forked from phoenix-oss/llama-stack-mirror
7fb2c1c48d
# What does this PR do? This PR changes our API to follow more idiomatic REST API approaches of having paths being resources and methods indicating the action being performed. Changes made to generator: 1) removed the prefix check of "get" as its not required and is actually needed for other method types too 2) removed _ check on path since variables can have "_" ## Test Plan LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v tests/client-sdk/agents/test_agents.py
100 lines
3.2 KiB
Python
100 lines
3.2 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 typing import Any, Dict, List, Literal, Optional, Protocol, Union
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from llama_models.schema_utils import json_schema_type, webmethod
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from pydantic import BaseModel, Field
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from typing_extensions import Annotated
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from llama_stack.apis.agents import AgentConfig
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from llama_stack.apis.common.job_types import Job, JobStatus
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from llama_stack.apis.inference import SamplingParams, SystemMessage
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from llama_stack.apis.scoring import ScoringResult
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from llama_stack.apis.scoring_functions import ScoringFnParams
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@json_schema_type
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class ModelCandidate(BaseModel):
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type: Literal["model"] = "model"
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model: str
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sampling_params: SamplingParams
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system_message: Optional[SystemMessage] = None
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@json_schema_type
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class AgentCandidate(BaseModel):
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type: Literal["agent"] = "agent"
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config: AgentConfig
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EvalCandidate = Annotated[
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Union[ModelCandidate, AgentCandidate], Field(discriminator="type")
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]
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@json_schema_type
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class BenchmarkEvalTaskConfig(BaseModel):
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type: Literal["benchmark"] = "benchmark"
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eval_candidate: EvalCandidate
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num_examples: Optional[int] = Field(
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description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
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default=None,
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)
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@json_schema_type
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class AppEvalTaskConfig(BaseModel):
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type: Literal["app"] = "app"
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eval_candidate: EvalCandidate
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scoring_params: Dict[str, ScoringFnParams] = Field(
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description="Map between scoring function id and parameters for each scoring function you want to run",
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default_factory=dict,
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)
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num_examples: Optional[int] = Field(
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description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
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default=None,
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)
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# we could optinally add any specific dataset config here
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EvalTaskConfig = Annotated[
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Union[BenchmarkEvalTaskConfig, AppEvalTaskConfig], Field(discriminator="type")
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]
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@json_schema_type
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class EvaluateResponse(BaseModel):
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generations: List[Dict[str, Any]]
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# each key in the dict is a scoring function name
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scores: Dict[str, ScoringResult]
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class Eval(Protocol):
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@webmethod(route="/eval/run", method="POST")
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async def run_eval(
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self,
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task_id: str,
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task_config: EvalTaskConfig,
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) -> Job: ...
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@webmethod(route="/eval/evaluate-rows", method="POST")
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async def evaluate_rows(
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self,
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task_id: str,
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input_rows: List[Dict[str, Any]],
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scoring_functions: List[str],
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task_config: EvalTaskConfig,
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) -> EvaluateResponse: ...
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@webmethod(route="/eval/jobs/{job_id}", method="GET")
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async def job_status(self, job_id: str, task_id: str) -> Optional[JobStatus]: ...
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@webmethod(route="/eval/jobs/cancel", method="POST")
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async def job_cancel(self, job_id: str, task_id: str) -> None: ...
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@webmethod(route="/eval/jobs/{job_id}/result", method="GET")
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async def job_result(self, job_id: str, task_id: str) -> EvaluateResponse: ...
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