LCORE-1094: Rag chunks are not parsed

[are-ces]

Description

Added parsing of rag_chunks, then added them to TurnSummary to store the rag chunks in the transcript.

Type of change

• Refactor
• New feature
• Bug fix
• CVE fix
• Optimization
• Documentation Update
• Configuration Update
• Bump-up service version
• Bump-up dependent library
• Bump-up library or tool used for development (does not change the final image)
• CI configuration change
• Konflux configuration change
• Unit tests improvement
• Integration tests improvement
• End to end tests improvement

Tools used to create PR

Identify any AI code assistants used in this PR (for transparency and review context)

• Assisted-by: Claude

Related Tickets & Documents

• Related Issue # LCORE-1094
• Closes # LCORE-1094

Checklist before requesting a review

• I have performed a self-review of my code.
• PR has passed all pre-merge test jobs.
• If it is a core feature, I have added thorough tests.

Testing

• Set up LCS with a rag db, called the rag and checked that the transcript stores the rag chunks.

Summary by CodeRabbit

• New Features
  • RAG chunk extraction from API responses now properly captures source information and relevance scores, enabling accurate source attribution and content tracking in conversations.

_{✏️ Tip: You can customize this high-level summary in your review settings.}

[coderabbitai]

Important

Review skipped

Draft detected.

Please check the settings in the CodeRabbit UI or the .coderabbit.yaml file in this repository. To trigger a single review, invoke the @coderabbitai review command.

You can disable this status message by setting the reviews.review_status to false in the CodeRabbit configuration file.

Walkthrough

A new function parse_rag_chunks_from_responses_api() was introduced to extract RAG chunks from the Responses API output. The retrieve_response function was modified to use this function to populate TurnSummary.rag_chunks instead of leaving it empty. Minor import reordering was also performed.

Changes

Cohort / File(s)	Summary
RAG chunk extraction from Responses API src/app/endpoints/query_v2.py	Added parse_rag_chunks_from_responses_api() function to extract RAG chunks by scanning for file_search_call items in API responses. Modified retrieve_response() to invoke this function and populate TurnSummary.rag_chunks. Minor import reordering of utility functions.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

• New function logic is straightforward—iterating through response items and aggregating structured data
• Integration point is minimal (single function call assignment)
• No complex state management or error handling paths introduced

Pre-merge checks and finishing touches

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly references the main change: parsing of rag chunks from the Responses API output and storing them in TurnSummary.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

---

Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out.

❤️ Share

• X
• Mastodon
• Reddit
• LinkedIn

_{Comment @coderabbitai help to get the list of available commands and usage tips.}

[coderabbitai]

Actionable comments posted: 1

♻️ Duplicate comments (1)

src/app/endpoints/query_v2.py (1)

422-429: Fix the parser function to return RAGChunk objects.

The integration of RAG chunk parsing is correct, but the parse_rag_chunks_from_responses_api function returns a list of dicts instead of list[RAGChunk] objects, causing the TurnSummary validation to fail.

See the detailed fix in the review comment for lines 455-482.

📜 Review details

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 4269f0b and ad277ff.

📒 Files selected for processing (1)

• src/app/endpoints/query_v2.py (3 hunks)

🧰 Additional context used

📓 Path-based instructions (3)

src/**/*.py

📄 CodeRabbit inference engine (CLAUDE.md)

src/**/*.py: Use absolute imports for internal modules in LCS project (e.g., from auth import get_auth_dependency)
All modules must start with descriptive docstrings explaining their purpose
Use logger = logging.getLogger(__name__) pattern for module logging
All functions must include complete type annotations for parameters and return types, using modern syntax (str | int) and Optional[Type] or Type | None
All functions must have docstrings with brief descriptions following Google Python docstring conventions
Function names must use snake_case with descriptive, action-oriented names (get_, validate_, check_)
Avoid in-place parameter modification anti-patterns; return new data structures instead of modifying input parameters
Use async def for I/O operations and external API calls
All classes must include descriptive docstrings explaining their purpose following Google Python docstring conventions
Class names must use PascalCase with descriptive names and standard suffixes: Configuration for config classes, Error/Exception for exceptions, Resolver for strategy patterns, Interface for abstract base classes
Abstract classes must use ABC with @abstractmethod decorators
Include complete type annotations for all class attributes in Python classes
Use import logging and module logger pattern with standard log levels: debug, info, warning, error

Files:

• src/app/endpoints/query_v2.py

src/app/endpoints/**/*.py

📄 CodeRabbit inference engine (CLAUDE.md)

Use FastAPI HTTPException with appropriate status codes for API endpoint error handling

Files:

• src/app/endpoints/query_v2.py

src/**/{client,app/endpoints/**}.py

📄 CodeRabbit inference engine (CLAUDE.md)

Handle APIConnectionError from Llama Stack in integration code

Files:

• src/app/endpoints/query_v2.py

🧬 Code graph analysis (1)

src/app/endpoints/query_v2.py (2)

src/utils/suid.py (2)

• normalize_conversation_id (101-122)
• to_llama_stack_conversation_id (125-145)

src/utils/types.py (1)

• TurnSummary (135-220)

🪛 GitHub Actions: Integration tests

src/app/endpoints/query_v2.py

[error] 425-425: TurnSummary validation failed: rag_chunks[0].content must be a string. Received MagicMock during test_query_v2_endpoint_with_tool_calls. Traceback points to query_v2.py:425.

🪛 GitHub Actions: Unit tests

src/app/endpoints/query_v2.py

[error] 476-476: AttributeError: 'dict' object has no attribute 'text' while parsing rag chunks from responses API in parse_rag_chunks_from_responses_api. Expected items to have 'text' and 'score' attributes (triggered during tests/unit/app/endpoints/test_query_v2.py::test_retrieve_response_parses_referenced_documents).

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (6)

• GitHub Check: build-pr
• GitHub Check: Konflux kflux-prd-rh02 / lightspeed-stack-on-pull-request
• GitHub Check: E2E: server mode / azure
• GitHub Check: E2E: server mode / ci
• GitHub Check: E2E: library mode / ci
• GitHub Check: E2E: library mode / azure

🔇 Additional comments (1)

src/app/endpoints/query_v2.py (1)

46-46: LGTM! Import consolidation improves organization.

The import reorganization properly consolidates related utility functions into a single import statement.

[coderabbitai]

⚠️ Potential issue | 🔴 Critical

Fix AttributeError and type mismatch issues in RAG chunk parsing.

The function has critical issues confirmed by pipeline failures:

1. Line 476: result.text causes AttributeError: 'dict' object has no attribute 'text' when result is a dict.
2. Return type: Returns list[dict] but TurnSummary.rag_chunks expects list[RAGChunk] objects, causing validation failures.
3. Type annotation: Incomplete return type -> list violates coding guidelines requiring complete type annotations.

The function must handle both dict and object access patterns like other functions in this file (e.g., _build_tool_call_summary and parse_referenced_documents_from_responses_api).

Apply this diff to fix all issues:

+from utils.types import RAGChunk
+
 # ... (in the imports section)

-def parse_rag_chunks_from_responses_api(response_obj: Any) -> list:
+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 rag chunk dicts with content, source, score
+        List of RAGChunk objects with content, source, and score
     """
-    rag_chunks = []
+    rag_chunks: list[RAGChunk] = []
 
     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 = {
-                    "content": result.text,
-                    "source": "file_search",
-                    "score": result.score,
-                }
-                rag_chunks.append(rag_chunk)
+                # Handle both dict and object access patterns
+                if isinstance(result, dict):
+                    content = result.get("text", "")
+                    score = result.get("score")
+                else:
+                    content = getattr(result, "text", "")
+                    score = getattr(result, "score", None)
+                
+                if content:  # Only add if content exists
+                    rag_chunks.append(
+                        RAGChunk(
+                            content=content,
+                            source="file_search",
+                            score=score,
+                        )
+                    )
 
     return rag_chunks

🧰 Tools

🪛 GitHub Actions: Unit tests

[error] 476-476: AttributeError: 'dict' object has no attribute 'text' while parsing rag chunks from responses API in parse_rag_chunks_from_responses_api. Expected items to have 'text' and 'score' attributes (triggered during tests/unit/app/endpoints/test_query_v2.py::test_retrieve_response_parses_referenced_documents).