Sambanova inference provider (#555)

# What does this PR do?

This PR adds SambaNova as one of the Provider

- Add SambaNova as a provider

## Test Plan
Test the functional command
```
pytest -s -v --providers inference=sambanova llama_stack/providers/tests/inference/test_embeddings.py llama_stack/providers/tests/inference/test_prompt_adapter.py llama_stack/providers/tests/inference/test_text_inference.py llama_stack/providers/tests/inference/test_vision_inference.py --env SAMBANOVA_API_KEY=<sambanova-api-key>
```

Test the distribution template:
```
# Docker
LLAMA_STACK_PORT=5001
docker run -it -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  llamastack/distribution-sambanova \
  --port $LLAMA_STACK_PORT \
  --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY

# Conda
llama stack build --template sambanova --image-type conda
llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
```

## Source
[SambaNova API Documentation](https://cloud.sambanova.ai/apis)

## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [Y] Ran pre-commit to handle lint / formatting issues.
- [Y] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [Y] Updated relevant documentation.
- [Y ] Wrote necessary unit or integration tests.

---------

Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

distributions/sambanova/build.yaml

@@ -0,0 +1,19 @@
+version: '2'
+name: sambanova
+distribution_spec:
+  description: Use SambaNova.AI for running LLM inference
+  docker_image: null
+  providers:
+    inference:
+    - remote::sambanova
+    memory:
+    - inline::faiss
+    - remote::chromadb
+    - remote::pgvector
+    safety:
+    - inline::llama-guard
+    agents:
+    - inline::meta-reference
+    telemetry:
+    - inline::meta-reference
+image_type: conda

distributions/sambanova/compose.yaml

@@ -0,0 +1,16 @@
+services:
+  llamastack:
+    image: llamastack/distribution-sambanova
+    network_mode: "host"
+    volumes:
+      - ~/.llama:/root/.llama
+      - ./run.yaml:/root/llamastack-run-sambanova.yaml
+    ports:
+      - "5000:5000"
+    entrypoint: bash -c "python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-sambanova.yaml"
+    deploy:
+      restart_policy:
+        condition: on-failure
+        delay: 3s
+        max_attempts: 5
+        window: 60s

distributions/sambanova/run.yaml

@@ -0,0 +1,83 @@
+version: '2'
+image_name: sambanova
+docker_image: null
+conda_env: sambanova
+apis:
+- agents
+- inference
+- memory
+- safety
+- telemetry
+providers:
+  inference:
+  - provider_id: sambanova
+    provider_type: remote::sambanova
+    config:
+      url: https://api.sambanova.ai/v1/
+      api_key: ${env.SAMBANOVA_API_KEY}
+  memory:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/faiss_store.db
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config: {}
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/agents_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config: {}
+metadata_store:
+  namespace: null
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/registry.db
+models:
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-8B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.1-8B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-70B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.1-70B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-405B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.1-405B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-1B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.2-1B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-3B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.2-3B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
+  provider_id: null
+  provider_model_id: Llama-3.2-11B-Vision-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
+  provider_id: null
+  provider_model_id: Llama-3.2-90B-Vision-Instruct
+shields:
+- params: null
+  shield_id: meta-llama/Llama-Guard-3-8B
+  provider_id: null
+  provider_shield_id: null
+memory_banks: []
+datasets: []
+scoring_fns: []
+eval_tasks: []

docs/source/concepts/index.md

@@ -24,7 +24,7 @@ We are working on adding a few more APIs to complete the application lifecycle.
 ## API Providers
 
 The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Obvious examples for these include
-- LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, etc.),
+- LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, SambaNova, etc.),
 - Vector databases (e.g., ChromaDB, Weaviate, Qdrant, etc.),
 - Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
 

docs/source/distributions/self_hosted_distro/sambanova.md

@@ -0,0 +1,74 @@
+---
+orphan: true
+---
+# SambaNova Distribution
+
+```{toctree}
+:maxdepth: 2
+:hidden:
+
+self
+```
+
+The `llamastack/distribution-sambanova` distribution consists of the following provider configurations.
+
+| API | Provider(s) |
+|-----|-------------|
+| agents | `inline::meta-reference` |
+| inference | `remote::sambanova` |
+| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
+| safety | `inline::llama-guard` |
+| telemetry | `inline::meta-reference` |
+
+
+### Environment Variables
+
+The following environment variables can be configured:
+
+- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
+- `SAMBANOVA_API_KEY`: SambaNova.AI API Key (default: ``)
+
+### Models
+
+The following models are available by default:
+
+- `meta-llama/Llama-3.1-8B-Instruct`
+- `meta-llama/Llama-3.1-70B-Instruct`
+- `meta-llama/Llama-3.1-405B-Instruct`
+- `meta-llama/Llama-3.2-1B-Instruct`
+- `meta-llama/Llama-3.2-3B-Instruct`
+- `meta-llama/Llama-3.2-11B-Vision-Instruct`
+- `meta-llama/Llama-3.2-90B-Vision-Instruct`
+
+
+### Prerequisite: API Keys
+
+Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaBova.ai](https://sambanova.ai/).
+
+
+## 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.
+
+```bash
+LLAMA_STACK_PORT=5001
+docker run \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  llamastack/distribution-sambanova \
+  --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 \
+  --port $LLAMA_STACK_PORT \
+  --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
+```

docs/source/index.md

@@ -40,6 +40,7 @@ A number of "adapters" are available for some popular Inference and Memory (Vect
 |  Fireworks  |  Hosted  | Y  | Y  |  Y  |    |   |
 |  AWS Bedrock  |  Hosted  |    |  Y  |    | Y  | |
 |  Together  |  Hosted  |  Y  |  Y  |   | Y  |  |
+|  SambaNova  |  Hosted  |    |  Y  |   |   |  |
 |  Ollama  | Single Node   |    |  Y  |    |   |
 |  TGI  |  Hosted and Single Node  |    |  Y  |    |   |
 |  NVIDIA NIM  |  Hosted and Single Node  |    |  Y  |    |   |

llama_stack/distribution/ui/modules/api.py

@@ -18,6 +18,7 @@ class LlamaStackApi:
             provider_data={
                 "fireworks_api_key": os.environ.get("FIREWORKS_API_KEY", ""),
                 "together_api_key": os.environ.get("TOGETHER_API_KEY", ""),
+                "sambanova_api_key": os.environ.get("SAMBANOVA_API_KEY", ""),
                 "openai_api_key": os.environ.get("OPENAI_API_KEY", ""),
             },
         )

llama_stack/providers/registry/inference.py

@@ -204,4 +204,15 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.adapters.inference.runpod.RunpodImplConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="sambanova",
+                pip_packages=[
+                    "openai",
+                ],
+                module="llama_stack.providers.remote.inference.sambanova",
+                config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
+            ),
+        ),
     ]

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

@@ -0,0 +1,23 @@
+# 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 pydantic import BaseModel
+
+from .config import SambaNovaImplConfig
+from .sambanova import SambaNovaInferenceAdapter
+
+
+class SambaNovaProviderDataValidator(BaseModel):
+    sambanova_api_key: str
+
+
+async def get_adapter_impl(config: SambaNovaImplConfig, _deps):
+    assert isinstance(
+        config, SambaNovaImplConfig
+    ), f"Unexpected config type: {type(config)}"
+    impl = SambaNovaInferenceAdapter(config)
+    await impl.initialize()
+    return impl

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

@@ -0,0 +1,29 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any, Dict, Optional
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+
+@json_schema_type
+class SambaNovaImplConfig(BaseModel):
+    url: str = Field(
+        default="https://api.sambanova.ai/v1",
+        description="The URL for the SambaNova AI server",
+    )
+    api_key: Optional[str] = Field(
+        default=None,
+        description="The SambaNova.ai API Key",
+    )
+
+    @classmethod
+    def sample_run_config(cls) -> Dict[str, Any]:
+        return {
+            "url": "https://api.sambanova.ai/v1",
+            "api_key": "${env.SAMBANOVA_API_KEY}",
+        }

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -0,0 +1,333 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import json
+from typing import AsyncGenerator
+
+from llama_models.datatypes import CoreModelId, SamplingStrategy
+from llama_models.llama3.api.chat_format import ChatFormat
+from llama_models.llama3.api.tokenizer import Tokenizer
+from openai import OpenAI
+
+from llama_stack.apis.common.content_types import (
+    ImageContentItem,
+    InterleavedContent,
+    TextContentItem,
+)
+from llama_stack.apis.inference import *  # noqa: F403
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    process_chat_completion_stream_response,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    convert_image_content_to_url,
+)
+
+from .config import SambaNovaImplConfig
+
+MODEL_ALIASES = [
+    build_model_alias(
+        "Meta-Llama-3.1-8B-Instruct",
+        CoreModelId.llama3_1_8b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.1-70B-Instruct",
+        CoreModelId.llama3_1_70b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.1-405B-Instruct",
+        CoreModelId.llama3_1_405b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.2-1B-Instruct",
+        CoreModelId.llama3_2_1b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.2-3B-Instruct",
+        CoreModelId.llama3_2_3b_instruct.value,
+    ),
+    build_model_alias(
+        "Llama-3.2-11B-Vision-Instruct",
+        CoreModelId.llama3_2_11b_vision_instruct.value,
+    ),
+    build_model_alias(
+        "Llama-3.2-90B-Vision-Instruct",
+        CoreModelId.llama3_2_90b_vision_instruct.value,
+    ),
+]
+
+
+class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
+    def __init__(self, config: SambaNovaImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self,
+            model_aliases=MODEL_ALIASES,
+        )
+
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+    async def initialize(self) -> None:
+        return
+
+    async def shutdown(self) -> None:
+        pass
+
+    def _get_client(self) -> OpenAI:
+        return OpenAI(base_url=self.config.url, api_key=self.config.api_key)
+
+    async def completion(
+        self,
+        model_id: str,
+        content: InterleavedContent,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        raise NotImplementedError()
+
+    async def chat_completion(
+        self,
+        model_id: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        model = await self.model_store.get_model(model_id)
+
+        request = ChatCompletionRequest(
+            model=model.provider_resource_id,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        request_sambanova = await self.convert_chat_completion_request(request)
+
+        if stream:
+            return self._stream_chat_completion(request_sambanova)
+        else:
+            return await self._nonstream_chat_completion(request_sambanova)
+
+    async def _nonstream_chat_completion(
+        self, request: ChatCompletionRequest
+    ) -> ChatCompletionResponse:
+        response = self._get_client().chat.completions.create(**request)
+
+        choice = response.choices[0]
+
+        result = ChatCompletionResponse(
+            completion_message=CompletionMessage(
+                content=choice.message.content or "",
+                stop_reason=self.convert_to_sambanova_finish_reason(
+                    choice.finish_reason
+                ),
+                tool_calls=self.convert_to_sambanova_tool_calls(
+                    choice.message.tool_calls
+                ),
+            ),
+            logprobs=None,
+        )
+
+        return result
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest
+    ) -> AsyncGenerator:
+        async def _to_async_generator():
+            streaming = self._get_client().chat.completions.create(**request)
+            for chunk in streaming:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_chat_completion_stream_response(
+            stream, self.formatter
+        ):
+            yield chunk
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedContent],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()
+
+    async def convert_chat_completion_request(
+        self, request: ChatCompletionRequest
+    ) -> dict:
+        compatible_request = self.convert_sampling_params(request.sampling_params)
+        compatible_request["model"] = request.model
+        compatible_request["messages"] = await self.convert_to_sambanova_messages(
+            request.messages
+        )
+        compatible_request["stream"] = request.stream
+        compatible_request["logprobs"] = False
+        compatible_request["extra_headers"] = {
+            b"User-Agent": b"llama-stack: sambanova-inference-adapter",
+        }
+        compatible_request["tools"] = self.convert_to_sambanova_tool(request.tools)
+        return compatible_request
+
+    def convert_sampling_params(
+        self, sampling_params: SamplingParams, legacy: bool = False
+    ) -> dict:
+        params = {}
+
+        if sampling_params:
+            params["frequency_penalty"] = sampling_params.repetition_penalty
+
+            if sampling_params.max_tokens:
+                if legacy:
+                    params["max_tokens"] = sampling_params.max_tokens
+                else:
+                    params["max_completion_tokens"] = sampling_params.max_tokens
+
+            if sampling_params.strategy == SamplingStrategy.top_p:
+                params["top_p"] = sampling_params.top_p
+            elif sampling_params.strategy == "top_k":
+                params["extra_body"]["top_k"] = sampling_params.top_k
+            elif sampling_params.strategy == "greedy":
+                params["temperature"] = sampling_params.temperature
+
+        return params
+
+    async def convert_to_sambanova_messages(
+        self, messages: List[Message]
+    ) -> List[dict]:
+        conversation = []
+        for message in messages:
+            content = {}
+
+            content["content"] = await self.convert_to_sambanova_content(message)
+
+            if isinstance(message, UserMessage):
+                content["role"] = "user"
+            elif isinstance(message, CompletionMessage):
+                content["role"] = "assistant"
+                tools = []
+                for tool_call in message.tool_calls:
+                    tools.append(
+                        {
+                            "id": tool_call.call_id,
+                            "function": {
+                                "name": tool_call.name,
+                                "arguments": json.dumps(tool_call.arguments),
+                            },
+                            "type": "function",
+                        }
+                    )
+                content["tool_calls"] = tools
+            elif isinstance(message, ToolResponseMessage):
+                content["role"] = "tool"
+                content["tool_call_id"] = message.call_id
+            elif isinstance(message, SystemMessage):
+                content["role"] = "system"
+
+            conversation.append(content)
+
+        return conversation
+
+    async def convert_to_sambanova_content(self, message: Message) -> dict:
+        async def _convert_content(content) -> dict:
+            if isinstance(content, ImageContentItem):
+                url = await convert_image_content_to_url(content, download=True)
+                # A fix to make sure the call sucess.
+                components = url.split(";base64")
+                url = f"{components[0].lower()};base64{components[1]}"
+                return {
+                    "type": "image_url",
+                    "image_url": {"url": url},
+                }
+            else:
+                text = content.text if isinstance(content, TextContentItem) else content
+                assert isinstance(text, str)
+                return {"type": "text", "text": text}
+
+        if isinstance(message.content, list):
+            # If it is a list, the text content should be wrapped in dict
+            content = [await _convert_content(c) for c in message.content]
+        else:
+            content = message.content
+
+        return content
+
+    def convert_to_sambanova_tool(self, tools: List[ToolDefinition]) -> List[dict]:
+        if tools is None:
+            return tools
+
+        compatiable_tools = []
+
+        for tool in tools:
+            properties = {}
+            compatiable_required = []
+            if tool.parameters:
+                for tool_key, tool_param in tool.parameters.items():
+                    properties[tool_key] = {"type": tool_param.param_type}
+                    if tool_param.description:
+                        properties[tool_key]["description"] = tool_param.description
+                    if tool_param.default:
+                        properties[tool_key]["default"] = tool_param.default
+                    if tool_param.required:
+                        compatiable_required.append(tool_key)
+
+            compatiable_tool = {
+                "type": "function",
+                "function": {
+                    "name": tool.tool_name,
+                    "description": tool.description,
+                    "parameters": {
+                        "type": "object",
+                        "properties": properties,
+                        "required": compatiable_required,
+                    },
+                },
+            }
+
+            compatiable_tools.append(compatiable_tool)
+
+        if len(compatiable_tools) > 0:
+            return compatiable_tools
+        return None
+
+    def convert_to_sambanova_finish_reason(self, finish_reason: str) -> StopReason:
+        return {
+            "stop": StopReason.end_of_turn,
+            "length": StopReason.out_of_tokens,
+            "tool_calls": StopReason.end_of_message,
+        }.get(finish_reason, StopReason.end_of_turn)
+
+    def convert_to_sambanova_tool_calls(
+        self,
+        tool_calls,
+    ) -> List[ToolCall]:
+        if not tool_calls:
+            return []
+
+        for call in tool_calls:
+            call_function_arguments = json.loads(call.function.arguments)
+
+        compitable_tool_calls = [
+            ToolCall(
+                call_id=call.id,
+                tool_name=call.function.name,
+                arguments=call_function_arguments,
+            )
+            for call in tool_calls
+        ]
+
+        return compitable_tool_calls

llama_stack/providers/tests/inference/fixtures.py

@@ -23,6 +23,7 @@ from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig
 from llama_stack.providers.remote.inference.groq import GroqConfig
 from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
 from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
+from llama_stack.providers.remote.inference.sambanova import SambaNovaImplConfig
 from llama_stack.providers.remote.inference.tgi import TGIImplConfig
 from llama_stack.providers.remote.inference.together import TogetherImplConfig
 from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
@@ -232,6 +233,23 @@ def inference_tgi() -> ProviderFixture:
 
 
 @pytest.fixture(scope="session")
+def inference_sambanova() -> ProviderFixture:
+    return ProviderFixture(
+        providers=[
+            Provider(
+                provider_id="sambanova",
+                provider_type="remote::sambanova",
+                config=SambaNovaImplConfig(
+                    api_key=get_env_or_fail("SAMBANOVA_API_KEY"),
+                ).model_dump(),
+            )
+        ],
+        provider_data=dict(
+            sambanova_api_key=get_env_or_fail("SAMBANOVA_API_KEY"),
+        ),
+    )
+
+
 def inference_sentence_transformers() -> ProviderFixture:
     return ProviderFixture(
         providers=[
@@ -282,6 +300,7 @@ INFERENCE_FIXTURES = [
     "cerebras",
     "nvidia",
     "tgi",
+    "sambanova",
 ]
 
 

llama_stack/providers/tests/inference/test_model_registration.py

@@ -59,7 +59,7 @@ class TestModelRegistration:
             },
         )
 
-        with pytest.raises(AssertionError) as exc_info:
+        with pytest.raises(ValueError) as exc_info:
             await models_impl.register_model(
                 model_id="custom-model-2",
                 metadata={

llama_stack/providers/tests/inference/test_text_inference.py

@@ -385,6 +385,12 @@ class TestInference:
             # TODO(aidand): Remove this skip once Groq's tool calling for Llama3.2 works better
             pytest.skip("Groq's tool calling for Llama3.2 doesn't work very well")
 
+        if provider.__provider_spec__.provider_type == "remote::sambanova" and (
+            "-1B-" in inference_model or "-3B-" in inference_model
+        ):
+            # TODO(snova-edawrdm): Remove this skip once SambaNova's tool calling for 1B/ 3B
+            pytest.skip("Sambanova's tool calling for lightweight models don't work")
+
         messages = sample_messages + [
             UserMessage(
                 content="What's the weather like in San Francisco?",
@@ -431,6 +437,9 @@ class TestInference:
         ):
             # TODO(aidand): Remove this skip once Groq's tool calling for Llama3.2 works better
             pytest.skip("Groq's tool calling for Llama3.2 doesn't work very well")
+        if provider.__provider_spec__.provider_type == "remote::sambanova":
+            # TODO(snova-edawrdm): Remove this skip once SambaNova's tool calling under streaming is supported (we are working on it)
+            pytest.skip("Sambanova's tool calling for streaming doesn't work")
 
         messages = sample_messages + [
             UserMessage(

llama_stack/providers/tests/inference/test_vision_inference.py

@@ -59,6 +59,7 @@ class TestVisionModelInference:
             "remote::fireworks",
             "remote::ollama",
             "remote::vllm",
+            "remote::sambanova",
         ):
             pytest.skip(
                 "Other inference providers don't support vision chat completion() yet"
@@ -98,6 +99,7 @@ class TestVisionModelInference:
             "remote::fireworks",
             "remote::ollama",
             "remote::vllm",
+            "remote::sambanova",
         ):
             pytest.skip(
                 "Other inference providers don't support vision chat completion() yet"

llama_stack/templates/sambanova/__init__.py

@@ -0,0 +1,7 @@
+# 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 .sambanova import get_distribution_template  # noqa: F401

llama_stack/templates/sambanova/build.yaml

@@ -0,0 +1,19 @@
+version: '2'
+name: sambanova
+distribution_spec:
+  description: Use SambaNova.AI for running LLM inference
+  docker_image: null
+  providers:
+    inference:
+    - remote::sambanova
+    memory:
+    - inline::faiss
+    - remote::chromadb
+    - remote::pgvector
+    safety:
+    - inline::llama-guard
+    agents:
+    - inline::meta-reference
+    telemetry:
+    - inline::meta-reference
+image_type: conda

llama_stack/templates/sambanova/doc_template.md

@@ -0,0 +1,68 @@
+---
+orphan: true
+---
+# SambaNova Distribution
+
+```{toctree}
+:maxdepth: 2
+:hidden:
+
+self
+```
+
+The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
+
+{{ providers_table }}
+
+{% if run_config_env_vars %}
+### Environment Variables
+
+The following environment variables can be configured:
+
+{% for var, (default_value, description) in run_config_env_vars.items() %}
+- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
+{% endfor %}
+{% endif %}
+
+{% if default_models %}
+### Models
+
+The following models are available by default:
+
+{% for model in default_models %}
+- `{{ model.model_id }} ({{ model.provider_model_id }})`
+{% endfor %}
+{% endif %}
+
+
+### Prerequisite: API Keys
+
+Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaBova.ai](https://sambanova.ai/).
+
+
+## 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.
+
+```bash
+LLAMA_STACK_PORT=5001
+docker run \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  llamastack/distribution-{{ name }} \
+  --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 \
+  --port $LLAMA_STACK_PORT \
+  --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
+```

llama_stack/templates/sambanova/run.yaml

@@ -0,0 +1,83 @@
+version: '2'
+image_name: sambanova
+docker_image: null
+conda_env: sambanova
+apis:
+- agents
+- inference
+- memory
+- safety
+- telemetry
+providers:
+  inference:
+  - provider_id: sambanova
+    provider_type: remote::sambanova
+    config:
+      url: https://api.sambanova.ai/v1/
+      api_key: ${env.SAMBANOVA_API_KEY}
+  memory:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/faiss_store.db
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config: {}
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/agents_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config: {}
+metadata_store:
+  namespace: null
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/registry.db
+models:
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-8B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.1-8B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-70B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.1-70B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-405B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.1-405B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-1B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.2-1B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-3B-Instruct
+  provider_id: null
+  provider_model_id: Meta-Llama-3.2-3B-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
+  provider_id: null
+  provider_model_id: Llama-3.2-11B-Vision-Instruct
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
+  provider_id: null
+  provider_model_id: Llama-3.2-90B-Vision-Instruct
+shields:
+- params: null
+  shield_id: meta-llama/Llama-Guard-3-8B
+  provider_id: null
+  provider_shield_id: null
+memory_banks: []
+datasets: []
+scoring_fns: []
+eval_tasks: []

llama_stack/templates/sambanova/sambanova.py

@@ -0,0 +1,71 @@
+# 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 pathlib import Path
+
+from llama_models.sku_list import all_registered_models
+
+from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
+from llama_stack.providers.remote.inference.sambanova import SambaNovaImplConfig
+from llama_stack.providers.remote.inference.sambanova.sambanova import MODEL_ALIASES
+
+from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
+
+
+def get_distribution_template() -> DistributionTemplate:
+    providers = {
+        "inference": ["remote::sambanova"],
+        "memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
+        "safety": ["inline::llama-guard"],
+        "agents": ["inline::meta-reference"],
+        "telemetry": ["inline::meta-reference"],
+    }
+
+    inference_provider = Provider(
+        provider_id="sambanova",
+        provider_type="remote::sambanova",
+        config=SambaNovaImplConfig.sample_run_config(),
+    )
+
+    core_model_to_hf_repo = {
+        m.descriptor(): m.huggingface_repo for m in all_registered_models()
+    }
+    default_models = [
+        ModelInput(
+            model_id=core_model_to_hf_repo[m.llama_model],
+            provider_model_id=m.provider_model_id,
+        )
+        for m in MODEL_ALIASES
+    ]
+
+    return DistributionTemplate(
+        name="sambanova",
+        distro_type="self_hosted",
+        description="Use SambaNova.AI for running LLM inference",
+        docker_image=None,
+        template_path=Path(__file__).parent / "doc_template.md",
+        providers=providers,
+        default_models=default_models,
+        run_configs={
+            "run.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": [inference_provider],
+                },
+                default_models=default_models,
+                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+            ),
+        },
+        run_config_env_vars={
+            "LLAMASTACK_PORT": (
+                "5001",
+                "Port for the Llama Stack distribution server",
+            ),
+            "SAMBANOVA_API_KEY": (
+                "",
+                "SambaNova.AI API Key",
+            ),
+        },
+    )