Add performance benchmark documentation with tiktoken measurements

[Copilot]

Adds BENCHMARK.md documenting token efficiency of structural context generation vs. full-code dumping, measured on the amdb codebase itself (30 Rust modules).

Key Metrics

• 74.6% global token reduction while maintaining 100% file coverage
• 88-97% compression on heavyweight modules (query, parser, indexer, vector_store, generator)
• Measurements via tiktoken cl100k_base encoder (GPT-4 tokenizer)

Structure

• Executive Summary: Tabular metrics overview
• Heavyweight Analysis: Detailed breakdown of top 5 token-intensive modules with before/after counts
• Context Efficiency: LLM token window implications and cost analysis
• Methodology: Reproducible measurement process with complete benchmark.py script

Example Output

Module: parser
Original: 1,177 tokens → Compressed: 63 tokens
Reduction: 94.6%

The document serves as production evidence that structural extraction preserves semantic relationships while dramatically reducing token overhead for AI context windows.

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