feat: Add rerank models and rerank API change (#3831)

# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- Extend the model type to include rerank models.
- Implement `rerank()` method in inference router.
- Add `rerank_model_list` to `OpenAIMixin` to enable providers to
register and identify rerank models
- Update documentation.

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
```
pytest tests/unit/providers/utils/inference/test_openai_mixin.py
```

docs/docs/providers/inference/index.mdx

@@ -3,9 +3,10 @@ description: "Inference
 
     Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
+    This API provides the raw interface to the underlying models. Three kinds of models are supported:
     - LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search."
+    - Embedding models: these models generate embeddings to be used for semantic search.
+    - Rerank models: these models reorder the documents based on their relevance to a query."
 sidebar_label: Inference
 title: Inference
 ---
@@ -18,8 +19,9 @@ Inference
 
     Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
+    This API provides the raw interface to the underlying models. Three kinds of models are supported:
     - LLM models: these models generate "raw" and "chat" (conversational) completions.
     - Embedding models: these models generate embeddings to be used for semantic search.
+    - Rerank models: these models reorder the documents based on their relevance to a query.
 
 This section contains documentation for all available providers for the **inference** API.

docs/static/deprecated-llama-stack-spec.html

@@ -13467,7 +13467,7 @@
         },
         {
             "name": "Inference",
-            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Three kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.\n- Rerank models: these models reorder the documents based on their relevance to a query.",
             "x-displayName": "Inference"
         },
         {

docs/static/deprecated-llama-stack-spec.yaml

@@ -10218,13 +10218,16 @@ tags:
       embeddings.
 
 
-      This API provides the raw interface to the underlying models. Two kinds of models
-      are supported:
+      This API provides the raw interface to the underlying models. Three kinds of
+      models are supported:
 
       - LLM models: these models generate "raw" and "chat" (conversational) completions.
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
+
+      - Rerank models: these models reorder the documents based on their relevance
+      to a query.
     x-displayName: Inference
   - name: Models
     description: ''

docs/static/llama-stack-spec.html

@@ -6859,7 +6859,8 @@
                 "type": "string",
                 "enum": [
                     "llm",
-                    "embedding"
+                    "embedding",
+                    "rerank"
                 ],
                 "title": "ModelType",
                 "description": "Enumeration of supported model types in Llama Stack."
@@ -13269,7 +13270,7 @@
         },
         {
             "name": "Inference",
-            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Three kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.\n- Rerank models: these models reorder the documents based on their relevance to a query.",
             "x-displayName": "Inference"
         },
         {

docs/static/llama-stack-spec.yaml

@@ -5269,6 +5269,7 @@ components:
       enum:
         - llm
         - embedding
+        - rerank
       title: ModelType
       description: >-
         Enumeration of supported model types in Llama Stack.
@@ -10190,13 +10191,16 @@ tags:
       embeddings.
 
 
-      This API provides the raw interface to the underlying models. Two kinds of models
-      are supported:
+      This API provides the raw interface to the underlying models. Three kinds of
+      models are supported:
 
       - LLM models: these models generate "raw" and "chat" (conversational) completions.
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
+
+      - Rerank models: these models reorder the documents based on their relevance
+      to a query.
     x-displayName: Inference
   - name: Inspect
     description: >-

docs/static/stainless-llama-stack-spec.html

@@ -8531,7 +8531,8 @@
                 "type": "string",
                 "enum": [
                     "llm",
-                    "embedding"
+                    "embedding",
+                    "rerank"
                 ],
                 "title": "ModelType",
                 "description": "Enumeration of supported model types in Llama Stack."
@@ -17959,7 +17960,7 @@
         },
         {
             "name": "Inference",
-            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Three kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.\n- Rerank models: these models reorder the documents based on their relevance to a query.",
             "x-displayName": "Inference"
         },
         {

docs/static/stainless-llama-stack-spec.yaml

@@ -6482,6 +6482,7 @@ components:
       enum:
         - llm
         - embedding
+        - rerank
       title: ModelType
       description: >-
         Enumeration of supported model types in Llama Stack.
@@ -13585,13 +13586,16 @@ tags:
       embeddings.
 
 
-      This API provides the raw interface to the underlying models. Two kinds of models
-      are supported:
+      This API provides the raw interface to the underlying models. Three kinds of
+      models are supported:
 
       - LLM models: these models generate "raw" and "chat" (conversational) completions.
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
+
+      - Rerank models: these models reorder the documents based on their relevance
+      to a query.
     x-displayName: Inference
   - name: Inspect
     description: >-

llama_stack/apis/inference/inference.py

@@ -1234,9 +1234,10 @@ class Inference(InferenceProvider):
 
     Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
+    This API provides the raw interface to the underlying models. Three kinds of models are supported:
     - LLM models: these models generate "raw" and "chat" (conversational) completions.
     - Embedding models: these models generate embeddings to be used for semantic search.
+    - Rerank models: these models reorder the documents based on their relevance to a query.
     """
 
     @webmethod(route="/openai/v1/chat/completions", method="GET", level=LLAMA_STACK_API_V1, deprecated=True)

llama_stack/apis/models/models.py

@@ -27,10 +27,12 @@ class ModelType(StrEnum):
     """Enumeration of supported model types in Llama Stack.
     :cvar llm: Large language model for text generation and completion
     :cvar embedding: Embedding model for converting text to vector representations
+    :cvar rerank: Reranking model for reordering documents based on their relevance to a query
     """
 
     llm = "llm"
     embedding = "embedding"
+    rerank = "rerank"
 
 
 @json_schema_type

llama_stack/core/routers/inference.py

@@ -44,9 +44,14 @@ from llama_stack.apis.inference import (
     OpenAIEmbeddingsResponse,
     OpenAIMessageParam,
     Order,
+    RerankResponse,
     StopReason,
     ToolPromptFormat,
 )
+from llama_stack.apis.inference.inference import (
+    OpenAIChatCompletionContentPartImageParam,
+    OpenAIChatCompletionContentPartTextParam,
+)
 from llama_stack.apis.models import Model, ModelType
 from llama_stack.apis.telemetry import MetricEvent, MetricInResponse, Telemetry
 from llama_stack.log import get_logger
@@ -182,6 +187,23 @@ class InferenceRouter(Inference):
             raise ModelTypeError(model_id, model.model_type, expected_model_type)
         return model
 
+    async def rerank(
+        self,
+        model: str,
+        query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
+        items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
+        max_num_results: int | None = None,
+    ) -> RerankResponse:
+        logger.debug(f"InferenceRouter.rerank: {model}")
+        model_obj = await self._get_model(model, ModelType.rerank)
+        provider = await self.routing_table.get_provider_impl(model_obj.identifier)
+        return await provider.rerank(
+            model=model_obj.identifier,
+            query=query,
+            items=items,
+            max_num_results=max_num_results,
+        )
+
     async def openai_completion(
         self,
         params: Annotated[OpenAICompletionRequestWithExtraBody, Body(...)],

llama_stack/providers/utils/inference/openai_mixin.py

@@ -48,6 +48,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
     - overwrite_completion_id: If True, overwrites the 'id' field in OpenAI responses
     - download_images: If True, downloads images and converts to base64 for providers that require it
     - embedding_model_metadata: A dictionary mapping model IDs to their embedding metadata
+    - construct_model_from_identifier: Method to construct a Model instance corresponding to the given identifier
     - provider_data_api_key_field: Optional field name in provider data to look for API key
     - list_provider_model_ids: Method to list available models from the provider
     - get_extra_client_params: Method to provide extra parameters to the AsyncOpenAI client
@@ -121,6 +122,30 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
         """
         return {}
 
+    def construct_model_from_identifier(self, identifier: str) -> Model:
+        """
+        Construct a Model instance corresponding to the given identifier
+
+        Child classes can override this to customize model typing/metadata.
+
+        :param identifier: The provider's model identifier
+        :return: A Model instance
+        """
+        if metadata := self.embedding_model_metadata.get(identifier):
+            return Model(
+                provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+                provider_resource_id=identifier,
+                identifier=identifier,
+                model_type=ModelType.embedding,
+                metadata=metadata,
+            )
+        return Model(
+            provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+            provider_resource_id=identifier,
+            identifier=identifier,
+            model_type=ModelType.llm,
+        )
+
     async def list_provider_model_ids(self) -> Iterable[str]:
         """
         List available models from the provider.
@@ -416,21 +441,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
             if self.allowed_models and provider_model_id not in self.allowed_models:
                 logger.info(f"Skipping model {provider_model_id} as it is not in the allowed models list")
                 continue
-            if metadata := self.embedding_model_metadata.get(provider_model_id):
-                model = Model(
-                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
-                    provider_resource_id=provider_model_id,
-                    identifier=provider_model_id,
-                    model_type=ModelType.embedding,
-                    metadata=metadata,
-                )
-            else:
-                model = Model(
-                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
-                    provider_resource_id=provider_model_id,
-                    identifier=provider_model_id,
-                    model_type=ModelType.llm,
-                )
+            model = self.construct_model_from_identifier(provider_model_id)
             self._model_cache[provider_model_id] = model
 
         return list(self._model_cache.values())

tests/unit/providers/utils/inference/test_openai_mixin.py

@@ -38,6 +38,28 @@ class OpenAIMixinWithEmbeddingsImpl(OpenAIMixinImpl):
     }
 
 
+class OpenAIMixinWithCustomModelConstruction(OpenAIMixinImpl):
+    """Test implementation that uses construct_model_from_identifier to add rerank models"""
+
+    embedding_model_metadata: dict[str, dict[str, int]] = {
+        "text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192},
+        "text-embedding-ada-002": {"embedding_dimension": 1536, "context_length": 8192},
+    }
+
+    # Adds rerank models via construct_model_from_identifier
+    rerank_model_ids: set[str] = {"rerank-model-1", "rerank-model-2"}
+
+    def construct_model_from_identifier(self, identifier: str) -> Model:
+        if identifier in self.rerank_model_ids:
+            return Model(
+                provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+                provider_resource_id=identifier,
+                identifier=identifier,
+                model_type=ModelType.rerank,
+            )
+        return super().construct_model_from_identifier(identifier)
+
+
 @pytest.fixture
 def mixin():
     """Create a test instance of OpenAIMixin with mocked model_store"""
@@ -62,6 +84,13 @@ def mixin_with_embeddings():
     return OpenAIMixinWithEmbeddingsImpl(config=config)
 
 
+@pytest.fixture
+def mixin_with_custom_model_construction():
+    """Create a test instance using custom construct_model_from_identifier"""
+    config = RemoteInferenceProviderConfig()
+    return OpenAIMixinWithCustomModelConstruction(config=config)
+
+
 @pytest.fixture
 def mock_models():
     """Create multiple mock OpenAI model objects"""
@@ -113,6 +142,19 @@ def mock_client_context():
     return _mock_client_context
 
 
+def _assert_models_match_expected(actual_models, expected_models):
+    """Verify the models match expected attributes.
+
+    Args:
+        actual_models: List of models to verify
+        expected_models: Mapping of model identifier to expected attribute values
+    """
+    for identifier, expected_attrs in expected_models.items():
+        model = next(m for m in actual_models if m.identifier == identifier)
+        for attr_name, expected_value in expected_attrs.items():
+            assert getattr(model, attr_name) == expected_value
+
+
 class TestOpenAIMixinListModels:
     """Test cases for the list_models method"""
 
@@ -342,21 +384,71 @@ class TestOpenAIMixinEmbeddingModelMetadata:
             assert result is not None
             assert len(result) == 2
 
-            # Find the models in the result
-            embedding_model = next(m for m in result if m.identifier == "text-embedding-3-small")
-            llm_model = next(m for m in result if m.identifier == "gpt-4")
+            expected_models = {
+                "text-embedding-3-small": {
+                    "model_type": ModelType.embedding,
+                    "metadata": {"embedding_dimension": 1536, "context_length": 8192},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "text-embedding-3-small",
+                },
+                "gpt-4": {
+                    "model_type": ModelType.llm,
+                    "metadata": {},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "gpt-4",
+                },
+            }
 
-            # Check embedding model
-            assert embedding_model.model_type == ModelType.embedding
-            assert embedding_model.metadata == {"embedding_dimension": 1536, "context_length": 8192}
-            assert embedding_model.provider_id == "test-provider"
-            assert embedding_model.provider_resource_id == "text-embedding-3-small"
+            _assert_models_match_expected(result, expected_models)
 
-            # Check LLM model
-            assert llm_model.model_type == ModelType.llm
-            assert llm_model.metadata == {}  # No metadata for LLMs
-            assert llm_model.provider_id == "test-provider"
-            assert llm_model.provider_resource_id == "gpt-4"
+
+class TestOpenAIMixinCustomModelConstruction:
+    """Test cases for mixed model types (LLM, embedding, rerank) through construct_model_from_identifier"""
+
+    async def test_mixed_model_types_identification(self, mixin_with_custom_model_construction, mock_client_context):
+        """Test that LLM, embedding, and rerank models are correctly identified with proper types and metadata"""
+        # Create mock models: 1 embedding, 1 rerank, 1 LLM
+        mock_embedding_model = MagicMock(id="text-embedding-3-small")
+        mock_rerank_model = MagicMock(id="rerank-model-1")
+        mock_llm_model = MagicMock(id="gpt-4")
+        mock_models = [mock_embedding_model, mock_rerank_model, mock_llm_model]
+
+        mock_client = MagicMock()
+
+        async def mock_models_list():
+            for model in mock_models:
+                yield model
+
+        mock_client.models.list.return_value = mock_models_list()
+
+        with mock_client_context(mixin_with_custom_model_construction, mock_client):
+            result = await mixin_with_custom_model_construction.list_models()
+
+            assert result is not None
+            assert len(result) == 3
+
+            expected_models = {
+                "text-embedding-3-small": {
+                    "model_type": ModelType.embedding,
+                    "metadata": {"embedding_dimension": 1536, "context_length": 8192},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "text-embedding-3-small",
+                },
+                "rerank-model-1": {
+                    "model_type": ModelType.rerank,
+                    "metadata": {},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "rerank-model-1",
+                },
+                "gpt-4": {
+                    "model_type": ModelType.llm,
+                    "metadata": {},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "gpt-4",
+                },
+            }
+
+            _assert_models_match_expected(result, expected_models)
 
 
 class TestOpenAIMixinAllowedModels: