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https://github.com/meta-llama/llama-stack.git
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c4cb6aa8d9
Prevent sensitive information from being logged in telemetry output by assigning SecretStr type to sensitive fields. API keys, password from KV store are now covered. All providers have been converted. Signed-off-by: Sébastien Han <seb@redhat.com>
61 lines
1.9 KiB
Python
61 lines
1.9 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 pathlib import Path
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from pydantic import BaseModel, Field, SecretStr, field_validator
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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 VLLMInferenceAdapterConfig(BaseModel):
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url: str | None = Field(
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default=None,
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description="The URL for the vLLM model serving endpoint",
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)
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max_tokens: int = Field(
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default=4096,
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description="Maximum number of tokens to generate.",
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)
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api_token: SecretStr | None = Field(
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default=SecretStr("fake"),
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description="The API token",
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)
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tls_verify: bool | str = Field(
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default=True,
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description="Whether to verify TLS certificates. Can be a boolean or a path to a CA certificate file.",
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)
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refresh_models: bool = Field(
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default=False,
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description="Whether to refresh models periodically",
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)
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@field_validator("tls_verify")
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@classmethod
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def validate_tls_verify(cls, v):
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if isinstance(v, str):
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# Otherwise, treat it as a cert path
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cert_path = Path(v).expanduser().resolve()
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if not cert_path.exists():
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raise ValueError(f"TLS certificate file does not exist: {v}")
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if not cert_path.is_file():
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raise ValueError(f"TLS certificate path is not a file: {v}")
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return v
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return v
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@classmethod
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def sample_run_config(
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cls,
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url: str = "${env.VLLM_URL:=}",
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**kwargs,
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):
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return {
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"url": url,
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"max_tokens": "${env.VLLM_MAX_TOKENS:=4096}",
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"api_token": "${env.VLLM_API_TOKEN:=fake}",
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"tls_verify": "${env.VLLM_TLS_VERIFY:=true}",
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}
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