Intelligent Context Management for LLM APIs

[nikzasel]

Problem: Sending full LLM context (e.g., 1M tokens) with every request is inefficient and costly, especially for minor interactions.

Proposed Solution: Implement intelligent, user-configurable context management when the context window is full, near capacity, or a manual limit is set.

Key Features:

1) Manual Context Size Limit:
Allow users to set a maximum token limit for the context, overriding the model's default.

2) Configurable Truncation Strategies (when context is full/near capacity):

• Clear Oldest: Automatically remove tokens from the beginning of the context.
• Summarize/Condense Middle: Intelligently reduce less critical or redundant parts of the context (e.g., via internal summarization/compression).
• Integrated LLM Rewriting/Condensing:
  • Leverage existing text condensation capabilities.
  • Provide an option to trigger rewriting/condensing based on a configurable percentage of context capacity, not just when full.

This will significantly improve efficiency and cost-effectiveness for LLM interactions.