chore: trivial idiom cleanups (ruff PIE810, FURB167, B009)

m_flow/api/v1/prompts/routers/get_prompts_router.py

@@ -118,23 +118,22 @@ def _categorize_prompt(filename: str) -> str:
     # Answering: question answering and context generation
     # Note: summarize_search_results is for Q&A response, not general summarization
     if (
-        name.startswith("answer")
-        or name.startswith("direct_answer")
+        name.startswith(("answer", "direct_answer"))
         or "retrieval_context" in name
         or name == "search_result_deduplicator.txt"
     ):
         return "answering"
 
     # Episodic: Episode/Facet extraction and routing
-    if name.startswith("episodic") or name.startswith("episode"):
+    if name.startswith(("episodic", "episode")):
         return "episodic"
 
     # Entity: entity extraction and description
     if "entity" in name or name == "optimize_merged_description.txt":
         return "entity"
 
     # Graph: knowledge graph generation
-    if name.startswith("generate_graph") or name.startswith("knowledge_graph_extractor"):
+    if name.startswith(("generate_graph", "knowledge_graph_extractor")):
         return "graph"
 
     # Summarization: content summarization (general purpose)

m_flow/knowledge/summarization/summarize_by_event.py

@@ -108,7 +108,7 @@ def _extract_episode_name(raw_text: str) -> str:
     """Extract 'Episode Name: ...' from LLM output."""
     import re
 
-    match = re.search(r"^Episode Name:\s*(.+?)(?:\n|$)", raw_text, re.I | re.M)
+    match = re.search(r"^Episode Name:\s*(.+?)(?:\n|$)", raw_text, re.IGNORECASE | re.MULTILINE)
     return match.group(1).strip() if match else ""
 
 

m_flow/knowledge/summarization/text_summary_parser.py

@@ -97,7 +97,7 @@ def parse(
     def _extract_topic(cls, text: str) -> Optional[str]:
         """Extract overall topic from text."""
         # Try "Topic: ..." format
-        topic_match = re.search(r"^(?:Topic|Overall Topic|Summary):\s*(.+?)(?:\n|$)", text, re.I)
+        topic_match = re.search(r"^(?:Topic|Overall Topic|Summary):\s*(.+?)(?:\n|$)", text, re.IGNORECASE)
         if topic_match:
             return topic_match.group(1).strip()
 
@@ -106,7 +106,7 @@ def _extract_topic(cls, text: str) -> Optional[str]:
         if (
             10 < len(first_line) < 100
             and not first_line.startswith(("【", "-", "*", "#"))
-            and not re.match(r"^Episode Name:", first_line, re.I)
+            and not re.match(r"^Episode Name:", first_line, re.IGNORECASE)
         ):
             return first_line
 

m_flow/memory/episodic/routing/document_router.py

@@ -224,7 +224,7 @@ async def route_documents_to_episodes(
         doc = getattr(first_chunk, "is_part_of", None)
 
         # Sentence-level mode: doc_id is actually event_id (precomputed once)
-        is_single_event = doc_id.startswith("evt_") or doc_id.startswith("atomic_")
+        is_single_event = doc_id.startswith(("evt_", "atomic_"))
 
         # Get doc_title (use extracted helper function to reduce nesting)
         doc_title = ""

m_flow/memory/procedural/procedural_points_builder.py

@@ -79,7 +79,7 @@ def extract_anchor_tokens(text: str, min_len: int = 2, max_count: int = 50) -> L
     seen: Set[str] = set()
 
     # Numbers with units
-    for m in re.finditer(r"\d+\.?\d*\s*(?:TB|GB|MB|KB|%|次|个|分钟|秒|小时|天|ms|s|min)", text, re.I):
+    for m in re.finditer(r"\d+\.?\d*\s*(?:TB|GB|MB|KB|%|次|个|分钟|秒|小时|天|ms|s|min)", text, re.IGNORECASE):
         t = m.group(0).strip()
         k = t.lower()
         if k not in seen and len(t) >= min_len:

m_flow/retrieval/episodic/bundle_search.py

@@ -142,11 +142,7 @@ async def episodic_bundle_search(
         results_by_collection = {c: len(r) for c, r in node_distances.items()}
         total_hits = sum(results_by_collection.values())
         all_ids = {
-            str(getattr(r, "id"))
-            for results in node_distances.values()
-            if results
-            for r in results
-            if getattr(r, "id", None)
+            str(r.id) for results in node_distances.values() if results for r in results if getattr(r, "id", None)
         }
         rlog.log_vector_search(
             collections=cfg.collections,
@@ -425,7 +421,7 @@ async def _two_phase_projection(
     """Two-phase graph projection."""
     # Collect relevant IDs
     all_hit_ids = {
-        str(getattr(r, "id"))
+        str(r.id)
         for collection_name, results in node_distances.items()
         if collection_name != "RelationType_relationship_name" and results
         for r in results
@@ -485,7 +481,7 @@ async def _two_phase_projection_with_stats(
 
     # Collect relevant IDs
     all_hit_ids = {
-        str(getattr(r, "id"))
+        str(r.id)
         for collection_name, results in node_distances.items()
         if collection_name != "RelationType_relationship_name" and results
         for r in results

m_flow/retrieval/utils/fine_grained_triplet_search.py

@@ -259,7 +259,7 @@ async def search_in_collection(collection_name: str):
         if wide_search_limit is not None:
             relevant_ids_to_filter = list(
                 {
-                    str(getattr(scored_node, "id"))
+                    str(scored_node.id)
                     for collection_name, score_collection in node_distances.items()
                     if collection_name != "RelationType_relationship_name"
                     and isinstance(score_collection, (list, tuple))

m_flow/retrieval/utils/procedural_bundle_search.py

@@ -298,7 +298,7 @@ async def search_col(col: str):
 
         # Collect hit IDs
         all_hit_ids = {
-            str(getattr(r, "id"))
+            str(r.id)
             for col, scored in node_distances.items()
             if col != "RelationType_relationship_name"
             for r in (scored or [])

m_flow/shared/files/utils/get_data_file_path.py

@@ -16,7 +16,7 @@ def _normalise_file_uri(raw: str) -> str:
     normalised = os.path.normpath(without_scheme)
     # On Windows the URI ``file:///C:/dir`` normalises to ``/C:\\dir``
     if os.name == "nt":
-        if (normalised.startswith("/") or normalised.startswith("\\")) and len(normalised) > 2 and normalised[2] == ":":
+        if (normalised.startswith(("/", "\\"))) and len(normalised) > 2 and normalised[2] == ":":
             normalised = normalised[1:]
     return normalised