implement embedding generation in supported inference providers (#589)

This PR adds the ability to generate embeddings in all supported
inference providers.

```
pytest -v -s llama_stack/providers/tests/inference/test_embeddings.py -k "bedrock" --inference-model="amazon.titan-embed-text-v2:0"  --env EMBEDDING_DIMENSION=1024

 pytest -v -s -k "vllm"  --inferrence-model="intfloat/e5-mistral-7b-instruct"  llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=4096  --env VLLM_URL="http://localhost:9798/v1"

pytest -v -s --inference-model="nomic-ai/nomic-embed-text-v1.5"  llama_stack/providers/tests/inference/test_embeddings.py  -k "fireworks"  --env FIREWORKS_API_KEY=<API_KEY>--env EMBEDDING_DIMENSION=128

pytest -v -s --inference-model="togethercomputer/m2-bert-80M-2k-retrieval"  llama_stack/providers/tests/inference/test_embeddings.py  -k "together"  --env TOGETHER_API_KEY=<API_KEY>--env EMBEDDING_DIMENSION=768

pytest -v -s -k "ollama"  --inference-model="all-minilm:v8"  llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=384

 torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="sentence-transformers/all-MiniLM-L6-v2"  llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=384

```

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

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 from typing import *  # noqa: F403
+import json
 
 from botocore.client import BaseClient
 from llama_models.datatypes import CoreModelId
@@ -19,8 +20,10 @@ from llama_stack.providers.utils.inference.model_registry import (
 
 from llama_stack.apis.inference import *  # noqa: F403
 
+
 from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
 from llama_stack.providers.utils.bedrock.client import create_bedrock_client
+from llama_stack.providers.utils.inference.prompt_adapter import content_has_media
 
 
 model_aliases = [
@@ -448,4 +451,21 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
         model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
-        raise NotImplementedError()
+        model = await self.model_store.get_model(model_id)
+        embeddings = []
+        for content in contents:
+            assert not content_has_media(
+                content
+            ), "Bedrock does not support media for embeddings"
+            input_text = interleaved_text_media_as_str(content)
+            input_body = {"inputText": input_text}
+            body = json.dumps(input_body)
+            response = self.client.invoke_model(
+                body=body,
+                modelId=model.provider_resource_id,
+                accept="application/json",
+                contentType="application/json",
+            )
+            response_body = json.loads(response.get("body").read())
+            embeddings.append(response_body.get("embedding"))
+        return EmbeddingsResponse(embeddings=embeddings)

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

@@ -13,7 +13,7 @@ from pydantic import BaseModel, Field
 @json_schema_type
 class FireworksImplConfig(BaseModel):
     url: str = Field(
-        default="https://api.fireworks.ai/inference",
+        default="https://api.fireworks.ai/inference/v1",
         description="The URL for the Fireworks server",
     )
     api_key: Optional[str] = Field(
@@ -24,6 +24,6 @@ class FireworksImplConfig(BaseModel):
     @classmethod
     def sample_run_config(cls) -> Dict[str, Any]:
         return {
-            "url": "https://api.fireworks.ai/inference",
+            "url": "https://api.fireworks.ai/inference/v1",
             "api_key": "${env.FIREWORKS_API_KEY}",
         }

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

@@ -4,7 +4,7 @@
 # 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 typing import AsyncGenerator, List, Optional, Union
 
 from fireworks.client import Fireworks
 from llama_models.datatypes import CoreModelId
@@ -28,6 +28,7 @@ from llama_stack.providers.utils.inference.openai_compat import (
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
     completion_request_to_prompt,
+    content_has_media,
     convert_message_to_dict,
     request_has_media,
 )
@@ -89,17 +90,19 @@ class FireworksInferenceAdapter(
     async def shutdown(self) -> None:
         pass
 
-    def _get_client(self) -> Fireworks:
-        fireworks_api_key = None
+    def _get_api_key(self) -> str:
         if self.config.api_key is not None:
-            fireworks_api_key = self.config.api_key
+            return self.config.api_key
         else:
             provider_data = self.get_request_provider_data()
             if provider_data is None or not provider_data.fireworks_api_key:
                 raise ValueError(
                     'Pass Fireworks API Key in the header X-LlamaStack-ProviderData as { "fireworks_api_key": <your api key>}'
                 )
-            fireworks_api_key = provider_data.fireworks_api_key
+            return provider_data.fireworks_api_key
+
+    def _get_client(self) -> Fireworks:
+        fireworks_api_key = self._get_api_key()
         return Fireworks(api_key=fireworks_api_key)
 
     async def completion(
@@ -264,4 +267,19 @@ class FireworksInferenceAdapter(
         model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
-        raise NotImplementedError()
+        model = await self.model_store.get_model(model_id)
+
+        kwargs = {}
+        if model.metadata.get("embedding_dimensions"):
+            kwargs["dimensions"] = model.metadata.get("embedding_dimensions")
+        assert all(
+            not content_has_media(content) for content in contents
+        ), "Fireworks does not support media for embeddings"
+        response = self._get_client().embeddings.create(
+            model=model.provider_resource_id,
+            input=[interleaved_text_media_as_str(content) for content in contents],
+            **kwargs,
+        )
+
+        embeddings = [data.embedding for data in response.data]
+        return EmbeddingsResponse(embeddings=embeddings)

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

@@ -36,6 +36,7 @@ from llama_stack.providers.utils.inference.openai_compat import (
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
     completion_request_to_prompt,
+    content_has_media,
     convert_image_media_to_url,
     request_has_media,
 )
@@ -321,9 +322,30 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
         model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
-        raise NotImplementedError()
+        model = await self.model_store.get_model(model_id)
+
+        assert all(
+            not content_has_media(content) for content in contents
+        ), "Ollama does not support media for embeddings"
+        response = await self.client.embed(
+            model=model.provider_resource_id,
+            input=[interleaved_text_media_as_str(content) for content in contents],
+        )
+        embeddings = response["embeddings"]
+
+        return EmbeddingsResponse(embeddings=embeddings)
 
     async def register_model(self, model: Model) -> Model:
+        # ollama does not have embedding models running. Check if the model is in list of available models.
+        if model.model_type == ModelType.embedding_model:
+            response = await self.client.list()
+            available_models = [m["model"] for m in response["models"]]
+            if model.provider_resource_id not in available_models:
+                raise ValueError(
+                    f"Model '{model.provider_resource_id}' is not available in Ollama. "
+                    f"Available models: {', '.join(available_models)}"
+                )
+            return model
         model = await self.register_helper.register_model(model)
         models = await self.client.ps()
         available_models = [m["model"] for m in models["models"]]

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

@@ -31,6 +31,7 @@ from llama_stack.providers.utils.inference.openai_compat import (
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
     completion_request_to_prompt,
+    content_has_media,
     convert_message_to_dict,
     request_has_media,
 )
@@ -253,4 +254,13 @@ class TogetherInferenceAdapter(
         model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
-        raise NotImplementedError()
+        model = await self.model_store.get_model(model_id)
+        assert all(
+            not content_has_media(content) for content in contents
+        ), "Together does not support media for embeddings"
+        r = self._get_client().embeddings.create(
+            model=model.provider_resource_id,
+            input=[interleaved_text_media_as_str(content) for content in contents],
+        )
+        embeddings = [item.embedding for item in r.data]
+        return EmbeddingsResponse(embeddings=embeddings)

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

@@ -29,6 +29,7 @@ from llama_stack.providers.utils.inference.openai_compat import (
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
     completion_request_to_prompt,
+    content_has_media,
     convert_message_to_dict,
     request_has_media,
 )
@@ -203,4 +204,20 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
-        raise NotImplementedError()
+        model = await self.model_store.get_model(model_id)
+
+        kwargs = {}
+        assert model.model_type == ModelType.embedding_model
+        assert model.metadata.get("embedding_dimensions")
+        kwargs["dimensions"] = model.metadata.get("embedding_dimensions")
+        assert all(
+            not content_has_media(content) for content in contents
+        ), "VLLM does not support media for embeddings"
+        response = self.client.embeddings.create(
+            model=model.provider_resource_id,
+            input=[interleaved_text_media_as_str(content) for content in contents],
+            **kwargs,
+        )
+
+        embeddings = [data.embedding for data in response.data]
+        return EmbeddingsResponse(embeddings=embeddings)