forked from phoenix-oss/llama-stack-mirror
9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
69 lines
2.1 KiB
Python
69 lines
2.1 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 pydantic import BaseModel, Field, SecretStr
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from llama_stack.schema_utils import json_schema_type
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@json_schema_type
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class TGIImplConfig(BaseModel):
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url: str = Field(
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description="The URL for the TGI serving endpoint",
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)
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@classmethod
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def sample_run_config(cls, url: str = "${env.TGI_URL}", **kwargs):
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return {
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"url": url,
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}
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@json_schema_type
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class InferenceEndpointImplConfig(BaseModel):
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endpoint_name: str = Field(
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description="The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided.",
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)
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api_token: SecretStr | None = Field(
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default=None,
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description="Your Hugging Face user access token (will default to locally saved token if not provided)",
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)
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@classmethod
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def sample_run_config(
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cls,
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endpoint_name: str = "${env.INFERENCE_ENDPOINT_NAME}",
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api_token: str = "${env.HF_API_TOKEN}",
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**kwargs,
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):
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return {
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"endpoint_name": endpoint_name,
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"api_token": api_token,
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}
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@json_schema_type
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class InferenceAPIImplConfig(BaseModel):
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huggingface_repo: str = Field(
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description="The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct')",
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)
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api_token: SecretStr | None = Field(
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default=None,
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description="Your Hugging Face user access token (will default to locally saved token if not provided)",
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)
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@classmethod
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def sample_run_config(
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cls,
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repo: str = "${env.INFERENCE_MODEL}",
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api_token: str = "${env.HF_API_TOKEN}",
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**kwargs,
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):
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return {
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"huggingface_repo": repo,
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"api_token": api_token,
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}
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