build: configure ruff from pyproject.toml (#1100)

# What does this PR do?

- Remove hardcoded configurations from pre-commit.
- Allow configuration to be set via pyproject.toml.
- Merge .ruff.toml settings into pyproject.toml.
- Ensure the linter and formatter use the defined configuration instead
of being overridden by pre-commit.

Signed-off-by: Sébastien Han <seb@redhat.com>

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]

[//]: # (## Documentation)

Signed-off-by: Sébastien Han <seb@redhat.com>

llama_stack/providers/inline/agents/meta_reference/safety.py

@@ -41,7 +41,7 @@ class ShieldRunnerMixin:
                 for identifier in identifiers
             ]
         )
-        for identifier, response in zip(identifiers, responses):
+        for identifier, response in zip(identifiers, responses, strict=False):
             if not response.violation:
                 continue
 

llama_stack/providers/inline/eval/meta_reference/eval.py

@@ -201,7 +201,9 @@ class MetaReferenceEvalImpl(
             raise ValueError(f"Invalid candidate type: {candidate.type}")
 
         # scoring with generated_answer
-        score_input_rows = [input_r | generated_r for input_r, generated_r in zip(input_rows, generations)]
+        score_input_rows = [
+            input_r | generated_r for input_r, generated_r in zip(input_rows, generations, strict=False)
+        ]
 
         if task_config.scoring_params is not None:
             scoring_functions_dict = {

llama_stack/providers/inline/tool_runtime/code_interpreter/code_env_prefix.py

@@ -83,12 +83,6 @@ import sys as _sys
 from contextlib import (  # noqa
     contextmanager as _contextmanager,
 )
-from contextlib import (
-    redirect_stderr as _redirect_stderr,
-)
-from contextlib import (
-    redirect_stdout as _redirect_stdout,
-)
 from multiprocessing.connection import Connection as _Connection
 
 # Mangle imports to avoid polluting model execution namespace.

llama_stack/providers/inline/tool_runtime/rag/memory.py

@@ -118,7 +118,7 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
             return RAGQueryResult(content=None)
 
         # sort by score
-        chunks, scores = zip(*sorted(zip(chunks, scores), key=lambda x: x[1], reverse=True))
+        chunks, scores = zip(*sorted(zip(chunks, scores, strict=False), key=lambda x: x[1], reverse=True), strict=False)
 
         tokens = 0
         picked = []

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -103,7 +103,7 @@ class FaissIndex(EmbeddingIndex):
 
         chunks = []
         scores = []
-        for d, i in zip(distances[0], indices[0]):
+        for d, i in zip(distances[0], indices[0], strict=False):
             if i < 0:
                 continue
             chunks.append(self.chunk_by_index[int(i)])

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -80,7 +80,7 @@ class SQLiteVecIndex(EmbeddingIndex):
         try:
             # Start transaction
             cur.execute("BEGIN TRANSACTION")
-            for chunk, emb in zip(chunks, embeddings):
+            for chunk, emb in zip(chunks, embeddings, strict=False):
                 # Serialize and insert the chunk metadata.
                 chunk_json = chunk.model_dump_json()
                 cur.execute(f"INSERT INTO {self.metadata_table} (chunk) VALUES (?)", (chunk_json,))