feat: create dynamic model registration for OpenAI and Llama compat remote inference providers (#2745)

# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
The purpose of this task is to create a solution that can automatically
detect when new models are added, deprecated, or removed by OpenAI and
Llama API providers, and automatically update the list of supported
models in LLamaStack.

This feature is vitally important in order to avoid missing new models
and editing the entries manually hence I created automation allowing
users to dynamically register:
- any models from OpenAI provider available at 
[https://api.openai.com/v1/models](https://api.openai.com/v1/models)
that are not in
[https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/openai/models.py](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/openai/models.py)

- any models from Llama API provider available at
[https://api.llama.com/v1/models](https://api.llama.com/v1/models) that
are not in
[https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/llama_openai_compat/models.py](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/llama_openai_compat/models.py)

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes #2504

this PR is dependant on #2710

## 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.* -->

1. Create venv at root llamastack directory:
`uv venv .venv --python 3.12 --seed`    
2. Activate venv:
`source .venv/bin/activate`   
3. `uv pip install -e .`
4. Create OpenAI distro modifying run.yaml
5. Build distro:
`llama stack build --template starter --image-type venv`
6. Then run LlamaStack, but before navigate to templates/starter folder:
`llama stack run run.yaml --image-type venv OPENAI_API_KEY=<YOUR_KEY>
ENABLE_OPENAI=openai`
7. Then try to register dummy llm that doesn't exist in OpenAI provider:
` llama-stack-client models register ianm/ianllm
--provider-model-id=ianllm --provider-id=openai `
 
You should receive this output - combined list of static config +
fetched available models from OpenAI:
 
<img width="1380" height="474" alt="Screenshot 2025-07-14 at 12 48 50"
src="https://github.com/user-attachments/assets/d26aad18-6b15-49ee-9c49-b01b2d33f883"
/>

8. Then register real llm from OpenAI:
llama-stack-client models register openai/gpt-4-turbo-preview
--provider-model-id=gpt-4-turbo-preview --provider-id=openai

<img width="1253" height="613" alt="Screenshot 2025-07-14 at 13 43 02"
src="https://github.com/user-attachments/assets/60a5c9b1-3468-4eb9-9e92-cd7d21de3ca0"
/>
<img width="1288" height="655" alt="Screenshot 2025-07-14 at 13 43 11"
src="https://github.com/user-attachments/assets/c1e48871-0e24-4bd9-a0b8-8c95552a51ee"
/>

We correctly fetched all available models from OpenAI

As for Llama API, as a non-US person I don't have access to Llama API
Key but I joined wait list. The implementation for Llama is the same as
for OpenAI since Llama is openai compatible. So, the response from GET
endpoint has the same structure as OpenAI
https://llama.developer.meta.com/docs/api/models

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

@@ -8,7 +8,7 @@ import logging
 from collections.abc import AsyncIterator
 from typing import Any
 
-from openai import AsyncOpenAI
+from openai import AsyncOpenAI, NotFoundError
 
 from llama_stack.apis.inference import (
     OpenAIChatCompletion,
@@ -60,6 +60,27 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin):
         # litellm specific model names, an abstraction leak.
         self.is_openai_compat = True
 
+    async def check_model_availability(self, model: str) -> bool:
+        """
+        Check if a specific model is available from OpenAI.
+
+        :param model: The model identifier to check.
+        :return: True if the model is available dynamically, False otherwise.
+        """
+        try:
+            openai_client = self._get_openai_client()
+            retrieved_model = await openai_client.models.retrieve(model)
+            logger.info(f"Model {retrieved_model.id} is available from OpenAI")
+            return True
+
+        except NotFoundError:
+            logger.error(f"Model {model} is not available from OpenAI")
+            return False
+
+        except Exception as e:
+            logger.error(f"Failed to check model availability from OpenAI: {e}")
+            return False
+
     async def initialize(self) -> None:
         await super().initialize()