Litellm support in llama stack:

llama_stack/providers/remote/inference/litellm/litellm.py

@@ -0,0 +1,116 @@
+# 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 os
+from typing import AsyncIterator, List, Optional, Union, Any
+
+from litellm import completion as litellm_completion
+from litellm.types.utils import ModelResponse
+
+from llama_models.datatypes import SamplingParams
+from llama_models.llama3.api.datatypes import ToolDefinition, ToolPromptFormat, StopReason
+from llama_stack.apis.inference import (
+    ChatCompletionResponse,
+    ChatCompletionResponseStreamChunk,
+    CompletionResponse,
+    CompletionMessage,
+    CompletionResponseStreamChunk,
+    EmbeddingsResponse,
+    Inference,
+    InterleavedContent,
+    LogProbConfig,
+    Message,
+    ResponseFormat,
+    ToolChoice,
+)
+# from llama_stack.distribution.request_headers import NeedsRequestProviderData
+from llama_stack.providers.remote.inference.litellm.config import LitellmConfig
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
+
+_MODEL_ALIASES = [
+    build_model_alias(
+        "gpt-4o",  # provider_model_id
+        "gpt-4o",  # model_descriptor
+    ),
+]
+
+class LitellmInferenceAdapter(Inference, ModelRegistryHelper):
+    _config: LitellmConfig
+
+    def __init__(self, config: LitellmConfig):
+        ModelRegistryHelper.__init__(self, model_aliases=_MODEL_ALIASES)
+        self._config = config
+
+    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,
+    ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]:
+        # litellm doesn't support non-chat completion as of time of writing
+        raise NotImplementedError()
+
+    def _messages_to_litellm_messages(
+        self,
+        messages: List[Message],
+    ) -> list[dict[str, Any]]:
+        litellm_messages = []
+        for message in messages:
+            lm_message = {
+                "role": message.role,
+                "content": message.content,
+            }
+            litellm_messages.append(lm_message)
+        return litellm_messages
+
+    def _convert_to_llama_stack_response(
+        self,
+        litellm_response: ModelResponse,
+    ) -> ChatCompletionResponse:
+        assert litellm_response.choices is not None
+        assert len(litellm_response.choices) == 1
+        message = litellm_response.choices[0].message
+        completion_message = CompletionMessage(content=message["content"], role=message["role"], stop_reason=StopReason.end_of_message, tool_calls=[])
+        return ChatCompletionResponse(completion_message=completion_message)
+
+    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] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> Union[
+        ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
+    ]:
+        assert stream is False, "streaming not supported"
+        model_id = self.get_provider_model_id(model_id)
+        response = litellm_completion(
+            model=model_id,
+            custom_llm_provider=self._config.llm_provider,
+            messages=self._messages_to_litellm_messages(messages),
+            api_key=self._config.openai_api_key,
+        )
+
+        return self._convert_to_llama_stack_response(response)
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedContent],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()
+