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ac5fd57387
# What does this PR do? * Given that our API packages use "import *" in `__init.py__` we don't need to do `from llama_stack.apis.models.models` but simply from llama_stack.apis.models. The decision to use `import *` is debatable and should probably be revisited at one point. * Remove unneeded Ruff F401 rule * Consolidate Ruff F403 rule in the pyprojectfrom llama_stack.apis.models.models Signed-off-by: Sébastien Han <seb@redhat.com>
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
# 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 dataclasses import dataclass
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from llama_stack.apis.models import ModelType
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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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LLM_MODEL_IDS = [
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# the models w/ "openai/" prefix are the litellm specific model names.
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# they should be deprecated in favor of the canonical openai model names.
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"openai/gpt-4o",
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"openai/gpt-4o-mini",
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"openai/chatgpt-4o-latest",
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"gpt-3.5-turbo-0125",
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-instruct",
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"gpt-4",
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"gpt-4-turbo",
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"gpt-4o",
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"gpt-4o-2024-08-06",
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"gpt-4o-mini",
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"gpt-4o-audio-preview",
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"chatgpt-4o-latest",
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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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]
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@dataclass
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class EmbeddingModelInfo:
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"""Structured representation of embedding model information."""
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embedding_dimension: int
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context_length: int
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EMBEDDING_MODEL_IDS: dict[str, EmbeddingModelInfo] = {
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"openai/text-embedding-3-small": EmbeddingModelInfo(1536, 8192),
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"openai/text-embedding-3-large": EmbeddingModelInfo(3072, 8192),
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"text-embedding-3-small": EmbeddingModelInfo(1536, 8192),
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"text-embedding-3-large": EmbeddingModelInfo(3072, 8192),
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}
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MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + [
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ProviderModelEntry(
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provider_model_id=model_id,
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": model_info.embedding_dimension,
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"context_length": model_info.context_length,
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},
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)
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for model_id, model_info in EMBEDDING_MODEL_IDS.items()
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]
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