fix: rag retrieval

[vishnurk6247]

Summary by CodeRabbit

• Refactor
  • Improved internal LLM configuration handling to use structured configuration objects, ensuring more robust and maintainable code architecture.

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[coderabbitai]

Walkthrough

A single file in the knowledge base module's embeddings layer was refactored to replace dictionary-based LLM configuration with a structured LLMConfigModel. The change introduces type safety while preserving existing behavior and external interfaces.

Changes

Cohort / File(s)	Summary
LLM Configuration Refactoring wavefront/server/modules/knowledge_base_module/knowledge_base_module/embeddings/llm.py	Added import for LLMConfigModel; refactored _create_llm_instance to construct an LLMConfigModel instance (with provider='rootflo' and model_id=config.id) before passing it to LLMFactory.create_llm, replacing inline dictionary configuration. No functional changes to public interfaces.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

• Verify that LLMConfigModel correctly encapsulates the required configuration fields (provider, model_id)
• Confirm that config.id is reliably available and correctly mapped to model_id
• Ensure the hardcoded provider='rootflo' value is intentional and appropriate for all call sites

Poem

🐰 Config takes form, no longer plain,
Typed and structured, crystal-clear chain,
Dicts fade away, models arise,
Stronger LLM beneath cleaner skies! ✨

Pre-merge checks and finishing touches

❌ Failed checks (1 inconclusive)

Check name	Status	Explanation	Resolution
Title check	❓ Inconclusive	The title 'fix: rag retrieval' is vague and does not clearly describe the specific change made. The actual change involves refactoring LLM configuration to use typed LLMConfigModel instead of dict literals, which is not reflected in the generic 'rag retrieval' description.	Replace with a more specific title that describes the actual change, such as 'refactor: use typed LLMConfigModel for LLM configuration' or 'refactor: switch to structured LLM configuration object'.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch fix/rag-retrieval

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[coderabbitai]

Actionable comments posted: 2

📜 Review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 4c52481 and 477e877.

📒 Files selected for processing (1)

• wavefront/server/modules/knowledge_base_module/knowledge_base_module/embeddings/llm.py (2 hunks)

🧰 Additional context used

🧬 Code graph analysis (1)

wavefront/server/modules/knowledge_base_module/knowledge_base_module/embeddings/llm.py (2)

flo_ai/flo_ai/models/agent.py (1)

• LLMConfigModel (86-151)

flo_ai/flo_ai/helpers/llm_factory.py (2)

• LLMFactory (17-200)
• create_llm (31-61)

⏰ 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). (1)

• GitHub Check: build-push-artifact

🔇 Additional comments (1)

wavefront/server/modules/knowledge_base_module/knowledge_base_module/embeddings/llm.py (1)

5-5: LGTM!

The import of LLMConfigModel is necessary for the type-safe refactoring and aligns well with the structured configuration approach.