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chore: remove nested imports

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* Since our API packages use import * in __init__.py, we can import
  directly from llama_stack.apis.models instead of
  llama_stack.apis.models.models.  However, the choice to use import *
  is debatable and may need to be reconsidered in the future.

* Remove the unnecessary Ruff F401 suppression.

* Consolidate the Ruff F403 rule configuration in
pyproject.toml.

Signed-off-by: Sébastien Han <seb@redhat.com>
This commit is contained in:
Sébastien Han 2025-06-25 13:07:15 +02:00
parent cfee63bd0d
commit c245cb580c
No known key found for this signature in database
82 changed files with 143 additions and 164 deletions
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4
llama_stack/providers/inline/agents/meta_reference/openai_responses.py
View file

@ -44,7 +44,7 @@ from llama_stack.apis.agents.openai_responses import (
OpenAIResponseTextFormat,
)
from llama_stack.apis.common.content_types import TextContentItem
from llama_stack.apis.inference.inference import (
from llama_stack.apis.inference import (
Inference,
OpenAIAssistantMessageParam,
OpenAIChatCompletion,
@ -583,7 +583,7 @@ class OpenAIResponsesImpl:
from llama_stack.apis.agents.openai_responses import (
MCPListToolsTool,
)
from llama_stack.apis.tools.tools import Tool
from llama_stack.apis.tools import Tool
mcp_tool_to_server = {}

2
llama_stack/providers/inline/eval/meta_reference/eval.py
View file

@ -208,7 +208,7 @@ class MetaReferenceEvalImpl(
for scoring_fn_id in scoring_functions
}
else:
scoring_functions_dict = {scoring_fn_id: None for scoring_fn_id in scoring_functions}
scoring_functions_dict = dict.fromkeys(scoring_functions)
score_response = await self.scoring_api.score(
input_rows=score_input_rows, scoring_functions=scoring_functions_dict

2
llama_stack/providers/inline/scoring/llm_as_judge/scoring.py
View file

@ -7,7 +7,7 @@ from typing import Any
from llama_stack.apis.datasetio import DatasetIO
from llama_stack.apis.datasets import Datasets
from llama_stack.apis.inference.inference import Inference
from llama_stack.apis.inference import Inference
from llama_stack.apis.scoring import (
ScoreBatchResponse,
ScoreResponse,

2
llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py
View file

@ -6,7 +6,7 @@
import re
from typing import Any
from llama_stack.apis.inference.inference import Inference, UserMessage
from llama_stack.apis.inference import Inference, UserMessage
from llama_stack.apis.scoring import ScoringResultRow
from llama_stack.apis.scoring_functions import ScoringFnParams
from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn

3
llama_stack/providers/inline/vector_io/faiss/faiss.py
View file

@ -16,8 +16,7 @@ import numpy as np
from numpy.typing import NDArray
from llama_stack.apis.files import Files
from llama_stack.apis.inference import InterleavedContent
from llama_stack.apis.inference.inference import Inference
from llama_stack.apis.inference import Inference, InterleavedContent
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import (
Chunk,

6
llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
View file

@ -17,8 +17,8 @@ import numpy as np
import sqlite_vec
from numpy.typing import NDArray
from llama_stack.apis.files.files import Files
from llama_stack.apis.inference.inference import Inference
from llama_stack.apis.files import Files
from llama_stack.apis.inference import Inference
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import (
Chunk,
@ -66,7 +66,7 @@ def _normalize_scores(scores: dict[str, float]) -> dict[str, float]:
score_range = max_score - min_score
if score_range > 0:
return {doc_id: (score - min_score) / score_range for doc_id, score in scores.items()}
return {doc_id: 1.0 for doc_id in scores}
return dict.fromkeys(scores, 1.0)
def _weighted_rerank(