feat(internal): add image_url download feature to OpenAIMixin (#3516)

# What does this PR do?

simplify Ollama inference adapter by -
 - moving image_url download code to OpenAIMixin
- being a ModelRegistryHelper instead of having one (mypy blocks
check_model_availability method assignment)

## Test Plan

 - add unit tests for new download feature
- add integration tests for openai_chat_completion w/ image_url (close
test gap)

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

@@ -6,8 +6,7 @@
 
 
 import asyncio
-import base64
-from collections.abc import AsyncGenerator, AsyncIterator
+from collections.abc import AsyncGenerator
 from typing import Any
 
 from ollama import AsyncClient as AsyncOllamaClient
@@ -33,10 +32,6 @@ from llama_stack.apis.inference import (
     JsonSchemaResponseFormat,
     LogProbConfig,
     Message,
-    OpenAIChatCompletion,
-    OpenAIChatCompletionChunk,
-    OpenAIMessageParam,
-    OpenAIResponseFormatParam,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -62,7 +57,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
     OpenAICompatCompletionChoice,
     OpenAICompatCompletionResponse,
     get_sampling_options,
-    prepare_openai_completion_params,
     process_chat_completion_response,
     process_chat_completion_stream_response,
     process_completion_response,
@@ -75,7 +69,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
     content_has_media,
     convert_image_content_to_url,
     interleaved_content_as_str,
-    localize_image_content,
     request_has_media,
 )
 
@@ -84,6 +77,7 @@ logger = get_logger(name=__name__, category="inference::ollama")
 
 class OllamaInferenceAdapter(
     OpenAIMixin,
+    ModelRegistryHelper,
     InferenceProvider,
     ModelsProtocolPrivate,
 ):
@@ -129,6 +123,8 @@ class OllamaInferenceAdapter(
             ],
         )
         self.config = config
+        # Ollama does not support image urls, so we need to download the image and convert it to base64
+        self.download_images = True
         self._clients: dict[asyncio.AbstractEventLoop, AsyncOllamaClient] = {}
 
     @property
@@ -173,9 +169,6 @@ class OllamaInferenceAdapter(
     async def shutdown(self) -> None:
         self._clients.clear()
 
-    async def unregister_model(self, model_id: str) -> None:
-        pass
-
     async def _get_model(self, model_id: str) -> Model:
         if not self.model_store:
             raise ValueError("Model store not set")
@@ -403,75 +396,6 @@ class OllamaInferenceAdapter(
 
         raise UnsupportedModelError(model.provider_model_id, list(self._model_cache.keys()))
 
-    async def openai_chat_completion(
-        self,
-        model: str,
-        messages: list[OpenAIMessageParam],
-        frequency_penalty: float | None = None,
-        function_call: str | dict[str, Any] | None = None,
-        functions: list[dict[str, Any]] | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_completion_tokens: int | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        parallel_tool_calls: bool | None = None,
-        presence_penalty: float | None = None,
-        response_format: OpenAIResponseFormatParam | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        tools: list[dict[str, Any]] | None = None,
-        top_logprobs: int | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        model_obj = await self._get_model(model)
-
-        # Ollama does not support image urls, so we need to download the image and convert it to base64
-        async def _convert_message(m: OpenAIMessageParam) -> OpenAIMessageParam:
-            if isinstance(m.content, list):
-                for c in m.content:
-                    if c.type == "image_url" and c.image_url and c.image_url.url:
-                        localize_result = await localize_image_content(c.image_url.url)
-                        if localize_result is None:
-                            raise ValueError(f"Failed to localize image content from {c.image_url.url}")
-
-                        content, format = localize_result
-                        c.image_url.url = f"data:image/{format};base64,{base64.b64encode(content).decode('utf-8')}"
-            return m
-
-        messages = [await _convert_message(m) for m in messages]
-        params = await prepare_openai_completion_params(
-            model=model_obj.provider_resource_id,
-            messages=messages,
-            frequency_penalty=frequency_penalty,
-            function_call=function_call,
-            functions=functions,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_completion_tokens=max_completion_tokens,
-            max_tokens=max_tokens,
-            n=n,
-            parallel_tool_calls=parallel_tool_calls,
-            presence_penalty=presence_penalty,
-            response_format=response_format,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            tool_choice=tool_choice,
-            tools=tools,
-            top_logprobs=top_logprobs,
-            top_p=top_p,
-            user=user,
-        )
-        return await OpenAIMixin.openai_chat_completion(self, **params)
-
 
 async def convert_message_to_openai_dict_for_ollama(message: Message) -> list[dict]:
     async def _convert_content(content) -> dict:

llama_stack/providers/utils/inference/openai_mixin.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import base64
 import uuid
 from abc import ABC, abstractmethod
 from collections.abc import AsyncIterator
@@ -26,6 +27,7 @@ from llama_stack.apis.models import ModelType
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
+from llama_stack.providers.utils.inference.prompt_adapter import localize_image_content
 
 logger = get_logger(name=__name__, category="providers::utils")
 
@@ -51,6 +53,10 @@ class OpenAIMixin(ModelRegistryHelper, ABC):
     # This is useful for providers that do not return a unique id in the response.
     overwrite_completion_id: bool = False
 
+    # Allow subclasses to control whether to download images and convert to base64
+    # for providers that require base64 encoded images instead of URLs.
+    download_images: bool = False
+
     # Embedding model metadata for this provider
     # Can be set by subclasses or instances to provide embedding models
     # Format: {"model_id": {"embedding_dimension": 1536, "context_length": 8192}}
@@ -239,6 +245,24 @@ class OpenAIMixin(ModelRegistryHelper, ABC):
         """
         Direct OpenAI chat completion API call.
         """
+        if self.download_images:
+
+            async def _localize_image_url(m: OpenAIMessageParam) -> OpenAIMessageParam:
+                if isinstance(m.content, list):
+                    for c in m.content:
+                        if c.type == "image_url" and c.image_url and c.image_url.url and "http" in c.image_url.url:
+                            localize_result = await localize_image_content(c.image_url.url)
+                            if localize_result is None:
+                                raise ValueError(
+                                    f"Failed to localize image content from {c.image_url.url[:42]}{'...' if len(c.image_url.url) > 42 else ''}"
+                                )
+                            content, format = localize_result
+                            c.image_url.url = f"data:image/{format};base64,{base64.b64encode(content).decode('utf-8')}"
+                # else it's a string and we don't need to modify it
+                return m
+
+            messages = [await _localize_image_url(m) for m in messages]
+
         resp = await self.client.chat.completions.create(
             **await prepare_openai_completion_params(
                 model=await self._get_provider_model_id(model),