test: split embedding lane tests (#4389)

* test: split embedding lane tests

* test: preserve embedding focus selector after lane split

tests/helpers/embedding_lanes.py

@@ -0,0 +1,124 @@
+"""Shared fakes for embedding-lane tests."""
+
+
+class FakeEmbedder:
+    def __init__(self, dim, model, url):
+        self.dim = dim
+        self.model = model
+        self.url = url
+
+    def get_sentence_embedding_dimension(self):
+        return self.dim
+
+    def encode(self, texts, normalize_embeddings=True):
+        return [[float(i + 1)] * self.dim for i, _ in enumerate(texts)]
+
+
+class FailingEmbedder(FakeEmbedder):
+    def encode(self, texts, normalize_embeddings=True):
+        raise RuntimeError("embedding endpoint rate limited")
+
+
+class FakeCollection:
+    def __init__(self, name, metadata=None):
+        self.name = name
+        self.metadata = metadata or {}
+        self.rows = {}
+        self.dim = None
+
+    def count(self):
+        return len(self.rows)
+
+    def add(self, ids, embeddings, documents=None, metadatas=None):
+        self._check_dim(embeddings)
+        documents = documents or [None] * len(ids)
+        metadatas = metadatas or [{}] * len(ids)
+        for row_id, emb, doc, meta in zip(ids, embeddings, documents, metadatas):
+            self.rows[row_id] = {"embedding": emb, "document": doc, "metadata": meta}
+
+    def upsert(self, ids, embeddings, documents=None, metadatas=None):
+        self.add(ids, embeddings, documents=documents, metadatas=metadatas)
+
+    def get(self, ids=None, include=None, where=None, limit=None):
+        selected = list(self.rows.items())
+        if ids is not None:
+            id_set = set(ids)
+            selected = [(row_id, row) for row_id, row in selected if row_id in id_set]
+        if where:
+            selected = [
+                (row_id, row)
+                for row_id, row in selected
+                if all(row["metadata"].get(k) == v for k, v in where.items())
+            ]
+        if limit is not None:
+            selected = selected[:limit]
+        return {
+            "ids": [row_id for row_id, _ in selected],
+            "documents": [row["document"] for _, row in selected],
+            "metadatas": [row["metadata"] for _, row in selected],
+            "embeddings": [row["embedding"] for _, row in selected],
+        }
+
+    def query(self, query_embeddings, n_results, where=None, include=None):
+        self._check_dim(query_embeddings)
+        rows = self.get(where=where)
+        ids = rows["ids"][:n_results]
+        docs = rows["documents"][:n_results]
+        metas = rows["metadatas"][:n_results]
+        return {
+            "ids": [ids],
+            "documents": [docs],
+            "metadatas": [metas],
+            "distances": [[0.1 + i * 0.01 for i in range(len(ids))]],
+        }
+
+    def delete(self, ids):
+        for row_id in ids:
+            self.rows.pop(row_id, None)
+
+    def _check_dim(self, embeddings):
+        if not embeddings:
+            return
+        dim = len(embeddings[0])
+        if self.dim is None:
+            self.dim = dim
+        elif self.dim != dim:
+            raise RuntimeError(f"Collection expecting embedding with dimension of {self.dim}, got {dim}")
+
+
+class FakeChroma:
+    def __init__(self):
+        self.collections = {}
+        self.deleted = []
+        self.fail_next_add_for = {}
+
+    def get_or_create_collection(self, name, metadata=None):
+        if name not in self.collections:
+            self.collections[name] = FakeCollection(name, metadata=metadata)
+            if self.fail_next_add_for.get(name, 0) > 0:
+                original_add = self.collections[name].add
+
+                def fail_once(*args, **kwargs):
+                    self.fail_next_add_for[name] -= 1
+                    self.collections[name].add = original_add
+                    raise RuntimeError("chroma write failed")
+
+                self.collections[name].add = fail_once
+        elif metadata is not None:
+            self.collections[name].metadata = metadata
+        return self.collections[name]
+
+    def get_collection(self, name):
+        if name not in self.collections:
+            raise KeyError(name)
+        return self.collections[name]
+
+    def delete_collection(self, name):
+        self.deleted.append(name)
+        self.collections.pop(name, None)
+
+
+def patch_chroma(monkeypatch, fake):
+    import src.chroma_client as chroma_client
+
+    monkeypatch.setattr(chroma_client, "get_chroma_client", lambda: fake)

tests/run_focus.py

@@ -47,6 +47,12 @@ AREAS: tuple[str, ...] = (
     "uncategorized",
 )
 
+# Backward-compatible aggregate selectors for focused runs whose original
+# monolithic files were split into more specific taxonomy sub-areas.
+SUB_AREA_ALIASES: dict[str, tuple[str, ...]] = {
+    "embedding": ("embedding", "embedding_memory"),
+}
+
 
 def normalize_sub_area(value: str) -> str:
     """Normalize a CLI sub-area value and remove an optional ``sub_`` prefix."""
@@ -102,6 +108,13 @@ def sub_area_type(valid_sub_areas: frozenset[str]) -> Callable[[str], str]:
     return validate
 
 
+def _sub_area_marker_expression(sub_area: str) -> str:
+    """Build the marker expression for a sub-area, including narrow aliases."""
+    aliases = SUB_AREA_ALIASES.get(sub_area, (sub_area,))
+    markers = [f"sub_{alias}" for alias in aliases]
+    return " or ".join(markers)
+
+
 @dataclass(frozen=True)
 class FocusSelection:
     """A single focused-selection request, decoupled from argparse and pytest."""
@@ -143,7 +156,10 @@ def build_marker_expression(
     if area:
         parts.append(f"area_{area}")
     if sub_area:
-        parts.append(f"sub_{sub_area}")
+        sub_expression = _sub_area_marker_expression(sub_area)
+        if " or " in sub_expression:
+            sub_expression = f"({sub_expression})"
+        parts.append(sub_expression)
     if fast:
         parts.append("not slow")
     if not parts:

tests/test_embedding_lane_ndarray_restore.py

@@ -13,7 +13,7 @@ in test_embedding_lanes.py, but the preserved embeddings come back as ndarray.
 import numpy as np
 
 from src.embedding_lanes import build_embedding_lanes
-from tests.test_embedding_lanes import FakeChroma, FakeEmbedder, _patch_chroma
+from tests.helpers.embedding_lanes import FakeChroma, FakeEmbedder, patch_chroma
 
 
 def test_lane_reset_restores_when_chroma_returns_numpy_embeddings(monkeypatch):
@@ -46,7 +46,7 @@ def test_lane_reset_restores_when_chroma_returns_numpy_embeddings(monkeypatch):
 
     # Force the post-reset rewrite to fail so the restore branch runs.
     fake.fail_next_add_for["odysseus_memories_custom"] = 1
-    _patch_chroma(monkeypatch, fake)
+    patch_chroma(monkeypatch, fake)
 
     import src.embedding_lanes as lanes
 

tests/test_embedding_lanes.py

@@ -1,139 +1,21 @@
 import pytest
 
 from src.embedding_lanes import (
-    EmbeddingLane,
     LANE_CUSTOM,
     LANE_FASTEMBED,
     build_embedding_lanes,
 )
-
+from tests.helpers.embedding_lanes import (
-
+    FakeChroma,
-class FakeEmbedder:
+    FakeEmbedder,
-    def __init__(self, dim, model, url):
+    FailingEmbedder,
-        self.dim = dim
+    patch_chroma,
-        self.model = model
+)
-        self.url = url
-
-    def get_sentence_embedding_dimension(self):
-        return self.dim
-
-    def encode(self, texts, normalize_embeddings=True):
-        return [[float(i + 1)] * self.dim for i, _ in enumerate(texts)]
-
-
-class FailingEmbedder(FakeEmbedder):
-    def encode(self, texts, normalize_embeddings=True):
-        raise RuntimeError("embedding endpoint rate limited")
-
-
-class FakeCollection:
-    def __init__(self, name, metadata=None):
-        self.name = name
-        self.metadata = metadata or {}
-        self.rows = {}
-        self.dim = None
-
-    def count(self):
-        return len(self.rows)
-
-    def add(self, ids, embeddings, documents=None, metadatas=None):
-        self._check_dim(embeddings)
-        documents = documents or [None] * len(ids)
-        metadatas = metadatas or [{}] * len(ids)
-        for row_id, emb, doc, meta in zip(ids, embeddings, documents, metadatas):
-            self.rows[row_id] = {"embedding": emb, "document": doc, "metadata": meta}
-
-    def upsert(self, ids, embeddings, documents=None, metadatas=None):
-        self.add(ids, embeddings, documents=documents, metadatas=metadatas)
-
-    def get(self, ids=None, include=None, where=None, limit=None):
-        selected = list(self.rows.items())
-        if ids is not None:
-            id_set = set(ids)
-            selected = [(row_id, row) for row_id, row in selected if row_id in id_set]
-        if where:
-            selected = [
-                (row_id, row)
-                for row_id, row in selected
-                if all(row["metadata"].get(k) == v for k, v in where.items())
-            ]
-        if limit is not None:
-            selected = selected[:limit]
-        return {
-            "ids": [row_id for row_id, _ in selected],
-            "documents": [row["document"] for _, row in selected],
-            "metadatas": [row["metadata"] for _, row in selected],
-            "embeddings": [row["embedding"] for _, row in selected],
-        }
-
-    def query(self, query_embeddings, n_results, where=None, include=None):
-        self._check_dim(query_embeddings)
-        rows = self.get(where=where)
-        ids = rows["ids"][:n_results]
-        docs = rows["documents"][:n_results]
-        metas = rows["metadatas"][:n_results]
-        return {
-            "ids": [ids],
-            "documents": [docs],
-            "metadatas": [metas],
-            "distances": [[0.1 + i * 0.01 for i in range(len(ids))]],
-        }
-
-    def delete(self, ids):
-        for row_id in ids:
-            self.rows.pop(row_id, None)
-
-    def _check_dim(self, embeddings):
-        if not embeddings:
-            return
-        dim = len(embeddings[0])
-        if self.dim is None:
-            self.dim = dim
-        elif self.dim != dim:
-            raise RuntimeError(f"Collection expecting embedding with dimension of {self.dim}, got {dim}")
-
-
-class FakeChroma:
-    def __init__(self):
-        self.collections = {}
-        self.deleted = []
-        self.fail_next_add_for = {}
-
-    def get_or_create_collection(self, name, metadata=None):
-        if name not in self.collections:
-            self.collections[name] = FakeCollection(name, metadata=metadata)
-            if self.fail_next_add_for.get(name, 0) > 0:
-                original_add = self.collections[name].add
-
-                def fail_once(*args, **kwargs):
-                    self.fail_next_add_for[name] -= 1
-                    self.collections[name].add = original_add
-                    raise RuntimeError("chroma write failed")
-
-                self.collections[name].add = fail_once
-        elif metadata is not None:
-            self.collections[name].metadata = metadata
-        return self.collections[name]
-
-    def get_collection(self, name):
-        if name not in self.collections:
-            raise KeyError(name)
-        return self.collections[name]
-
-    def delete_collection(self, name):
-        self.deleted.append(name)
-        self.collections.pop(name, None)
-
-
-def _patch_chroma(monkeypatch, fake):
-    import src.chroma_client as chroma_client
-
-    monkeypatch.setattr(chroma_client, "get_chroma_client", lambda: fake)
 
 
 def test_build_embedding_lanes_keeps_custom_and_fastembed_dimensions_separate(monkeypatch):
     fake = FakeChroma()
-    _patch_chroma(monkeypatch, fake)
+    patch_chroma(monkeypatch, fake)
 
     import src.embedding_lanes as lanes
 
@@ -182,7 +64,7 @@ def test_build_embedding_lanes_recreates_only_custom_when_fingerprint_changes(mo
         },
     )
     fast.add(ids=["fast"], embeddings=[[0.0] * 384], documents=["fast"])
-    _patch_chroma(monkeypatch, fake)
+    patch_chroma(monkeypatch, fake)
 
     import src.embedding_lanes as lanes
 
@@ -214,7 +96,7 @@ def test_lane_reset_reembeds_existing_documents_on_fingerprint_change(monkeypatc
         documents=["existing custom memory"],
         metadatas=[{"source": "memory"}],
     )
-    _patch_chroma(monkeypatch, fake)
+    patch_chroma(monkeypatch, fake)
 
     import src.embedding_lanes as lanes
 
@@ -251,7 +133,7 @@ def test_lane_reset_keeps_existing_collection_when_reembed_fails(monkeypatch):
         documents=["existing custom memory"],
         metadatas=[{"source": "memory"}],
     )
-    _patch_chroma(monkeypatch, fake)
+    patch_chroma(monkeypatch, fake)
 
     import src.embedding_lanes as lanes
 
@@ -287,7 +169,7 @@ def test_lane_reset_keeps_existing_collection_when_preserve_read_fails(monkeypat
         raise RuntimeError("chroma read failed")
 
     old_custom.get = fail_get
-    _patch_chroma(monkeypatch, fake)
+    patch_chroma(monkeypatch, fake)
 
     import src.embedding_lanes as lanes
 
@@ -322,7 +204,7 @@ def test_lane_reset_restores_existing_collection_when_rewrite_fails(monkeypatch)
         metadatas=[{"source": "memory"}],
     )
     fake.fail_next_add_for["odysseus_memories_custom"] = 1
-    _patch_chroma(monkeypatch, fake)
+    patch_chroma(monkeypatch, fake)
 
     import src.embedding_lanes as lanes
 
@@ -344,7 +226,7 @@ def test_lane_reset_restores_existing_collection_when_rewrite_fails(monkeypatch)
 
 def test_build_embedding_lanes_uses_fastembed_when_custom_unavailable(monkeypatch):
     fake = FakeChroma()
-    _patch_chroma(monkeypatch, fake)
+    patch_chroma(monkeypatch, fake)
 
     import src.embedding_lanes as lanes
 
@@ -411,694 +293,3 @@ def test_custom_lane_uses_http_down_latch(monkeypatch):
 
     assert calls == [{"url": None, "model": None, "api_key": None}]
     embeddings.reset_http_embed_state()
-
-
-def test_memory_vector_store_writes_both_lanes_and_prefers_custom(monkeypatch):
-    fake = FakeChroma()
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FakeEmbedder(768, "nomic", "http://embeddings/v1"))
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore("data")
-    store.add("mem-1", "Nicholai likes direct memory systems")
-
-    assert fake.collections["odysseus_memories_custom"].count() == 1
-    assert fake.collections["odysseus_memories_fastembed"].count() == 1
-
-    results = store.search("direct memory", k=5)
-    assert results[0]["memory_id"] == "mem-1"
-    assert results[0]["embedding_lane"] == LANE_CUSTOM
-
-
-def test_memory_search_merges_fallback_only_results_before_limit():
-    custom_collection = FakeCollection("odysseus_memories_custom", metadata={"embedding_lane": "custom"})
-    fast_collection = FakeCollection("odysseus_memories_fastembed", metadata={"embedding_lane": "fastembed"})
-    custom_collection.add(
-        ids=["old-1", "old-2"],
-        embeddings=[[0.0] * 768, [0.0] * 768],
-        documents=["older custom memory", "another custom memory"],
-        metadatas=[{"source": "memory"}, {"source": "memory"}],
-    )
-    fast_collection.add(
-        ids=["fallback-only"],
-        embeddings=[[0.0] * 384],
-        documents=["fallback only relevant memory"],
-        metadatas=[{"source": "memory"}],
-    )
-
-    custom_collection.query = lambda **_kwargs: {
-        "ids": [["old-1", "old-2"]],
-        "distances": [[0.20, 0.21]],
-    }
-    fast_collection.query = lambda **_kwargs: {
-        "ids": [["fallback-only"]],
-        "distances": [[0.05]],
-    }
-
-    custom_lane = EmbeddingLane(
-        name=LANE_CUSTOM,
-        client=FakeEmbedder(768, "nomic", "http://embeddings/v1"),
-        collection=custom_collection,
-        collection_name="odysseus_memories_custom",
-        model="nomic",
-        url="http://embeddings/v1",
-        dimension=768,
-        fingerprint="custom",
-    )
-    fast_lane = EmbeddingLane(
-        name=LANE_FASTEMBED,
-        client=FakeEmbedder(384, "mini", "local://fastembed"),
-        collection=fast_collection,
-        collection_name="odysseus_memories_fastembed",
-        model="mini",
-        url="local://fastembed",
-        dimension=384,
-        fingerprint="fast",
-    )
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore.__new__(MemoryVectorStore)
-    store._lanes = [custom_lane, fast_lane]
-    store._healthy = True
-
-    results = store.search("fallback relevant", k=2)
-
-    assert [row["memory_id"] for row in results] == ["fallback-only", "old-1"]
-
-
-def test_vector_rag_writes_both_lanes_and_falls_back_to_fastembed(monkeypatch):
-    fake = FakeChroma()
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.rag_vector import VectorRAG
-
-    rag = VectorRAG()
-    assert rag.add_document("session search belongs in tools", {"source": "/tmp/a.md", "owner": "alice"})
-    assert "odysseus_rag_custom" not in fake.collections
-    assert fake.collections["odysseus_rag_fastembed"].count() == 1
-
-    results = rag.search("session search", k=3, owner="alice")
-    assert results[0]["document"] == "session search belongs in tools"
-    assert results[0]["embedding_lane"] == LANE_FASTEMBED
-
-
-def test_vector_rag_batch_index_continues_when_custom_lane_fails(monkeypatch, tmp_path):
-    fake = FakeChroma()
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FailingEmbedder(768, "nomic", "http://embeddings/v1"))
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.rag_vector import VectorRAG
-
-    rag = VectorRAG(persist_directory=str(tmp_path))
-    result = rag.add_documents_batch([
-        ("batch fallback document", {"source": "/tmp/a.md", "owner": "alice"}),
-    ])
-
-    assert result["success"]
-    assert result["added_count"] == 1
-    assert fake.collections["odysseus_rag_custom"].count() == 0
-    assert fake.collections["odysseus_rag_fastembed"].count() == 1
-
-
-def test_vector_rag_batch_index_reports_failure_when_all_lanes_fail(monkeypatch, tmp_path):
-    fake = FakeChroma()
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FailingEmbedder(768, "nomic", "http://embeddings/v1"))
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FailingEmbedder(384, "mini", "local://fastembed"))
-
-    from src.rag_vector import VectorRAG
-
-    rag = VectorRAG(persist_directory=str(tmp_path))
-    result = rag.add_documents_batch([
-        ("batch outage document", {"source": "/tmp/a.md", "owner": "alice"}),
-    ])
-
-    assert not result["success"]
-    assert fake.collections["odysseus_rag_custom"].count() == 0
-    assert fake.collections["odysseus_rag_fastembed"].count() == 0
-
-
-def test_tool_index_indexes_and_retrieves_from_available_lanes(monkeypatch):
-    fake = FakeChroma()
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FakeEmbedder(768, "nomic", "http://embeddings/v1"))
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.tool_index import ToolIndex
-
-    index = ToolIndex()
-    index.index_builtin_tools()
-
-    assert fake.collections["odysseus_tool_index_custom"].count() > 0
-    assert fake.collections["odysseus_tool_index_fastembed"].count() > 0
-    assert "bash" in index.retrieve("run a shell command", k=10)
-
-
-def test_tool_index_builtin_indexing_fails_when_all_lanes_fail():
-    custom_lane = EmbeddingLane(
-        name=LANE_CUSTOM,
-        client=FailingEmbedder(768, "nomic", "http://embeddings/v1"),
-        collection=FakeCollection("odysseus_tool_index_custom", metadata={"embedding_lane": "custom"}),
-        collection_name="odysseus_tool_index_custom",
-        model="nomic",
-        url="http://embeddings/v1",
-        dimension=768,
-        fingerprint="custom",
-    )
-    fast_lane = EmbeddingLane(
-        name=LANE_FASTEMBED,
-        client=FailingEmbedder(384, "mini", "local://fastembed"),
-        collection=FakeCollection("odysseus_tool_index_fastembed", metadata={"embedding_lane": "fastembed"}),
-        collection_name="odysseus_tool_index_fastembed",
-        model="mini",
-        url="local://fastembed",
-        dimension=384,
-        fingerprint="fast",
-    )
-
-    from src.tool_index import ToolIndex
-
-    index = ToolIndex.__new__(ToolIndex)
-    index._lanes = [custom_lane, fast_lane]
-    index._healthy = True
-
-    with pytest.raises(RuntimeError, match="all embedding lanes"):
-        index.index_builtin_tools()
-    assert not index.healthy
-
-
-def test_tool_index_retrieval_continues_when_custom_lane_query_fails():
-    custom_collection = FakeCollection("odysseus_tool_index_custom", metadata={"embedding_lane": "custom"})
-    fast_collection = FakeCollection("odysseus_tool_index_fastembed", metadata={"embedding_lane": "fastembed"})
-    fast_collection.add(
-        ids=["builtin_bash"],
-        embeddings=[[0.0] * 384],
-        documents=["Tool: bash\nRun shell commands"],
-        metadatas=[{"tool_name": "bash", "tool_type": "builtin"}],
-    )
-
-    def fail_query(*_args, **_kwargs):
-        raise RuntimeError("custom endpoint down")
-
-    custom_collection.add(
-        ids=["builtin_python"],
-        embeddings=[[0.0] * 768],
-        documents=["Tool: python\nRun Python"],
-        metadatas=[{"tool_name": "python", "tool_type": "builtin"}],
-    )
-    custom_collection.query = fail_query
-
-    custom_lane = EmbeddingLane(
-        name=LANE_CUSTOM,
-        client=FakeEmbedder(768, "nomic", "http://embeddings/v1"),
-        collection=custom_collection,
-        collection_name="odysseus_tool_index_custom",
-        model="nomic",
-        url="http://embeddings/v1",
-        dimension=768,
-        fingerprint="custom",
-    )
-    fast_lane = EmbeddingLane(
-        name=LANE_FASTEMBED,
-        client=FakeEmbedder(384, "mini", "local://fastembed"),
-        collection=fast_collection,
-        collection_name="odysseus_tool_index_fastembed",
-        model="mini",
-        url="local://fastembed",
-        dimension=384,
-        fingerprint="fast",
-    )
-
-    from src.tool_index import ToolIndex
-
-    index = ToolIndex.__new__(ToolIndex)
-    index._lanes = [custom_lane, fast_lane]
-
-    assert index.retrieve("run shell", k=5) == ["bash"]
-
-
-def test_tool_index_merges_fallback_tool_results_before_limit():
-    custom_collection = FakeCollection("odysseus_tool_index_custom", metadata={"embedding_lane": "custom"})
-    fast_collection = FakeCollection("odysseus_tool_index_fastembed", metadata={"embedding_lane": "fastembed"})
-    custom_collection.add(
-        ids=["builtin_one", "builtin_two"],
-        embeddings=[[0.0] * 768, [0.0] * 768],
-        documents=["Tool: one", "Tool: two"],
-        metadatas=[
-            {"tool_name": "one", "tool_type": "builtin"},
-            {"tool_name": "two", "tool_type": "builtin"},
-        ],
-    )
-    fast_collection.add(
-        ids=["mcp_current"],
-        embeddings=[[0.0] * 384],
-        documents=["Tool: current MCP"],
-        metadatas=[{"tool_name": "current_mcp", "tool_type": "mcp"}],
-    )
-
-    custom_collection.query = lambda **_kwargs: {
-        "ids": [["builtin_one", "builtin_two"]],
-        "metadatas": [[
-            {"tool_name": "one", "tool_type": "builtin"},
-            {"tool_name": "two", "tool_type": "builtin"},
-        ]],
-        "distances": [[0.20, 0.21]],
-    }
-    fast_collection.query = lambda **_kwargs: {
-        "ids": [["mcp_current"]],
-        "metadatas": [[{"tool_name": "current_mcp", "tool_type": "mcp"}]],
-        "distances": [[0.05]],
-    }
-
-    custom_lane = EmbeddingLane(
-        name=LANE_CUSTOM,
-        client=FakeEmbedder(768, "nomic", "http://embeddings/v1"),
-        collection=custom_collection,
-        collection_name="odysseus_tool_index_custom",
-        model="nomic",
-        url="http://embeddings/v1",
-        dimension=768,
-        fingerprint="custom",
-    )
-    fast_lane = EmbeddingLane(
-        name=LANE_FASTEMBED,
-        client=FakeEmbedder(384, "mini", "local://fastembed"),
-        collection=fast_collection,
-        collection_name="odysseus_tool_index_fastembed",
-        model="mini",
-        url="local://fastembed",
-        dimension=384,
-        fingerprint="fast",
-    )
-
-    from src.tool_index import ToolIndex
-
-    index = ToolIndex.__new__(ToolIndex)
-    index._lanes = [custom_lane, fast_lane]
-
-    assert index.retrieve("current mcp", k=2) == ["current_mcp", "one"]
-
-
-def test_legacy_collection_backfills_fastembed_lane(monkeypatch):
-    fake = FakeChroma()
-    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
-    legacy.add(
-        ids=["legacy-memory"],
-        embeddings=[[0.0] * 384],
-        documents=["legacy memory row"],
-        metadatas=[{"source": "memory"}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore("data")
-
-    assert store.count() == 1
-    assert fake.collections["odysseus_memories"].count() == 1
-    assert fake.collections["odysseus_memories_fastembed"].count() == 1
-
-
-def test_legacy_collection_backfills_custom_only_lane(monkeypatch):
-    fake = FakeChroma()
-    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
-    legacy.add(
-        ids=["legacy-memory"],
-        embeddings=[[0.0] * 384],
-        documents=["legacy memory row"],
-        metadatas=[{"source": "memory"}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FakeEmbedder(768, "nomic", "http://embeddings/v1"))
-
-    def fail_fastembed():
-        raise RuntimeError("fastembed missing")
-
-    monkeypatch.setattr(lanes, "_build_fastembed_client", fail_fastembed)
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore("data")
-
-    assert store.count() == 1
-    assert "odysseus_memories_fastembed" not in fake.collections
-    assert fake.collections["odysseus_memories_custom"].count() == 1
-    assert len(fake.collections["odysseus_memories_custom"].rows["legacy-memory"]["embedding"]) == 768
-
-
-def test_legacy_migration_continues_when_custom_backfill_fails(monkeypatch):
-    fake = FakeChroma()
-    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
-    legacy.add(
-        ids=["legacy-memory"],
-        embeddings=[[0.0] * 384],
-        documents=["legacy memory row"],
-        metadatas=[{"source": "memory"}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FailingEmbedder(768, "nomic", "http://embeddings/v1"))
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore("data")
-
-    assert store.healthy
-    assert fake.collections["odysseus_memories_custom"].count() == 0
-    assert fake.collections["odysseus_memories_fastembed"].count() == 1
-
-
-def test_legacy_migration_resumes_partial_lane_backfill(monkeypatch):
-    fake = FakeChroma()
-    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
-    legacy.add(
-        ids=["legacy-1", "legacy-2"],
-        embeddings=[[0.0] * 384, [0.0] * 384],
-        documents=["legacy memory one", "legacy memory two"],
-        metadatas=[{"source": "memory"}, {"source": "memory"}],
-    )
-    partial = fake.get_or_create_collection("odysseus_memories_fastembed", metadata={"embedding_lane": "fastembed"})
-    partial.add(
-        ids=["legacy-1"],
-        embeddings=[[0.0] * 384],
-        documents=["legacy memory one"],
-        metadatas=[{"source": "memory"}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore("data")
-
-    assert store.count() == 2
-    assert set(fake.collections["odysseus_memories_fastembed"].get()["ids"]) == {"legacy-1", "legacy-2"}
-
-
-def test_memory_rebuild_does_not_reimport_legacy_collection(monkeypatch):
-    fake = FakeChroma()
-    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
-    legacy.add(
-        ids=["stale-memory"],
-        embeddings=[[0.0] * 384],
-        documents=["stale legacy memory"],
-        metadatas=[{"source": "memory"}],
-    )
-    inactive_custom = fake.get_or_create_collection("odysseus_memories_custom", metadata={"embedding_lane": "custom"})
-    inactive_custom.add(
-        ids=["stale-custom"],
-        embeddings=[[0.0] * 768],
-        documents=["stale inactive custom memory"],
-        metadatas=[{"source": "memory"}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore("data")
-    assert fake.collections["odysseus_memories_fastembed"].count() == 1
-
-    store.rebuild([{"id": "current-memory", "text": "current rebuilt memory"}])
-
-    assert "odysseus_memories" not in fake.collections
-    assert "odysseus_memories_custom" not in fake.collections
-    assert fake.collections["odysseus_memories_fastembed"].count() == 1
-    assert fake.collections["odysseus_memories_fastembed"].get()["ids"] == ["current-memory"]
-
-
-def test_memory_remove_deletes_inactive_lane_collection(monkeypatch):
-    fake = FakeChroma()
-    custom_collection = fake.get_or_create_collection("odysseus_memories_custom", metadata={"embedding_lane": "custom"})
-    fast_collection = fake.get_or_create_collection("odysseus_memories_fastembed", metadata={"embedding_lane": "fastembed"})
-    custom_collection.add(
-        ids=["mem-1"],
-        embeddings=[[0.0] * 768],
-        documents=["custom stale memory"],
-        metadatas=[{"source": "memory"}],
-    )
-    fast_collection.add(
-        ids=["mem-1"],
-        embeddings=[[0.0] * 384],
-        documents=["fast memory"],
-        metadatas=[{"source": "memory"}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    fast_lane = EmbeddingLane(
-        name=LANE_FASTEMBED,
-        client=FakeEmbedder(384, "mini", "local://fastembed"),
-        collection=fast_collection,
-        collection_name="odysseus_memories_fastembed",
-        model="mini",
-        url="local://fastembed",
-        dimension=384,
-        fingerprint="fast",
-    )
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore.__new__(MemoryVectorStore)
-    store._lanes = [fast_lane]
-    store._healthy = True
-
-    store.remove("mem-1")
-
-    assert custom_collection.count() == 0
-    assert fast_collection.count() == 0
-
-
-def test_memory_rebuild_continues_when_custom_lane_fails(monkeypatch):
-    fake = FakeChroma()
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FailingEmbedder(768, "nomic", "http://embeddings/v1"))
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.memory_vector import MemoryVectorStore
-
-    store = MemoryVectorStore("data")
-    store.rebuild([{"id": "current-memory", "text": "current rebuilt memory"}])
-
-    assert fake.collections["odysseus_memories_custom"].count() == 0
-    assert fake.collections["odysseus_memories_fastembed"].count() == 1
-    assert fake.collections["odysseus_memories_fastembed"].get()["ids"] == ["current-memory"]
-
-
-def test_rag_rebuild_does_not_reimport_legacy_collection(monkeypatch, tmp_path):
-    fake = FakeChroma()
-    legacy = fake.get_or_create_collection("odysseus_rag", metadata={"hnsw:space": "cosine"})
-    legacy.add(
-        ids=["stale-doc"],
-        embeddings=[[0.0] * 384],
-        documents=["stale legacy document"],
-        metadatas=[{"source": "/tmp/stale.md"}],
-    )
-    inactive_custom = fake.get_or_create_collection("odysseus_rag_custom", metadata={"embedding_lane": "custom"})
-    inactive_custom.add(
-        ids=["stale-custom-doc"],
-        embeddings=[[0.0] * 768],
-        documents=["stale inactive custom document"],
-        metadatas=[{"source": "/tmp/stale.md"}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    import src.embedding_lanes as lanes
-
-    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
-    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
-
-    from src.rag_vector import VectorRAG
-
-    rag = VectorRAG(persist_directory=str(tmp_path))
-    assert fake.collections["odysseus_rag_fastembed"].count() == 1
-
-    assert rag.rebuild_index()
-
-    assert "odysseus_rag" not in fake.collections
-    assert "odysseus_rag_custom" not in fake.collections
-    assert fake.collections["odysseus_rag_fastembed"].count() == 0
-    assert rag.search("stale legacy", k=3) == []
-
-
-def test_rag_remove_directory_deletes_inactive_lane_collection(monkeypatch, tmp_path):
-    fake = FakeChroma()
-    legacy_collection = fake.get_or_create_collection("odysseus_rag", metadata={"hnsw:space": "cosine"})
-    custom_collection = fake.get_or_create_collection("odysseus_rag_custom", metadata={"embedding_lane": "custom"})
-    fast_collection = fake.get_or_create_collection("odysseus_rag_fastembed", metadata={"embedding_lane": "fastembed"})
-    source = str(tmp_path / "docs" / "note.md")
-    directory = str(tmp_path / "docs")
-    legacy_collection.add(
-        ids=["legacy-doc"],
-        embeddings=[[0.0] * 384],
-        documents=["legacy stale doc"],
-        metadatas=[{"source": source}],
-    )
-    custom_collection.add(
-        ids=["custom-doc"],
-        embeddings=[[0.0] * 768],
-        documents=["custom stale doc"],
-        metadatas=[{"source": source}],
-    )
-    fast_collection.add(
-        ids=["fast-doc"],
-        embeddings=[[0.0] * 384],
-        documents=["fast current doc"],
-        metadatas=[{"source": source}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    fast_lane = EmbeddingLane(
-        name=LANE_FASTEMBED,
-        client=FakeEmbedder(384, "mini", "local://fastembed"),
-        collection=fast_collection,
-        collection_name="odysseus_rag_fastembed",
-        model="mini",
-        url="local://fastembed",
-        dimension=384,
-        fingerprint="fast",
-    )
-
-    from src.rag_vector import VectorRAG
-
-    rag = VectorRAG.__new__(VectorRAG)
-    rag._lanes = [fast_lane]
-    rag._collection = fast_collection
-    rag._healthy = True
-
-    result = rag.remove_directory(directory)
-
-    assert result["success"]
-    assert result["removed_count"] == 3
-    assert legacy_collection.count() == 0
-    assert custom_collection.count() == 0
-    assert fast_collection.count() == 0
-
-
-def test_rag_delete_by_source_deletes_inactive_lane_collection(monkeypatch, tmp_path):
-    fake = FakeChroma()
-    legacy_collection = fake.get_or_create_collection("odysseus_rag", metadata={"hnsw:space": "cosine"})
-    custom_collection = fake.get_or_create_collection("odysseus_rag_custom", metadata={"embedding_lane": "custom"})
-    fast_collection = fake.get_or_create_collection("odysseus_rag_fastembed", metadata={"embedding_lane": "fastembed"})
-    source = str(tmp_path / "docs" / "note.md")
-    legacy_collection.add(
-        ids=["legacy-doc"],
-        embeddings=[[0.0] * 384],
-        documents=["legacy stale doc"],
-        metadatas=[{"source": source}],
-    )
-    custom_collection.add(
-        ids=["shared-doc"],
-        embeddings=[[0.0] * 768],
-        documents=["custom stale doc"],
-        metadatas=[{"source": source}],
-    )
-    fast_collection.add(
-        ids=["shared-doc"],
-        embeddings=[[0.0] * 384],
-        documents=["fast current doc"],
-        metadatas=[{"source": source}],
-    )
-    _patch_chroma(monkeypatch, fake)
-
-    fast_lane = EmbeddingLane(
-        name=LANE_FASTEMBED,
-        client=FakeEmbedder(384, "mini", "local://fastembed"),
-        collection=fast_collection,
-        collection_name="odysseus_rag_fastembed",
-        model="mini",
-        url="local://fastembed",
-        dimension=384,
-        fingerprint="fast",
-    )
-
-    from src.rag_vector import VectorRAG
-
-    rag = VectorRAG.__new__(VectorRAG)
-    rag._lanes = [fast_lane]
-    rag._collection = fast_collection
-    rag._healthy = True
-
-    assert rag.delete_by_source(source) == 2
-    assert legacy_collection.count() == 0
-    assert custom_collection.count() == 0
-    assert fast_collection.count() == 0
-
-
-def test_vector_rag_uses_keyword_fallback_when_all_lanes_query_fail():
-    collection = FakeCollection("odysseus_rag_fastembed", metadata={"embedding_lane": "fastembed"})
-    collection.add(
-        ids=["doc-1"],
-        embeddings=[[0.0] * 384],
-        documents=["fallback keyword document"],
-        metadatas=[{"source": "/tmp/doc.md"}],
-    )
-
-    def fail_query(*_args, **_kwargs):
-        raise RuntimeError("embedding query down")
-
-    collection.query = fail_query
-    lane = EmbeddingLane(
-        name=LANE_FASTEMBED,
-        client=FakeEmbedder(384, "mini", "local://fastembed"),
-        collection=collection,
-        collection_name="odysseus_rag_fastembed",
-        model="mini",
-        url="local://fastembed",
-        dimension=384,
-        fingerprint="fp",
-    )
-
-    from src.rag_vector import VectorRAG
-
-    rag = VectorRAG.__new__(VectorRAG)
-    rag._lanes = [lane]
-    rag._collection = collection
-    rag._healthy = True
-
-    results = rag.search("fallback keyword", k=3)
-
-    assert results[0]["id"] == "doc-1"
-    assert results[0]["search_type"] == "keyword_fallback"

tests/test_embedding_lanes_legacy.py

@@ -0,0 +1,117 @@
+from tests.helpers.embedding_lanes import (
+    FakeChroma,
+    FakeEmbedder,
+    FailingEmbedder,
+    patch_chroma,
+)
+
+
+def test_legacy_collection_backfills_fastembed_lane(monkeypatch):
+    fake = FakeChroma()
+    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
+    legacy.add(
+        ids=["legacy-memory"],
+        embeddings=[[0.0] * 384],
+        documents=["legacy memory row"],
+        metadatas=[{"source": "memory"}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore("data")
+
+    assert store.count() == 1
+    assert fake.collections["odysseus_memories"].count() == 1
+    assert fake.collections["odysseus_memories_fastembed"].count() == 1
+
+
+def test_legacy_collection_backfills_custom_only_lane(monkeypatch):
+    fake = FakeChroma()
+    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
+    legacy.add(
+        ids=["legacy-memory"],
+        embeddings=[[0.0] * 384],
+        documents=["legacy memory row"],
+        metadatas=[{"source": "memory"}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FakeEmbedder(768, "nomic", "http://embeddings/v1"))
+
+    def fail_fastembed():
+        raise RuntimeError("fastembed missing")
+
+    monkeypatch.setattr(lanes, "_build_fastembed_client", fail_fastembed)
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore("data")
+
+    assert store.count() == 1
+    assert "odysseus_memories_fastembed" not in fake.collections
+    assert fake.collections["odysseus_memories_custom"].count() == 1
+    assert len(fake.collections["odysseus_memories_custom"].rows["legacy-memory"]["embedding"]) == 768
+
+
+def test_legacy_migration_continues_when_custom_backfill_fails(monkeypatch):
+    fake = FakeChroma()
+    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
+    legacy.add(
+        ids=["legacy-memory"],
+        embeddings=[[0.0] * 384],
+        documents=["legacy memory row"],
+        metadatas=[{"source": "memory"}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FailingEmbedder(768, "nomic", "http://embeddings/v1"))
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore("data")
+
+    assert store.healthy
+    assert fake.collections["odysseus_memories_custom"].count() == 0
+    assert fake.collections["odysseus_memories_fastembed"].count() == 1
+
+
+def test_legacy_migration_resumes_partial_lane_backfill(monkeypatch):
+    fake = FakeChroma()
+    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
+    legacy.add(
+        ids=["legacy-1", "legacy-2"],
+        embeddings=[[0.0] * 384, [0.0] * 384],
+        documents=["legacy memory one", "legacy memory two"],
+        metadatas=[{"source": "memory"}, {"source": "memory"}],
+    )
+    partial = fake.get_or_create_collection("odysseus_memories_fastembed", metadata={"embedding_lane": "fastembed"})
+    partial.add(
+        ids=["legacy-1"],
+        embeddings=[[0.0] * 384],
+        documents=["legacy memory one"],
+        metadatas=[{"source": "memory"}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore("data")
+
+    assert store.count() == 2
+    assert set(fake.collections["odysseus_memories_fastembed"].get()["ids"]) == {"legacy-1", "legacy-2"}

tests/test_embedding_lanes_memory.py

@@ -0,0 +1,187 @@
+from src.embedding_lanes import (
+    EmbeddingLane,
+    LANE_CUSTOM,
+    LANE_FASTEMBED,
+)
+from tests.helpers.embedding_lanes import (
+    FakeChroma,
+    FakeCollection,
+    FakeEmbedder,
+    FailingEmbedder,
+    patch_chroma,
+)
+
+
+def test_memory_vector_store_writes_both_lanes_and_prefers_custom(monkeypatch):
+    fake = FakeChroma()
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FakeEmbedder(768, "nomic", "http://embeddings/v1"))
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore("data")
+    store.add("mem-1", "Nicholai likes direct memory systems")
+
+    assert fake.collections["odysseus_memories_custom"].count() == 1
+    assert fake.collections["odysseus_memories_fastembed"].count() == 1
+
+    results = store.search("direct memory", k=5)
+    assert results[0]["memory_id"] == "mem-1"
+    assert results[0]["embedding_lane"] == LANE_CUSTOM
+
+
+def test_memory_search_merges_fallback_only_results_before_limit():
+    custom_collection = FakeCollection("odysseus_memories_custom", metadata={"embedding_lane": "custom"})
+    fast_collection = FakeCollection("odysseus_memories_fastembed", metadata={"embedding_lane": "fastembed"})
+    custom_collection.add(
+        ids=["old-1", "old-2"],
+        embeddings=[[0.0] * 768, [0.0] * 768],
+        documents=["older custom memory", "another custom memory"],
+        metadatas=[{"source": "memory"}, {"source": "memory"}],
+    )
+    fast_collection.add(
+        ids=["fallback-only"],
+        embeddings=[[0.0] * 384],
+        documents=["fallback only relevant memory"],
+        metadatas=[{"source": "memory"}],
+    )
+
+    custom_collection.query = lambda **_kwargs: {
+        "ids": [["old-1", "old-2"]],
+        "distances": [[0.20, 0.21]],
+    }
+    fast_collection.query = lambda **_kwargs: {
+        "ids": [["fallback-only"]],
+        "distances": [[0.05]],
+    }
+
+    custom_lane = EmbeddingLane(
+        name=LANE_CUSTOM,
+        client=FakeEmbedder(768, "nomic", "http://embeddings/v1"),
+        collection=custom_collection,
+        collection_name="odysseus_memories_custom",
+        model="nomic",
+        url="http://embeddings/v1",
+        dimension=768,
+        fingerprint="custom",
+    )
+    fast_lane = EmbeddingLane(
+        name=LANE_FASTEMBED,
+        client=FakeEmbedder(384, "mini", "local://fastembed"),
+        collection=fast_collection,
+        collection_name="odysseus_memories_fastembed",
+        model="mini",
+        url="local://fastembed",
+        dimension=384,
+        fingerprint="fast",
+    )
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore.__new__(MemoryVectorStore)
+    store._lanes = [custom_lane, fast_lane]
+    store._healthy = True
+
+    results = store.search("fallback relevant", k=2)
+
+    assert [row["memory_id"] for row in results] == ["fallback-only", "old-1"]
+
+
+def test_memory_rebuild_does_not_reimport_legacy_collection(monkeypatch):
+    fake = FakeChroma()
+    legacy = fake.get_or_create_collection("odysseus_memories", metadata={"hnsw:space": "cosine"})
+    legacy.add(
+        ids=["stale-memory"],
+        embeddings=[[0.0] * 384],
+        documents=["stale legacy memory"],
+        metadatas=[{"source": "memory"}],
+    )
+    inactive_custom = fake.get_or_create_collection("odysseus_memories_custom", metadata={"embedding_lane": "custom"})
+    inactive_custom.add(
+        ids=["stale-custom"],
+        embeddings=[[0.0] * 768],
+        documents=["stale inactive custom memory"],
+        metadatas=[{"source": "memory"}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore("data")
+    assert fake.collections["odysseus_memories_fastembed"].count() == 1
+
+    store.rebuild([{"id": "current-memory", "text": "current rebuilt memory"}])
+
+    assert "odysseus_memories" not in fake.collections
+    assert "odysseus_memories_custom" not in fake.collections
+    assert fake.collections["odysseus_memories_fastembed"].count() == 1
+    assert fake.collections["odysseus_memories_fastembed"].get()["ids"] == ["current-memory"]
+
+
+def test_memory_remove_deletes_inactive_lane_collection(monkeypatch):
+    fake = FakeChroma()
+    custom_collection = fake.get_or_create_collection("odysseus_memories_custom", metadata={"embedding_lane": "custom"})
+    fast_collection = fake.get_or_create_collection("odysseus_memories_fastembed", metadata={"embedding_lane": "fastembed"})
+    custom_collection.add(
+        ids=["mem-1"],
+        embeddings=[[0.0] * 768],
+        documents=["custom stale memory"],
+        metadatas=[{"source": "memory"}],
+    )
+    fast_collection.add(
+        ids=["mem-1"],
+        embeddings=[[0.0] * 384],
+        documents=["fast memory"],
+        metadatas=[{"source": "memory"}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    fast_lane = EmbeddingLane(
+        name=LANE_FASTEMBED,
+        client=FakeEmbedder(384, "mini", "local://fastembed"),
+        collection=fast_collection,
+        collection_name="odysseus_memories_fastembed",
+        model="mini",
+        url="local://fastembed",
+        dimension=384,
+        fingerprint="fast",
+    )
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore.__new__(MemoryVectorStore)
+    store._lanes = [fast_lane]
+    store._healthy = True
+
+    store.remove("mem-1")
+
+    assert custom_collection.count() == 0
+    assert fast_collection.count() == 0
+
+
+def test_memory_rebuild_continues_when_custom_lane_fails(monkeypatch):
+    fake = FakeChroma()
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FailingEmbedder(768, "nomic", "http://embeddings/v1"))
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.memory_vector import MemoryVectorStore
+
+    store = MemoryVectorStore("data")
+    store.rebuild([{"id": "current-memory", "text": "current rebuilt memory"}])
+
+    assert fake.collections["odysseus_memories_custom"].count() == 0
+    assert fake.collections["odysseus_memories_fastembed"].count() == 1
+    assert fake.collections["odysseus_memories_fastembed"].get()["ids"] == ["current-memory"]

tests/test_embedding_lanes_rag.py

@@ -0,0 +1,252 @@
+from src.embedding_lanes import (
+    EmbeddingLane,
+    LANE_FASTEMBED,
+)
+from tests.helpers.embedding_lanes import (
+    FakeChroma,
+    FakeCollection,
+    FakeEmbedder,
+    FailingEmbedder,
+    patch_chroma,
+)
+
+
+def test_vector_rag_writes_both_lanes_and_falls_back_to_fastembed(monkeypatch):
+    fake = FakeChroma()
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.rag_vector import VectorRAG
+
+    rag = VectorRAG()
+    assert rag.add_document("session search belongs in tools", {"source": "/tmp/a.md", "owner": "alice"})
+    assert "odysseus_rag_custom" not in fake.collections
+    assert fake.collections["odysseus_rag_fastembed"].count() == 1
+
+    results = rag.search("session search", k=3, owner="alice")
+    assert results[0]["document"] == "session search belongs in tools"
+    assert results[0]["embedding_lane"] == LANE_FASTEMBED
+
+
+def test_vector_rag_batch_index_continues_when_custom_lane_fails(monkeypatch, tmp_path):
+    fake = FakeChroma()
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FailingEmbedder(768, "nomic", "http://embeddings/v1"))
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.rag_vector import VectorRAG
+
+    rag = VectorRAG(persist_directory=str(tmp_path))
+    result = rag.add_documents_batch([
+        ("batch fallback document", {"source": "/tmp/a.md", "owner": "alice"}),
+    ])
+
+    assert result["success"]
+    assert result["added_count"] == 1
+    assert fake.collections["odysseus_rag_custom"].count() == 0
+    assert fake.collections["odysseus_rag_fastembed"].count() == 1
+
+
+def test_vector_rag_batch_index_reports_failure_when_all_lanes_fail(monkeypatch, tmp_path):
+    fake = FakeChroma()
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FailingEmbedder(768, "nomic", "http://embeddings/v1"))
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FailingEmbedder(384, "mini", "local://fastembed"))
+
+    from src.rag_vector import VectorRAG
+
+    rag = VectorRAG(persist_directory=str(tmp_path))
+    result = rag.add_documents_batch([
+        ("batch outage document", {"source": "/tmp/a.md", "owner": "alice"}),
+    ])
+
+    assert not result["success"]
+    assert fake.collections["odysseus_rag_custom"].count() == 0
+    assert fake.collections["odysseus_rag_fastembed"].count() == 0
+
+
+def test_rag_rebuild_does_not_reimport_legacy_collection(monkeypatch, tmp_path):
+    fake = FakeChroma()
+    legacy = fake.get_or_create_collection("odysseus_rag", metadata={"hnsw:space": "cosine"})
+    legacy.add(
+        ids=["stale-doc"],
+        embeddings=[[0.0] * 384],
+        documents=["stale legacy document"],
+        metadatas=[{"source": "/tmp/stale.md"}],
+    )
+    inactive_custom = fake.get_or_create_collection("odysseus_rag_custom", metadata={"embedding_lane": "custom"})
+    inactive_custom.add(
+        ids=["stale-custom-doc"],
+        embeddings=[[0.0] * 768],
+        documents=["stale inactive custom document"],
+        metadatas=[{"source": "/tmp/stale.md"}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: None)
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.rag_vector import VectorRAG
+
+    rag = VectorRAG(persist_directory=str(tmp_path))
+    assert fake.collections["odysseus_rag_fastembed"].count() == 1
+
+    assert rag.rebuild_index()
+
+    assert "odysseus_rag" not in fake.collections
+    assert "odysseus_rag_custom" not in fake.collections
+    assert fake.collections["odysseus_rag_fastembed"].count() == 0
+    assert rag.search("stale legacy", k=3) == []
+
+
+def test_rag_remove_directory_deletes_inactive_lane_collection(monkeypatch, tmp_path):
+    fake = FakeChroma()
+    legacy_collection = fake.get_or_create_collection("odysseus_rag", metadata={"hnsw:space": "cosine"})
+    custom_collection = fake.get_or_create_collection("odysseus_rag_custom", metadata={"embedding_lane": "custom"})
+    fast_collection = fake.get_or_create_collection("odysseus_rag_fastembed", metadata={"embedding_lane": "fastembed"})
+    source = str(tmp_path / "docs" / "note.md")
+    directory = str(tmp_path / "docs")
+    legacy_collection.add(
+        ids=["legacy-doc"],
+        embeddings=[[0.0] * 384],
+        documents=["legacy stale doc"],
+        metadatas=[{"source": source}],
+    )
+    custom_collection.add(
+        ids=["custom-doc"],
+        embeddings=[[0.0] * 768],
+        documents=["custom stale doc"],
+        metadatas=[{"source": source}],
+    )
+    fast_collection.add(
+        ids=["fast-doc"],
+        embeddings=[[0.0] * 384],
+        documents=["fast current doc"],
+        metadatas=[{"source": source}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    fast_lane = EmbeddingLane(
+        name=LANE_FASTEMBED,
+        client=FakeEmbedder(384, "mini", "local://fastembed"),
+        collection=fast_collection,
+        collection_name="odysseus_rag_fastembed",
+        model="mini",
+        url="local://fastembed",
+        dimension=384,
+        fingerprint="fast",
+    )
+
+    from src.rag_vector import VectorRAG
+
+    rag = VectorRAG.__new__(VectorRAG)
+    rag._lanes = [fast_lane]
+    rag._collection = fast_collection
+    rag._healthy = True
+
+    result = rag.remove_directory(directory)
+
+    assert result["success"]
+    assert result["removed_count"] == 3
+    assert legacy_collection.count() == 0
+    assert custom_collection.count() == 0
+    assert fast_collection.count() == 0
+
+
+def test_rag_delete_by_source_deletes_inactive_lane_collection(monkeypatch, tmp_path):
+    fake = FakeChroma()
+    legacy_collection = fake.get_or_create_collection("odysseus_rag", metadata={"hnsw:space": "cosine"})
+    custom_collection = fake.get_or_create_collection("odysseus_rag_custom", metadata={"embedding_lane": "custom"})
+    fast_collection = fake.get_or_create_collection("odysseus_rag_fastembed", metadata={"embedding_lane": "fastembed"})
+    source = str(tmp_path / "docs" / "note.md")
+    legacy_collection.add(
+        ids=["legacy-doc"],
+        embeddings=[[0.0] * 384],
+        documents=["legacy stale doc"],
+        metadatas=[{"source": source}],
+    )
+    custom_collection.add(
+        ids=["shared-doc"],
+        embeddings=[[0.0] * 768],
+        documents=["custom stale doc"],
+        metadatas=[{"source": source}],
+    )
+    fast_collection.add(
+        ids=["shared-doc"],
+        embeddings=[[0.0] * 384],
+        documents=["fast current doc"],
+        metadatas=[{"source": source}],
+    )
+    patch_chroma(monkeypatch, fake)
+
+    fast_lane = EmbeddingLane(
+        name=LANE_FASTEMBED,
+        client=FakeEmbedder(384, "mini", "local://fastembed"),
+        collection=fast_collection,
+        collection_name="odysseus_rag_fastembed",
+        model="mini",
+        url="local://fastembed",
+        dimension=384,
+        fingerprint="fast",
+    )
+
+    from src.rag_vector import VectorRAG
+
+    rag = VectorRAG.__new__(VectorRAG)
+    rag._lanes = [fast_lane]
+    rag._collection = fast_collection
+    rag._healthy = True
+
+    assert rag.delete_by_source(source) == 2
+    assert legacy_collection.count() == 0
+    assert custom_collection.count() == 0
+    assert fast_collection.count() == 0
+
+
+def test_vector_rag_uses_keyword_fallback_when_all_lanes_query_fail():
+    collection = FakeCollection("odysseus_rag_fastembed", metadata={"embedding_lane": "fastembed"})
+    collection.add(
+        ids=["doc-1"],
+        embeddings=[[0.0] * 384],
+        documents=["fallback keyword document"],
+        metadatas=[{"source": "/tmp/doc.md"}],
+    )
+
+    def fail_query(*_args, **_kwargs):
+        raise RuntimeError("embedding query down")
+
+    collection.query = fail_query
+    lane = EmbeddingLane(
+        name=LANE_FASTEMBED,
+        client=FakeEmbedder(384, "mini", "local://fastembed"),
+        collection=collection,
+        collection_name="odysseus_rag_fastembed",
+        model="mini",
+        url="local://fastembed",
+        dimension=384,
+        fingerprint="fp",
+    )
+
+    from src.rag_vector import VectorRAG
+
+    rag = VectorRAG.__new__(VectorRAG)
+    rag._lanes = [lane]
+    rag._collection = collection
+    rag._healthy = True
+
+    results = rag.search("fallback keyword", k=3)
+
+    assert results[0]["id"] == "doc-1"
+    assert results[0]["search_type"] == "keyword_fallback"

tests/test_embedding_lanes_tool_index.py

@@ -0,0 +1,178 @@
+import pytest
+
+from src.embedding_lanes import (
+    EmbeddingLane,
+    LANE_CUSTOM,
+    LANE_FASTEMBED,
+)
+from tests.helpers.embedding_lanes import (
+    FakeChroma,
+    FakeCollection,
+    FakeEmbedder,
+    FailingEmbedder,
+    patch_chroma,
+)
+
+
+def test_tool_index_indexes_and_retrieves_from_available_lanes(monkeypatch):
+    fake = FakeChroma()
+    patch_chroma(monkeypatch, fake)
+
+    import src.embedding_lanes as lanes
+
+    monkeypatch.setattr(lanes, "_build_custom_client", lambda: FakeEmbedder(768, "nomic", "http://embeddings/v1"))
+    monkeypatch.setattr(lanes, "_build_fastembed_client", lambda: FakeEmbedder(384, "mini", "local://fastembed"))
+
+    from src.tool_index import ToolIndex
+
+    index = ToolIndex()
+    index.index_builtin_tools()
+
+    assert fake.collections["odysseus_tool_index_custom"].count() > 0
+    assert fake.collections["odysseus_tool_index_fastembed"].count() > 0
+    assert "bash" in index.retrieve("run a shell command", k=10)
+
+
+def test_tool_index_builtin_indexing_fails_when_all_lanes_fail():
+    custom_lane = EmbeddingLane(
+        name=LANE_CUSTOM,
+        client=FailingEmbedder(768, "nomic", "http://embeddings/v1"),
+        collection=FakeCollection("odysseus_tool_index_custom", metadata={"embedding_lane": "custom"}),
+        collection_name="odysseus_tool_index_custom",
+        model="nomic",
+        url="http://embeddings/v1",
+        dimension=768,
+        fingerprint="custom",
+    )
+    fast_lane = EmbeddingLane(
+        name=LANE_FASTEMBED,
+        client=FailingEmbedder(384, "mini", "local://fastembed"),
+        collection=FakeCollection("odysseus_tool_index_fastembed", metadata={"embedding_lane": "fastembed"}),
+        collection_name="odysseus_tool_index_fastembed",
+        model="mini",
+        url="local://fastembed",
+        dimension=384,
+        fingerprint="fast",
+    )
+
+    from src.tool_index import ToolIndex
+
+    index = ToolIndex.__new__(ToolIndex)
+    index._lanes = [custom_lane, fast_lane]
+    index._healthy = True
+
+    with pytest.raises(RuntimeError, match="all embedding lanes"):
+        index.index_builtin_tools()
+    assert not index.healthy
+
+
+def test_tool_index_retrieval_continues_when_custom_lane_query_fails():
+    custom_collection = FakeCollection("odysseus_tool_index_custom", metadata={"embedding_lane": "custom"})
+    fast_collection = FakeCollection("odysseus_tool_index_fastembed", metadata={"embedding_lane": "fastembed"})
+    fast_collection.add(
+        ids=["builtin_bash"],
+        embeddings=[[0.0] * 384],
+        documents=["Tool: bash\nRun shell commands"],
+        metadatas=[{"tool_name": "bash", "tool_type": "builtin"}],
+    )
+
+    def fail_query(*_args, **_kwargs):
+        raise RuntimeError("custom endpoint down")
+
+    custom_collection.add(
+        ids=["builtin_python"],
+        embeddings=[[0.0] * 768],
+        documents=["Tool: python\nRun Python"],
+        metadatas=[{"tool_name": "python", "tool_type": "builtin"}],
+    )
+    custom_collection.query = fail_query
+
+    custom_lane = EmbeddingLane(
+        name=LANE_CUSTOM,
+        client=FakeEmbedder(768, "nomic", "http://embeddings/v1"),
+        collection=custom_collection,
+        collection_name="odysseus_tool_index_custom",
+        model="nomic",
+        url="http://embeddings/v1",
+        dimension=768,
+        fingerprint="custom",
+    )
+    fast_lane = EmbeddingLane(
+        name=LANE_FASTEMBED,
+        client=FakeEmbedder(384, "mini", "local://fastembed"),
+        collection=fast_collection,
+        collection_name="odysseus_tool_index_fastembed",
+        model="mini",
+        url="local://fastembed",
+        dimension=384,
+        fingerprint="fast",
+    )
+
+    from src.tool_index import ToolIndex
+
+    index = ToolIndex.__new__(ToolIndex)
+    index._lanes = [custom_lane, fast_lane]
+
+    assert index.retrieve("run shell", k=5) == ["bash"]
+
+
+def test_tool_index_merges_fallback_tool_results_before_limit():
+    custom_collection = FakeCollection("odysseus_tool_index_custom", metadata={"embedding_lane": "custom"})
+    fast_collection = FakeCollection("odysseus_tool_index_fastembed", metadata={"embedding_lane": "fastembed"})
+    custom_collection.add(
+        ids=["builtin_one", "builtin_two"],
+        embeddings=[[0.0] * 768, [0.0] * 768],
+        documents=["Tool: one", "Tool: two"],
+        metadatas=[
+            {"tool_name": "one", "tool_type": "builtin"},
+            {"tool_name": "two", "tool_type": "builtin"},
+        ],
+    )
+    fast_collection.add(
+        ids=["mcp_current"],
+        embeddings=[[0.0] * 384],
+        documents=["Tool: current MCP"],
+        metadatas=[{"tool_name": "current_mcp", "tool_type": "mcp"}],
+    )
+
+    custom_collection.query = lambda **_kwargs: {
+        "ids": [["builtin_one", "builtin_two"]],
+        "metadatas": [[
+            {"tool_name": "one", "tool_type": "builtin"},
+            {"tool_name": "two", "tool_type": "builtin"},
+        ]],
+        "distances": [[0.20, 0.21]],
+    }
+    fast_collection.query = lambda **_kwargs: {
+        "ids": [["mcp_current"]],
+        "metadatas": [[{"tool_name": "current_mcp", "tool_type": "mcp"}]],
+        "distances": [[0.05]],
+    }
+
+    custom_lane = EmbeddingLane(
+        name=LANE_CUSTOM,
+        client=FakeEmbedder(768, "nomic", "http://embeddings/v1"),
+        collection=custom_collection,
+        collection_name="odysseus_tool_index_custom",
+        model="nomic",
+        url="http://embeddings/v1",
+        dimension=768,
+        fingerprint="custom",
+    )
+    fast_lane = EmbeddingLane(
+        name=LANE_FASTEMBED,
+        client=FakeEmbedder(384, "mini", "local://fastembed"),
+        collection=fast_collection,
+        collection_name="odysseus_tool_index_fastembed",
+        model="mini",
+        url="local://fastembed",
+        dimension=384,
+        fingerprint="fast",
+    )
+
+    from src.tool_index import ToolIndex
+
+    index = ToolIndex.__new__(ToolIndex)
+    index._lanes = [custom_lane, fast_lane]
+
+    assert index.retrieve("current mcp", k=2) == ["current_mcp", "one"]

tests/test_run_focus.py

@@ -41,10 +41,24 @@ def test_sub_area_only_marker_expression():
     assert build_marker_expression(None, "cookbook") == "sub_cookbook"
 
 
+def test_embedding_sub_area_marker_expression_includes_memory_split():
+    assert (
+        build_marker_expression(None, "embedding")
+        == "(sub_embedding or sub_embedding_memory)"
+    )
+
+
 def test_area_and_sub_area_marker_expression():
     assert build_marker_expression("services", "cookbook") == "area_services and sub_cookbook"
 
 
+def test_area_and_embedding_sub_area_marker_expression_includes_memory_split():
+    assert (
+        build_marker_expression("services", "embedding")
+        == "area_services and (sub_embedding or sub_embedding_memory)"
+    )
+
+
 def test_no_selection_marker_expression_is_none():
     assert build_marker_expression(None, None) is None
 
@@ -75,6 +89,12 @@ def test_sub_area_only_command():
     assert _cmd(sub_area="cookbook") == [PY, "-m", "pytest", "-m", "sub_cookbook"]
 
 
+def test_embedding_sub_area_command_includes_memory_split():
+    assert _cmd(sub_area="embedding") == [
+        PY, "-m", "pytest", "-m", "(sub_embedding or sub_embedding_memory)",
+    ]
+
+
 def test_area_and_sub_area_command():
     assert _cmd(area="services", sub_area="cookbook") == [
         PY, "-m", "pytest", "-m", "area_services and sub_cookbook",
@@ -130,6 +150,13 @@ def test_fast_with_area_and_sub_area_command():
     ]
 
 
+def test_fast_with_embedding_sub_area_command_includes_memory_split():
+    assert _cmd(sub_area="embedding", fast=True) == [
+        PY, "-m", "pytest", "-m",
+        "(sub_embedding or sub_embedding_memory) and not slow",
+    ]
+
+
 def test_durations_appends_flag():
     assert _cmd(fast=True, durations=25) == [
         PY, "-m", "pytest", "-m", "not slow", "--durations=25",
@@ -252,6 +279,30 @@ def test_run_accepts_both_sub_area_forms(value):
     ]]
 
 
+def test_run_keeps_embedding_memory_selector_specific():
+    executor = _FakeExecutor()
+    run(["--sub-area", "embedding_memory"], executor=executor)
+    assert executor.calls == [[
+        sys.executable,
+        "-m",
+        "pytest",
+        "-m",
+        "sub_embedding_memory",
+    ]]
+
+
+def test_run_expands_embedding_selector_to_memory_split():
+    executor = _FakeExecutor()
+    run(["--sub-area", "embedding"], executor=executor)
+    assert executor.calls == [[
+        sys.executable,
+        "-m",
+        "pytest",
+        "-m",
+        "(sub_embedding or sub_embedding_memory)",
+    ]]
+
+
 def test_invalid_area_exits_with_error():
     with pytest.raises(SystemExit) as excinfo:
         run(["--area", "bogus"], executor=_FakeExecutor())

tests/test_taxonomy.py

@@ -50,6 +50,12 @@ def test_classify_examples(filename, expected_area, expected_sub):
     assert result.sub_area == expected_sub
 
 
+def test_embedding_lanes_memory_file_keeps_specific_sub_area():
+    result = classify_test_path("tests/test_embedding_lanes_memory.py")
+    assert result.area == "services"
+    assert result.sub_area == "embedding_memory"
+
+
 # --- classify_test_path: fallback --------------------------------------------
 
 def test_unknown_filename_is_uncategorized():