phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-07-16 18:08:09 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

chore: remove nested imports (#2515)

Browse source 
# What does this PR do?

* Given that our API packages use "import *" in `__init.py__` we don't
need to do `from llama_stack.apis.models.models` but simply from
llama_stack.apis.models. The decision to use `import *` is debatable and
should probably be revisited at one point.

* Remove unneeded Ruff F401 rule
* Consolidate Ruff F403 rule in the pyprojectfrom
llama_stack.apis.models.models

Signed-off-by: Sébastien Han <seb@redhat.com>
This commit is contained in:
Sébastien Han 2025-06-26 04:31:05 +02:00 • committed by GitHub
parent 2d9fd041eb
commit ac5fd57387
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
82 changed files with 143 additions and 164 deletions
Download patch file Download diff file Expand all files Collapse all files

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

@ -15,8 +15,8 @@ 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,
@ -64,7 +64,7 @@ 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(