diff --git a/studio/backend/auth/authentication.py b/studio/backend/auth/authentication.py
index ec53467189..04eefbd1b0 100644
--- a/studio/backend/auth/authentication.py
+++ b/studio/backend/auth/authentication.py
@@ -168,7 +168,7 @@ async def get_current_subject_sse(
     """
     raw = token
     if not raw and authorization and authorization.lower().startswith("bearer "):
-        raw = authorization[len("bearer "):].strip()
+        raw = authorization[len("bearer ") :].strip()
     if not raw:
         raise HTTPException(
             status_code = status.HTTP_401_UNAUTHORIZED,
diff --git a/studio/backend/core/inference/tools.py b/studio/backend/core/inference/tools.py
index 7af5eddd7f..0d277bb65b 100644
--- a/studio/backend/core/inference/tools.py
+++ b/studio/backend/core/inference/tools.py
@@ -502,6 +502,7 @@ TERMINAL_TOOL = {
     },
 }
 
+
 # Lazy import — keeps studio.db init lazy so tools.py doesn't pull in
 # the whole rag stack on inference paths that never see RAG.
 def _get_rag_tool_spec():
diff --git a/studio/backend/core/rag/chunking.py b/studio/backend/core/rag/chunking.py
index 86488a9ef6..3d07a7ce24 100644
--- a/studio/backend/core/rag/chunking.py
+++ b/studio/backend/core/rag/chunking.py
@@ -57,7 +57,7 @@ def _atomic_split(
             if count(piece) <= max_tokens:
                 out.append(piece)
             else:
-                tail = separators[separators.index(sep) + 1:]
+                tail = separators[separators.index(sep) + 1 :]
                 out.extend(_atomic_split(piece, tail, max_tokens, count))
         return out
     # No separator made progress — hard-slice by characters.
diff --git a/studio/backend/core/rag/embeddings.py b/studio/backend/core/rag/embeddings.py
index 73f884b84f..60f1b562fe 100644
--- a/studio/backend/core/rag/embeddings.py
+++ b/studio/backend/core/rag/embeddings.py
@@ -109,10 +109,14 @@ class _BGEVLAdapter:
         from PIL import Image
 
         if inputs is None or len(inputs) == 0:
-            return np.zeros((0, self.get_sentence_embedding_dimension()), dtype = np.float32)
+            return np.zeros(
+                (0, self.get_sentence_embedding_dimension()), dtype = np.float32
+            )
 
         sample = inputs[0]
-        is_image = isinstance(sample, Image.Image) or isinstance(sample, (bytes, bytearray))
+        is_image = isinstance(sample, Image.Image) or isinstance(
+            sample, (bytes, bytearray)
+        )
 
         chunks_out = []
         for start in range(0, len(inputs), batch_size):
@@ -513,9 +517,7 @@ def _windowed_late_chunk_encode(
         ws, _we = windows[best_window]
         emb = _window_embeddings(best_window)
         local_indices = [
-            ti - ws
-            for ti in chunk_token_indices
-            if ws <= ti < ws + emb.shape[0]
+            ti - ws for ti in chunk_token_indices if ws <= ti < ws + emb.shape[0]
         ]
         if not local_indices:
             vec = model.encode(
diff --git a/studio/backend/core/rag/ingestion.py b/studio/backend/core/rag/ingestion.py
index 830195c504..bc24c1c84a 100644
--- a/studio/backend/core/rag/ingestion.py
+++ b/studio/backend/core/rag/ingestion.py
@@ -78,7 +78,9 @@ def _subprocess_worker(
         parsed = parse(Path(stored_path), want_images = (mode == "multimodal"))
         pages = parsed.pages
         if not pages and not parsed.images:
-            out_queue.put({"type": "error", "error": "no extractable content in document"})
+            out_queue.put(
+                {"type": "error", "error": "no extractable content in document"}
+            )
             return
 
         out_queue.put({"type": "progress", "stage": "load_model", "progress": 0.1})
@@ -249,9 +251,7 @@ def _stream_image_chunks(
     image_vectors = encode_images(bytes_for_encoding, model_name = model_name)
 
     # Embed only the non-empty captions; track which images they map to.
-    caption_to_image: list[int] = [
-        i for i, cap in enumerate(captions) if cap.strip()
-    ]
+    caption_to_image: list[int] = [i for i, cap in enumerate(captions) if cap.strip()]
     if caption_to_image:
         caption_vectors_arr = encode(
             [captions[i] for i in caption_to_image],
@@ -369,6 +369,7 @@ def _run_late_chunking(
 # Job manager (parent side)
 # ------------------------------------------------------------------
 
+
 class _JobState:
     def __init__(self, job_id: str, document_id: str, scope: str) -> None:
         self.job_id = job_id
@@ -531,7 +532,7 @@ def _insert_chunks_and_collect_for_bm25(
 
 def _all_scope_chunks(scope: str) -> list[dict]:
     if scope.startswith("kb_"):
-        kb_id = scope[len("kb_"):]
+        kb_id = scope[len("kb_") :]
         sql = (
             "SELECT c.id, c.text FROM rag_chunks c "
             "JOIN rag_documents d ON d.id = c.document_id "
@@ -539,7 +540,7 @@ def _all_scope_chunks(scope: str) -> list[dict]:
         )
         bind = (kb_id,)
     elif scope.startswith("thread_"):
-        thread_id = scope[len("thread_"):]
+        thread_id = scope[len("thread_") :]
         sql = (
             "SELECT c.id, c.text FROM rag_chunks c "
             "JOIN rag_documents d ON d.id = c.document_id "
diff --git a/studio/backend/core/rag/parsers/__init__.py b/studio/backend/core/rag/parsers/__init__.py
index 8b2bcff1ae..b3d910ad0e 100644
--- a/studio/backend/core/rag/parsers/__init__.py
+++ b/studio/backend/core/rag/parsers/__init__.py
@@ -18,6 +18,7 @@ class ParsedPage:
     pipe-tables, and list bullets survive extraction so the chunker can
     split on them. Parsers MUST emit Markdown, not bare plain text.
     """
+
     text: str
     page_number: int | None = None
 
@@ -32,6 +33,7 @@ class ParsedImage:
     when no caption could be paired (the image still ingests, just
     without the paired-caption chunk).
     """
+
     image_bytes: bytes
     mime_type: str
     page_number: int | None = None
@@ -46,6 +48,7 @@ class ParseResult:
     passed `want_images=True`. Iteration aliases for `pages` so legacy
     code that did `for page in parse(path)` keeps working.
     """
+
     pages: list[ParsedPage] = field(default_factory = list)
     images: list[ParsedImage] = field(default_factory = list)
 
diff --git a/studio/backend/core/rag/parsers/pdf.py b/studio/backend/core/rag/parsers/pdf.py
index 994b1aaed9..0d312072f1 100644
--- a/studio/backend/core/rag/parsers/pdf.py
+++ b/studio/backend/core/rag/parsers/pdf.py
@@ -56,7 +56,9 @@ def _extract_with_pymupdf(path: Path, want_images: bool) -> ParseResult:
 
 def _extract_images_pymupdf(doc, pages: list[ParsedPage]) -> list[ParsedImage]:
     """Pull embedded images and pair each with the nearest text on the same page."""
-    captions_by_page: dict[int, str] = {p.page_number: p.text for p in pages if p.page_number}
+    captions_by_page: dict[int, str] = {
+        p.page_number: p.text for p in pages if p.page_number
+    }
     out: list[ParsedImage] = []
     for page_index in range(len(doc)):
         page_number = page_index + 1
diff --git a/studio/backend/core/rag/retrieval.py b/studio/backend/core/rag/retrieval.py
index bbf4b8ff5a..4c48519151 100644
--- a/studio/backend/core/rag/retrieval.py
+++ b/studio/backend/core/rag/retrieval.py
@@ -101,7 +101,9 @@ def _rrf_fuse(
     dense_scores: dict[str, float] = {}
     for ranking in rankings:
         for rank, hit in enumerate(ranking):
-            fused[hit.chunk_id] = fused.get(hit.chunk_id, 0.0) + 1.0 / (rrf_k + rank + 1)
+            fused[hit.chunk_id] = fused.get(hit.chunk_id, 0.0) + 1.0 / (
+                rrf_k + rank + 1
+            )
             if hit.chunk_id not in seen:
                 seen[hit.chunk_id] = hit
             if hit.dense_score is not None:
diff --git a/studio/backend/core/rag/tool.py b/studio/backend/core/rag/tool.py
index 019fedf959..8bd05ce8c9 100644
--- a/studio/backend/core/rag/tool.py
+++ b/studio/backend/core/rag/tool.py
@@ -114,9 +114,7 @@ def search_knowledge_base(
     from core.rag.vector_store import kb_scope, thread_scope
     from storage.studio_db import get_connection
 
-    scope = (
-        kb_scope(scope_kb_id) if scope_kb_id else thread_scope(scope_thread_id)
-    )
+    scope = kb_scope(scope_kb_id) if scope_kb_id else thread_scope(scope_thread_id)
     k = top_k if top_k is not None else default_top_k
 
     if enable_rerank:
diff --git a/studio/backend/routes/inference.py b/studio/backend/routes/inference.py
index 7c2a4e06bb..53bac5d9ac 100644
--- a/studio/backend/routes/inference.py
+++ b/studio/backend/routes/inference.py
@@ -2484,9 +2484,7 @@ async def openai_chat_completions(
                     else 300,
                     session_id = payload.session_id,
                     tool_context = (
-                        {"rag_scope": payload.rag_scope}
-                        if payload.rag_scope
-                        else None
+                        {"rag_scope": payload.rag_scope} if payload.rag_scope else None
                     ),
                 )
 
@@ -2956,9 +2954,7 @@ async def openai_chat_completions(
         def sf_generate_with_tools():
             return backend.generate_chat_completion_with_tools(
                 tool_context = (
-                    {"rag_scope": payload.rag_scope}
-                    if payload.rag_scope
-                    else None
+                    {"rag_scope": payload.rag_scope} if payload.rag_scope else None
                 ),
                 messages = _sf_chat_messages,
                 tools = _sf_tools_to_use,
diff --git a/studio/backend/routes/rag.py b/studio/backend/routes/rag.py
index bcc0a66a4f..fc59c508aa 100644
--- a/studio/backend/routes/rag.py
+++ b/studio/backend/routes/rag.py
@@ -28,7 +28,15 @@ from pathlib import Path
 from typing import Any, Literal, Optional
 from uuid import uuid4
 
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Request, UploadFile
+from fastapi import (
+    APIRouter,
+    Depends,
+    Header,
+    HTTPException,
+    Query,
+    Request,
+    UploadFile,
+)
 from fastapi.responses import FileResponse, StreamingResponse
 from pydantic import BaseModel, Field
 
@@ -40,6 +48,8 @@ async def _sse_auth(
     authorization: str | None = Header(None),
 ) -> str:
     return await get_current_subject_sse(token, authorization)
+
+
 from core.rag import embeddings, ingestion, reranker, retrieval, vector_store
 from core.rag.vector_store import kb_scope, thread_scope
 from loggers import get_logger
@@ -161,6 +171,7 @@ class SearchResponse(BaseModel):
 # Helpers
 # ------------------------------------------------------------------
 
+
 def _sanitize_filename(filename: str) -> str:
     name = Path(filename).name.strip().replace("\x00", "")
     return name or "document"
@@ -182,7 +193,7 @@ def _resolve_scope_embedder(scope: str) -> str | None:
     from utils.rag.config import resolve_embedder
 
     if scope.startswith("kb_"):
-        kb_id = scope[len("kb_"):]
+        kb_id = scope[len("kb_") :]
         with get_connection() as conn:
             row = conn.execute(
                 "SELECT embedding_model FROM rag_knowledge_bases WHERE id = ?",
@@ -190,7 +201,7 @@ def _resolve_scope_embedder(scope: str) -> str | None:
             ).fetchone()
         return row["embedding_model"] if row else None
     if scope.startswith("thread_"):
-        thread_id = scope[len("thread_"):]
+        thread_id = scope[len("thread_") :]
         settings = _load_thread_settings(thread_id)
         return settings.embedding_model or resolve_embedder(
             settings.mode,
@@ -204,9 +215,7 @@ def _row_to_kb(row: Any) -> KBResponse:
     # pre-Phase-3 connection in tests; fall back to the schema defaults.
     keys = row.keys() if hasattr(row, "keys") else ()
     chunking_strategy = (
-        row["chunking_strategy"]
-        if "chunking_strategy" in keys
-        else "standard"
+        row["chunking_strategy"] if "chunking_strategy" in keys else "standard"
     )
     mode = row["mode"] if "mode" in keys else "text"
     return KBResponse(
@@ -378,6 +387,7 @@ def _unlink_if_under_uploads(path: Path) -> None:
 # Knowledge bases
 # ------------------------------------------------------------------
 
+
 @router.post("/knowledge-bases", response_model = KBResponse)
 def create_knowledge_base(
     payload: CreateKBRequest,
@@ -391,9 +401,8 @@ def create_knowledge_base(
     # If the caller didn't override embedding_model, resolve from the
     # Phase-3 matrix using their (mode, strategy) selection. Unknown
     # combos fall back to the legacy default — see resolve_embedder.
-    embedding_model = (
-        payload.embedding_model
-        or resolve_embedder(payload.mode, payload.chunking_strategy)
+    embedding_model = payload.embedding_model or resolve_embedder(
+        payload.mode, payload.chunking_strategy
     )
     created_at = _now_ms()
     with get_connection() as conn:
@@ -452,6 +461,7 @@ class RagDefaults(BaseModel):
 
 class UpdateRagDefaultsRequest(BaseModel):
     """Patch shape — only fields present overwrite stored values."""
+
     chunking_strategy: ChunkingStrategy | None = None
     mode: KBMode | None = None
     embedding_model: str | None = None
@@ -541,9 +551,7 @@ def _load_thread_settings(thread_id: str) -> ThreadRagSettings:
         raw = {}
     fallback = _load_rag_defaults()
     return ThreadRagSettings(
-        chunking_strategy = (
-            raw.get("chunking_strategy") or fallback.chunking_strategy
-        ),
+        chunking_strategy = (raw.get("chunking_strategy") or fallback.chunking_strategy),
         mode = raw.get("mode") or fallback.mode,
         embedding_model = raw.get("embedding_model") or fallback.embedding_model,
     )
@@ -598,6 +606,7 @@ def set_thread_rag_settings(
 
 class ReingestKBRequest(BaseModel):
     """All fields optional — omitting one keeps the KB's current value."""
+
     chunking_strategy: ChunkingStrategy | None = None
     mode: KBMode | None = None
     embedding_model: str | None = None
@@ -689,9 +698,7 @@ def reingest_knowledge_base(
     kb_row = _kb_or_404(kb_id)
     keys = kb_row.keys() if hasattr(kb_row, "keys") else ()
     current_strategy = (
-        kb_row["chunking_strategy"]
-        if "chunking_strategy" in keys
-        else "standard"
+        kb_row["chunking_strategy"] if "chunking_strategy" in keys else "standard"
     )
     current_mode = kb_row["mode"] if "mode" in keys else "text"
     current_embedder = kb_row["embedding_model"]
@@ -700,13 +707,10 @@ def reingest_knowledge_base(
     new_mode = payload.mode or current_mode
     _validate_mode_combo(new_mode, new_strategy)
 
-    new_embedder = (
-        payload.embedding_model
-        or (
-            current_embedder
-            if (new_strategy == current_strategy and new_mode == current_mode)
-            else resolve_embedder(new_mode, new_strategy)
-        )
+    new_embedder = payload.embedding_model or (
+        current_embedder
+        if (new_strategy == current_strategy and new_mode == current_mode)
+        else resolve_embedder(new_mode, new_strategy)
     )
 
     with get_connection() as conn:
@@ -800,6 +804,7 @@ def delete_knowledge_base(
 # Document upload (KB and per-thread)
 # ------------------------------------------------------------------
 
+
 @router.post("/knowledge-bases/{kb_id}/documents", response_model = UploadResponse)
 async def upload_kb_document(
     kb_id: str,
@@ -813,9 +818,7 @@ async def upload_kb_document(
     # fall back to the same defaults as the column.
     kb_keys = kb_row.keys() if hasattr(kb_row, "keys") else ()
     chunking_strategy = (
-        kb_row["chunking_strategy"]
-        if "chunking_strategy" in kb_keys
-        else "standard"
+        kb_row["chunking_strategy"] if "chunking_strategy" in kb_keys else "standard"
     )
     mode = kb_row["mode"] if "mode" in kb_keys else "text"
     return _start_ingestion(
@@ -867,6 +870,7 @@ async def upload_thread_document(
 # Document list / delete
 # ------------------------------------------------------------------
 
+
 @router.get("/knowledge-bases/{kb_id}/documents", response_model = DocumentListResponse)
 def list_kb_documents(
     kb_id: str,
@@ -910,7 +914,7 @@ def get_rag_image(
     if "/" in filename or "\\" in filename or filename.startswith("."):
         raise HTTPException(status_code = 400, detail = "Invalid filename")
     root = Path(os.path.realpath(rag_uploads_root() / "images"))
-    candidate = (rag_uploads_root() / "images" / document_id / filename)
+    candidate = rag_uploads_root() / "images" / document_id / filename
     try:
         real = Path(os.path.realpath(candidate))
         real.relative_to(root)
@@ -927,9 +931,7 @@ def delete_document(
     current_subject: str = Depends(get_current_subject),
 ) -> dict:
     row = _document_or_404(document_id)
-    scope = (
-        kb_scope(row["kb_id"]) if row["kb_id"] else thread_scope(row["thread_id"])
-    )
+    scope = kb_scope(row["kb_id"]) if row["kb_id"] else thread_scope(row["thread_id"])
     with get_connection() as conn:
         conn.execute("DELETE FROM rag_documents WHERE id = ?", (document_id,))
         conn.commit()
@@ -995,6 +997,7 @@ def clear_thread_documents(
 # Ingestion job SSE
 # ------------------------------------------------------------------
 
+
 @router.get("/jobs/{job_id}/events")
 async def job_events(
     job_id: str,
@@ -1065,6 +1068,7 @@ async def _replay_terminal_state(row: Any):
 # Search
 # ------------------------------------------------------------------
 
+
 @router.post("/search", response_model = SearchResponse)
 def search(
     payload: SearchRequest,
diff --git a/studio/backend/utils/rag/config.py b/studio/backend/utils/rag/config.py
index fbe5366260..73edbf41b6 100644
--- a/studio/backend/utils/rag/config.py
+++ b/studio/backend/utils/rag/config.py
@@ -62,6 +62,7 @@ def resolve_embedder(mode: str, chunking_strategy: str) -> str:
         RAG_EMBEDDING_MODEL,
     )
 
+
 RAG_CHUNK_SIZE: int = _env_int("UNSLOTH_RAG_CHUNK_SIZE", 512)
 RAG_CHUNK_OVERLAP: int = _env_int("UNSLOTH_RAG_CHUNK_OVERLAP", 64)
 
@@ -79,8 +80,7 @@ RAG_EMBED_BATCH_SIZE: int = _env_int("UNSLOTH_RAG_EMBED_BATCH_SIZE", 32)
 # with the active chat model — callers opt in per-request via
 # `enable_rerank` on SearchRequest.
 RAG_RERANKER_MODEL: str = (
-    os.environ.get("UNSLOTH_RAG_RERANKER_MODEL", "").strip()
-    or "BAAI/bge-reranker-base"
+    os.environ.get("UNSLOTH_RAG_RERANKER_MODEL", "").strip() or "BAAI/bge-reranker-base"
 )
 RAG_RERANK_CANDIDATE_K: int = _env_int("UNSLOTH_RAG_RERANK_CANDIDATE_K", 50)
 RAG_RERANK_BATCH_SIZE: int = _env_int("UNSLOTH_RAG_RERANK_BATCH_SIZE", 16)
diff --git a/tests/python/test_rag_chunking.py b/tests/python/test_rag_chunking.py
index 835b96878d..2fd0376ccc 100644
--- a/tests/python/test_rag_chunking.py
+++ b/tests/python/test_rag_chunking.py
@@ -26,7 +26,9 @@ def test_chunk_pages_splits_long_text():
     )
     assert len(chunks) > 1
     for chunk in chunks:
-        assert _wc_counter(chunk.text) <= 55  # max + small slack from atomic split granularity
+        assert (
+            _wc_counter(chunk.text) <= 55
+        )  # max + small slack from atomic split granularity
 
 
 def test_chunk_pages_short_text_is_one_chunk():
diff --git a/tests/python/test_rag_late_chunking.py b/tests/python/test_rag_late_chunking.py
index 73d2182ab5..71b3f1a277 100644
--- a/tests/python/test_rag_late_chunking.py
+++ b/tests/python/test_rag_late_chunking.py
@@ -82,6 +82,7 @@ def test_late_chunk_encode_returns_one_vector_per_span():
     pytest.importorskip("torch")
     # all-MiniLM-L6-v2 is ~80MB and embeds at 384 dims.
     import os
+
     os.environ.setdefault(
         "UNSLOTH_RAG_EMBEDDING_MODEL",
         "sentence-transformers/all-MiniLM-L6-v2",
diff --git a/tests/python/test_rag_multimodal.py b/tests/python/test_rag_multimodal.py
index 5901c62dac..78737c5b14 100644
--- a/tests/python/test_rag_multimodal.py
+++ b/tests/python/test_rag_multimodal.py
@@ -34,10 +34,10 @@ def test_html_parser_returns_images_when_requested(tmp_path):
     img_path.write_bytes(png_bytes)
     html_path = tmp_path / "sample.html"
     html_path.write_text(
-        f'<html><body><h1>Doc</h1>'
-        f'<p>Body text.</p>'
+        f"<html><body><h1>Doc</h1>"
+        f"<p>Body text.</p>"
         f'<img src="tiny.png" alt="A tiny figure">'
-        f'</body></html>',
+        f"</body></html>",
         encoding = "utf-8",
     )
 
diff --git a/tests/python/test_rag_multimodal_integration.py b/tests/python/test_rag_multimodal_integration.py
index 36654b8ad8..f9e94ebf3a 100644
--- a/tests/python/test_rag_multimodal_integration.py
+++ b/tests/python/test_rag_multimodal_integration.py
@@ -168,6 +168,6 @@ def test_text_and_image_vectors_share_dimension(monkeypatch):
     image_vectors = embeddings_module.encode_images([buf.getvalue()])
     text_vectors = embeddings_module.encode(["a blue square"])
 
-    assert image_vectors[0].shape == text_vectors[0].shape, (
-        f"text dim {text_vectors[0].shape} != image dim {image_vectors[0].shape}"
-    )
+    assert (
+        image_vectors[0].shape == text_vectors[0].shape
+    ), f"text dim {text_vectors[0].shape} != image dim {image_vectors[0].shape}"
diff --git a/tests/python/test_rag_reranker.py b/tests/python/test_rag_reranker.py
index d63b94c089..c19f22f1c4 100644
--- a/tests/python/test_rag_reranker.py
+++ b/tests/python/test_rag_reranker.py
@@ -27,7 +27,9 @@ def test_rerank_empty_returns_empty():
 @pytest.mark.server
 def test_rerank_reorders_by_relevance(monkeypatch):
     """Hide the relevant chunk at the back of the input and check it bubbles up."""
-    monkeypatch.setenv("UNSLOTH_RAG_RERANKER_MODEL", "cross-encoder/ms-marco-MiniLM-L-6-v2")
+    monkeypatch.setenv(
+        "UNSLOTH_RAG_RERANKER_MODEL", "cross-encoder/ms-marco-MiniLM-L-6-v2"
+    )
     from core.rag.reranker import rerank, unload
     from core.rag.retrieval import Hit
 
@@ -35,7 +37,10 @@ def test_rerank_reorders_by_relevance(monkeypatch):
         (Hit("noise1", 0.0), "Cats are small carnivorous mammals."),
         (Hit("noise2", 0.0), "The Eiffel Tower is in Paris, France."),
         (Hit("noise3", 0.0), "Python is a programming language."),
-        (Hit("answer", 0.0), "The speed of light in vacuum is approximately 299792458 meters per second."),
+        (
+            Hit("answer", 0.0),
+            "The speed of light in vacuum is approximately 299792458 meters per second.",
+        ),
     ]
     try:
         ranked = rerank("How fast does light travel?", pairs, top_k = 2)
@@ -47,7 +52,9 @@ def test_rerank_reorders_by_relevance(monkeypatch):
 
 @pytest.mark.server
 def test_unload_clears_singleton(monkeypatch):
-    monkeypatch.setenv("UNSLOTH_RAG_RERANKER_MODEL", "cross-encoder/ms-marco-MiniLM-L-6-v2")
+    monkeypatch.setenv(
+        "UNSLOTH_RAG_RERANKER_MODEL", "cross-encoder/ms-marco-MiniLM-L-6-v2"
+    )
     from core.rag import reranker
     from core.rag.retrieval import Hit
 
diff --git a/tests/python/test_rag_tool_handler.py b/tests/python/test_rag_tool_handler.py
index 18af735d1d..c28b49cbbe 100644
--- a/tests/python/test_rag_tool_handler.py
+++ b/tests/python/test_rag_tool_handler.py
@@ -14,8 +14,10 @@ if str(STUDIO_BACKEND) not in sys.path:
 
 def _make_hit(chunk_id: str):
     """Minimal stand-in for retrieval.Hit — just needs .chunk_id."""
+
     class _Hit:
         pass
+
     h = _Hit()
     h.chunk_id = chunk_id
     h.score = 1.0
@@ -55,9 +57,11 @@ def test_kb_takes_precedence_over_thread():
         captured["scope"] = scope
         return []
 
-    with patch.object(tool.__import__("core.rag.retrieval", fromlist = ["retrieve_hybrid"]),
-                       "retrieve_hybrid",
-                       _stub_retrieve):
+    with patch.object(
+        tool.__import__("core.rag.retrieval", fromlist = ["retrieve_hybrid"]),
+        "retrieve_hybrid",
+        _stub_retrieve,
+    ):
         result = tool.search_knowledge_base(
             query = "x",
             scope_kb_id = "kb-abc",
@@ -78,9 +82,11 @@ def test_thread_scope_when_only_thread_set():
         captured["scope"] = scope
         return []
 
-    with patch.object(tool.__import__("core.rag.retrieval", fromlist = ["retrieve_hybrid"]),
-                       "retrieve_hybrid",
-                       _stub_retrieve):
+    with patch.object(
+        tool.__import__("core.rag.retrieval", fromlist = ["retrieve_hybrid"]),
+        "retrieve_hybrid",
+        _stub_retrieve,
+    ):
         tool.search_knowledge_base(
             query = "x",
             scope_thread_id = "thread-xyz",
@@ -137,9 +143,17 @@ def test_execute_tool_dispatches_to_search_knowledge_base():
 
     called = {}
 
-    def _stub(*, query, top_k = None, scope_kb_id = None, scope_thread_id = None,
-              enable_rerank = False, reranker_model = None, default_top_k = 5,
-              min_score = 0.0):
+    def _stub(
+        *,
+        query,
+        top_k = None,
+        scope_kb_id = None,
+        scope_thread_id = None,
+        enable_rerank = False,
+        reranker_model = None,
+        default_top_k = 5,
+        min_score = 0.0,
+    ):
         called["query"] = query
         called["top_k"] = top_k
         called["scope_kb_id"] = scope_kb_id