up

reme2/component/file_store/base_file_store.py

@@ -16,7 +16,6 @@ class BaseFileStore(BaseComponent):
             store_name: str,
             embedding_model: str = "default",
             keyword_index: str = "default",
-            fts_enabled: bool = True,
             **kwargs,
     ):
         super().__init__(**kwargs)
@@ -25,30 +24,26 @@ class BaseFileStore(BaseComponent):
         self.store_name = store_name or self.name
         self._embedding_model_name = embedding_model
         self._keyword_index_name = keyword_index
-        self.fts_enabled = fts_enabled
 
         self.embedding_model: BaseEmbeddingModel | None = None
         self.keyword_index: BaseKeywordIndex | None = None
-        self.vector_enabled = bool(embedding_model)
         self.store_path = self.working_path / self.component_type.value / store_name
         self.store_path.mkdir(parents=True, exist_ok=True)
-        if not self.vector_enabled and not self.fts_enabled:
-            raise ValueError("At least one of embedding_model or fts_enabled must be set.")
+        if not embedding_model and not keyword_index:
+            raise ValueError("At least one of embedding_model or keyword_index must be set.")
 
         self.file_nodes: dict[str, FileNode] = {}
 
     async def _start(self) -> None:
-        if self.vector_enabled:
+        if self._embedding_model_name:
             self.embedding_model = self.get_component(ComponentEnum.EMBEDDING_MODEL, self._embedding_model_name)
-        if self.fts_enabled:
+        if self._keyword_index_name:
             self.keyword_index = self.get_component(ComponentEnum.KEYWORD_INDEX, self._keyword_index_name)
         await self.load_file_nodes()
 
     async def _close(self) -> None:
-        if self.vector_enabled:
-            self.embedding_model = None
-        if self.fts_enabled:
-            self.keyword_index = None
+        self.embedding_model = None
+        self.keyword_index = None
         await self.dump_file_nodes()
 
     async def load_file_nodes(self):

reme2/component/file_store/local_file_store.py

@@ -79,7 +79,7 @@ class LocalFileStore(BaseFileStore):
         for node, chunks in file:
             old_node = self.file_nodes.pop(node.path, None)
             cached = {}
-            if old_node and self.vector_enabled:
+            if old_node and self.embedding_model:
                 for cid in old_node.chunk_ids:
                     old = self.file_chunks.pop(cid, None)
                     if old and old.embedding:
@@ -88,7 +88,7 @@ class LocalFileStore(BaseFileStore):
             node.chunk_ids = []
             needs_embed = []
             for c in chunks:
-                if self.vector_enabled and not c.embedding:
+                if self.embedding_model and not c.embedding:
                     if c.id in cached:
                         c.embedding = cached[c.id]
                     elif c.text:
@@ -100,7 +100,7 @@ class LocalFileStore(BaseFileStore):
             if needs_embed and self.embedding_model:
                 await self.embedding_model.get_node_embeddings(needs_embed)
 
-            if self.fts_enabled and self.keyword_index:
+            if self.keyword_index:
                 await self.keyword_index.add_docs({c.id: c.text for c in chunks if c.text})
 
     async def delete_by_path(self, path: str | list[str]) -> None:
@@ -114,13 +114,13 @@ class LocalFileStore(BaseFileStore):
                     self.file_chunks.pop(cid, None)
                     deleted_chunk_ids.append(cid)
 
-        if self.fts_enabled and self.keyword_index and deleted_chunk_ids:
+        if self.keyword_index and deleted_chunk_ids:
             await self.keyword_index.delete_docs(deleted_chunk_ids)
 
     async def clear(self) -> None:
         self.file_nodes.clear()
         self.file_chunks.clear()
-        if self.fts_enabled and self.keyword_index:
+        if self.keyword_index:
             await self.keyword_index.clear()
 
     # Search
@@ -148,7 +148,7 @@ class LocalFileStore(BaseFileStore):
         return results[:limit]
 
     async def keyword_search(self, query: str, limit: int, search_filter: dict) -> list[FileChunk]:
-        if not self.fts_enabled or self.keyword_index is None:
+        if not self.keyword_index:
             return []
 
         query = query.strip()

reme2/config/expert.yaml

@@ -364,7 +364,6 @@ components:
       store_name: "reme"
       db_path: "./vault/.reme"
       working_dir: "./vault"
-      fts_enabled: true
 
   file_watcher:
     default:

reme2/config/service.yaml

@@ -257,7 +257,6 @@ components:
       store_name: "reme"
       db_path: "./vault/.reme"
       working_dir: "./vault"
-      fts_enabled: true
 
   file_watcher:
     default:

reme2/memory/retriever.py

@@ -172,7 +172,7 @@ class HybridRetriever(BaseRetriever):
         candidates = min(200, max(1, int(max_results * self.candidate_multiplier)))
         text_weight = 1.0 - self.vector_weight
 
-        if fs.vector_enabled and fs.fts_enabled:
+        if fs.embedding_model and fs.keyword_index:
             v_task = memory_io.search_vector(fs, query, limit=candidates, chunk_filter=chunk_filter)
             k_task = memory_io.search_keyword(fs, query, limit=candidates, chunk_filter=chunk_filter)
             v_results, k_results = await asyncio.gather(v_task, k_task)
@@ -185,9 +185,9 @@ class HybridRetriever(BaseRetriever):
                 results = self._merge_vk(
                     v_results, k_results, self.vector_weight, text_weight,
                 )[:max_results]
-        elif fs.vector_enabled:
+        elif fs.embedding_model:
             results = await memory_io.search_vector(fs, query, limit=max_results, chunk_filter=chunk_filter)
-        elif fs.fts_enabled:
+        elif fs.keyword_index:
             results = await memory_io.search_keyword(fs, query, limit=max_results, chunk_filter=chunk_filter)
         else:
             results = []