fix(llm): add retries to RAG orchestrator to handle output validation issues#399
fix(llm): add retries to RAG orchestrator to handle output validation issues#399
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request enhances the reliability of the RAG orchestrator by implementing a retry strategy. This change aims to mitigate transient failures that can occur during the LLM's output validation process, ensuring a more stable and resilient agent initialization without altering its core functionality. Highlights
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Code Review
This pull request adds a retry mechanism to the RAG orchestrator to improve resilience against transient LLM failures. A security audit confirmed that no new vulnerabilities were introduced by these changes. However, the current implementation hardcodes the number of retries to 3. It is recommended to make this value configurable, similar to existing patterns like thinking_budget, to enhance flexibility.
| model=llm, | ||
| system_prompt=RAG_ORCHESTRATOR_SYSTEM_PROMPT, | ||
| tools=tools, | ||
| retries=3, # Increase retries to handle output validation issues |
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While adding retries is a good way to handle transient LLM issues, hardcoding the value 3 makes it inflexible. It would be better to make this configurable, similar to other settings like thinking_budget.
This would involve:
- Adding
ORCHESTRATOR_RETRIES: int | None = NonetoAppConfigincodebase_rag/config.py. - Adding a
retries: int | None = Nonefield to theModelConfigdataclass. - Populating this field from the environment variable in
AppConfig._get_default_config.
Then you could use it here like this:
| retries=3, # Increase retries to handle output validation issues | |
| retries=config.retries or 3, # Use configured retries, default to 3 |
Greptile SummaryThis PR adds Key changes:
Minor issue:
Confidence Score: 4/5
Important Files Changed
Last reviewed commit: bee8526 |
| model=llm, | ||
| system_prompt=RAG_ORCHESTRATOR_SYSTEM_PROMPT, | ||
| tools=tools, | ||
| retries=3, # Increase retries to handle output validation issues |
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inline comment violates project's "No Comments" policy - must be prefixed with (H) or removed
| retries=3, # Increase retries to handle output validation issues | |
| retries=3, # (H) Increase retries to handle output validation issues |
Context Used: Rule from dashboard - ## Technical Requirements
Agentic Framework
- PydanticAI Only: This project uses PydanticAI... (source)
Prompt To Fix With AI
This is a comment left during a code review.
Path: codebase_rag/services/llm.py
Line: 114
Comment:
inline comment violates project's "No Comments" policy - must be prefixed with `(H)` or removed
```suggestion
retries=3, # (H) Increase retries to handle output validation issues
```
**Context Used:** Rule from `dashboard` - ## Technical Requirements
### Agentic Framework
- **PydanticAI Only**: This project uses PydanticAI... ([source](https://app.greptile.com/review/custom-context?memory=d4240b05-b763-467a-a6bf-94f73e8b6859))
How can I resolve this? If you propose a fix, please make it concise.Note: If this suggestion doesn't match your team's coding style, reply to this and let me know. I'll remember it for next time!
Add retries=3 to the RAG orchestrator agent creation to handle transient output validation failures.
Dependency graph
Merge in this order: