feat: add (openai, anthropic, gemini) providers via litellm (#1267)

# What does this PR do?

This PR introduces more non-llama model support to llama stack.
Providers introduced: openai, anthropic and gemini. All of these
providers use essentially the same piece of code -- the implementation
works via the `litellm` library.

We will expose only specific models for providers we enable making sure
they all work well and pass tests. This setup (instead of automatically
enabling _all_ providers and models allowed by LiteLLM) ensures we can
also perform any needed prompt tuning on a per-model basis as needed
(just like we do it for llama models.)

## Test Plan

```bash
#!/bin/bash

args=("$@")
for model in openai/gpt-4o anthropic/claude-3-5-sonnet-latest gemini/gemini-1.5-flash; do
    LLAMA_STACK_CONFIG=dev pytest -s -v tests/client-sdk/inference/test_text_inference.py \
        --embedding-model=all-MiniLM-L6-v2 \
        --vision-inference-model="" \
        --inference-model=$model "${args[@]}"
done
```

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -0,0 +1,171 @@
+# 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, AsyncIterator, List, Optional, Union
+
+import litellm
+
+from llama_stack.apis.common.content_types import (
+    InterleavedContent,
+    InterleavedContentItem,
+)
+from llama_stack.apis.inference import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    ChatCompletionResponseStreamChunk,
+    EmbeddingsResponse,
+    EmbeddingTaskType,
+    Inference,
+    JsonSchemaResponseFormat,
+    LogProbConfig,
+    Message,
+    ResponseFormat,
+    SamplingParams,
+    TextTruncation,
+    ToolChoice,
+    ToolConfig,
+    ToolDefinition,
+    ToolPromptFormat,
+)
+from llama_stack.apis.models.models import Model
+from llama_stack.providers.utils.inference.model_registry import (
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    convert_message_to_openai_dict_new,
+    convert_openai_chat_completion_choice,
+    convert_openai_chat_completion_stream,
+    convert_tooldef_to_openai_tool,
+    get_sampling_options,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    interleaved_content_as_str,
+)
+
+
+class LiteLLMOpenAIMixin(
+    ModelRegistryHelper,
+    Inference,
+):
+    def __init__(self, model_entries) -> None:
+        self.model_entries = model_entries
+        ModelRegistryHelper.__init__(self, model_entries)
+
+    async def register_model(self, model: Model) -> Model:
+        model_id = self.get_provider_model_id(model.provider_resource_id)
+        if model_id is None:
+            raise ValueError(f"Unsupported model: {model.provider_resource_id}")
+        return model
+
+    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("LiteLLM does not support completion requests")
+
+    async def chat_completion(
+        self,
+        model_id: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+        tool_config: Optional[ToolConfig] = None,
+    ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
+        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 [],
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+            tool_config=tool_config,
+        )
+
+        params = await self._get_params(request)
+
+        # unfortunately, we need to use synchronous litellm.completion here because litellm
+        # caches various httpx.client objects in a non-eventloop aware manner
+        response = litellm.completion(**params)
+        if stream:
+            return self._stream_chat_completion(response)
+        else:
+            return convert_openai_chat_completion_choice(response.choices[0])
+
+    async def _stream_chat_completion(
+        self, response: litellm.ModelResponse
+    ) -> AsyncIterator[ChatCompletionResponseStreamChunk]:
+        async def _stream_generator():
+            for chunk in response:
+                yield chunk
+
+        async for chunk in convert_openai_chat_completion_stream(
+            _stream_generator(), enable_incremental_tool_calls=True
+        ):
+            yield chunk
+
+    async def _get_params(self, request: ChatCompletionRequest) -> dict:
+        input_dict = {}
+
+        input_dict["messages"] = [await convert_message_to_openai_dict_new(m) for m in request.messages]
+        if fmt := request.response_format:
+            if not isinstance(fmt, JsonSchemaResponseFormat):
+                raise ValueError(
+                    f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported."
+                )
+
+            fmt = fmt.json_schema
+            name = fmt["title"]
+            del fmt["title"]
+            fmt["additionalProperties"] = False
+            input_dict["response_format"] = {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": name,
+                    "schema": fmt,
+                    "strict": True,
+                },
+            }
+        if request.tools:
+            input_dict["tools"] = [convert_tooldef_to_openai_tool(tool) for tool in request.tools]
+            if request.tool_config.tool_choice:
+                input_dict["tool_choice"] = request.tool_config.tool_choice.value
+
+        return {
+            "model": request.model,
+            **input_dict,
+            "stream": request.stream,
+            **get_sampling_options(request.sampling_params),
+        }
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[str] | List[InterleavedContentItem],
+        text_truncation: Optional[TextTruncation] = TextTruncation.none,
+        output_dimension: Optional[int] = None,
+        task_type: Optional[EmbeddingTaskType] = None,
+    ) -> EmbeddingsResponse:
+        model = await self.model_store.get_model(model_id)
+
+        response = litellm.embedding(
+            model=model.provider_resource_id,
+            input=[interleaved_content_as_str(content) for content in contents],
+        )
+
+        embeddings = [data["embedding"] for data in response["data"]]
+        return EmbeddingsResponse(embeddings=embeddings)