[memory refactor][1/n] Rename Memory -> VectorIO, MemoryBanks -> VectorDBs (#828)

See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.

This is the first part:

- delete other kinds of memory banks (keyvalue, keyword, graph) for now;
we will introduce a keyvalue store API as part of this design but not
use it in the RAG tool yet.
- renaming of the APIs

llama_stack/apis/vector_io/vector_io.py

@@ -0,0 +1,57 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+
+from llama_models.schema_utils import json_schema_type, webmethod
+from pydantic import BaseModel, Field
+
+from llama_stack.apis.inference import InterleavedContent
+from llama_stack.apis.vector_dbs import VectorDB
+from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
+
+
+class Chunk(BaseModel):
+    content: InterleavedContent
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+
+@json_schema_type
+class QueryChunksResponse(BaseModel):
+    chunks: List[Chunk]
+    scores: List[float]
+
+
+class VectorDBStore(Protocol):
+    def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]: ...
+
+
+@runtime_checkable
+@trace_protocol
+class VectorIO(Protocol):
+    vector_db_store: VectorDBStore
+
+    # this will just block now until documents are inserted, but it should
+    # probably return a Job instance which can be polled for completion
+    @webmethod(route="/vector-io/insert", method="POST")
+    async def insert_chunks(
+        self,
+        vector_db_id: str,
+        chunks: List[Chunk],
+        ttl_seconds: Optional[int] = None,
+    ) -> None: ...
+
+    @webmethod(route="/vector-io/query", method="POST")
+    async def query_chunks(
+        self,
+        vector_db_id: str,
+        query: InterleavedContent,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> QueryChunksResponse: ...