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https://github.com/meta-llama/llama-stack.git
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928a39d17b
Groq has never supported raw completions anyhow. So this makes it easier to switch it to LiteLLM. All our test suite passes. I also updated all the openai-compat providers so they work with api keys passed from headers. `provider_data` ## Test Plan ```bash LLAMA_STACK_CONFIG=groq \ pytest -s -v tests/client-sdk/inference/test_text_inference.py \ --inference-model=groq/llama-3.3-70b-versatile --vision-inference-model="" ``` Also tested (openai, anthropic, gemini) providers. No regressions.
32 lines
861 B
Python
32 lines
861 B
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 typing import Any, Dict, Optional
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from pydantic import BaseModel, Field
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from llama_stack.schema_utils import json_schema_type
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class AnthropicProviderDataValidator(BaseModel):
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anthropic_api_key: Optional[str] = Field(
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default=None,
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description="API key for Anthropic models",
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)
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@json_schema_type
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class AnthropicConfig(BaseModel):
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api_key: Optional[str] = Field(
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default=None,
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description="API key for Anthropic models",
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)
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@classmethod
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def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY}", **kwargs) -> Dict[str, Any]:
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return {
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"api_key": api_key,
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}
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