fix: raise an error when no vector DB IDs are provided to the RAG tool (#1911)

# What does this PR do?
This PR fixes the behavior of the `/tool-runtime/rag-tool/query`
endpoint when invoked with an empty `vector_db_ids` parameter.
As of now, it simply returns an empty result, which leads to a
misleading error message from the server and makes it difficult and
time-consuming to detect the problem with the input parameter.
The proposed fix is to return an indicative error message in this case.


## Test Plan
Running the following script:
```
agent = Agent(
    client,
    model=MODEL_ID,
    instructions=SYSTEM_PROMPT,
    tools=[
        dict(
            name="builtin::rag/knowledge_search",
            args={
                "vector_db_ids": [],
            },
        )
    ],
)

response = agent.create_turn(
    messages=[
        {
            "role": "user",
            "content": "How to install OpenShift?",
        }
    ],
    session_id=agent.create_session(f"rag-session")
)
```
results in the following error message in the non-patched version:
```
{"type": "function", "name": "knowledge_search", "parameters": {"query": "installing OpenShift"}}400: Invalid value: Tool call result (id: 494b8020-90bb-449b-aa76-10960d6b2cc2, name: knowledge_search) does not have any content
```
and in the following one in the patched version:
```
{"type": "function", "name": "knowledge_search", "parameters": {"query": "installing OpenShift"}}400: Invalid value: No vector DBs were provided to the RAG tool. Please provide at least one DB.
```

llama_stack/providers/inline/tool_runtime/rag/memory.py

@@ -105,7 +105,9 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
         query_config: RAGQueryConfig | None = None,
     ) -> RAGQueryResult:
         if not vector_db_ids:
-            return RAGQueryResult(content=None)
+            raise ValueError(
+                "No vector DBs were provided to the knowledge search tool. Please provide at least one vector DB ID."
+            )
 
         query_config = query_config or RAGQueryConfig()
         query = await generate_rag_query(

tests/unit/rag/test_rag_query.py

@@ -0,0 +1,19 @@
+# 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 unittest.mock import MagicMock
+
+import pytest
+
+from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRuntimeImpl
+
+
+class TestRagQuery:
+    @pytest.mark.asyncio
+    async def test_query_raises_on_empty_vector_db_ids(self):
+        rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock())
+        with pytest.raises(ValueError):
+            await rag_tool.query(content=MagicMock(), vector_db_ids=[])