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41431d8bdd
currently providers have a `pip_package` list. Rather than make our own form of python dependency management, we should use `pyproject.toml` files in each provider declaring the dependencies in a more trackable manner. Each provider can then be installed using the already in place `module` field in the ProviderSpec, pointing to the directory the provider lives in we can then simply `uv pip install` this directory as opposed to installing the dependencies one by one Signed-off-by: Charlie Doern <cdoern@redhat.com>
42 lines
1.6 KiB
Python
42 lines
1.6 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 Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec
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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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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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RemoteProviderSpec(
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api=Api.eval,
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adapter_type="nvidia",
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provider_type="remote::nvidia",
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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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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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