diff --git a/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb b/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb
index fa527f1a0..d061603c8 100644
--- a/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb
+++ b/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",
diff --git a/docs/source/distributions/building_distro.md b/docs/source/distributions/building_distro.md
index 67d39159c..cc94fa9db 100644
--- a/docs/source/distributions/building_distro.md
+++ b/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
-    telemetry: meta-reference
+    agents: inline::meta-reference
+    telemetry: inline::meta-reference
 image_type: conda
 ```
 
diff --git a/docs/zero_to_hero_guide/00_Inference101.ipynb b/docs/zero_to_hero_guide/00_Inference101.ipynb
index 2aced6ef9..687f5606b 100644
--- a/docs/zero_to_hero_guide/00_Inference101.ipynb
+++ b/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": {
diff --git a/docs/zero_to_hero_guide/README.md b/docs/zero_to_hero_guide/README.md
index 68c012164..b451e0af7 100644
--- a/docs/zero_to_hero_guide/README.md
+++ b/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  \
-      --port $LLAMA_STACK_PORT \
-      --env INFERENCE_MODEL=$INFERENCE_MODEL \
-      --env SAFETY_MODEL=$SAFETY_MODEL \
+   llama stack run ollama
+      --port $LLAMA_STACK_PORT
+      --env INFERENCE_MODEL=$INFERENCE_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
-   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
+   llama-stack-client configure --endpoint http://localhost:5001
    ```
    **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:$LLAMA_STACK_PORT/inference/chat_completion \
--H "Content-Type: application/json" \
--d '{
-    "model": "Llama3.2-3B-Instruct",
+curl http://localhost:$LLAMA_STACK_PORT/alpha/inference/chat-completion
+-H "Content-Type: application/json"
+-d @- <<EOF
+{
+    "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
-The `llama-stack-client` library offers a robust and efficient python methods for interacting with the Llama Stack server.
+### 1. Activate Conda Environmen
 
 ```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
-client = LlamaStackClient(base_url="http://localhost:5051")
+# Get the model ID from the environment variable
+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)
 ```
diff --git a/llama_stack/apis/eval/eval.py b/llama_stack/apis/eval/eval.py
index 2592bca37..1073d6310 100644
--- a/llama_stack/apis/eval/eval.py
+++ b/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
diff --git a/llama_stack/apis/scoring/scoring.py b/llama_stack/apis/scoring/scoring.py
index 453e35f6d..996291dcc 100644
--- a/llama_stack/apis/scoring/scoring.py
+++ b/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: ...
diff --git a/llama_stack/distribution/build_container.sh b/llama_stack/distribution/build_container.sh
index a9aee8f14..49e65b8cb 100755
--- a/llama_stack/distribution/build_container.sh
+++ b/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"
 
diff --git a/llama_stack/distribution/library_client.py b/llama_stack/distribution/library_client.py
index 48fcc437b..01b8bb3b5 100644
--- a/llama_stack/distribution/library_client.py
+++ b/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 = {}
diff --git a/llama_stack/distribution/server/server.py b/llama_stack/distribution/server/server.py
index daaf8475b..e432cca4e 100644
--- a/llama_stack/distribution/server/server.py
+++ b/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)
diff --git a/llama_stack/distribution/stack.py b/llama_stack/distribution/stack.py
index 965df5f03..7fc2c7650 100644
--- a/llama_stack/distribution/stack.py
+++ b/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 = {}
diff --git a/llama_stack/distribution/ui/page/evaluations/app_eval.py b/llama_stack/distribution/ui/page/evaluations/app_eval.py
index 5ec47ed45..a9dd50a04 100644
--- a/llama_stack/distribution/ui/page/evaluations/app_eval.py
+++ b/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(),
diff --git a/llama_stack/distribution/ui/page/evaluations/native_eval.py b/llama_stack/distribution/ui/page/evaluations/native_eval.py
index b8cc8bfa6..2cbc8d63e 100644
--- a/llama_stack/distribution/ui/page/evaluations/native_eval.py
+++ b/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)
 
diff --git a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
index f225f5393..09738d7b7 100644
--- a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
+++ b/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
 
diff --git a/llama_stack/providers/inline/eval/meta_reference/eval.py b/llama_stack/providers/inline/eval/meta_reference/eval.py
index 00630132e..b555c9f2a 100644
--- a/llama_stack/providers/inline/eval/meta_reference/eval.py
+++ b/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.common.type_system import (
-    ChatCompletionInputType,
-    CompletionInputType,
-    StringType,
-)
+from llama_stack.apis.agents import Agents, StepType
 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):
-    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):
+class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate, DataSchemaValidatorMixin):
     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
-
-        await self.validate_eval_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.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(
-                {
-                    ColumnName.generated_answer.value: final_event.turn.output_message.content
-                }
+
+            # check if there's a memory retrieval step and extract the context
+            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
 
diff --git a/llama_stack/providers/inline/post_training/torchtune/common/utils.py b/llama_stack/providers/inline/post_training/torchtune/common/utils.py
index 8c73a1397..2b7a4ec93 100644
--- a/llama_stack/providers/inline/post_training/torchtune/common/utils.py
+++ b/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
diff --git a/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py b/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
index 517be6d89..1b6c508a7 100644
--- a/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
+++ b/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
diff --git a/llama_stack/providers/inline/scoring/basic/scoring.py b/llama_stack/providers/inline/scoring/basic/scoring.py
index f8b30cbcf..f612abda4 100644
--- a/llama_stack/providers/inline/scoring/basic/scoring.py
+++ b/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,
diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py
index 9991c5502..9b0566228 100644
--- a/llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py
+++ b/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.
     """
diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py
index 552f34d46..38014ca6f 100644
--- a/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py
+++ b/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.
     """
diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py
index 29ae12e44..71defc433 100644
--- a/llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py
+++ b/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.
     """
diff --git a/llama_stack/providers/inline/scoring/braintrust/braintrust.py b/llama_stack/providers/inline/scoring/braintrust/braintrust.py
index 0c6102645..4282ef6ec 100644
--- a/llama_stack/providers/inline/scoring/braintrust/braintrust.py
+++ b/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(),
-            "braintrust::answer-correctness": AnswerCorrectness(),
+            entry.identifier: entry.evaluator
+            for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
         }
         self.supported_fn_defs_registry = {
-            factuality_fn_def.identifier: factuality_fn_def,
-            answer_correctness_fn_def.identifier: answer_correctness_fn_def,
+            entry.identifier: entry.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]
-            agg_results = aggregate_average(score_results)
+            aggregation_functions = self.supported_fn_defs_registry[
+                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,
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_correctness.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_correctness.py
index dc5df8e78..526ba2c37 100644
--- a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_correctness.py
+++ b/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",
-    params=None,
+    description=(
+        "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]
+    ),
 )
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_relevancy.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_relevancy.py
new file mode 100644
index 000000000..3e3e6ac87
--- /dev/null
+++ b/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]
+    ),
+)
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_similarity.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_similarity.py
new file mode 100644
index 000000000..bea8dfd53
--- /dev/null
+++ b/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]
+    ),
+)
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_entity_recall.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_entity_recall.py
new file mode 100644
index 000000000..ac41df000
--- /dev/null
+++ b/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]
+    ),
+)
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_precision.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_precision.py
new file mode 100644
index 000000000..ef172d82c
--- /dev/null
+++ b/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]
+    ),
+)
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_recall.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_recall.py
new file mode 100644
index 000000000..d4561a5d4
--- /dev/null
+++ b/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]
+    ),
+)
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_relevancy.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/context_relevancy.py
new file mode 100644
index 000000000..06fc86a7b
--- /dev/null
+++ b/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]
+    ),
+)
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/factuality.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/factuality.py
index b733f10c8..a4d597c29 100644
--- a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/factuality.py
+++ b/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",
-    params=None,
+    description=(
+        "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]
+    ),
 )
diff --git a/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/faithfulness.py b/llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/faithfulness.py
new file mode 100644
index 000000000..9cffff558
--- /dev/null
+++ b/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]
+    ),
+)
diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py b/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py
index 09780e6fb..305c13665 100644
--- a/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py
+++ b/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,
diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py b/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py
index 00ea53c8f..027709f74 100644
--- a/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py
+++ b/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
     """
diff --git a/llama_stack/providers/remote/inference/cerebras/cerebras.py b/llama_stack/providers/remote/inference/cerebras/cerebras.py
index 40457e1ae..586447012 100644
--- a/llama_stack/providers/remote/inference/cerebras/cerebras.py
+++ b/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:
diff --git a/llama_stack/providers/remote/inference/cerebras/config.py b/llama_stack/providers/remote/inference/cerebras/config.py
index 9bae6ca4d..6eb4dffec 100644
--- a/llama_stack/providers/remote/inference/cerebras/config.py
+++ b/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",
     )
diff --git a/llama_stack/providers/remote/inference/fireworks/config.py b/llama_stack/providers/remote/inference/fireworks/config.py
index 979e8455a..d84a00d56 100644
--- a/llama_stack/providers/remote/inference/fireworks/config.py
+++ b/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",
     )
diff --git a/llama_stack/providers/remote/inference/fireworks/fireworks.py b/llama_stack/providers/remote/inference/fireworks/fireworks.py
index 7a00194ac..6706e9f4a 100644
--- a/llama_stack/providers/remote/inference/fireworks/fireworks.py
+++ b/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:
diff --git a/llama_stack/providers/remote/inference/nvidia/config.py b/llama_stack/providers/remote/inference/nvidia/config.py
index 28be43f4c..9e81211bd 100644
--- a/llama_stack/providers/remote/inference/nvidia/config.py
+++ b/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",
     )
diff --git a/llama_stack/providers/remote/inference/nvidia/nvidia.py b/llama_stack/providers/remote/inference/nvidia/nvidia.py
index 585ad83c7..42c4db53e 100644
--- a/llama_stack/providers/remote/inference/nvidia/nvidia.py
+++ b/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,
         )
 
diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py
index 88f985f3a..2de5a994e 100644
--- a/llama_stack/providers/remote/inference/ollama/ollama.py
+++ b/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,
diff --git a/llama_stack/providers/remote/inference/tgi/config.py b/llama_stack/providers/remote/inference/tgi/config.py
index 230eaacab..f05005b25 100644
--- a/llama_stack/providers/remote/inference/tgi/config.py
+++ b/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)",
     )
diff --git a/llama_stack/providers/remote/inference/tgi/tgi.py b/llama_stack/providers/remote/inference/tgi/tgi.py
index dd02c055a..25d2e0cb8 100644
--- a/llama_stack/providers/remote/inference/tgi/tgi.py
+++ b/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)
diff --git a/llama_stack/providers/remote/inference/together/config.py b/llama_stack/providers/remote/inference/together/config.py
index ecbe9ec06..a56cb5bb8 100644
--- a/llama_stack/providers/remote/inference/together/config.py
+++ b/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",
     )
diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py
index 6b5a6a3b0..f8e889ab3 100644
--- a/llama_stack/providers/remote/inference/together/together.py
+++ b/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:
diff --git a/llama_stack/providers/tests/datasetio/test_datasetio.py b/llama_stack/providers/tests/datasetio/test_datasetio.py
index 46c99f5b3..cf28045a4 100644
--- a/llama_stack/providers/tests/datasetio/test_datasetio.py
+++ b/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(),
diff --git a/llama_stack/providers/tests/datasetio/test_rag_dataset.csv b/llama_stack/providers/tests/datasetio/test_rag_dataset.csv
new file mode 100644
index 000000000..a0e1fce72
--- /dev/null
+++ b/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
diff --git a/llama_stack/providers/tests/inference/test_text_inference.py b/llama_stack/providers/tests/inference/test_text_inference.py
index 2eeda0dbf..fd93857a3 100644
--- a/llama_stack/providers/tests/inference/test_text_inference.py
+++ b/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",
diff --git a/llama_stack/providers/tests/scoring/test_scoring.py b/llama_stack/providers/tests/scoring/test_scoring.py
index 2643b8fd6..00dd5d27b 100644
--- a/llama_stack/providers/tests/scoring/test_scoring.py
+++ b/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,
diff --git a/llama_stack/providers/utils/common/__init__.py b/llama_stack/providers/utils/common/__init__.py
new file mode 100644
index 000000000..756f351d8
--- /dev/null
+++ b/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.
diff --git a/llama_stack/providers/utils/common/data_schema_validator.py b/llama_stack/providers/utils/common/data_schema_validator.py
new file mode 100644
index 000000000..d9e6cb6b5
--- /dev/null
+++ b/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}"
+        )
diff --git a/llama_stack/providers/utils/inference/prompt_adapter.py b/llama_stack/providers/utils/inference/prompt_adapter.py
index f7d2cd84e..ed0cabe1c 100644
--- a/llama_stack/providers/utils/inference/prompt_adapter.py
+++ b/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
-            img = c.data
-            if isinstance(img, URL):
-                if img.uri.startswith("data"):
-                    match = re.match(r"data:image/(\w+);base64,(.+)", img.uri)
+            if c.url:
+                # Load image bytes from URL
+                if c.url.uri.startswith("data"):
+                    match = re.match(r"data:image/(\w+);base64,(.+)", c.url.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://"):
-                    path = img.uri[len("file://") :]
+                elif c.url.uri.startswith("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)}")
diff --git a/llama_stack/providers/utils/scoring/base_scoring_fn.py b/llama_stack/providers/utils/scoring/base_scoring_fn.py
index 2db77fd2b..e0e557374 100644
--- a/llama_stack/providers/utils/scoring/base_scoring_fn.py
+++ b/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.
-    Each scoring_fn needs to implement the following methods:
+    Base interface class for Scoring Functions.
+    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 = {}
diff --git a/llama_stack/providers/utils/telemetry/trace_protocol.py b/llama_stack/providers/utils/telemetry/trace_protocol.py
index 31897c0ae..38a56fdac 100644
--- a/llama_stack/providers/utils/telemetry/trace_protocol.py
+++ b/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():