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…ment - Added `_attach_model_profile` function to attach model context metadata to `ChatLiteLLM`. - Updated `create_chat_litellm_from_config` and `create_chat_litellm_from_agent_config` to utilize the new profile attachment. - Improved context profile caching in `llm_router_service.py` to include both minimum and maximum input tokens, along with token model names for better context management. - Introduced new methods for token counting and context trimming based on model profiles.
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Review by RecurseML
🔍 Review performed on 5571e8a..eec4db4
✨ No bugs found, your code is sparkling clean
✅ Files analyzed, no issues (1)
• surfsense_backend/app/services/llm_router_service.py
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…ment
_attach_model_profilefunction to attach model context metadata toChatLiteLLM.create_chat_litellm_from_configandcreate_chat_litellm_from_agent_configto utilize the new profile attachment.llm_router_service.pyto include both minimum and maximum input tokens, along with token model names for better context management.Description
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High-level PR Summary
This PR enhances LLM configuration and routing by introducing model profile attachment and context-aware message trimming capabilities. The changes add a
_attach_model_profilefunction that captures model context metadata (likemax_input_tokens) from LiteLLM's model info and attaches it toChatLiteLLMinstances. The router service is updated to cache both minimum and maximum input token limits across all deployments, enabling smarter context management. Most significantly, this introduces sophisticated context trimming logic that uses binary search to intelligently truncate large messages (especially tool responses and document context) when they exceed the model's context window, preferring XML document boundaries for cleaner cuts and preserving system messages to maintain agent instructions.⏱️ Estimated Review Time: 30-90 minutes
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surfsense_backend/app/agents/new_chat/llm_config.pysurfsense_backend/app/services/llm_router_service.py