Mastering Model Context Protocol: Semantic Chunking and Dynamic Token Management for Efficient LLM Usage

Challenges in Managing Context for Large Language Models

Working with large language models (LLMs), especially under resource constraints like those in Google Colab, requires effective context management. Long documents and limited token windows can quickly exceed capacity, leading to performance issues.

Building the ModelContextManager

The ModelContextManager class implements the Model Context Protocol (MCP) by automatically chunking input text, generating semantic embeddings with Sentence-Transformers, and scoring each chunk based on recency, importance, and semantic relevance. This manager optimizes token usage by including only the most pertinent chunks in the context window.

Key Components and Functionality

• ContextChunk Dataclass: Stores text segments with embeddings, importance scores, timestamps, and metadata.
• Token Counting: Uses a GPT-2 tokenizer to monitor token usage and maintain the maximum context length.
• Relevance Scoring: Combines recency, user-assigned importance, and semantic similarity to score chunks.
• Context Optimization: Automatically prunes less relevant chunks when token limits are exceeded.
• Visualization Tools: Provides methods to visualize token distribution, scores, and chunk characteristics.

Integrating with Hugging Face Models

The MCPColabDemo class connects the context manager with a Hugging Face sequence-to-sequence model, demonstrated using FLAN-T5. It supports document chunking, query processing with context retrieval, and interactive sessions for real-time experimentation.

Practical Usage and Demo

The run_mcp_demo function showcases the entire workflow: adding sample chunks, retrieving relevant context for a query, displaying statistics, and visualizing the context window. This demonstration illustrates how MCP balances semantic relevance, token budget, and temporal factors to enhance LLM interactions.

By adopting this protocol, developers can efficiently manage context windows, ensuring that language models operate with concise yet highly relevant prompts, resulting in improved response quality and resource utilization.