Merge 6b4940806f into 188a56af5c

docs/docs/providers/inference/index.mdx

@@ -1,9 +1,10 @@
 ---
 description: "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
 ---
@@ -14,8 +15,9 @@ title: 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

@@ -13335,7 +13335,7 @@
         },
         {
             "name": "Inference",
-            "description": "This 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": "This 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": "Llama Stack Inference API for generating completions, chat completions, and embeddings."
         },
         {

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

@@ -9990,13 +9990,16 @@ tags:
     description: ''
   - name: Inference
     description: >-
-      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: >-
       Llama Stack Inference API for generating completions, chat completions, and
       embeddings.

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

@@ -4992,7 +4992,7 @@
                 "properties": {
                     "model": {
                         "type": "string",
-                        "description": "The identifier of the reranking model to use."
+                        "description": "The identifier of the reranking model to use. The model must be a reranking model registered with Llama Stack and available via the /models endpoint."
                     },
                     "query": {
                         "oneOf": [

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

@@ -3657,7 +3657,8 @@ components:
         model:
           type: string
           description: >-
-            The identifier of the reranking model to use.
+            The identifier of the reranking model to use. The model must be a reranking
+            model registered with Llama Stack and available via the /models endpoint.
         query:
           oneOf:
             - type: string

docs/static/llama-stack-spec.html

@@ -6829,7 +6829,8 @@
                 "type": "string",
                 "enum": [
                     "llm",
-                    "embedding"
+                    "embedding",
+                    "rerank"
                 ],
                 "title": "ModelType",
                 "description": "Enumeration of supported model types in Llama Stack."
@@ -12883,7 +12884,7 @@
         },
         {
             "name": "Inference",
-            "description": "This 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": "This 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": "Llama Stack Inference API for generating completions, chat completions, and embeddings."
         },
         {

docs/static/llama-stack-spec.yaml

@@ -5158,6 +5158,7 @@ components:
       enum:
         - llm
         - embedding
+        - rerank
       title: ModelType
       description: >-
         Enumeration of supported model types in Llama Stack.
@@ -9728,13 +9729,16 @@ tags:
     description: ''
   - name: Inference
     description: >-
-      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: >-
       Llama Stack Inference API for generating completions, chat completions, and
       embeddings.

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

@@ -8838,7 +8838,8 @@
                 "type": "string",
                 "enum": [
                     "llm",
-                    "embedding"
+                    "embedding",
+                    "rerank"
                 ],
                 "title": "ModelType",
                 "description": "Enumeration of supported model types in Llama Stack."
@@ -17033,7 +17034,7 @@
                 "properties": {
                     "model": {
                         "type": "string",
-                        "description": "The identifier of the reranking model to use."
+                        "description": "The identifier of the reranking model to use. The model must be a reranking model registered with Llama Stack and available via the /models endpoint."
                     },
                     "query": {
                         "oneOf": [
@@ -18456,7 +18457,7 @@
         },
         {
             "name": "Inference",
-            "description": "This 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": "This 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": "Llama Stack Inference API for generating completions, chat completions, and embeddings."
         },
         {

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

@@ -6603,6 +6603,7 @@ components:
       enum:
         - llm
         - embedding
+        - rerank
       title: ModelType
       description: >-
         Enumeration of supported model types in Llama Stack.
@@ -12693,7 +12694,8 @@ components:
         model:
           type: string
           description: >-
-            The identifier of the reranking model to use.
+            The identifier of the reranking model to use. The model must be a reranking
+            model registered with Llama Stack and available via the /models endpoint.
         query:
           oneOf:
             - type: string
@@ -13774,13 +13776,16 @@ tags:
     description: ''
   - name: Inference
     description: >-
-      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: >-
       Llama Stack Inference API for generating completions, chat completions, and
       embeddings.

llama_stack/apis/inference/inference.py

@@ -1016,7 +1016,7 @@ class InferenceProvider(Protocol):
     ) -> RerankResponse:
         """Rerank a list of documents based on their relevance to a query.
 
-        :param model: The identifier of the reranking model to use.
+        :param model: The identifier of the reranking model to use. The model must be a reranking model registered with Llama Stack and available via the /models endpoint.
         :param query: The search query to rank items against. Can be a string, text content part, or image content part. The input must not exceed the model's max input token length.
         :param items: List of items to rerank. Each item can be a string, text content part, or image content part. Each input must not exceed the model's max input token length.
         :param max_num_results: (Optional) Maximum number of results to return. Default: returns all.
@@ -1159,9 +1159,10 @@ class InferenceProvider(Protocol):
 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

@@ -41,9 +41,14 @@ from llama_stack.apis.inference import (
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
     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
@@ -179,6 +184,23 @@ 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,
         model: str,

llama_stack/providers/remote/inference/nvidia/NVIDIA.md

@@ -188,3 +188,22 @@ vlm_response = client.chat.completions.create(
 
 print(f"VLM Response: {vlm_response.choices[0].message.content}")
 ```
+
+### Rerank Example
+
+The following example shows how to rerank documents using an NVIDIA NIM.
+
+```python
+rerank_response = client.inference.rerank(
+    model="nvidia/llama-3.2-nv-rerankqa-1b-v2",
+    query="query",
+    items=[
+        "item_1",
+        "item_2",
+        "item_3",
+    ],
+)
+
+for i, result in enumerate(rerank_response):
+    print(f"{i+1}. [Index: {result.index}, " f"Score: {(result.relevance_score):.3f}]")
+```

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

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 
+import aiohttp
 from openai import NOT_GIVEN
 
 from llama_stack.apis.inference import (
@@ -12,7 +13,14 @@ from llama_stack.apis.inference import (
     OpenAIEmbeddingData,
     OpenAIEmbeddingsResponse,
     OpenAIEmbeddingUsage,
+    RerankData,
+    RerankResponse,
 )
+from llama_stack.apis.inference.inference import (
+    OpenAIChatCompletionContentPartImageParam,
+    OpenAIChatCompletionContentPartTextParam,
+)
+from llama_stack.apis.models import Model, ModelType
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -44,6 +52,18 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
         "snowflake/arctic-embed-l": {"embedding_dimension": 512, "context_length": 1024},
     }
 
+    rerank_model_list = [
+        "nv-rerank-qa-mistral-4b:1",
+        "nvidia/nv-rerankqa-mistral-4b-v3",
+        "nvidia/llama-3.2-nv-rerankqa-1b-v2",
+    ]
+
+    _rerank_model_endpoints = {
+        "nv-rerank-qa-mistral-4b:1": "https://ai.api.nvidia.com/v1/retrieval/nvidia/reranking",
+        "nvidia/nv-rerankqa-mistral-4b-v3": "https://ai.api.nvidia.com/v1/retrieval/nvidia/nv-rerankqa-mistral-4b-v3/reranking",
+        "nvidia/llama-3.2-nv-rerankqa-1b-v2": "https://ai.api.nvidia.com/v1/retrieval/nvidia/llama-3_2-nv-rerankqa-1b-v2/reranking",
+    }
+
     def __init__(self, config: NVIDIAConfig) -> None:
         logger.info(f"Initializing NVIDIAInferenceAdapter({config.url})...")
 
@@ -62,6 +82,8 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
         #         "Consider removing the api_key from the configuration."
         #     )
 
+        super().__init__()
+
         self._config = config
 
     def get_api_key(self) -> str:
@@ -80,6 +102,103 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
         """
         return f"{self._config.url}/v1" if self._config.append_api_version else self._config.url
 
+    async def list_models(self) -> list[Model] | None:
+        """
+        List available NVIDIA models by combining:
+        1. Dynamic models from https://integrate.api.nvidia.com/v1/models
+        2. Static rerank models (which use different API endpoints)
+        """
+        self._model_cache = {}
+        models = await super().list_models()
+
+        # Add rerank models
+        existing_ids = {m.identifier for m in models}
+        for model_id, _ in self._rerank_model_endpoints.items():
+            if self.allowed_models and model_id not in self.allowed_models:
+                continue
+            if model_id not in existing_ids:
+                model = Model(
+                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+                    provider_resource_id=model_id,
+                    identifier=model_id,
+                    model_type=ModelType.rerank,
+                )
+                models.append(model)
+                self._model_cache[model_id] = model
+
+        return models
+
+    async def rerank(
+        self,
+        model: str,
+        query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
+        items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
+        max_num_results: int | None = None,
+    ) -> RerankResponse:
+        provider_model_id = await self._get_provider_model_id(model)
+
+        ranking_url = self.get_base_url()
+
+        if _is_nvidia_hosted(self._config) and provider_model_id in self._rerank_model_endpoints:
+            ranking_url = self._rerank_model_endpoints[provider_model_id]
+
+        logger.debug(f"Using rerank endpoint: {ranking_url} for model: {provider_model_id}")
+
+        # Convert query to text format
+        if isinstance(query, str):
+            query_text = query
+        elif isinstance(query, OpenAIChatCompletionContentPartTextParam):
+            query_text = query.text
+        else:
+            raise ValueError("Query must be a string or text content part")
+
+        # Convert items to text format
+        passages = []
+        for item in items:
+            if isinstance(item, str):
+                passages.append({"text": item})
+            elif isinstance(item, OpenAIChatCompletionContentPartTextParam):
+                passages.append({"text": item.text})
+            else:
+                raise ValueError("Items must be strings or text content parts")
+
+        payload = {
+            "model": provider_model_id,
+            "query": {"text": query_text},
+            "passages": passages,
+        }
+
+        headers = {
+            "Authorization": f"Bearer {self.get_api_key()}",
+            "Content-Type": "application/json",
+        }
+
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.post(ranking_url, headers=headers, json=payload) as response:
+                    if response.status != 200:
+                        response_text = await response.text()
+                        raise ConnectionError(
+                            f"NVIDIA rerank API request failed with status {response.status}: {response_text}"
+                        )
+
+                    result = await response.json()
+                    rankings = result.get("rankings", [])
+
+                    # Convert to RerankData format
+                    rerank_data = []
+                    for ranking in rankings:
+                        rerank_data.append(RerankData(index=ranking["index"], relevance_score=ranking["logit"]))
+
+                    # Apply max_num_results limit
+                    if max_num_results is not None:
+                        rerank_data = rerank_data[:max_num_results]
+
+                    return RerankResponse(data=rerank_data)
+
+        except aiohttp.ClientError as e:
+            raise ConnectionError(f"Failed to connect to NVIDIA rerank API at {ranking_url}: {e}") from e
+
     async def openai_embeddings(
         self,
         model: str,

llama_stack/providers/utils/inference/openai_mixin.py

@@ -63,6 +63,10 @@ class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
     # Format: {"model_id": {"embedding_dimension": 1536, "context_length": 8192}}
     embedding_model_metadata: dict[str, dict[str, int]] = {}
 
+    # List of rerank model IDs for this provider
+    # Can be set by subclasses or instances to provide rerank models
+    rerank_model_list: list[str] = []
+
     # Cache of available models keyed by model ID
     # This is set in list_models() and used in check_model_availability()
     _model_cache: dict[str, Model] = {}
@@ -400,6 +404,14 @@ class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
                     model_type=ModelType.embedding,
                     metadata=metadata,
                 )
+            elif m.id in self.rerank_model_list:
+                # This is a rerank model
+                model = Model(
+                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+                    provider_resource_id=m.id,
+                    identifier=m.id,
+                    model_type=ModelType.rerank,
+                )
             else:
                 # This is an LLM
                 model = Model(

tests/integration/conftest.py

@@ -120,6 +120,10 @@ def pytest_addoption(parser):
         "--embedding-model",
         help="comma-separated list of embedding models. Fixture name: embedding_model_id",
     )
+    parser.addoption(
+        "--rerank-model",
+        help="comma-separated list of rerank models. Fixture name: rerank_model_id",
+    )
     parser.addoption(
         "--safety-shield",
         help="comma-separated list of safety shields. Fixture name: shield_id",
@@ -198,6 +202,7 @@ def pytest_generate_tests(metafunc):
         "shield_id": ("--safety-shield", "shield"),
         "judge_model_id": ("--judge-model", "judge"),
         "embedding_dimension": ("--embedding-dimension", "dim"),
+        "rerank_model_id": ("--rerank-model", "rerank"),
     }
 
     # Collect all parameters and their values

tests/integration/fixtures/common.py

@@ -119,6 +119,7 @@ def client_with_models(
     embedding_model_id,
     embedding_dimension,
     judge_model_id,
+    rerank_model_id,
 ):
     client = llama_stack_client
 
@@ -151,6 +152,20 @@ def client_with_models(
             model_type="embedding",
             metadata={"embedding_dimension": embedding_dimension or 384},
         )
+    if rerank_model_id and rerank_model_id not in model_ids:
+        selected_provider = None
+        for p in providers:
+            # Currently only NVIDIA inference provider supports reranking
+            if p.provider_type == "remote::nvidia":
+                selected_provider = p
+                break
+
+        selected_provider = selected_provider or providers[0]
+        client.models.register(
+            model_id=rerank_model_id,
+            provider_id=selected_provider.provider_id,
+            model_type="rerank",
+        )
     return client
 
 
@@ -166,7 +181,14 @@ def model_providers(llama_stack_client):
 
 @pytest.fixture(autouse=True)
 def skip_if_no_model(request):
-    model_fixtures = ["text_model_id", "vision_model_id", "embedding_model_id", "judge_model_id", "shield_id"]
+    model_fixtures = [
+        "text_model_id",
+        "vision_model_id",
+        "embedding_model_id",
+        "judge_model_id",
+        "shield_id",
+        "rerank_model_id",
+    ]
     test_func = request.node.function
 
     actual_params = inspect.signature(test_func).parameters.keys()

tests/integration/inference/test_rerank.py

@@ -0,0 +1,214 @@
+# 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 pytest
+from llama_stack_client import BadRequestError as LlamaStackBadRequestError
+from llama_stack_client.types.alpha import InferenceRerankResponse
+from llama_stack_client.types.shared.interleaved_content import (
+    ImageContentItem,
+    ImageContentItemImage,
+    ImageContentItemImageURL,
+    TextContentItem,
+)
+
+from llama_stack.core.library_client import LlamaStackAsLibraryClient
+
+# Test data
+DUMMY_STRING = "string_1"
+DUMMY_STRING2 = "string_2"
+DUMMY_TEXT = TextContentItem(text=DUMMY_STRING, type="text")
+DUMMY_TEXT2 = TextContentItem(text=DUMMY_STRING2, type="text")
+DUMMY_IMAGE_URL = ImageContentItem(
+    image=ImageContentItemImage(url=ImageContentItemImageURL(uri="https://example.com/image.jpg")), type="image"
+)
+DUMMY_IMAGE_BASE64 = ImageContentItem(image=ImageContentItemImage(data="base64string"), type="image")
+
+PROVIDERS_SUPPORTING_MEDIA = {}  # Providers that support media input for rerank models
+
+
+def skip_if_provider_doesnt_support_rerank(inference_provider_type):
+    supported_providers = {"remote::nvidia"}
+    if inference_provider_type not in supported_providers:
+        pytest.skip(f"{inference_provider_type} doesn't support rerank models")
+
+
+def _validate_rerank_response(response: InferenceRerankResponse, items: list) -> None:
+    """
+    Validate that a rerank response has the correct structure and ordering.
+
+    Args:
+        response: The InferenceRerankResponse to validate
+        items: The original items list that was ranked
+
+    Raises:
+        AssertionError: If any validation fails
+    """
+    seen = set()
+    last_score = float("inf")
+    for d in response:
+        assert 0 <= d.index < len(items), f"Index {d.index} out of bounds for {len(items)} items"
+        assert d.index not in seen, f"Duplicate index {d.index} found"
+        seen.add(d.index)
+        assert isinstance(d.relevance_score, float), f"Score must be float, got {type(d.relevance_score)}"
+        assert d.relevance_score <= last_score, f"Scores not in descending order: {d.relevance_score} > {last_score}"
+        last_score = d.relevance_score
+
+
+def _validate_semantic_ranking(response: InferenceRerankResponse, items: list, expected_first_item: str) -> None:
+    """
+    Validate that the expected most relevant item ranks first.
+
+    Args:
+        response: The InferenceRerankResponse to validate
+        items: The original items list that was ranked
+        expected_first_item: The expected first item in the ranking
+
+    Raises:
+        AssertionError: If any validation fails
+    """
+    if not response:
+        raise AssertionError("No ranking data returned in response")
+
+    actual_first_index = response[0].index
+    actual_first_item = items[actual_first_index]
+    assert actual_first_item == expected_first_item, (
+        f"Expected '{expected_first_item}' to rank first, but '{actual_first_item}' ranked first instead."
+    )
+
+
+@pytest.mark.parametrize(
+    "query,items",
+    [
+        (DUMMY_STRING, [DUMMY_STRING, DUMMY_STRING2]),
+        (DUMMY_TEXT, [DUMMY_TEXT, DUMMY_TEXT2]),
+        (DUMMY_STRING, [DUMMY_STRING2, DUMMY_TEXT]),
+        (DUMMY_TEXT, [DUMMY_STRING, DUMMY_TEXT2]),
+    ],
+    ids=[
+        "string-query-string-items",
+        "text-query-text-items",
+        "mixed-content-1",
+        "mixed-content-2",
+    ],
+)
+def test_rerank_text(client_with_models, rerank_model_id, query, items, inference_provider_type):
+    skip_if_provider_doesnt_support_rerank(inference_provider_type)
+
+    response = client_with_models.alpha.inference.rerank(model=rerank_model_id, query=query, items=items)
+    assert isinstance(response, list)
+    # TODO: Add type validation for response items once InferenceRerankResponseItem is exported from llama stack client.
+    assert len(response) <= len(items)
+    _validate_rerank_response(response, items)
+
+
+@pytest.mark.parametrize(
+    "query,items",
+    [
+        (DUMMY_IMAGE_URL, [DUMMY_STRING]),
+        (DUMMY_IMAGE_BASE64, [DUMMY_TEXT]),
+        (DUMMY_TEXT, [DUMMY_IMAGE_URL]),
+        (DUMMY_IMAGE_BASE64, [DUMMY_IMAGE_URL, DUMMY_STRING, DUMMY_IMAGE_BASE64, DUMMY_TEXT]),
+        (DUMMY_TEXT, [DUMMY_IMAGE_URL, DUMMY_STRING, DUMMY_IMAGE_BASE64, DUMMY_TEXT]),
+    ],
+    ids=[
+        "image-query-url",
+        "image-query-base64",
+        "text-query-image-item",
+        "mixed-content-1",
+        "mixed-content-2",
+    ],
+)
+def test_rerank_image(client_with_models, rerank_model_id, query, items, inference_provider_type):
+    skip_if_provider_doesnt_support_rerank(inference_provider_type)
+
+    if rerank_model_id not in PROVIDERS_SUPPORTING_MEDIA:
+        error_type = (
+            ValueError if isinstance(client_with_models, LlamaStackAsLibraryClient) else LlamaStackBadRequestError
+        )
+        with pytest.raises(error_type):
+            client_with_models.alpha.inference.rerank(model=rerank_model_id, query=query, items=items)
+    else:
+        response = client_with_models.alpha.inference.rerank(model=rerank_model_id, query=query, items=items)
+
+        assert isinstance(response, list)
+        assert len(response) <= len(items)
+        _validate_rerank_response(response, items)
+
+
+def test_rerank_max_results(client_with_models, rerank_model_id, inference_provider_type):
+    skip_if_provider_doesnt_support_rerank(inference_provider_type)
+
+    items = [DUMMY_STRING, DUMMY_STRING2, DUMMY_TEXT, DUMMY_TEXT2]
+    max_num_results = 2
+
+    response = client_with_models.alpha.inference.rerank(
+        model=rerank_model_id,
+        query=DUMMY_STRING,
+        items=items,
+        max_num_results=max_num_results,
+    )
+
+    assert isinstance(response, list)
+    assert len(response) == max_num_results
+    _validate_rerank_response(response, items)
+
+
+def test_rerank_max_results_larger_than_items(client_with_models, rerank_model_id, inference_provider_type):
+    skip_if_provider_doesnt_support_rerank(inference_provider_type)
+
+    items = [DUMMY_STRING, DUMMY_STRING2]
+    response = client_with_models.alpha.inference.rerank(
+        model=rerank_model_id,
+        query=DUMMY_STRING,
+        items=items,
+        max_num_results=10,  # Larger than items length
+    )
+
+    assert isinstance(response, list)
+    assert len(response) <= len(items)  # Should return at most len(items)
+
+
+@pytest.mark.parametrize(
+    "query,items,expected_first_item",
+    [
+        (
+            "What is a reranking model? ",
+            [
+                "A reranking model reranks a list of items based on the query. ",
+                "Machine learning algorithms learn patterns from data. ",
+                "Python is a programming language. ",
+            ],
+            "A reranking model reranks a list of items based on the query. ",
+        ),
+        (
+            "What is C++?",
+            [
+                "Learning new things is interesting. ",
+                "C++ is a programming language. ",
+                "Books provide knowledge and entertainment. ",
+            ],
+            "C++ is a programming language. ",
+        ),
+        (
+            "What are good learning habits? ",
+            [
+                "Cooking pasta is a fun activity. ",
+                "Plants need water and sunlight. ",
+                "Good learning habits include reading daily and taking notes. ",
+            ],
+            "Good learning habits include reading daily and taking notes. ",
+        ),
+    ],
+)
+def test_rerank_semantic_correctness(
+    client_with_models, rerank_model_id, query, items, expected_first_item, inference_provider_type
+):
+    skip_if_provider_doesnt_support_rerank(inference_provider_type)
+
+    response = client_with_models.alpha.inference.rerank(model=rerank_model_id, query=query, items=items)
+
+    _validate_rerank_response(response, items)
+    _validate_semantic_ranking(response, items, expected_first_item)

tests/unit/providers/nvidia/test_rerank_inference.py

@@ -0,0 +1,222 @@
+# 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 unittest.mock import AsyncMock, MagicMock, patch
+
+import aiohttp
+import pytest
+
+from llama_stack.apis.models import ModelType
+from llama_stack.providers.remote.inference.nvidia.config import NVIDIAConfig
+from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAInferenceAdapter
+
+
+class MockResponse:
+    def __init__(self, status=200, json_data=None, text_data="OK"):
+        self.status = status
+        self._json_data = json_data or {"rankings": []}
+        self._text_data = text_data
+
+    async def json(self):
+        return self._json_data
+
+    async def text(self):
+        return self._text_data
+
+
+class MockSession:
+    def __init__(self, response):
+        self.response = response
+        self.post_calls = []
+
+    async def __aenter__(self):
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        return False
+
+    def post(self, url, **kwargs):
+        self.post_calls.append((url, kwargs))
+
+        class PostContext:
+            def __init__(self, response):
+                self.response = response
+
+            async def __aenter__(self):
+                return self.response
+
+            async def __aexit__(self, exc_type, exc_val, exc_tb):
+                return False
+
+        return PostContext(self.response)
+
+
+def create_adapter(config=None, rerank_endpoints=None):
+    if config is None:
+        config = NVIDIAConfig(api_key="test-key")
+
+    adapter = NVIDIAInferenceAdapter(config)
+
+    class MockModel:
+        provider_resource_id = "test-model"
+        metadata = {}
+
+    adapter.model_store = AsyncMock()
+    adapter.model_store.get_model = AsyncMock(return_value=MockModel())
+
+    if rerank_endpoints is not None:
+        adapter._rerank_model_endpoints = rerank_endpoints
+
+    return adapter
+
+
+async def test_rerank_basic_functionality():
+    adapter = create_adapter()
+    mock_response = MockResponse(json_data={"rankings": [{"index": 0, "logit": 0.5}]})
+    mock_session = MockSession(mock_response)
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        result = await adapter.rerank(model="test-model", query="test query", items=["item1", "item2"])
+
+    assert len(result.data) == 1
+    assert result.data[0].index == 0
+    assert result.data[0].relevance_score == 0.5
+
+    url, kwargs = mock_session.post_calls[0]
+    payload = kwargs["json"]
+    assert payload["model"] == "test-model"
+    assert payload["query"] == {"text": "test query"}
+    assert payload["passages"] == [{"text": "item1"}, {"text": "item2"}]
+
+
+async def test_missing_rankings_key():
+    adapter = create_adapter()
+    mock_session = MockSession(MockResponse(json_data={}))
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        result = await adapter.rerank(model="test-model", query="q", items=["a"])
+
+    assert len(result.data) == 0
+
+
+async def test_hosted_with_endpoint():
+    adapter = create_adapter(
+        config=NVIDIAConfig(api_key="key"), rerank_endpoints={"test-model": "https://model.endpoint/rerank"}
+    )
+    mock_session = MockSession(MockResponse())
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        await adapter.rerank(model="test-model", query="q", items=["a"])
+
+    url, _ = mock_session.post_calls[0]
+    assert url == "https://model.endpoint/rerank"
+
+
+async def test_hosted_without_endpoint():
+    adapter = create_adapter(
+        config=NVIDIAConfig(api_key="key"),  # This creates hosted config (integrate.api.nvidia.com).
+        rerank_endpoints={},  # No endpoint mapping for test-model
+    )
+    mock_session = MockSession(MockResponse())
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        await adapter.rerank(model="test-model", query="q", items=["a"])
+
+    url, _ = mock_session.post_calls[0]
+    assert "https://integrate.api.nvidia.com" in url
+
+
+async def test_hosted_model_not_in_endpoint_mapping():
+    adapter = create_adapter(
+        config=NVIDIAConfig(api_key="key"), rerank_endpoints={"other-model": "https://other.endpoint/rerank"}
+    )
+    mock_session = MockSession(MockResponse())
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        await adapter.rerank(model="test-model", query="q", items=["a"])
+
+    url, _ = mock_session.post_calls[0]
+    assert "https://integrate.api.nvidia.com" in url
+    assert url != "https://other.endpoint/rerank"
+
+
+async def test_self_hosted_ignores_endpoint():
+    adapter = create_adapter(
+        config=NVIDIAConfig(url="http://localhost:8000", api_key=None),
+        rerank_endpoints={"test-model": "https://model.endpoint/rerank"},  # This should be ignored for self-hosted.
+    )
+    mock_session = MockSession(MockResponse())
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        await adapter.rerank(model="test-model", query="q", items=["a"])
+
+    url, _ = mock_session.post_calls[0]
+    assert "http://localhost:8000" in url
+    assert "model.endpoint/rerank" not in url
+
+
+async def test_max_num_results():
+    adapter = create_adapter()
+    rankings = [{"index": 0, "logit": 0.8}, {"index": 1, "logit": 0.6}]
+    mock_session = MockSession(MockResponse(json_data={"rankings": rankings}))
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        result = await adapter.rerank(model="test-model", query="q", items=["a", "b"], max_num_results=1)
+
+    assert len(result.data) == 1
+    assert result.data[0].index == 0
+    assert result.data[0].relevance_score == 0.8
+
+
+async def test_http_error():
+    adapter = create_adapter()
+    mock_session = MockSession(MockResponse(status=500, text_data="Server Error"))
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        with pytest.raises(ConnectionError, match="status 500.*Server Error"):
+            await adapter.rerank(model="test-model", query="q", items=["a"])
+
+
+async def test_client_error():
+    adapter = create_adapter()
+    mock_session = AsyncMock()
+    mock_session.__aenter__.side_effect = aiohttp.ClientError("Network error")
+
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        with pytest.raises(ConnectionError, match="Failed to connect.*Network error"):
+            await adapter.rerank(model="test-model", query="q", items=["a"])
+
+
+async def test_list_models_adds_rerank_models():
+    """Test that list_models adds rerank models to the dynamic model list."""
+    adapter = create_adapter()
+    adapter.__provider_id__ = "nvidia"
+
+    # Mock the list_models from the superclass to return some dynamic models
+    base_models = [
+        MagicMock(identifier="llm-1", model_type=ModelType.llm),
+        MagicMock(identifier="embedding-1", model_type=ModelType.embedding),
+    ]
+
+    with patch.object(NVIDIAInferenceAdapter.__bases__[0], "list_models", return_value=base_models):
+        result = await adapter.list_models()
+
+        assert result is not None
+
+        # Check that the rerank models are added
+        model_ids = [m.identifier for m in result]
+        assert "nv-rerank-qa-mistral-4b:1" in model_ids
+        assert "nvidia/nv-rerankqa-mistral-4b-v3" in model_ids
+        assert "nvidia/llama-3.2-nv-rerankqa-1b-v2" in model_ids
+
+        rerank_models = [m for m in result if m.model_type == ModelType.rerank]
+
+        assert len(rerank_models) == 3
+
+        for rerank_model in rerank_models:
+            assert rerank_model.provider_id == "nvidia"
+            assert rerank_model.metadata == {}
+            assert rerank_model.identifier in adapter._model_cache

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

@@ -35,6 +35,40 @@ class OpenAIMixinWithEmbeddingsImpl(OpenAIMixinImpl):
     }
 
 
+class OpenAIMixinWithRerankImpl(OpenAIMixin):
+    """Test implementation with rerank model list"""
+
+    rerank_model_list = ["rerank-model-1", "rerank-model-2"]
+
+    def __init__(self):
+        self.__provider_id__ = "test-provider"
+
+    def get_api_key(self) -> str:
+        raise NotImplementedError("This method should be mocked in tests")
+
+    def get_base_url(self) -> str:
+        raise NotImplementedError("This method should be mocked in tests")
+
+
+class OpenAIMixinWithEmbeddingsAndRerankImpl(OpenAIMixin):
+    """Test implementation with both embedding model metadata and rerank model list"""
+
+    embedding_model_metadata = {
+        "text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192},
+        "text-embedding-ada-002": {"embedding_dimension": 1536, "context_length": 8192},
+    }
+
+    rerank_model_list = ["rerank-model-1", "rerank-model-2"]
+
+    __provider_id__ = "test-provider"
+
+    def get_api_key(self) -> str:
+        raise NotImplementedError("This method should be mocked in tests")
+
+    def get_base_url(self) -> str:
+        raise NotImplementedError("This method should be mocked in tests")
+
+
 @pytest.fixture
 def mixin():
     """Create a test instance of OpenAIMixin with mocked model_store"""
@@ -56,6 +90,18 @@ def mixin_with_embeddings():
     return OpenAIMixinWithEmbeddingsImpl()
 
 
+@pytest.fixture
+def mixin_with_rerank():
+    """Create a test instance of OpenAIMixin with rerank model list"""
+    return OpenAIMixinWithRerankImpl()
+
+
+@pytest.fixture
+def mixin_with_embeddings_and_rerank():
+    """Create a test instance of OpenAIMixin with both embedding model metadata and rerank model list"""
+    return OpenAIMixinWithEmbeddingsAndRerankImpl()
+
+
 @pytest.fixture
 def mock_models():
     """Create multiple mock OpenAI model objects"""
@@ -107,6 +153,19 @@ 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"""
 
@@ -300,21 +359,113 @@ 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 TestOpenAIMixinRerankModelList:
+    """Test cases for rerank_model_list attribute functionality"""
+
+    async def test_rerank_model_identified(self, mixin_with_rerank, mock_client_context):
+        """Test that models in rerank_model_list are correctly identified as rerank models"""
+        # Create mock models: 1 rerank model and 1 LLM
+        mock_rerank_model = MagicMock(id="rerank-model-1")
+        mock_llm_model = MagicMock(id="gpt-4")
+        mock_models = [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_rerank, mock_client):
+            result = await mixin_with_rerank.list_models()
+
+            assert result is not None
+            assert len(result) == 2
+
+            expected_models = {
+                "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 TestOpenAIMixinMixedModelTypes:
+    """Test cases for mixed model types (LLM, embedding, rerank)"""
+
+    async def test_mixed_model_types_identification(self, mixin_with_embeddings_and_rerank, 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_embeddings_and_rerank, mock_client):
+            result = await mixin_with_embeddings_and_rerank.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: