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c9a49a80e8
# What does this PR do? Simple approach to get some provider pages in the docs. Add or update description fields in the provider configuration class using Pydantic’s Field, ensuring these descriptions are clear and complete, as they will be used to auto-generate provider documentation via ./scripts/distro_codegen.py instead of editing the docs manually. Signed-off-by: Sébastien Han <seb@redhat.com>
47 lines
1.8 KiB
Python
47 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 llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec
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def available_providers() -> list[ProviderSpec]:
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return [
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InlineProviderSpec(
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api=Api.eval,
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provider_type="inline::meta-reference",
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pip_packages=["tree_sitter", "pythainlp", "langdetect", "emoji", "nltk"],
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module="llama_stack.providers.inline.eval.meta_reference",
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config_class="llama_stack.providers.inline.eval.meta_reference.MetaReferenceEvalConfig",
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api_dependencies=[
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Api.datasetio,
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Api.datasets,
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Api.scoring,
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Api.inference,
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Api.agents,
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],
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description="Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics.",
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),
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remote_provider_spec(
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api=Api.eval,
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adapter=AdapterSpec(
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adapter_type="nvidia",
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pip_packages=[
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"requests",
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],
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module="llama_stack.providers.remote.eval.nvidia",
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config_class="llama_stack.providers.remote.eval.nvidia.NVIDIAEvalConfig",
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description="NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform.",
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),
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api_dependencies=[
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Api.datasetio,
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Api.datasets,
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Api.scoring,
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Api.inference,
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Api.agents,
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],
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),
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]
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