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# What does this PR do? Extract API definitions and provider specifications into a standalone llama-stack-api package that can be published to PyPI independently of the main llama-stack server. see: https://github.com/llamastack/llama-stack/pull/2978 and https://github.com/llamastack/llama-stack/pull/2978#issuecomment-3145115942 Motivation External providers currently import from llama-stack, which overrides the installed version and causes dependency conflicts. This separation allows external providers to: - Install only the type definitions they need without server dependencies - Avoid version conflicts with the installed llama-stack package - Be versioned and released independently This enables us to re-enable external provider module tests that were previously blocked by these import conflicts. Changes - Created llama-stack-api package with minimal dependencies (pydantic, jsonschema) - Moved APIs, providers datatypes, strong_typing, and schema_utils - Updated all imports from llama_stack.* to llama_stack_api.* - Configured local editable install for development workflow - Updated linting and type-checking configuration for both packages Next Steps - Publish llama-stack-api to PyPI - Update external provider dependencies - Re-enable external provider module tests Pre-cursor PRs to this one: - #4093 - #3954 - #4064 These PRs moved key pieces _out_ of the Api pkg, limiting the scope of change here. relates to #3237 ## Test Plan Package builds successfully and can be imported independently. All pre-commit hooks pass with expected exclusions maintained. --------- Signed-off-by: Charlie Doern <cdoern@redhat.com>
80 lines
3.1 KiB
Python
80 lines
3.1 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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import os
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from typing import Any
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from llama_stack_api import json_schema_type
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from pydantic import BaseModel, Field
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from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
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class NVIDIAProviderDataValidator(BaseModel):
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nvidia_api_key: str | None = Field(
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default=None,
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description="API key for NVIDIA NIM models",
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)
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@json_schema_type
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class NVIDIAConfig(RemoteInferenceProviderConfig):
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"""
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Configuration for the NVIDIA NIM inference endpoint.
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Attributes:
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url (str): A base url for accessing the NVIDIA NIM, e.g. http://localhost:8000
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api_key (str): The access key for the hosted NIM endpoints
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rerank_model_to_url (dict[str, str]): Mapping of rerank model identifiers to their API endpoints
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There are two ways to access NVIDIA NIMs -
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0. Hosted: Preview APIs hosted at https://integrate.api.nvidia.com
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1. Self-hosted: You can run NVIDIA NIMs on your own infrastructure
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By default the configuration is set to use the hosted APIs. This requires
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an API key which can be obtained from https://ngc.nvidia.com/.
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By default the configuration will attempt to read the NVIDIA_API_KEY environment
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variable to set the api_key. Please do not put your API key in code.
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If you are using a self-hosted NVIDIA NIM, you can set the url to the
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URL of your running NVIDIA NIM and do not need to set the api_key.
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"""
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url: str = Field(
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default_factory=lambda: os.getenv("NVIDIA_BASE_URL", "https://integrate.api.nvidia.com"),
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description="A base url for accessing the NVIDIA NIM",
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)
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timeout: int = Field(
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default=60,
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description="Timeout for the HTTP requests",
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)
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append_api_version: bool = Field(
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default_factory=lambda: os.getenv("NVIDIA_APPEND_API_VERSION", "True").lower() != "false",
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description="When set to false, the API version will not be appended to the base_url. By default, it is true.",
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)
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rerank_model_to_url: dict[str, str] = Field(
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default_factory=lambda: {
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"nv-rerank-qa-mistral-4b:1": "https://ai.api.nvidia.com/v1/retrieval/nvidia/reranking",
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"nvidia/nv-rerankqa-mistral-4b-v3": "https://ai.api.nvidia.com/v1/retrieval/nvidia/nv-rerankqa-mistral-4b-v3/reranking",
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"nvidia/llama-3.2-nv-rerankqa-1b-v2": "https://ai.api.nvidia.com/v1/retrieval/nvidia/llama-3_2-nv-rerankqa-1b-v2/reranking",
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},
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description="Mapping of rerank model identifiers to their API endpoints. ",
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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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url: str = "${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}",
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api_key: str = "${env.NVIDIA_API_KEY:=}",
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append_api_version: bool = "${env.NVIDIA_APPEND_API_VERSION:=True}",
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**kwargs,
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) -> dict[str, Any]:
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return {
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"url": url,
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"api_key": api_key,
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"append_api_version": append_api_version,
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}
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