fix(search): honor min_score in plain search steps (#338)

reme/steps/index/bm25_search.py

@@ -47,6 +47,7 @@ class Bm25SearchStep(BaseStep):
         assert self.context is not None
         query: str = (self.context.get("query", "") or "").strip()
         limit: int = int(self.context.get("limit") or 5)
+        min_score: float = float(self.context.get("min_score") or 0.0)
         tool_context_id: str = (self.context.get("tool_context_id", "") or "").strip()
 
         if not query:
@@ -59,6 +60,9 @@ class Bm25SearchStep(BaseStep):
         results = await self.file_store.keyword_search(query, candidates, {})
         self.logger.info(f"[{self.name}] query={query!r} candidates={candidates} hits={len(results)}")
 
+        if min_score > 0.0:
+            results = [chunk for chunk in results if chunk.score >= min_score]
+
         if tool_context_id:
             results = self._dedupe_tool_context(results, tool_context_id, limit)
         else:

reme/steps/index/vector_search.py

@@ -47,6 +47,7 @@ class VectorSearchStep(BaseStep):
         assert self.context is not None
         query: str = (self.context.get("query", "") or "").strip()
         limit: int = int(self.context.get("limit") or 5)
+        min_score: float = float(self.context.get("min_score") or 0.0)
         tool_context_id: str = (self.context.get("tool_context_id", "") or "").strip()
 
         if not query:
@@ -59,6 +60,9 @@ class VectorSearchStep(BaseStep):
         results = await self.file_store.vector_search(query, candidates, {})
         self.logger.info(f"[{self.name}] query={query!r} candidates={candidates} hits={len(results)}")
 
+        if min_score > 0.0:
+            results = [chunk for chunk in results if chunk.score >= min_score]
+
         if tool_context_id:
             results = self._dedupe_tool_context(results, tool_context_id, limit)
         else:

tests/unit/test_search_step.py

@@ -7,7 +7,7 @@ from reme.components import ApplicationContext
 from reme.components.runtime_context import RuntimeContext
 from reme.enumeration import LinkScopeEnum
 from reme.schema import FileChunk, FileLink, FileNode
-from reme.steps.index import AddDraftStep, ReadAllDraftStep, SearchStep
+from reme.steps.index import AddDraftStep, Bm25SearchStep, ReadAllDraftStep, SearchStep, VectorSearchStep
 
 
 class FakeSearchStore(BaseFileStore):
@@ -151,6 +151,38 @@ def test_search_step_keyword_only_uses_keyword_scores_and_min_score():
     asyncio.run(run())
 
 
+def test_plain_search_steps_apply_min_score_before_truncation():
+    """Vector-only and BM25-only tools should not return hits below ``min_score``."""
+
+    async def run():
+        vector_store = FakeSearchStore(
+            vector_results=[
+                _chunk("vector-high", "daily/high.md", "strong vector hit", "vector", 0.9),
+                _chunk("vector-low", "daily/low.md", "weak vector hit", "vector", 0.2),
+            ],
+        )
+        keyword_store = FakeSearchStore(
+            keyword_results=[
+                _chunk("keyword-high", "daily/high.md", "strong keyword hit", "keyword", 4.0),
+                _chunk("keyword-low", "daily/low.md", "weak keyword hit", "keyword", 0.2),
+            ],
+        )
+
+        vector = await VectorSearchStep(file_store=vector_store)(
+            RuntimeContext(query="alpha", limit=5, min_score=0.5),
+        )
+        keyword = await Bm25SearchStep(file_store=keyword_store)(
+            RuntimeContext(query="alpha", limit=5, min_score=1.0),
+        )
+
+        assert [result["id"] for result in vector.metadata["results"]] == ["vector-high"]
+        assert [result["id"] for result in keyword.metadata["results"]] == ["keyword-high"]
+        assert vector.answer == "strong vector hit"
+        assert keyword.answer == "strong keyword hit"
+
+    asyncio.run(run())
+
+
 def test_search_step_tool_context_deduplicates_returned_chunks_only():
     """When tool_context_id is supplied, repeated searches skip previously returned chunks."""