Nutanix AI on!

README.md

@@ -88,6 +88,7 @@ Additionally, we have designed every element of the Stack such that APIs as well
 | Chroma | Single Node |  |  | :heavy_check_mark: |  |  |
 | PG Vector | Single Node |  |  | :heavy_check_mark: |  |  |
 | PyTorch ExecuTorch | On-device iOS | :heavy_check_mark:  | :heavy_check_mark:  |  |  |
+|  Nutanix AI  |  Hosted  |  | :heavy_check_mark:  |  |    |   |
 
 ### Distributions
 

distributions/nutanix/README.md

@@ -0,0 +1,40 @@
+# Nutanix Distribution
+
+The `llamastack/distribution-nutanix` distribution consists of the following provider configurations.
+
+
+| **API**         	| **Inference** 	| **Agents**     	| **Memory**                                       	| **Safety**     	| **Telemetry**  	|
+|-----------------	|---------------	|----------------	|--------------------------------------------------	|----------------	|----------------	|
+| **Provider(s)** 	| remote::nutanix   	| meta-reference 	| meta-reference 	| meta-reference 	| meta-reference 	|
+
+
+### Start the Distribution (Hosted remote)
+
+> [!NOTE]
+> This assumes you have an hosted Nutanix AI endpoint and an API Key.
+
+1. Clone the repo
+```
+git clone git@github.com:meta-llama/llama-stack.git
+cd llama-stack
+```
+
+2. Config the model name
+
+Please adjust the `NUTANIX_SUPPORTED_MODELS` variable at line 29 in `llama_stack/providers/adapters/inference/nutanix/nutanix.py` according to your deployment.
+
+3. Build the distrbution
+```
+pip install -e .
+llama stack build --template nutanix --name ntnx --image-type conda
+```
+
+4. Set the endpoint URL and API Key
+```
+llama stack configure ntnx
+```
+
+5. Serve and enjoy!
+```
+llama stack run ntnx --port 174
+```

distributions/nutanix/build.yaml

@@ -0,0 +1 @@
+../../llama_stack/templates/nutanix/build.yaml

llama_stack/providers/adapters/inference/nutanix/__init__.py

@@ -0,0 +1,16 @@
+# 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 .config import NutanixImplConfig
+from .nutanix import NutanixInferenceAdapter
+
+async def get_adapter_impl(config: NutanixInferenceAdapter, _deps):
+    assert isinstance(
+        config, NutanixImplConfig
+    ), f"Unexpected config type: {type(config)}"
+    impl = NutanixInferenceAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/adapters/inference/nutanix/config.py

@@ -0,0 +1,22 @@
+# 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 Optional
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+
+@json_schema_type
+class NutanixImplConfig(BaseModel):
+    url: str = Field(
+        default=None,
+        description="The URL of the Nutanix AI endpoint",
+    )
+    api_token: str = Field(
+        default=None,
+        description="The API token of the Nutanix AI endpoint",
+    )

llama_stack/providers/adapters/inference/nutanix/nutanix.py

@@ -0,0 +1,125 @@
+# 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 AsyncGenerator
+
+from openai import OpenAI
+
+from llama_models.llama3.api.chat_format import ChatFormat
+
+from llama_models.llama3.api.datatypes import Message
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from llama_stack.apis.inference import *  # noqa: F403
+from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+from llama_stack.providers.utils.inference.openai_compat import (
+    get_sampling_options,
+    process_chat_completion_response,
+    process_chat_completion_stream_response,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    chat_completion_request_to_messages,
+)
+
+from .config import NutanixImplConfig
+
+NUTANIX_SUPPORTED_MODELS = {
+    "Llama3.1-8B-Instruct": "vllm-llama-3-1",
+}
+
+
+class NutanixInferenceAdapter(ModelRegistryHelper, Inference):
+    def __init__(self, config: NutanixImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self, stack_to_provider_models_map=NUTANIX_SUPPORTED_MODELS
+        )
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+    async def initialize(self) -> None:
+        return
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def completion(
+        self,
+        model: str,
+        content: InterleavedTextMedia,
+        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: 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:
+        request = ChatCompletionRequest(
+            model=model,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+
+        client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
+        if stream:
+            return self._stream_chat_completion(request, client)
+        else:
+            return await self._nonstream_chat_completion(request, client)
+
+    async def _nonstream_chat_completion(
+        self, request: ChatCompletionRequest, client: OpenAI
+    ) -> ChatCompletionResponse:
+        params = self._get_params(request)
+        r = client.chat.completions.create(**params)
+        return process_chat_completion_response(r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest, client: OpenAI
+    ) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        async def _to_async_generator():
+            s = client.chat.completions.create(**params)
+            for chunk in s:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_chat_completion_stream_response(
+            stream, self.formatter
+        ):
+            yield chunk
+
+    def _get_params(self, request: ChatCompletionRequest) -> dict:
+        params = {
+            "model": self.map_to_provider_model(request.model),
+            "messages": chat_completion_request_to_messages(request, return_dict=True),
+            "stream": request.stream,
+            **get_sampling_options(request.sampling_params),
+        }
+        return params
+
+    async def embeddings(
+        self,
+        model: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/registry/inference.py

@@ -161,4 +161,15 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="nutanix",
+                pip_packages=[
+                    "openai",
+                ],
+                module="llama_stack.providers.remote.inference.nutanix",
+                config_class="llama_stack.providers.remote.inference.nutanix.NutanixImplConfig",
+            ),
+        ),
     ]

llama_stack/providers/utils/inference/openai_compat.py

@@ -43,6 +43,8 @@ def get_sampling_options(params: SamplingParams) -> dict:
 
 
 def text_from_choice(choice) -> str:
+    if hasattr(choice, "message") and choice.message:
+        return choice.message.content
     if hasattr(choice, "delta") and choice.delta:
         return choice.delta.content
 

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -169,10 +169,12 @@ def chat_completion_request_to_model_input_info(
 def chat_completion_request_to_messages(
     request: ChatCompletionRequest,
     llama_model: str,
-) -> List[Message]:
+    return_dict: bool = False,
+) -> Union[List[Message], List[Dict[str, str]]]:
     """Reads chat completion request and augments the messages to handle tools.
     For eg. for llama_3_1, add system message with the appropriate tools or
     add user messsage for custom tools, etc.
+    If return_dict is set, returns a list of the messages dictionaries instead of objects.
     """
     model = resolve_model(llama_model)
     if model is None:
@@ -199,7 +201,10 @@ def chat_completion_request_to_messages(
     if fmt_prompt := response_format_prompt(request.response_format):
         messages.append(UserMessage(content=fmt_prompt))
 
-    return messages
+    if return_dict:
+        return [{'role': m.role, 'content': m.content} for m in messages]
+    else:
+        return messages
 
 
 def response_format_prompt(fmt: Optional[ResponseFormat]):

llama_stack/templates/nutanix/build.yaml

@@ -0,0 +1,9 @@
+name: nutanix
+distribution_spec:
+  description: Use Nutanix AI Endpoint for running LLM inference
+  providers:
+    inference: remote::nutanix
+    memory: meta-reference
+    safety: meta-reference
+    agents: meta-reference
+    telemetry: meta-reference