/json"
--d '{
+-d @- <<EOF
-    "model": "Llama3.2-3B-Instruct",
+{
+    "model_id": "$INFERENCE_MODEL",
     "messages": [
         {"role": "system", "content": "You are a helpful assistant."},
         {"role": "user", "content": "Write me a 2-sentence poem about the moon"}
     ],
     "sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512}
-}'
+}
+EOF
 ```
 
 You can check the available models with the command `llama-stack-client models list`.
@@ -186,16 +183,12 @@ You can check the available models with the command `llama-stack-client models l
 
 You can also interact with the Llama Stack server using a simple Python script. Below is an example:
 
-### 1. Activate Conda Environment and Install Required Python Packages
+### 1. Activate Conda Environmen
-The `llama-stack-client` library offers a robust and efficient python methods for interacting with the Llama Stack server.
 
 ```bash
 conda activate ollama
-pip install llama-stack-client
 ```
 
-Note, the client library gets installed by default if you install the server library
-
 ### 2. Create Python Script (`test_llama_stack.py`)
 ```bash
 touch test_llama_stack.py
@@ -206,19 +199,28 @@ touch test_llama_stack.py
 In `test_llama_stack.py`, write the following code:
 
 ```python
-from llama_stack_client import LlamaStackClient
+import os
+from llama_stack_client import LlamaStackClien
 
-# Initialize the client
+# Get the model ID from the environment variable
-client = LlamaStackClient(base_url="http://localhost:5051")
+INFERENCE_MODEL = os.environ.get("INFERENCE_MODEL")
 
-# Create a chat completion request
+# Check if the environment variable is se
+if INFERENCE_MODEL is None:
+    raise ValueError("The environment variable 'INFERENCE_MODEL' is not set.")
+
+# Initialize the clien
+client = LlamaStackClient(base_url="http://localhost:5001")
+
+# Create a chat completion reques
 response = client.inference.chat_completion(
     messages=[
         {"role": "system", "content": "You are a friendly assistant."},
         {"role": "user", "content": "Write a two-sentence poem about llama."}
     ],
-    model_id=MODEL_NAME,
+    model_id=INFERENCE_MODEL,
 )
+
 # Print the response
 print(response.completion_message.content)
 ```

llama_stack/apis/eval/eval.py

@@ -6,9 +6,10 @@
 
 from typing import Any, Dict, List, Literal, Optional, Protocol, Union
 
-from llama_models.llama3.api.datatypes import BaseModel, Field
 from llama_models.schema_utils import json_schema_type, webmethod
 
+from pydantic import BaseModel, Field
+
 from typing_extensions import Annotated
 
 from llama_stack.apis.agents import AgentConfig

llama_stack/apis/scoring/scoring.py

@@ -47,7 +47,7 @@ class Scoring(Protocol):
     async def score_batch(
         self,
         dataset_id: str,
-        scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
+        scoring_functions: Dict[str, Optional[ScoringFnParams]],
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse: ...
 
@@ -55,5 +55,5 @@ class Scoring(Protocol):
     async def score(
         self,
         input_rows: List[Dict[str, Any]],
-        scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
+        scoring_functions: Dict[str, Optional[ScoringFnParams]],
     ) -> ScoreResponse: ...

llama_stack/distribution/build_container.sh

@@ -126,7 +126,7 @@ ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--templat
 
 EOF
 
-printf "Dockerfile created successfully in $TEMP_DIR/Dockerfile"
+printf "Dockerfile created successfully in $TEMP_DIR/Dockerfile\n\n"
 cat $TEMP_DIR/Dockerfile
 printf "\n"
 

llama_stack/distribution/library_client.py

@@ -39,6 +39,7 @@ from llama_stack.distribution.server.endpoints import get_all_api_endpoints
 from llama_stack.distribution.stack import (
     construct_stack,
     get_stack_run_config_from_template,
+    redact_sensitive_fields,
     replace_env_vars,
 )
 
@@ -273,7 +274,10 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
 
         console = Console()
         console.print(f"Using config [blue]{self.config_path_or_template_name}[/blue]:")
-        console.print(yaml.dump(self.config.model_dump(), indent=2))
+
+        # Redact sensitive information before printing
+        safe_config = redact_sensitive_fields(self.config.model_dump())
+        console.print(yaml.dump(safe_config, indent=2))
 
         endpoints = get_all_api_endpoints()
         endpoint_impls = {}

llama_stack/distribution/server/server.py

@@ -35,6 +35,7 @@ from llama_stack.distribution.request_headers import set_request_provider_data
 from llama_stack.distribution.resolver import InvalidProviderError
 from llama_stack.distribution.stack import (
     construct_stack,
+    redact_sensitive_fields,
     replace_env_vars,
     validate_env_pair,
 )
@@ -280,7 +281,8 @@ def main():
         config = StackRunConfig(**config)
 
     print("Run configuration:")
-    print(yaml.dump(config.model_dump(), indent=2))
+    safe_config = redact_sensitive_fields(config.model_dump())
+    print(yaml.dump(safe_config, indent=2))
 
     app = FastAPI(lifespan=lifespan)
     app.add_middleware(TracingMiddleware)

llama_stack/distribution/stack.py

@@ -112,6 +112,26 @@ class EnvVarError(Exception):
         )
 
 
+def redact_sensitive_fields(data: Dict[str, Any]) -> Dict[str, Any]:
+    """Redact sensitive information from config before printing."""
+    sensitive_patterns = ["api_key", "api_token", "password", "secret"]
+
+    def _redact_dict(d: Dict[str, Any]) -> Dict[str, Any]:
+        result = {}
+        for k, v in d.items():
+            if isinstance(v, dict):
+                result[k] = _redact_dict(v)
+            elif isinstance(v, list):
+                result[k] = [_redact_dict(i) if isinstance(i, dict) else i for i in v]
+            elif any(pattern in k.lower() for pattern in sensitive_patterns):
+                result[k] = "********"
+            else:
+                result[k] = v
+        return result
+
+    return _redact_dict(data)
+
+
 def replace_env_vars(config: Any, path: str = "") -> Any:
     if isinstance(config, dict):
         result = {}

llama_stack/distribution/ui/page/evaluations/app_eval.py

@@ -129,7 +129,7 @@ def application_evaluation_page():
 
                 # Display current row results using separate containers
                 progress_text_container.write(
-                    f"Expand to see current processed result ({i+1}/{len(rows)})"
+                    f"Expand to see current processed result ({i + 1} / {len(rows)})"
                 )
                 results_container.json(
                     score_res.to_json(),

llama_stack/distribution/ui/page/evaluations/native_eval.py

@@ -232,7 +232,7 @@ def run_evaluation_3():
                 output_res[scoring_fn].append(eval_res.scores[scoring_fn].score_rows[0])
 
             progress_text_container.write(
-                f"Expand to see current processed result ({i+1}/{len(rows)})"
+                f"Expand to see current processed result ({i + 1} / {len(rows)})"
             )
             results_container.json(eval_res, expanded=2)
 

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

@@ -584,7 +584,7 @@ class ChatAgent(ShieldRunnerMixin):
                 tool_call = message.tool_calls[0]
 
                 name = tool_call.tool_name
-                if not isinstance(name, BuiltinTool):
+                if not isinstance(name, BuiltinTool) or name not in enabled_tools:
                     yield message
                     return
 

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

@@ -3,23 +3,24 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-from enum import Enum
 from typing import Any, Dict, List, Optional
 
 from tqdm import tqdm
 
-from llama_stack.apis.agents import Agents
+from llama_stack.apis.agents import Agents, StepType
-from llama_stack.apis.common.type_system import (
-    ChatCompletionInputType,
-    CompletionInputType,
-    StringType,
-)
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
 from llama_stack.apis.eval_tasks import EvalTask
 from llama_stack.apis.inference import Inference, UserMessage
 from llama_stack.apis.scoring import Scoring
+from llama_stack.distribution.datatypes import Api
 from llama_stack.providers.datatypes import EvalTasksProtocolPrivate
+
+from llama_stack.providers.utils.common.data_schema_validator import (
+    ColumnName,
+    DataSchemaValidatorMixin,
+    get_valid_schemas,
+)
 from llama_stack.providers.utils.kvstore import kvstore_impl
 
 from .....apis.common.job_types import Job
@@ -30,15 +31,7 @@ from .config import MetaReferenceEvalConfig
 EVAL_TASKS_PREFIX = "eval_tasks:"
 
 
-class ColumnName(Enum):
+class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate, DataSchemaValidatorMixin):
-    input_query = "input_query"
-    expected_answer = "expected_answer"
-    chat_completion_input = "chat_completion_input"
-    completion_input = "completion_input"
-    generated_answer = "generated_answer"
-
-
-class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
     def __init__(
         self,
         config: MetaReferenceEvalConfig,
@@ -82,29 +75,6 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
         )
         self.eval_tasks[task_def.identifier] = task_def
 
-    async def validate_eval_input_dataset_schema(self, dataset_id: str) -> None:
-        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
-            raise ValueError(f"Dataset {dataset_id} does not have a schema defined.")
-
-        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(),
-            },
-        ]
-
-        if dataset_def.dataset_schema not in expected_schemas:
-            raise ValueError(
-                f"Dataset {dataset_id} does not have a correct input schema in {expected_schemas}"
-            )
-
     async def run_eval(
         self,
         task_id: str,
@@ -114,8 +84,10 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
         dataset_id = task_def.dataset_id
         candidate = task_config.eval_candidate
         scoring_functions = task_def.scoring_functions
-
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        await self.validate_eval_input_dataset_schema(dataset_id=dataset_id)
+        self.validate_dataset_schema(
+            dataset_def.dataset_schema, get_valid_schemas(Api.eval.value)
+        )
         all_rows = await self.datasetio_api.get_rows_paginated(
             dataset_id=dataset_id,
             rows_in_page=(
@@ -167,11 +139,21 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
                 )
             ]
             final_event = turn_response[-1].event.payload
-            generations.append(
+
-                {
+            # check if there's a memory retrieval step and extract the context
-                    ColumnName.generated_answer.value: final_event.turn.output_message.content
+            memory_rag_context = None
-                }
+            for step in final_event.turn.steps:
+                if step.step_type == StepType.memory_retrieval.value:
+                    memory_rag_context = " ".join(x.text for x in step.inserted_context)
+
+            agent_generation = {}
+            agent_generation[ColumnName.generated_answer.value] = (
+                final_event.turn.output_message.content
             )
+            if memory_rag_context:
+                agent_generation[ColumnName.context.value] = memory_rag_context
+
+            generations.append(agent_generation)
 
         return generations
 

llama_stack/providers/inline/post_training/torchtune/common/utils.py

@@ -18,6 +18,7 @@ from llama_models.datatypes import Model
 from llama_models.sku_list import resolve_model
 from llama_stack.apis.common.type_system import ParamType, StringType
 from llama_stack.apis.datasets import Datasets
+
 from pydantic import BaseModel
 
 from torchtune.models.llama3 import llama3_tokenizer

llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py

@@ -7,6 +7,7 @@
 import logging
 import os
 import time
+from datetime import datetime
 from functools import partial
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple

llama_stack/providers/inline/scoring/basic/scoring.py

@@ -14,8 +14,13 @@ from llama_stack.apis.scoring import (
     ScoringResult,
 )
 from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
-from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 
+from llama_stack.distribution.datatypes import Api
+from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
+from llama_stack.providers.utils.common.data_schema_validator import (
+    DataSchemaValidatorMixin,
+    get_valid_schemas,
+)
 from .config import BasicScoringConfig
 from .scoring_fn.equality_scoring_fn import EqualityScoringFn
 from .scoring_fn.regex_parser_scoring_fn import RegexParserScoringFn
@@ -24,7 +29,9 @@ from .scoring_fn.subset_of_scoring_fn import SubsetOfScoringFn
 FIXED_FNS = [EqualityScoringFn, SubsetOfScoringFn, RegexParserScoringFn]
 
 
-class BasicScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
+class BasicScoringImpl(
+    Scoring, ScoringFunctionsProtocolPrivate, DataSchemaValidatorMixin
+):
     def __init__(
         self,
         config: BasicScoringConfig,
@@ -61,30 +68,17 @@ class BasicScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     async def register_scoring_function(self, function_def: ScoringFn) -> None:
         raise NotImplementedError("Register scoring function not implemented yet")
 
-    async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
-        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
-            raise ValueError(
-                f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
-            )
-
-        for required_column in ["generated_answer", "expected_answer", "input_query"]:
-            if required_column not in dataset_def.dataset_schema:
-                raise ValueError(
-                    f"Dataset {dataset_id} does not have a '{required_column}' column."
-                )
-            if dataset_def.dataset_schema[required_column].type != "string":
-                raise ValueError(
-                    f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
-                )
-
     async def score_batch(
         self,
         dataset_id: str,
         scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
-        await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id)
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
+        self.validate_dataset_schema(
+            dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
+        )
+
         all_rows = await self.datasetio_api.get_rows_paginated(
             dataset_id=dataset_id,
             rows_in_page=-1,

llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py

@@ -9,12 +9,12 @@ from typing import Any, Dict, Optional
 from llama_stack.apis.scoring import ScoringResultRow
 
 from llama_stack.apis.scoring_functions import ScoringFnParams
-from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
 
 from .fn_defs.equality import equality
 
 
-class EqualityScoringFn(BaseScoringFn):
+class EqualityScoringFn(RegisteredBaseScoringFn):
     """
     A scoring_fn that assigns a score of 1.0 if the input string matches the target string, and 0.0 otherwise.
     """

llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py

@@ -9,14 +9,14 @@ from typing import Any, Dict, Optional
 
 from llama_stack.apis.scoring import ScoringResultRow
 from llama_stack.apis.scoring_functions import ScoringFnParams, ScoringFnParamsType
-from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
 
 from .fn_defs.regex_parser_multiple_choice_answer import (
     regex_parser_multiple_choice_answer,
 )
 
 
-class RegexParserScoringFn(BaseScoringFn):
+class RegexParserScoringFn(RegisteredBaseScoringFn):
     """
     A scoring_fn that parses answer from generated response according to context and check match with expected_answer.
     """

llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py

@@ -8,12 +8,12 @@ from typing import Any, Dict, Optional
 
 from llama_stack.apis.scoring import ScoringResultRow
 from llama_stack.apis.scoring_functions import ScoringFnParams
-from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
 
 from .fn_defs.subset_of import subset_of
 
 
-class SubsetOfScoringFn(BaseScoringFn):
+class SubsetOfScoringFn(RegisteredBaseScoringFn):
     """
     A scoring_fn that assigns a score of 1.0 if the expected string is included in the generated string, and 0.0 otherwise.
     """

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -7,7 +7,17 @@ import os
 from typing import Any, Dict, List, Optional
 
 from autoevals.llm import Factuality
-from autoevals.ragas import AnswerCorrectness
+from autoevals.ragas import (
+    AnswerCorrectness,
+    AnswerRelevancy,
+    AnswerSimilarity,
+    ContextEntityRecall,
+    ContextPrecision,
+    ContextRecall,
+    ContextRelevancy,
+    Faithfulness,
+)
+from pydantic import BaseModel
 
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
@@ -18,20 +28,90 @@ from llama_stack.apis.scoring import (
     ScoringResult,
     ScoringResultRow,
 )
-from llama_stack.apis.scoring_functions import AggregationFunctionType, ScoringFn
+from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
+
+from llama_stack.distribution.datatypes import Api
 
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
+from llama_stack.providers.utils.common.data_schema_validator import (
+    DataSchemaValidatorMixin,
+    get_valid_schemas,
+)
 
-from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_average
+from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_metrics
-
 from .config import BraintrustScoringConfig
 from .scoring_fn.fn_defs.answer_correctness import answer_correctness_fn_def
+from .scoring_fn.fn_defs.answer_relevancy import answer_relevancy_fn_def
+from .scoring_fn.fn_defs.answer_similarity import answer_similarity_fn_def
+from .scoring_fn.fn_defs.context_entity_recall import context_entity_recall_fn_def
+from .scoring_fn.fn_defs.context_precision import context_precision_fn_def
+from .scoring_fn.fn_defs.context_recall import context_recall_fn_def
+from .scoring_fn.fn_defs.context_relevancy import context_relevancy_fn_def
 from .scoring_fn.fn_defs.factuality import factuality_fn_def
+from .scoring_fn.fn_defs.faithfulness import faithfulness_fn_def
+
+
+class BraintrustScoringFnEntry(BaseModel):
+    identifier: str
+    evaluator: Any
+    fn_def: ScoringFn
+
+
+SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY = [
+    BraintrustScoringFnEntry(
+        identifier="braintrust::factuality",
+        evaluator=Factuality(),
+        fn_def=factuality_fn_def,
+    ),
+    BraintrustScoringFnEntry(
+        identifier="braintrust::answer-correctness",
+        evaluator=AnswerCorrectness(),
+        fn_def=answer_correctness_fn_def,
+    ),
+    BraintrustScoringFnEntry(
+        identifier="braintrust::answer-relevancy",
+        evaluator=AnswerRelevancy(),
+        fn_def=answer_relevancy_fn_def,
+    ),
+    BraintrustScoringFnEntry(
+        identifier="braintrust::answer-similarity",
+        evaluator=AnswerSimilarity(),
+        fn_def=answer_similarity_fn_def,
+    ),
+    BraintrustScoringFnEntry(
+        identifier="braintrust::faithfulness",
+        evaluator=Faithfulness(),
+        fn_def=faithfulness_fn_def,
+    ),
+    BraintrustScoringFnEntry(
+        identifier="braintrust::context-entity-recall",
+        evaluator=ContextEntityRecall(),
+        fn_def=context_entity_recall_fn_def,
+    ),
+    BraintrustScoringFnEntry(
+        identifier="braintrust::context-precision",
+        evaluator=ContextPrecision(),
+        fn_def=context_precision_fn_def,
+    ),
+    BraintrustScoringFnEntry(
+        identifier="braintrust::context-recall",
+        evaluator=ContextRecall(),
+        fn_def=context_recall_fn_def,
+    ),
+    BraintrustScoringFnEntry(
+        identifier="braintrust::context-relevancy",
+        evaluator=ContextRelevancy(),
+        fn_def=context_relevancy_fn_def,
+    ),
+]
 
 
 class BraintrustScoringImpl(
-    Scoring, ScoringFunctionsProtocolPrivate, NeedsRequestProviderData
+    Scoring,
+    ScoringFunctionsProtocolPrivate,
+    NeedsRequestProviderData,
+    DataSchemaValidatorMixin,
 ):
     def __init__(
         self,
@@ -44,12 +124,12 @@ class BraintrustScoringImpl(
         self.datasets_api = datasets_api
 
         self.braintrust_evaluators = {
-            "braintrust::factuality": Factuality(),
+            entry.identifier: entry.evaluator
-            "braintrust::answer-correctness": AnswerCorrectness(),
+            for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
         }
         self.supported_fn_defs_registry = {
-            factuality_fn_def.identifier: factuality_fn_def,
+            entry.identifier: entry.fn_def
-            answer_correctness_fn_def.identifier: answer_correctness_fn_def,
+            for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
         }
 
     async def initialize(self) -> None: ...
@@ -70,23 +150,6 @@ class BraintrustScoringImpl(
             "Registering scoring function not allowed for braintrust provider"
         )
 
-    async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
-        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
-            raise ValueError(
-                f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
-            )
-
-        for required_column in ["generated_answer", "expected_answer", "input_query"]:
-            if required_column not in dataset_def.dataset_schema:
-                raise ValueError(
-                    f"Dataset {dataset_id} does not have a '{required_column}' column."
-                )
-            if dataset_def.dataset_schema[required_column].type != "string":
-                raise ValueError(
-                    f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
-                )
-
     async def set_api_key(self) -> None:
         # api key is in the request headers
         if not self.config.openai_api_key:
@@ -102,11 +165,16 @@ class BraintrustScoringImpl(
     async def score_batch(
         self,
         dataset_id: str,
-        scoring_functions: List[str],
+        scoring_functions: Dict[str, Optional[ScoringFnParams]],
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
         await self.set_api_key()
-        await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id)
+
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
+        self.validate_dataset_schema(
+            dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
+        )
+
         all_rows = await self.datasetio_api.get_rows_paginated(
             dataset_id=dataset_id,
             rows_in_page=-1,
@@ -126,6 +194,7 @@ class BraintrustScoringImpl(
     async def score_row(
         self, input_row: Dict[str, Any], scoring_fn_identifier: Optional[str] = None
     ) -> ScoringResultRow:
+        self.validate_row_schema(input_row, get_valid_schemas(Api.scoring.value))
         await self.set_api_key()
         assert scoring_fn_identifier is not None, "scoring_fn_identifier cannot be None"
         expected_answer = input_row["expected_answer"]
@@ -133,12 +202,19 @@ class BraintrustScoringImpl(
         input_query = input_row["input_query"]
         evaluator = self.braintrust_evaluators[scoring_fn_identifier]
 
-        result = evaluator(generated_answer, expected_answer, input=input_query)
+        result = evaluator(
+            generated_answer,
+            expected_answer,
+            input=input_query,
+            context=input_row["context"] if "context" in input_row else None,
+        )
         score = result.score
         return {"score": score, "metadata": result.metadata}
 
     async def score(
-        self, input_rows: List[Dict[str, Any]], scoring_functions: List[str]
+        self,
+        input_rows: List[Dict[str, Any]],
+        scoring_functions: Dict[str, Optional[ScoringFnParams]],
     ) -> ScoreResponse:
         await self.set_api_key()
         res = {}
@@ -150,8 +226,17 @@ class BraintrustScoringImpl(
                 await self.score_row(input_row, scoring_fn_id)
                 for input_row in input_rows
             ]
-            aggregation_functions = [AggregationFunctionType.average]
+            aggregation_functions = self.supported_fn_defs_registry[
-            agg_results = aggregate_average(score_results)
+                scoring_fn_id
+            ].params.aggregation_functions
+
+            # override scoring_fn params if provided
+            if scoring_functions[scoring_fn_id] is not None:
+                override_params = scoring_functions[scoring_fn_id]
+                if override_params.aggregation_functions:
+                    aggregation_functions = override_params.aggregation_functions
+
+            agg_results = aggregate_metrics(score_results, aggregation_functions)
             res[scoring_fn_id] = ScoringResult(
                 score_rows=score_results,
                 aggregated_results=agg_results,

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_correctness.py

@@ -5,14 +5,23 @@
 # the root directory of this source tree.
 
 from llama_stack.apis.common.type_system import NumberType
-from llama_stack.apis.scoring_functions import ScoringFn
+from llama_stack.apis.scoring_functions import (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
 
 
 answer_correctness_fn_def = ScoringFn(
     identifier="braintrust::answer-correctness",
-    description="Scores the correctness of the answer based on the ground truth.. One of Braintrust LLM basd scorer https://github.com/braintrustdata/autoevals/blob/main/py/autoevals/llm.py",
+    description=(
-    params=None,
+        "Scores the correctness of the answer based on the ground truth. "
+        "Uses Braintrust LLM-based scorer from autoevals library."
+    ),
     provider_id="braintrust",
     provider_resource_id="answer-correctness",
     return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
 )

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_relevancy.py

@@ -0,0 +1,26 @@
+# 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 (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
+
+answer_relevancy_fn_def = ScoringFn(
+    identifier="braintrust::answer-relevancy",
+    description=(
+        "Test output relevancy against the input query using Braintrust LLM scorer. "
+        "See: github.com/braintrustdata/autoevals"
+    ),
+    provider_id="braintrust",
+    provider_resource_id="answer-relevancy",
+    return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
+)

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_similarity.py

@@ -0,0 +1,26 @@
+# 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 (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
+
+answer_similarity_fn_def = ScoringFn(
+    identifier="braintrust::answer-similarity",
+    description=(
+        "Test output similarity against expected value using Braintrust LLM scorer. "
+        "See: github.com/braintrustdata/autoevals"
+    ),
+    provider_id="braintrust",
+    provider_resource_id="answer-similarity",
+    return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
+)

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_entity_recall.py

@@ -0,0 +1,26 @@
+# 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 (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
+
+context_entity_recall_fn_def = ScoringFn(
+    identifier="braintrust::context-entity-recall",
+    description=(
+        "Evaluates how well the context captures the named entities present in the "
+        "reference answer. See: github.com/braintrustdata/autoevals"
+    ),
+    provider_id="braintrust",
+    provider_resource_id="context-entity-recall",
+    return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
+)

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_precision.py

@@ -0,0 +1,26 @@
+# 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 (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
+
+context_precision_fn_def = ScoringFn(
+    identifier="braintrust::context-precision",
+    description=(
+        "Measures how much of the provided context is actually relevant to answering the "
+        "question. See: github.com/braintrustdata/autoevals"
+    ),
+    provider_id="braintrust",
+    provider_resource_id="context-precision",
+    return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
+)

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_recall.py

@@ -0,0 +1,26 @@
+# 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 (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
+
+context_recall_fn_def = ScoringFn(
+    identifier="braintrust::context-recall",
+    description=(
+        "Evaluates how well the context covers the information needed to answer the "
+        "question. See: github.com/braintrustdata/autoevals"
+    ),
+    provider_id="braintrust",
+    provider_resource_id="context-recall",
+    return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
+)

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_relevancy.py

@@ -0,0 +1,26 @@
+# 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 (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
+
+context_relevancy_fn_def = ScoringFn(
+    identifier="braintrust::context-relevancy",
+    description=(
+        "Assesses how relevant the provided context is to the given question. "
+        "See: github.com/braintrustdata/autoevals"
+    ),
+    provider_id="braintrust",
+    provider_resource_id="context-relevancy",
+    return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
+)

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/factuality.py

@@ -5,14 +5,23 @@
 # the root directory of this source tree.
 
 from llama_stack.apis.common.type_system import NumberType
-from llama_stack.apis.scoring_functions import ScoringFn
+from llama_stack.apis.scoring_functions import (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
 
 
 factuality_fn_def = ScoringFn(
     identifier="braintrust::factuality",
-    description="Test whether an output is factual, compared to an original (`expected`) value. One of Braintrust LLM basd scorer https://github.com/braintrustdata/autoevals/blob/main/py/autoevals/llm.py",
+    description=(
-    params=None,
+        "Test output factuality against expected value using Braintrust LLM scorer. "
+        "See: github.com/braintrustdata/autoevals"
+    ),
     provider_id="braintrust",
     provider_resource_id="factuality",
     return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
 )

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/faithfulness.py

@@ -0,0 +1,26 @@
+# 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 (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
+
+faithfulness_fn_def = ScoringFn(
+    identifier="braintrust::faithfulness",
+    description=(
+        "Test output faithfulness to the input query using Braintrust LLM scorer. "
+        "See: github.com/braintrustdata/autoevals"
+    ),
+    provider_id="braintrust",
+    provider_resource_id="faithfulness",
+    return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
+)

llama_stack/providers/inline/scoring/llm_as_judge/scoring.py

@@ -16,7 +16,12 @@ from llama_stack.apis.scoring import (
     ScoringResult,
 )
 from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
+from llama_stack.distribution.datatypes import Api
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
+from llama_stack.providers.utils.common.data_schema_validator import (
+    DataSchemaValidatorMixin,
+    get_valid_schemas,
+)
 
 from .config import LlmAsJudgeScoringConfig
 from .scoring_fn.llm_as_judge_scoring_fn import LlmAsJudgeScoringFn
@@ -25,7 +30,9 @@ from .scoring_fn.llm_as_judge_scoring_fn import LlmAsJudgeScoringFn
 LLM_JUDGE_FNS = [LlmAsJudgeScoringFn]
 
 
-class LlmAsJudgeScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
+class LlmAsJudgeScoringImpl(
+    Scoring, ScoringFunctionsProtocolPrivate, DataSchemaValidatorMixin
+):
     def __init__(
         self,
         config: LlmAsJudgeScoringConfig,
@@ -65,30 +72,17 @@ class LlmAsJudgeScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     async def register_scoring_function(self, function_def: ScoringFn) -> None:
         raise NotImplementedError("Register scoring function not implemented yet")
 
-    async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
-        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
-            raise ValueError(
-                f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
-            )
-
-        for required_column in ["generated_answer", "expected_answer", "input_query"]:
-            if required_column not in dataset_def.dataset_schema:
-                raise ValueError(
-                    f"Dataset {dataset_id} does not have a '{required_column}' column."
-                )
-            if dataset_def.dataset_schema[required_column].type != "string":
-                raise ValueError(
-                    f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
-                )
-
     async def score_batch(
         self,
         dataset_id: str,
         scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
-        await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id)
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
+        self.validate_dataset_schema(
+            dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
+        )
+
         all_rows = await self.datasetio_api.get_rows_paginated(
             dataset_id=dataset_id,
             rows_in_page=-1,

llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py

@@ -12,14 +12,14 @@ from llama_stack.apis.inference.inference import Inference
 from llama_stack.apis.scoring import ScoringResultRow
 from llama_stack.apis.scoring_functions import ScoringFnParams
 
-from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
 
 from .fn_defs.llm_as_judge_405b_simpleqa import llm_as_judge_405b_simpleqa
 
 from .fn_defs.llm_as_judge_base import llm_as_judge_base
 
 
-class LlmAsJudgeScoringFn(BaseScoringFn):
+class LlmAsJudgeScoringFn(RegisteredBaseScoringFn):
     """
     A scoring_fn that assigns
     """

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -71,7 +71,8 @@ class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
         self.formatter = ChatFormat(Tokenizer.get_instance())
 
         self.client = AsyncCerebras(
-            base_url=self.config.base_url, api_key=self.config.api_key
+            base_url=self.config.base_url,
+            api_key=self.config.api_key.get_secret_value(),
         )
 
     async def initialize(self) -> None:

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

@@ -8,7 +8,7 @@ import os
 from typing import Any, Dict, Optional
 
 from llama_models.schema_utils import json_schema_type
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 DEFAULT_BASE_URL = "https://api.cerebras.ai"
 
@@ -19,7 +19,7 @@ class CerebrasImplConfig(BaseModel):
         default=os.environ.get("CEREBRAS_BASE_URL", DEFAULT_BASE_URL),
         description="Base URL for the Cerebras API",
     )
-    api_key: Optional[str] = Field(
+    api_key: Optional[SecretStr] = Field(
         default=os.environ.get("CEREBRAS_API_KEY"),
         description="Cerebras API Key",
     )

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

@@ -7,7 +7,7 @@
 from typing import Any, Dict, Optional
 
 from llama_models.schema_utils import json_schema_type
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 
 @json_schema_type
@@ -16,7 +16,7 @@ class FireworksImplConfig(BaseModel):
         default="https://api.fireworks.ai/inference/v1",
         description="The URL for the Fireworks server",
     )
-    api_key: Optional[str] = Field(
+    api_key: Optional[SecretStr] = Field(
         default=None,
         description="The Fireworks.ai API Key",
     )

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

@@ -113,7 +113,7 @@ class FireworksInferenceAdapter(
 
     def _get_api_key(self) -> str:
         if self.config.api_key is not None:
-            return self.config.api_key
+            return self.config.api_key.get_secret_value()
         else:
             provider_data = self.get_request_provider_data()
             if provider_data is None or not provider_data.fireworks_api_key:

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

@@ -8,7 +8,7 @@ import os
 from typing import Optional
 
 from llama_models.schema_utils import json_schema_type
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 
 @json_schema_type
@@ -40,7 +40,7 @@ class NVIDIAConfig(BaseModel):
         ),
         description="A base url for accessing the NVIDIA NIM",
     )
-    api_key: Optional[str] = Field(
+    api_key: Optional[SecretStr] = Field(
         default_factory=lambda: os.getenv("NVIDIA_API_KEY"),
         description="The NVIDIA API key, only needed of using the hosted service",
     )

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -113,7 +113,11 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         # make sure the client lives longer than any async calls
         self._client = AsyncOpenAI(
             base_url=f"{self._config.url}/v1",
-            api_key=self._config.api_key or "NO KEY",
+            api_key=(
+                self._config.api_key.get_secret_value()
+                if self._config.api_key
+                else "NO KEY"
+            ),
             timeout=self._config.timeout,
         )
 

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -236,6 +236,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
             tool_prompt_format=tool_prompt_format,
             stream=stream,
             logprobs=logprobs,
+            response_format=response_format,
         )
         if stream:
             return self._stream_chat_completion(request)
@@ -279,6 +280,14 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
             )
             input_dict["raw"] = True
 
+        if fmt := request.response_format:
+            if fmt.type == "json_schema":
+                input_dict["format"] = fmt.json_schema
+            elif fmt.type == "grammar":
+                raise NotImplementedError("Grammar response format is not supported")
+            else:
+                raise ValueError(f"Unknown response format type: {fmt.type}")
+
         return {
             "model": request.model,
             **input_dict,

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

@@ -7,7 +7,7 @@
 from typing import Optional
 
 from llama_models.schema_utils import json_schema_type
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 
 @json_schema_type
@@ -15,7 +15,7 @@ class TGIImplConfig(BaseModel):
     url: str = Field(
         description="The URL for the TGI serving endpoint",
     )
-    api_token: Optional[str] = Field(
+    api_token: Optional[SecretStr] = Field(
         default=None,
         description="A bearer token if your TGI endpoint is protected.",
     )
@@ -32,7 +32,7 @@ class InferenceEndpointImplConfig(BaseModel):
     endpoint_name: str = Field(
         description="The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided.",
     )
-    api_token: Optional[str] = Field(
+    api_token: Optional[SecretStr] = Field(
         default=None,
         description="Your Hugging Face user access token (will default to locally saved token if not provided)",
     )
@@ -55,7 +55,7 @@ class InferenceAPIImplConfig(BaseModel):
     huggingface_repo: str = Field(
         description="The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct')",
     )
-    api_token: Optional[str] = Field(
+    api_token: Optional[SecretStr] = Field(
         default=None,
         description="Your Hugging Face user access token (will default to locally saved token if not provided)",
     )

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -290,7 +290,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
 class TGIAdapter(_HfAdapter):
     async def initialize(self, config: TGIImplConfig) -> None:
         log.info(f"Initializing TGI client with url={config.url}")
-        self.client = AsyncInferenceClient(model=config.url, token=config.api_token)
+        self.client = AsyncInferenceClient(
+            model=config.url, token=config.api_token.get_secret_value()
+        )
         endpoint_info = await self.client.get_endpoint_info()
         self.max_tokens = endpoint_info["max_total_tokens"]
         self.model_id = endpoint_info["model_id"]
@@ -299,7 +301,7 @@ class TGIAdapter(_HfAdapter):
 class InferenceAPIAdapter(_HfAdapter):
     async def initialize(self, config: InferenceAPIImplConfig) -> None:
         self.client = AsyncInferenceClient(
-            model=config.huggingface_repo, token=config.api_token
+            model=config.huggingface_repo, token=config.api_token.get_secret_value()
         )
         endpoint_info = await self.client.get_endpoint_info()
         self.max_tokens = endpoint_info["max_total_tokens"]
@@ -309,7 +311,7 @@ class InferenceAPIAdapter(_HfAdapter):
 class InferenceEndpointAdapter(_HfAdapter):
     async def initialize(self, config: InferenceEndpointImplConfig) -> None:
         # Get the inference endpoint details
-        api = HfApi(token=config.api_token)
+        api = HfApi(token=config.api_token.get_secret_value())
         endpoint = api.get_inference_endpoint(config.endpoint_name)
 
         # Wait for the endpoint to be ready (if not already)

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

@@ -7,7 +7,7 @@
 from typing import Any, Dict, Optional
 
 from llama_models.schema_utils import json_schema_type
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 
 @json_schema_type
@@ -16,7 +16,7 @@ class TogetherImplConfig(BaseModel):
         default="https://api.together.xyz/v1",
         description="The URL for the Together AI server",
     )
-    api_key: Optional[str] = Field(
+    api_key: Optional[SecretStr] = Field(
         default=None,
         description="The Together AI API Key",
     )

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

@@ -130,7 +130,7 @@ class TogetherInferenceAdapter(
     def _get_client(self) -> Together:
         together_api_key = None
         if self.config.api_key is not None:
-            together_api_key = self.config.api_key
+            together_api_key = self.config.api_key.get_secret_value()
         else:
             provider_data = self.get_request_provider_data()
             if provider_data is None or not provider_data.together_api_key:

llama_stack/providers/tests/datasetio/test_datasetio.py

@@ -38,9 +38,15 @@ def data_url_from_file(file_path: str) -> str:
 
 
 async def register_dataset(
-    datasets_impl: Datasets, for_generation=False, dataset_id="test_dataset"
+    datasets_impl: Datasets,
+    for_generation=False,
+    for_rag=False,
+    dataset_id="test_dataset",
 ):
-    test_file = Path(os.path.abspath(__file__)).parent / "test_dataset.csv"
+    if for_rag:
+        test_file = Path(os.path.abspath(__file__)).parent / "test_rag_dataset.csv"
+    else:
+        test_file = Path(os.path.abspath(__file__)).parent / "test_dataset.csv"
     test_url = data_url_from_file(str(test_file))
 
     if for_generation:
@@ -49,6 +55,13 @@ async def register_dataset(
             "input_query": StringType(),
             "chat_completion_input": ChatCompletionInputType(),
         }
+    elif for_rag:
+        dataset_schema = {
+            "expected_answer": StringType(),
+            "input_query": StringType(),
+            "generated_answer": StringType(),
+            "context": StringType(),
+        }
     else:
         dataset_schema = {
             "expected_answer": StringType(),

llama_stack/providers/tests/datasetio/test_rag_dataset.csv

@@ -0,0 +1,6 @@
+input_query,context,generated_answer,expected_answer
+What is the capital of France?,"France is a country in Western Europe with a population of about 67 million people. Its capital city has been a major European cultural center since the 17th century and is known for landmarks like the Eiffel Tower and the Louvre Museum.",London,Paris
+Who is the CEO of Meta?,"Meta Platforms, formerly known as Facebook, is one of the world's largest technology companies. Founded by Mark Zuckerberg in 2004, the company has expanded to include platforms like Instagram, WhatsApp, and virtual reality technologies.",Mark Zuckerberg,Mark Zuckerberg
+What is the largest planet in our solar system?,"The solar system consists of eight planets orbiting around the Sun. These planets, in order from the Sun, are Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune. Gas giants are significantly larger than terrestrial planets.",Jupiter,Jupiter
+What is the smallest country in the world?,"Independent city-states and micronations are among the world's smallest sovereign territories. Some notable examples include Monaco, San Marino, and Vatican City, which is an enclave within Rome, Italy.",China,Vatican City
+What is the currency of Japan?,"Japan is an island country in East Asia with a rich cultural heritage and one of the world's largest economies. Its financial system has been established since the Meiji period, with its modern currency being introduced in 1871.",Yen,Yen

llama_stack/providers/tests/inference/test_text_inference.py

@@ -210,6 +210,7 @@ class TestInference:
         provider = inference_impl.routing_table.get_provider_impl(inference_model)
         if provider.__provider_spec__.provider_type not in (
             "inline::meta-reference",
+            "remote::ollama",
             "remote::tgi",
             "remote::together",
             "remote::fireworks",
@@ -272,6 +273,7 @@ class TestInference:
         provider = inference_impl.routing_table.get_provider_impl(inference_model)
         if provider.__provider_spec__.provider_type not in (
             "inline::meta-reference",
+            "remote::ollama",
             "remote::fireworks",
             "remote::tgi",
             "remote::together",

llama_stack/providers/tests/scoring/test_scoring.py

@@ -60,7 +60,7 @@ class TestScoring:
                 f"{provider_id} provider does not support scoring without params"
             )
 
-        await register_dataset(datasets_impl)
+        await register_dataset(datasets_impl, for_rag=True)
         response = await datasets_impl.list_datasets()
         assert len(response) == 1
 
@@ -112,7 +112,7 @@ class TestScoring:
             scoring_stack[Api.datasets],
             scoring_stack[Api.models],
         )
-        await register_dataset(datasets_impl)
+        await register_dataset(datasets_impl, for_rag=True)
         response = await datasets_impl.list_datasets()
         assert len(response) == 1
 
@@ -173,7 +173,7 @@ class TestScoring:
             scoring_stack[Api.datasets],
             scoring_stack[Api.models],
         )
-        await register_dataset(datasets_impl)
+        await register_dataset(datasets_impl, for_rag=True)
         rows = await datasetio_impl.get_rows_paginated(
             dataset_id="test_dataset",
             rows_in_page=3,

llama_stack/providers/utils/common/__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/utils/common/data_schema_validator.py

@@ -0,0 +1,87 @@
+# 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 enum import Enum
+from typing import Any, Dict, List
+
+from llama_stack.apis.common.type_system import (
+    ChatCompletionInputType,
+    CompletionInputType,
+    StringType,
+)
+
+from llama_stack.distribution.datatypes import Api
+
+
+class ColumnName(Enum):
+    input_query = "input_query"
+    expected_answer = "expected_answer"
+    chat_completion_input = "chat_completion_input"
+    completion_input = "completion_input"
+    generated_answer = "generated_answer"
+    context = "context"
+
+
+VALID_SCHEMAS_FOR_SCORING = [
+    {
+        ColumnName.input_query.value: StringType(),
+        ColumnName.expected_answer.value: StringType(),
+        ColumnName.generated_answer.value: StringType(),
+    },
+    {
+        ColumnName.input_query.value: StringType(),
+        ColumnName.expected_answer.value: StringType(),
+        ColumnName.generated_answer.value: StringType(),
+        ColumnName.context.value: StringType(),
+    },
+]
+
+VALID_SCHEMAS_FOR_EVAL = [
+    {
+        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(),
+    },
+]
+
+
+def get_valid_schemas(api_str: str):
+    if api_str == Api.scoring.value:
+        return VALID_SCHEMAS_FOR_SCORING
+    elif api_str == Api.eval.value:
+        return VALID_SCHEMAS_FOR_EVAL
+    else:
+        raise ValueError(f"Invalid API string: {api_str}")
+
+
+class DataSchemaValidatorMixin:
+    def validate_dataset_schema(
+        self,
+        dataset_schema: Dict[str, Any],
+        expected_schemas: List[Dict[str, Any]],
+    ):
+        if dataset_schema not in expected_schemas:
+            raise ValueError(
+                f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}"
+            )
+
+    def validate_row_schema(
+        self,
+        input_row: Dict[str, Any],
+        expected_schemas: List[Dict[str, Any]],
+    ):
+        for schema in expected_schemas:
+            if all(key in input_row for key in schema):
+                return
+
+        raise ValueError(
+            f"Input row {input_row} does not match any of the expected schemas in {expected_schemas}"
+        )

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -40,7 +40,6 @@ from llama_stack.apis.common.content_types import (
     InterleavedContent,
     InterleavedContentItem,
     TextContentItem,
-    URL,
 )
 
 from llama_stack.apis.inference import (
@@ -117,27 +116,31 @@ async def interleaved_content_convert_to_raw(
         elif isinstance(c, TextContentItem):
             return RawTextItem(text=c.text)
         elif isinstance(c, ImageContentItem):
-            # load image and return PIL version
+            if c.url:
-            img = c.data
+                # Load image bytes from URL
-            if isinstance(img, URL):
+                if c.url.uri.startswith("data"):
-                if img.uri.startswith("data"):
+                    match = re.match(r"data:image/(\w+);base64,(.+)", c.url.uri)
-                    match = re.match(r"data:image/(\w+);base64,(.+)", img.uri)
                     if not match:
-                        raise ValueError("Invalid data URL format")
+                        raise ValueError(
+                            f"Invalid data URL format, {c.url.uri[:40]}..."
+                        )
                     _, image_data = match.groups()
                     data = base64.b64decode(image_data)
-                elif img.uri.startswith("file://"):
+                elif c.url.uri.startswith("file://"):
-                    path = img.uri[len("file://") :]
+                    path = c.url.uri[len("file://") :]
                     with open(path, "rb") as f:
                         data = f.read()  # type: ignore
-                elif img.uri.startswith("http"):
+                elif c.url.uri.startswith("http"):
                     async with httpx.AsyncClient() as client:
-                        response = await client.get(img.uri)
+                        response = await client.get(c.url.uri)
                         data = response.content
                 else:
                     raise ValueError("Unsupported URL type")
-            else:
+            elif c.data:
                 data = c.data
+            else:
+                raise ValueError("No data or URL provided")
+
             return RawMediaItem(data=data)
         else:
             raise ValueError(f"Unsupported content type: {type(c)}")

llama_stack/providers/utils/scoring/base_scoring_fn.py

@@ -13,12 +13,51 @@ from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_metr
 
 class BaseScoringFn(ABC):
     """
-    Base interface class for all native scoring_fns.
+    Base interface class for Scoring Functions.
-    Each scoring_fn needs to implement the following methods:
+    Each scoring function needs to implement the following methods:
     - score_row(self, row)
     - aggregate(self, scoring_fn_results)
     """
 
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+    def __str__(self) -> str:
+        return self.__class__.__name__
+
+    @abstractmethod
+    async def score_row(
+        self,
+        input_row: Dict[str, Any],
+        scoring_fn_identifier: Optional[str] = None,
+        scoring_params: Optional[ScoringFnParams] = None,
+    ) -> ScoringResultRow:
+        raise NotImplementedError()
+
+    @abstractmethod
+    async def aggregate(
+        self,
+        scoring_results: List[ScoringResultRow],
+        scoring_fn_identifier: Optional[str] = None,
+        scoring_params: Optional[ScoringFnParams] = None,
+    ) -> Dict[str, Any]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    async def score(
+        self,
+        input_rows: List[Dict[str, Any]],
+        scoring_fn_identifier: Optional[str] = None,
+        scoring_params: Optional[ScoringFnParams] = None,
+    ) -> List[ScoringResultRow]:
+        raise NotImplementedError()
+
+
+class RegisteredBaseScoringFn(BaseScoringFn):
+    """
+    Interface for native scoring functions that are registered in LlamaStack.
+    """
+
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
         self.supported_fn_defs_registry = {}

llama_stack/providers/utils/telemetry/trace_protocol.py

@@ -53,7 +53,7 @@ def trace_protocol(cls: Type[T]) -> Type[T]:
             combined_args = {}
             for i, arg in enumerate(args):
                 param_name = (
-                    param_names[i] if i < len(param_names) else f"position_{i+1}"
+                    param_names[i] if i < len(param_names) else f"position_{i + 1}"
                 )
                 combined_args[param_name] = serialize_value(arg)
             for k, v in kwargs.items():