feat: New OpenAI compat embeddings API (#2314)

# What does this PR do?
Adds a new endpoint that is compatible with OpenAI for embeddings api. 
`/openai/v1/embeddings`
Added providers for OpenAI, LiteLLM and SentenceTransformer. 


## Test Plan
```
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/inference/test_openai_embeddings.py --embedding-model all-MiniLM-L6-v2,text-embedding-3-small,gemini/text-embedding-004
```

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -22,6 +22,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -197,3 +198,13 @@ class BedrockInferenceAdapter(
             response_body = json.loads(response.get("body").read())
             embeddings.append(response_body.get("embedding"))
         return EmbeddingsResponse(embeddings=embeddings)
+
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -21,6 +21,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -194,3 +195,13 @@ class CerebrasInferenceAdapter(
         task_type: EmbeddingTaskType | None = None,
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
+
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -20,6 +20,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -152,3 +153,13 @@ class DatabricksInferenceAdapter(
         task_type: EmbeddingTaskType | None = None,
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
+
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -37,6 +37,7 @@ from llama_stack.apis.inference.inference import (
     OpenAIChatCompletion,
     OpenAIChatCompletionChunk,
     OpenAICompletion,
+    OpenAIEmbeddingsResponse,
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
 )
@@ -286,6 +287,16 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
         embeddings = [data.embedding for data in response.data]
         return EmbeddingsResponse(embeddings=embeddings)
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()
+
     async def openai_completion(
         self,
         model: str,

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -29,6 +29,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -238,6 +239,16 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         #
         return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data])
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()
+
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -32,6 +32,7 @@ from llama_stack.apis.inference import (
     JsonSchemaResponseFormat,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -370,6 +371,16 @@ class OllamaInferenceAdapter(
 
         return model
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()
+
     async def openai_completion(
         self,
         model: str,

llama_stack/providers/remote/inference/openai/openai.py

@@ -14,6 +14,9 @@ from llama_stack.apis.inference.inference import (
     OpenAIChatCompletion,
     OpenAIChatCompletionChunk,
     OpenAICompletion,
+    OpenAIEmbeddingData,
+    OpenAIEmbeddingsResponse,
+    OpenAIEmbeddingUsage,
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
 )
@@ -38,6 +41,7 @@ logger = logging.getLogger(__name__)
 # | batch_chat_completion      | LiteLLMOpenAIMixin       |
 # | openai_completion          | AsyncOpenAI              |
 # | openai_chat_completion     | AsyncOpenAI              |
+# | openai_embeddings          | AsyncOpenAI              |
 #
 class OpenAIInferenceAdapter(LiteLLMOpenAIMixin):
     def __init__(self, config: OpenAIConfig) -> None:
@@ -171,3 +175,51 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin):
             user=user,
         )
         return await self._openai_client.chat.completions.create(**params)
+
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        model_id = (await self.model_store.get_model(model)).provider_resource_id
+        if model_id.startswith("openai/"):
+            model_id = model_id[len("openai/") :]
+
+        # Prepare parameters for OpenAI embeddings API
+        params = {
+            "model": model_id,
+            "input": input,
+        }
+
+        if encoding_format is not None:
+            params["encoding_format"] = encoding_format
+        if dimensions is not None:
+            params["dimensions"] = dimensions
+        if user is not None:
+            params["user"] = user
+
+        # Call OpenAI embeddings API
+        response = await self._openai_client.embeddings.create(**params)
+
+        data = []
+        for i, embedding_data in enumerate(response.data):
+            data.append(
+                OpenAIEmbeddingData(
+                    embedding=embedding_data.embedding,
+                    index=i,
+                )
+            )
+
+        usage = OpenAIEmbeddingUsage(
+            prompt_tokens=response.usage.prompt_tokens,
+            total_tokens=response.usage.total_tokens,
+        )
+
+        return OpenAIEmbeddingsResponse(
+            data=data,
+            model=response.model,
+            usage=usage,
+        )

llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -19,6 +19,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -210,6 +211,16 @@ class PassthroughInferenceAdapter(Inference):
             task_type=task_type,
         )
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()
+
     async def openai_completion(
         self,
         model: str,

llama_stack/providers/remote/inference/runpod/runpod.py

@@ -8,6 +8,7 @@ from collections.abc import AsyncGenerator
 from openai import OpenAI
 
 from llama_stack.apis.inference import *  # noqa: F403
+from llama_stack.apis.inference.inference import OpenAIEmbeddingsResponse
 
 # from llama_stack.providers.datatypes import ModelsProtocolPrivate
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
@@ -134,3 +135,13 @@ class RunpodInferenceAdapter(
         task_type: Optional[EmbeddingTaskType] = None,
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
+
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -23,6 +23,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     ResponseFormatType,
     SamplingParams,
@@ -291,6 +292,16 @@ class _HfAdapter(
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()
+
 
 class TGIAdapter(_HfAdapter):
     async def initialize(self, config: TGIImplConfig) -> None:

llama_stack/providers/remote/inference/together/together.py

@@ -23,6 +23,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     ResponseFormatType,
     SamplingParams,
@@ -267,6 +268,16 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
         embeddings = [item.embedding for item in r.data]
         return EmbeddingsResponse(embeddings=embeddings)
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()
+
     async def openai_completion(
         self,
         model: str,

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -38,6 +38,7 @@ from llama_stack.apis.inference import (
     JsonSchemaResponseFormat,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -507,6 +508,16 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         embeddings = [data.embedding for data in response.data]
         return EmbeddingsResponse(embeddings=embeddings)
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()
+
     async def openai_completion(
         self,
         model: str,

llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -21,6 +21,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -260,6 +261,16 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
     ) -> EmbeddingsResponse:
         raise NotImplementedError("embedding is not supported for watsonx")
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        raise NotImplementedError()
+
     async def openai_completion(
         self,
         model: str,