Add fallback model for DSPy inference

[Miyamura80]

Summary

• add fallback model configuration to default LLM settings
• initialize a secondary DSPy LM and switch on ServiceUnavailableError
• retry primary calls as before and fall back with logging

Testing

• make test_fast

[greptile-apps]

Greptile Overview

Greptile Summary

Added fallback model support to DSPy inference with automatic failover on service unavailability and rate limit errors.

• Added fallback_model field to DefaultLlm config model
• Configured gemini-2.5-flash as fallback for gemini-3-flash primary model
• Refactored LM initialization into _build_lm() helper method for reuse
• Implemented retry logic for both RateLimitError and ServiceUnavailableError
• Added fallback mechanism that switches to secondary model when primary fails after retries
• Enhanced error logging to distinguish between primary and fallback failures
• Properly guards against initializing fallback when same as primary or None

Confidence Score: 5/5

• This PR is safe to merge with minimal risk
• The implementation properly handles fallback model initialization with correct null checks, implements structured error handling with appropriate logging, and maintains backward compatibility. Previous thread issues about null checks and extra parentheses appear to have been addressed in subsequent commits. The code follows project style guides for logging.
• No files require special attention

Important Files Changed

Filename	Overview
common/config_models.py	Added optional fallback_model field to DefaultLlm config model - simple, safe schema change
common/global_config.yaml	Updated default model to gemini-3-flash and added gemini-2.5-flash as fallback - verify model names exist
utils/llm/dspy_inference.py	Implemented fallback LM with retry logic for RateLimitError and ServiceUnavailableError - well-structured error handling

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