Replacing RAG tool name with constant

[are-ces]

Description

Substituted all fixed references to the name of the rag tool "knowledge_search" with the constant.

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

Related Tickets & Documents

NA

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

NA

Summary by CodeRabbit

• New Features

  • None

• Bug Fixes

  • Improved reliability of referenced documents and streaming responses by consistently recognizing the configured retrieval tool, reducing missed results and edge-case failures across environments.

• Refactor

  • Standardized tool identification across query and streaming endpoints for consistent behavior and easier configuration management.

• Chores

  • Minor import reorganization with no impact on functionality or public APIs.

[coderabbitai]

Walkthrough

Replaces hard-coded tool name checks ("knowledge_search") with a centralized constant (DEFAULT_RAG_TOOL) in query and streaming_query endpoints. Adjusts and reorganizes imports accordingly. No public API changes.

Changes

Cohort / File(s)	Summary
RAG tool name normalization src/app/endpoints/query.py, src/app/endpoints/streaming_query.py	Replaced string literal "knowledge_search" comparisons with constants.DEFAULT_RAG_TOOL across tool-response handling and parsing logic; updated/imported constants accordingly; minor import reordering without behavior change otherwise.

Sequence Diagram(s)

sequenceDiagram
    autonumber
    participant C as Client
    participant Q as Query Endpoint
    participant T as Tool Executor
    participant S as Stream Handler

    C->>Q: Submit query
    Q->>T: Execute tools
    T-->>Q: Tool responses
    alt Response tool_name == DEFAULT_RAG_TOOL
        Q->>Q: Parse referenced documents
        Q->>S: Stream RAG content (docs/snippets)
    else Other tools
        Q->>S: Stream non-RAG tool output
    end
    S-->>C: Incremental results

Loading

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Possibly related PRs

• LCORE-601: Add RAG chunks in query response #550 — Similar replacement of "knowledge_search" with DEFAULT_RAG_TOOL in the query-processing path; directly touches related logic.

Suggested reviewers

• tisnik
• asamal4
• VladimirKadlec

Poem

A nibble of code, a hop through the pool,
I swapped a hard string for a constant so cool.
Now tools align, no guesswork or duel—
DEFAULT_RAG_TOOL leads the rule!
Thump-thump, I stream with glee,
Little bunny commits, bug-free. 🐇✨

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 and concisely describes the primary change in the pull request, namely replacing hard-coded references to the RAG tool name with a constant, which matches the modifications in both endpoint modules. It accurately reflects the main functional update without extraneous detail or ambiguity.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

✨ Finishing touches

• 📝 Generate Docstrings

🧪 Generate unit tests

• Create PR with unit tests
• Post copyable unit tests in a comment

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📜 Recent review details

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Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 6529c37 and d8f617e.

📒 Files selected for processing (2)

• src/app/endpoints/query.py (2 hunks)
• src/app/endpoints/streaming_query.py (2 hunks)

🧰 Additional context used

📓 Path-based instructions (5)

src/**/*.py

📄 CodeRabbit inference engine (CLAUDE.md)

Use absolute imports for internal modules (e.g., from auth import get_auth_dependency)

Files:

• src/app/endpoints/streaming_query.py
• src/app/endpoints/query.py

src/app/**/*.py

📄 CodeRabbit inference engine (CLAUDE.md)

Use standard FastAPI imports (from fastapi import APIRouter, HTTPException, Request, status, Depends) in FastAPI app code

Files:

• src/app/endpoints/streaming_query.py
• src/app/endpoints/query.py

**/*.py

📄 CodeRabbit inference engine (CLAUDE.md)

**/*.py: All modules start with descriptive module-level docstrings explaining purpose
Use logger = logging.getLogger(name) for module logging after import logging
Define type aliases at module level for clarity
All functions require docstrings with brief descriptions
Provide complete type annotations for all function parameters and return types
Use typing_extensions.Self in model validators where appropriate
Use modern union syntax (str | int) and Optional[T] or T | None consistently
Function names use snake_case with descriptive, action-oriented prefixes (get_, validate_, check_)
Avoid in-place parameter modification; return new data structures instead of mutating arguments
Use appropriate logging levels: debug, info, warning, error with clear messages
All classes require descriptive docstrings explaining purpose
Class names use PascalCase with conventional suffixes (Configuration, Error/Exception, Resolver, Interface)
Abstract base classes should use abc.ABC and @AbstractMethod for interfaces
Provide complete type annotations for all class attributes
Follow Google Python docstring style for modules, classes, and functions, including Args, Returns, Raises, Attributes sections as needed

Files:

• src/app/endpoints/streaming_query.py
• src/app/endpoints/query.py

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

📄 CodeRabbit inference engine (CLAUDE.md)

Use async def for I/O-bound operations and external API calls

Files:

• src/app/endpoints/streaming_query.py
• src/app/endpoints/query.py

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

📄 CodeRabbit inference engine (CLAUDE.md)

In API endpoints, raise FastAPI HTTPException with appropriate status codes for error handling

Files:

• src/app/endpoints/streaming_query.py
• src/app/endpoints/query.py

🧬 Code graph analysis (1)

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

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

• get_rag_toolgroups (709-736)

src/utils/types.py (1)

• TurnSummary (61-135)

⏰ 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). (2)

• GitHub Check: e2e_tests
• GitHub Check: build-pr

🔇 Additional comments (2)

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

516-517: Nice cleanup on the RAG tool check.

Switching to constants.DEFAULT_RAG_TOOL keeps the endpoint aligned with the shared definition and prevents future regressions when the tool name changes.

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

489-517: Good consistency update for streaming RAG responses.

The constant-based comparison mirrors the non-streaming path and ensures both flows stay in lockstep when the default tool identifier evolves.

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[tisnik]

LGTM