revert: "chore(cleanup)!: remove tool_runtime.rag_tool" (#3877)

Reverts llamastack/llama-stack#3871

This PR broke RAG (even from Responses -- there _is_ a dependency)

tests/unit/rag/test_rag_query.py

@@ -0,0 +1,138 @@
+# 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 AsyncMock, MagicMock
+
+import pytest
+
+from llama_stack.apis.tools.rag_tool import RAGQueryConfig
+from llama_stack.apis.vector_io import (
+    Chunk,
+    ChunkMetadata,
+    QueryChunksResponse,
+)
+from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRuntimeImpl
+
+
+class TestRagQuery:
+    async def test_query_raises_on_empty_vector_store_ids(self):
+        rag_tool = MemoryToolRuntimeImpl(
+            config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
+        )
+        with pytest.raises(ValueError):
+            await rag_tool.query(content=MagicMock(), vector_db_ids=[])
+
+    async def test_query_chunk_metadata_handling(self):
+        rag_tool = MemoryToolRuntimeImpl(
+            config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
+        )
+        content = "test query content"
+        vector_db_ids = ["db1"]
+
+        chunk_metadata = ChunkMetadata(
+            document_id="doc1",
+            chunk_id="chunk1",
+            source="test_source",
+            metadata_token_count=5,
+        )
+        interleaved_content = MagicMock()
+        chunk = Chunk(
+            content=interleaved_content,
+            metadata={
+                "key1": "value1",
+                "token_count": 10,
+                "metadata_token_count": 5,
+                # Note this is inserted into `metadata` during MemoryToolRuntimeImpl().insert()
+                "document_id": "doc1",
+            },
+            stored_chunk_id="chunk1",
+            chunk_metadata=chunk_metadata,
+        )
+
+        query_response = QueryChunksResponse(chunks=[chunk], scores=[1.0])
+
+        rag_tool.vector_io_api.query_chunks = AsyncMock(return_value=query_response)
+        result = await rag_tool.query(content=content, vector_db_ids=vector_db_ids)
+
+        assert result is not None
+        expected_metadata_string = (
+            "Metadata: {'chunk_id': 'chunk1', 'document_id': 'doc1', 'source': 'test_source', 'key1': 'value1'}"
+        )
+        assert expected_metadata_string in result.content[1].text
+        assert result.content is not None
+
+    async def test_query_raises_incorrect_mode(self):
+        with pytest.raises(ValueError):
+            RAGQueryConfig(mode="invalid_mode")
+
+    async def test_query_accepts_valid_modes(self):
+        default_config = RAGQueryConfig()  # Test default (vector)
+        assert default_config.mode == "vector"
+        vector_config = RAGQueryConfig(mode="vector")  # Test vector
+        assert vector_config.mode == "vector"
+        keyword_config = RAGQueryConfig(mode="keyword")  # Test keyword
+        assert keyword_config.mode == "keyword"
+        hybrid_config = RAGQueryConfig(mode="hybrid")  # Test hybrid
+        assert hybrid_config.mode == "hybrid"
+
+        # Test that invalid mode raises an error
+        with pytest.raises(ValueError):
+            RAGQueryConfig(mode="wrong_mode")
+
+    async def test_query_adds_vector_store_id_to_chunk_metadata(self):
+        rag_tool = MemoryToolRuntimeImpl(
+            config=MagicMock(),
+            vector_io_api=MagicMock(),
+            inference_api=MagicMock(),
+            files_api=MagicMock(),
+        )
+
+        vector_db_ids = ["db1", "db2"]
+
+        # Fake chunks from each DB
+        chunk_metadata1 = ChunkMetadata(
+            document_id="doc1",
+            chunk_id="chunk1",
+            source="test_source1",
+            metadata_token_count=5,
+        )
+        chunk1 = Chunk(
+            content="chunk from db1",
+            metadata={"vector_db_id": "db1", "document_id": "doc1"},
+            stored_chunk_id="c1",
+            chunk_metadata=chunk_metadata1,
+        )
+
+        chunk_metadata2 = ChunkMetadata(
+            document_id="doc2",
+            chunk_id="chunk2",
+            source="test_source2",
+            metadata_token_count=5,
+        )
+        chunk2 = Chunk(
+            content="chunk from db2",
+            metadata={"vector_db_id": "db2", "document_id": "doc2"},
+            stored_chunk_id="c2",
+            chunk_metadata=chunk_metadata2,
+        )
+
+        rag_tool.vector_io_api.query_chunks = AsyncMock(
+            side_effect=[
+                QueryChunksResponse(chunks=[chunk1], scores=[0.9]),
+                QueryChunksResponse(chunks=[chunk2], scores=[0.8]),
+            ]
+        )
+
+        result = await rag_tool.query(content="test", vector_db_ids=vector_db_ids)
+        returned_chunks = result.metadata["chunks"]
+        returned_scores = result.metadata["scores"]
+        returned_doc_ids = result.metadata["document_ids"]
+        returned_vector_db_ids = result.metadata["vector_db_ids"]
+
+        assert returned_chunks == ["chunk from db1", "chunk from db2"]
+        assert returned_scores == (0.9, 0.8)
+        assert returned_doc_ids == ["doc1", "doc2"]
+        assert returned_vector_db_ids == ["db1", "db2"]