feat(index-cache): add recall/remember service

surfsense_backend/app/indexing_pipeline/cache/service.py

@@ -0,0 +1,51 @@
+"""Recall and remember embedding sets, coordinating the index and blob store."""
+
+from __future__ import annotations
+
+import logging
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.indexing_pipeline.cache.persistence import CachedEmbeddingSetRepository
+from app.indexing_pipeline.cache.schemas import EmbeddingKey, EmbeddingSet
+from app.indexing_pipeline.cache.storage import EmbeddingCacheStore
+
+logger = logging.getLogger(__name__)
+
+
+class IndexCacheService:
+    def __init__(self, session: AsyncSession) -> None:
+        self._index = CachedEmbeddingSetRepository(session)
+        self._store = EmbeddingCacheStore()
+
+    async def recall(self, key: EmbeddingKey) -> EmbeddingSet | None:
+        """Return the cached embedding set, or None on a miss."""
+        row = await self._index.get(key)
+        if row is None:
+            return None
+
+        try:
+            embedding_set = await self._store.load(row.storage_key)
+        except Exception:
+            # Index points at a blob that is gone; treat as a miss and re-embed.
+            logger.warning("Cache blob missing: %s", row.storage_key, exc_info=True)
+            return None
+
+        if int(embedding_set.summary_embedding.shape[0]) != key.embedding_dim:
+            # A model swapped its dimension under a reused name; never serve it.
+            logger.warning("Cached embedding dimension mismatch: %s", row.storage_key)
+            return None
+
+        await self._index.mark_used(row.id)
+        return embedding_set
+
+    async def remember(self, key: EmbeddingKey, embedding_set: EmbeddingSet) -> None:
+        """Store a freshly embedded set for future reuse."""
+        storage_key, size_bytes = await self._store.save(key, embedding_set)
+        await self._index.insert(
+            key=key,
+            storage_backend=self._store.backend_name,
+            storage_key=storage_key,
+            size_bytes=size_bytes,
+            chunk_count=embedding_set.chunk_count,
+        )