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https://github.com/meta-llama/llama-stack.git
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8de8eb03c8
## What does this PR do?
This is a long-pending change and particularly important to get done
now.
Specifically:
- we cannot "localize" (aka download) any URLs from media attachments
anywhere near our modeling code. it must be done within llama-stack.
- `PIL.Image` is infesting all our APIs via `ImageMedia ->
InterleavedTextMedia` and that cannot be right at all. Anything in the
API surface must be "naturally serializable". We need a standard `{
type: "image", image_url: "<...>" }` which is more extensible
- `UserMessage`, `SystemMessage`, etc. are moved completely to
llama-stack from the llama-models repository.
See https://github.com/meta-llama/llama-models/pull/244 for the
corresponding PR in llama-models.
## Test Plan
```bash
cd llama_stack/providers/tests
pytest -s -v -k "fireworks or ollama or together" inference/test_vision_inference.py
pytest -s -v -k "(fireworks or ollama or together) and llama_3b" inference/test_text_inference.py
pytest -s -v -k chroma memory/test_memory.py \
--env EMBEDDING_DIMENSION=384 --env CHROMA_DB_PATH=/tmp/foobar
pytest -s -v -k fireworks agents/test_agents.py \
--safety-shield=meta-llama/Llama-Guard-3-8B \
--inference-model=meta-llama/Llama-3.1-8B-Instruct
```
Updated the client sdk (see PR ...), installed the SDK in the same
environment and then ran the SDK tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=together pytest -s -v agents/test_agents.py
LLAMA_STACK_CONFIG=ollama pytest -s -v memory/test_memory.py
# this one needed a bit of hacking in the run.yaml to ensure I could register the vision model correctly
INFERENCE_MODEL=llama3.2-vision:latest LLAMA_STACK_CONFIG=ollama pytest -s -v inference/test_inference.py
```
101 lines
3.2 KiB
Python
101 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 Literal, Optional, Protocol, Union
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from typing_extensions import Annotated
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_models.schema_utils import json_schema_type, webmethod
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from llama_stack.apis.scoring_functions import * # noqa: F403
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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.scoring import * # noqa: F403
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from llama_stack.apis.eval_tasks import * # noqa: F403
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from llama_stack.apis.inference import SamplingParams, SystemMessage
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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-eval", 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/job/status", method="GET")
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async def job_status(self, task_id: str, job_id: str) -> Optional[JobStatus]: ...
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@webmethod(route="/eval/job/cancel", method="POST")
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async def job_cancel(self, task_id: str, job_id: str) -> None: ...
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@webmethod(route="/eval/job/result", method="GET")
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async def job_result(self, task_id: str, job_id: str) -> EvaluateResponse: ...
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