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feat: add static embedding metadata to dynamic model listings for providers using OpenAIMixin (#3547)
# What does this PR do? - remove auto-download of ollama embedding models - add embedding model metadata to dynamic listing w/ unit test - add support and tests for allowed_models - removed inference provider models.py files where dynamic listing is enabled - store embedding metadata in embedding_model_metadata field on inference providers - make model_entries optional on ModelRegistryHelper and LiteLLMOpenAIMixin - make OpenAIMixin a ModelRegistryHelper - skip base64 embedding test for remote::ollama, always returns floats - only use OpenAI client for ollama model listing - remove unused build_model_entry function - remove unused get_huggingface_repo function ## Test Plan ci w/ new tests
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43 changed files with 368 additions and 1015 deletions
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@ -14,14 +14,12 @@ from llama_stack.providers.utils.inference.litellm_openai_mixin import (
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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from .config import AzureConfig
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from .models import MODEL_ENTRIES
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class AzureInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
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def __init__(self, config: AzureConfig) -> None:
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LiteLLMOpenAIMixin.__init__(
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self,
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MODEL_ENTRIES,
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litellm_provider_name="azure",
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api_key_from_config=config.api_key.get_secret_value(),
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provider_data_api_key_field="azure_api_key",
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@ -1,28 +0,0 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.providers.utils.inference.model_registry import (
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ProviderModelEntry,
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)
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# https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions
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LLM_MODEL_IDS = [
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"gpt-5",
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"gpt-5-mini",
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"gpt-5-nano",
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"gpt-5-chat",
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"o1",
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"o1-mini",
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"o3-mini",
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"o4-mini",
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"gpt-4.1",
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"gpt-4.1-mini",
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"gpt-4.1-nano",
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]
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SAFETY_MODELS_ENTRIES = list[ProviderModelEntry]()
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MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + SAFETY_MODELS_ENTRIES
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