feat(index-cache): add embedding blob store sharing the cache backend

surfsense_backend/app/indexing_pipeline/cache/storage/__init__.py

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+"""Blob storage for cached embedding sets."""
+
+from __future__ import annotations
+
+from .embedding_store import EmbeddingCacheStore
+
+__all__ = [
+    "EmbeddingCacheStore",
+]

surfsense_backend/app/indexing_pipeline/cache/storage/embedding_store.py

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+"""Read and write cached embedding blobs through the shared cache backend.
+
+The blob backend is shared with the ETL parse cache (same bucket / root), so
+markdown and its embeddings live side by side; only the object prefix differs.
+"""
+
+from __future__ import annotations
+
+from app.etl_pipeline.cache.storage.backend import resolve_cache_backend
+from app.indexing_pipeline.cache.serialization import deserialize, serialize
+from app.indexing_pipeline.cache.schemas import EmbeddingKey, EmbeddingSet
+from app.indexing_pipeline.cache.storage.object_keys import build_embedding_object_key
+
+_EMBEDDING_CONTENT_TYPE = "application/octet-stream"
+
+
+class EmbeddingCacheStore:
+    def __init__(self) -> None:
+        self._backend = resolve_cache_backend()
+
+    @property
+    def backend_name(self) -> str:
+        return self._backend.backend_name
+
+    async def save(self, key: EmbeddingKey, embedding_set: EmbeddingSet) -> tuple[str, int]:
+        """Persist the embedding set and return its storage key and byte size."""
+        blob = serialize(embedding_set)
+        storage_key = build_embedding_object_key(key)
+        await self._backend.put(
+            storage_key, blob, content_type=_EMBEDDING_CONTENT_TYPE
+        )
+        return storage_key, len(blob)
+
+    async def load(self, storage_key: str) -> EmbeddingSet:
+        chunks = [chunk async for chunk in self._backend.open_stream(storage_key)]
+        return deserialize(b"".join(chunks))
+
+    async def delete(self, storage_key: str) -> None:
+        await self._backend.delete(storage_key)

surfsense_backend/app/indexing_pipeline/cache/storage/object_keys.py

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+"""Object keys for cached embedding sets, namespaced under a dedicated prefix."""
+
+from __future__ import annotations
+
+from app.indexing_pipeline.cache.schemas import EmbeddingKey
+
+CACHE_PREFIX = "index_cache"
+
+
+def build_embedding_object_key(key: EmbeddingKey) -> str:
+    # Content-addressed: identical markdown + recipe always map to the same key.
+    return f"{CACHE_PREFIX}/{key.markdown_sha256}/{key.object_suffix}"