feat: Add vector_db_id to chunk metadata (#3304)

# What does this PR do?

When running RAG in a multi vector DB setting, it can be difficult to
trace where retrieved chunks originate from. This PR adds the
`vector_db_id` into each chunk’s metadata, making it easier to
understand which database a given chunk came from. This is helpful for
debugging and for analyzing retrieval behavior of multiple DBs.

Relevant code:

```python
for vector_db_id, result in zip(vector_db_ids, results):
    for chunk, score in zip(result.chunks, result.scores):
        if not hasattr(chunk, "metadata") or chunk.metadata is None:
            chunk.metadata = {}
        chunk.metadata["vector_db_id"] = vector_db_id

        chunks.append(chunk)
        scores.append(score)
```

## Test Plan

* Ran Llama Stack in debug mode.
* Verified that `vector_db_id` was added to each chunk’s metadata.
* Confirmed that the metadata was printed in the console when using the
RAG tool.

---------

Co-authored-by: are-ces <cpompeia@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>

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

@@ -167,8 +167,18 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
             for vector_db_id in vector_db_ids
         ]
         results: list[QueryChunksResponse] = await asyncio.gather(*tasks)
-        chunks = [c for r in results for c in r.chunks]
-        scores = [s for r in results for s in r.scores]
+
+        chunks = []
+        scores = []
+
+        for vector_db_id, result in zip(vector_db_ids, results, strict=False):
+            for chunk, score in zip(result.chunks, result.scores, strict=False):
+                if not hasattr(chunk, "metadata") or chunk.metadata is None:
+                    chunk.metadata = {}
+                chunk.metadata["vector_db_id"] = vector_db_id
+
+                chunks.append(chunk)
+                scores.append(score)
 
         if not chunks:
             return RAGQueryResult(content=None)
@@ -203,6 +213,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
             metadata_keys_to_exclude_from_context = [
                 "token_count",
                 "metadata_token_count",
+                "vector_db_id",
             ]
             metadata_for_context = {}
             for k in chunk_metadata_keys_to_include_from_context:
@@ -227,6 +238,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
                 "document_ids": [c.metadata["document_id"] for c in chunks[: len(picked)]],
                 "chunks": [c.content for c in chunks[: len(picked)]],
                 "scores": scores[: len(picked)],
+                "vector_db_ids": [c.metadata["vector_db_id"] for c in chunks[: len(picked)]],
             },
         )
 

tests/unit/rag/test_rag_query.py

@@ -81,3 +81,58 @@ class TestRagQuery:
         # Test that invalid mode raises an error
         with pytest.raises(ValueError):
             RAGQueryConfig(mode="wrong_mode")
+
+    @pytest.mark.asyncio
+    async def test_query_adds_vector_db_id_to_chunk_metadata(self):
+        rag_tool = MemoryToolRuntimeImpl(
+            config=MagicMock(),
+            vector_io_api=MagicMock(),
+            inference_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"]