feat: rebase and implement file API methods

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>

llama_stack/providers/inline/vector_io/qdrant/config.py

@@ -5,20 +5,28 @@
 # the root directory of this source tree.
 
 
-from typing import Any, Literal
+from typing import Any
 
 from pydantic import BaseModel
 
+from llama_stack.providers.utils.kvstore.config import (
+    KVStoreConfig,
+    SqliteKVStoreConfig,
+)
 from llama_stack.schema_utils import json_schema_type
 
 
 @json_schema_type
 class QdrantVectorIOConfig(BaseModel):
     path: str
-    distance_metric: Literal["COSINE", "DOT", "EUCLID", "MANHATTAN"] = "COSINE"
+    kvstore: KVStoreConfig
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
         return {
             "path": "${env.QDRANT_PATH:=~/.llama/" + __distro_dir__ + "}/" + "qdrant.db",
+            "kvstore": SqliteKVStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="qdrant_registry.db",
+            ),
         }

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -192,7 +192,9 @@ class SQLiteVecIndex(EmbeddingIndex):
 
         await asyncio.to_thread(_drop_tables)
 
-    async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray, batch_size: int = 500):
+    async def add_chunks(
+        self, chunks: list[Chunk], embeddings: NDArray, metadata: dict[str, Any] | None = None, batch_size: int = 500
+    ):
         """
         Add new chunks along with their embeddings using batch inserts.
         For each chunk, we insert its JSON into the metadata table and then insert its