LCORE-1094: Rag chunks are not parsed

src/app/endpoints/query_v2.py

@@ -40,12 +40,12 @@
     get_system_prompt,
     get_topic_summary_system_prompt,
 )
-from utils.suid import normalize_conversation_id, to_llama_stack_conversation_id
 from utils.mcp_headers import mcp_headers_dependency
 from utils.responses import extract_text_from_response_output_item
 from utils.shields import detect_shield_violations, get_available_shields
+from utils.suid import normalize_conversation_id, to_llama_stack_conversation_id
 from utils.token_counter import TokenCounter
-from utils.types import ToolCallSummary, ToolResultSummary, TurnSummary
+from utils.types import RAGChunk, ToolCallSummary, ToolResultSummary, TurnSummary
 
 logger = logging.getLogger("app.endpoints.handlers")
 router = APIRouter(tags=["query_v1"])
@@ -419,11 +419,14 @@ async def retrieve_response(  # pylint: disable=too-many-locals,too-many-branche
         len(llm_response),
     )
 
+    # Extract rag chunks
+    rag_chunks = parse_rag_chunks_from_responses_api(response)
+
     summary = TurnSummary(
         llm_response=llm_response,
         tool_calls=tool_calls,
         tool_results=tool_results,
-        rag_chunks=[],
+        rag_chunks=rag_chunks,
     )
 
     # Extract referenced documents and token usage from Responses API response
@@ -449,6 +452,34 @@ async def retrieve_response(  # pylint: disable=too-many-locals,too-many-branche
     return (summary, normalized_conversation_id, referenced_documents, token_usage)
 
 
+def parse_rag_chunks_from_responses_api(response_obj: Any) -> list[RAGChunk]:
+    """
+    Extract rag_chunks from the llama-stack OpenAI response.
+
+    Args:
+        response_obj: The ResponseObject from OpenAI compatible response API in llama-stack.
+
+    Returns:
+        List of RAGChunk with content, source, score
+    """
+    rag_chunks = []
+
+    for output_item in response_obj.output:
+        if (
+            hasattr(output_item, "type")
+            and output_item.type == "file_search_call"
+            and hasattr(output_item, "results")
+        ):
+
+            for result in output_item.results:
+                rag_chunk = RAGChunk(
+                    content=result.text, source="file_search", score=result.score
+                )
+                rag_chunks.append(rag_chunk)
+
+    return rag_chunks
+
+
 def parse_referenced_documents_from_responses_api(
     response: OpenAIResponseObject,  # pylint: disable=unused-argument
 ) -> list[ReferencedDocument]:

tests/integration/endpoints/test_query_v2_integration.py

@@ -347,6 +347,7 @@ async def test_query_v2_endpoint_with_tool_calls(
     mock_result.file_id = "doc-1"
     mock_result.filename = "ansible-docs.txt"
     mock_result.score = 0.95
+    mock_result.text = "Ansible is an open-source automation tool..."
     mock_result.attributes = {
         "doc_url": "https://example.com/ansible-docs.txt",
         "link": "https://example.com/ansible-docs.txt",

tests/unit/app/endpoints/test_query_v2.py

@@ -787,17 +787,12 @@ async def test_retrieve_response_no_violation_with_shields(
     validation_metric.inc.assert_not_called()
 
 
-@pytest.mark.asyncio
-async def test_retrieve_response_parses_referenced_documents(
-    mocker: MockerFixture,
-) -> None:
-    """Test that retrieve_response correctly parses referenced documents from response."""
-    mock_client = mocker.AsyncMock()
-
+def _create_message_output_with_citations(mocker: MockerFixture) -> Any:
+    """Create mock message output item with content annotations (citations)."""
     # 1. Output item with message content annotations (citations)
-    output_item_1 = mocker.Mock()
-    output_item_1.type = "message"
-    output_item_1.role = "assistant"
+    output_item = mocker.Mock()
+    output_item.type = "message"
+    output_item.role = "assistant"
 
     # Mock content with annotations
     content_part = mocker.Mock()
@@ -816,19 +811,48 @@ async def test_retrieve_response_parses_referenced_documents(
     annotation2.title = None
 
     content_part.annotations = [annotation1, annotation2]
-    output_item_1.content = [content_part]
+    output_item.content = [content_part]
+    return output_item
+
 
+def _create_file_search_output(mocker: MockerFixture) -> Any:
+    """Create mock file search tool call output with results."""
     # 2. Output item with file search tool call results
-    output_item_2 = mocker.Mock()
-    output_item_2.type = "file_search_call"
-    output_item_2.queries = (
+    output_item = mocker.Mock()
+    output_item.type = "file_search_call"
+    output_item.queries = (
         []
     )  # Ensure queries is a list to avoid iteration error in tool summary
-    output_item_2.status = "completed"
-    output_item_2.results = [
-        {"filename": "file2.pdf", "attributes": {"url": "http://example.com/doc2"}},
-        {"filename": "file3.docx", "attributes": {}},  # No URL
-    ]
+    output_item.status = "completed"
+    # Create mock result objects with proper attributes matching real llama-stack response
+    result_1 = mocker.Mock()
+    result_1.filename = "file2.pdf"
+    result_1.attributes = {"url": "http://example.com/doc2"}
+    result_1.text = "Sample text from file2.pdf"
+    result_1.score = 0.95
+    result_1.file_id = "file-123"
+
+    result_2 = mocker.Mock()
+    result_2.filename = "file3.docx"
+    result_2.attributes = {}
+    result_2.text = "Sample text from file3.docx"
+    result_2.score = 0.85
+    result_2.file_id = "file-456"
+
+    output_item.results = [result_1, result_2]
+    return output_item
+
+
+@pytest.mark.asyncio
+async def test_retrieve_response_parses_referenced_documents(
+    mocker: MockerFixture,
+) -> None:
+    """Test that retrieve_response correctly parses referenced documents from response."""
+    mock_client = mocker.AsyncMock()
+
+    # Create output items using helper functions
+    output_item_1 = _create_message_output_with_citations(mocker)
+    output_item_2 = _create_file_search_output(mocker)
 
     response_obj = mocker.Mock()
     response_obj.id = "resp-docs"
@@ -870,3 +894,12 @@ async def test_retrieve_response_parses_referenced_documents(
     doc4 = next((d for d in referenced_docs if d.doc_title == "file3.docx"), None)
     assert doc4
     assert doc4.doc_url is None
+
+    # Verify RAG chunks were extracted from file_search_call results
+    assert len(_summary.rag_chunks) == 2
+    assert _summary.rag_chunks[0].content == "Sample text from file2.pdf"
+    assert _summary.rag_chunks[0].source == "file_search"
+    assert _summary.rag_chunks[0].score == 0.95
+    assert _summary.rag_chunks[1].content == "Sample text from file3.docx"
+    assert _summary.rag_chunks[1].source == "file_search"
+    assert _summary.rag_chunks[1].score == 0.85