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feat: split API and provider specs into separate llama-stack-api pkg (#3895)
# 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>
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# 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 enum import Enum
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from typing import Any, Protocol, runtime_checkable
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from pydantic import BaseModel, Field
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from llama_stack.apis.common.tracing import telemetry_traceable
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from llama_stack.apis.inference import OpenAIMessageParam
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from llama_stack.apis.shields import Shield
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from llama_stack.apis.version import LLAMA_STACK_API_V1
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from llama_stack.schema_utils import json_schema_type, webmethod
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@json_schema_type
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class ModerationObjectResults(BaseModel):
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"""A moderation object.
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:param flagged: Whether any of the below categories are flagged.
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:param categories: A list of the categories, and whether they are flagged or not.
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:param category_applied_input_types: A list of the categories along with the input type(s) that the score applies to.
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:param category_scores: A list of the categories along with their scores as predicted by model.
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"""
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flagged: bool
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categories: dict[str, bool] | None = None
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category_applied_input_types: dict[str, list[str]] | None = None
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category_scores: dict[str, float] | None = None
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user_message: str | None = None
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metadata: dict[str, Any] = Field(default_factory=dict)
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@json_schema_type
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class ModerationObject(BaseModel):
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"""A moderation object.
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:param id: The unique identifier for the moderation request.
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:param model: The model used to generate the moderation results.
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:param results: A list of moderation objects
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"""
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id: str
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model: str
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results: list[ModerationObjectResults]
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@json_schema_type
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class ViolationLevel(Enum):
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"""Severity level of a safety violation.
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:cvar INFO: Informational level violation that does not require action
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:cvar WARN: Warning level violation that suggests caution but allows continuation
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:cvar ERROR: Error level violation that requires blocking or intervention
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"""
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INFO = "info"
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WARN = "warn"
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ERROR = "error"
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@json_schema_type
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class SafetyViolation(BaseModel):
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"""Details of a safety violation detected by content moderation.
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:param violation_level: Severity level of the violation
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:param user_message: (Optional) Message to convey to the user about the violation
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:param metadata: Additional metadata including specific violation codes for debugging and telemetry
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"""
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violation_level: ViolationLevel
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# what message should you convey to the user
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user_message: str | None = None
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# additional metadata (including specific violation codes) more for
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# debugging, telemetry
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metadata: dict[str, Any] = Field(default_factory=dict)
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@json_schema_type
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class RunShieldResponse(BaseModel):
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"""Response from running a safety shield.
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:param violation: (Optional) Safety violation detected by the shield, if any
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"""
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violation: SafetyViolation | None = None
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class ShieldStore(Protocol):
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async def get_shield(self, identifier: str) -> Shield: ...
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@runtime_checkable
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@telemetry_traceable
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class Safety(Protocol):
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"""Safety
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OpenAI-compatible Moderations API.
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"""
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shield_store: ShieldStore
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@webmethod(route="/safety/run-shield", method="POST", level=LLAMA_STACK_API_V1)
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async def run_shield(
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self,
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shield_id: str,
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messages: list[OpenAIMessageParam],
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params: dict[str, Any],
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) -> RunShieldResponse:
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"""Run shield.
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Run a shield.
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:param shield_id: The identifier of the shield to run.
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:param messages: The messages to run the shield on.
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:param params: The parameters of the shield.
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:returns: A RunShieldResponse.
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"""
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...
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@webmethod(route="/moderations", method="POST", level=LLAMA_STACK_API_V1)
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async def run_moderation(self, input: str | list[str], model: str | None = None) -> ModerationObject:
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"""Create moderation.
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Classifies if text and/or image inputs are potentially harmful.
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:param input: Input (or inputs) to classify.
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Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models.
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:param model: (Optional) The content moderation model you would like to use.
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:returns: A moderation object.
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"""
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...
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