feat(providers): sambanova safety provider (#2221)

# What does this PR do?

Includes SambaNova safety adaptor to use the sambanova cloud served
Meta-Llama-Guard-3-8B
minor updates in sambanova docs

## Test Plan
pytest -s -v tests/integration/safety/test_safety.py
--stack-config=sambanova --safety-shield=sambanova/Meta-Llama-Guard-3-8B

llama_stack/providers/registry/safety.py

@@ -63,4 +63,14 @@ def available_providers() -> list[ProviderSpec]:
                 config_class="llama_stack.providers.remote.safety.nvidia.NVIDIASafetyConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.safety,
+            adapter=AdapterSpec(
+                adapter_type="sambanova",
+                pip_packages=["litellm"],
+                module="llama_stack.providers.remote.safety.sambanova",
+                config_class="llama_stack.providers.remote.safety.sambanova.SambaNovaSafetyConfig",
+                provider_data_validator="llama_stack.providers.remote.safety.sambanova.config.SambaNovaProviderDataValidator",
+            ),
+        ),
     ]

llama_stack/providers/remote/safety/sambanova/__init__.py

@@ -0,0 +1,18 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+from typing import Any
+
+from .config import SambaNovaSafetyConfig
+
+
+async def get_adapter_impl(config: SambaNovaSafetyConfig, _deps) -> Any:
+    from .sambanova import SambaNovaSafetyAdapter
+
+    impl = SambaNovaSafetyAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/remote/safety/sambanova/config.py

@@ -0,0 +1,37 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any
+
+from pydantic import BaseModel, Field, SecretStr
+
+from llama_stack.schema_utils import json_schema_type
+
+
+class SambaNovaProviderDataValidator(BaseModel):
+    sambanova_api_key: str | None = Field(
+        default=None,
+        description="Sambanova Cloud API key",
+    )
+
+
+@json_schema_type
+class SambaNovaSafetyConfig(BaseModel):
+    url: str = Field(
+        default="https://api.sambanova.ai/v1",
+        description="The URL for the SambaNova AI server",
+    )
+    api_key: SecretStr | None = Field(
+        default=None,
+        description="The SambaNova cloud API Key",
+    )
+
+    @classmethod
+    def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> dict[str, Any]:
+        return {
+            "url": "https://api.sambanova.ai/v1",
+            "api_key": api_key,
+        }

llama_stack/providers/remote/safety/sambanova/sambanova.py

@@ -0,0 +1,100 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import json
+import logging
+from typing import Any
+
+import litellm
+import requests
+
+from llama_stack.apis.inference import Message
+from llama_stack.apis.safety import (
+    RunShieldResponse,
+    Safety,
+    SafetyViolation,
+    ViolationLevel,
+)
+from llama_stack.apis.shields import Shield
+from llama_stack.distribution.request_headers import NeedsRequestProviderData
+from llama_stack.providers.datatypes import ShieldsProtocolPrivate
+from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict_new
+
+from .config import SambaNovaSafetyConfig
+
+logger = logging.getLogger(__name__)
+
+CANNED_RESPONSE_TEXT = "I can't answer that. Can I help with something else?"
+
+
+class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProviderData):
+    def __init__(self, config: SambaNovaSafetyConfig) -> None:
+        self.config = config
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    def _get_api_key(self) -> str:
+        config_api_key = self.config.api_key if self.config.api_key else None
+        if config_api_key:
+            return config_api_key.get_secret_value()
+        else:
+            provider_data = self.get_request_provider_data()
+            if provider_data is None or not provider_data.sambanova_api_key:
+                raise ValueError(
+                    'Pass Sambanova API Key in the header X-LlamaStack-Provider-Data as { "sambanova_api_key": <your api key> }'
+                )
+            return provider_data.sambanova_api_key
+
+    async def register_shield(self, shield: Shield) -> None:
+        list_models_url = self.config.url + "/models"
+        try:
+            response = requests.get(list_models_url)
+            response.raise_for_status()
+        except requests.exceptions.RequestException as e:
+            raise RuntimeError(f"Request to {list_models_url} failed") from e
+        available_models = [model.get("id") for model in response.json().get("data", {})]
+        if (
+            len(available_models) == 0
+            or "guard" not in shield.provider_resource_id.lower()
+            or shield.provider_resource_id.split("sambanova/")[-1] not in available_models
+        ):
+            raise ValueError(f"Shield {shield.provider_resource_id} not found in SambaNova")
+
+    async def run_shield(
+        self, shield_id: str, messages: list[Message], params: dict[str, Any] | None = None
+    ) -> RunShieldResponse:
+        shield = await self.shield_store.get_shield(shield_id)
+        if not shield:
+            raise ValueError(f"Shield {shield_id} not found")
+
+        shield_params = shield.params
+        logger.debug(f"run_shield::{shield_params}::messages={messages}")
+        content_messages = [await convert_message_to_openai_dict_new(m) for m in messages]
+        logger.debug(f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:")
+
+        response = litellm.completion(
+            model=shield.provider_resource_id, messages=content_messages, api_key=self._get_api_key()
+        )
+        shield_message = response.choices[0].message.content
+
+        if "unsafe" in shield_message.lower():
+            user_message = CANNED_RESPONSE_TEXT
+            violation_type = shield_message.split("\n")[-1]
+            metadata = {"violation_type": violation_type}
+
+            return RunShieldResponse(
+                violation=SafetyViolation(
+                    user_message=user_message,
+                    violation_level=ViolationLevel.ERROR,
+                    metadata=metadata,
+                )
+            )
+
+        return RunShieldResponse()

llama_stack/templates/sambanova/build.yaml

@@ -1,6 +1,6 @@
 version: '2'
 distribution_spec:
-  description: Use SambaNova for running LLM inference
+  description: Use SambaNova for running LLM inference and safety
   providers:
     inference:
     - remote::sambanova
@@ -10,7 +10,7 @@ distribution_spec:
     - remote::chromadb
     - remote::pgvector
     safety:
-    - inline::llama-guard
+    - remote::sambanova
     agents:
     - inline::meta-reference
     telemetry:

llama_stack/templates/sambanova/doc_template.md

@@ -37,33 +37,44 @@ The following models are available by default:
 
 ### Prerequisite: API Keys
 
-Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://sambanova.ai/).
+Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](http://cloud.sambanova.ai?utm_source=llamastack&utm_medium=external&utm_campaign=cloud_signup).
 
 
 ## Running Llama Stack with SambaNova
 
 You can do this via Conda (build code) or Docker which has a pre-built image.
 
-### Via Docker
 
-This method allows you to get started quickly without having to build the distribution code.
+### Via Docker
 
 ```bash
 LLAMA_STACK_PORT=8321
+llama stack build --template sambanova --image-type container
 docker run \
   -it \
-  --pull always \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  llamastack/distribution-{{ name }} \
+  -v ~/.llama:/root/.llama \
+  distribution-{{ name }} \
   --port $LLAMA_STACK_PORT \
   --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
 ```
 
+
+### Via Venv
+
+```bash
+llama stack build --template sambanova --image-type venv
+llama stack run --image-type venv ~/.llama/distributions/sambanova/sambanova-run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
+```
+
+
 ### Via Conda
 
 ```bash
 llama stack build --template sambanova --image-type conda
-llama stack run ./run.yaml \
+llama stack run --image-type conda ~/.llama/distributions/sambanova/sambanova-run.yaml \
   --port $LLAMA_STACK_PORT \
   --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
 ```

llama_stack/templates/sambanova/run.yaml

@@ -38,10 +38,11 @@ providers:
       user: ${env.PGVECTOR_USER:}
       password: ${env.PGVECTOR_PASSWORD:}
   safety:
-  - provider_id: llama-guard
-    provider_type: inline::llama-guard
+  - provider_id: sambanova
+    provider_type: remote::sambanova
     config:
-      excluded_categories: []
+      url: https://api.sambanova.ai/v1
+      api_key: ${env.SAMBANOVA_API_KEY}
   agents:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
@@ -189,6 +190,9 @@ models:
   model_type: embedding
 shields:
 - shield_id: meta-llama/Llama-Guard-3-8B
+  provider_shield_id: sambanova/Meta-Llama-Guard-3-8B
+- shield_id: sambanova/Meta-Llama-Guard-3-8B
+  provider_shield_id: sambanova/Meta-Llama-Guard-3-8B
 vector_dbs: []
 datasets: []
 scoring_fns: []

llama_stack/templates/sambanova/sambanova.py

@@ -34,7 +34,7 @@ def get_distribution_template() -> DistributionTemplate:
     providers = {
         "inference": ["remote::sambanova", "inline::sentence-transformers"],
         "vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
-        "safety": ["inline::llama-guard"],
+        "safety": ["remote::sambanova"],
         "agents": ["inline::meta-reference"],
         "telemetry": ["inline::meta-reference"],
         "tool_runtime": [
@@ -110,7 +110,7 @@ def get_distribution_template() -> DistributionTemplate:
     return DistributionTemplate(
         name=name,
         distro_type="self_hosted",
-        description="Use SambaNova for running LLM inference",
+        description="Use SambaNova for running LLM inference and safety",
         container_image=None,
         template_path=Path(__file__).parent / "doc_template.md",
         providers=providers,
@@ -122,7 +122,15 @@ def get_distribution_template() -> DistributionTemplate:
                     "vector_io": vector_io_providers,
                 },
                 default_models=default_models + [embedding_model],
-                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+                default_shields=[
+                    ShieldInput(
+                        shield_id="meta-llama/Llama-Guard-3-8B", provider_shield_id="sambanova/Meta-Llama-Guard-3-8B"
+                    ),
+                    ShieldInput(
+                        shield_id="sambanova/Meta-Llama-Guard-3-8B",
+                        provider_shield_id="sambanova/Meta-Llama-Guard-3-8B",
+                    ),
+                ],
                 default_tool_groups=default_tool_groups,
             ),
         },