AST Syntax-Aware Normalization for JSON, XML, and YAML Prompts
Compressing Structural Data Payloads Without Invocation Failures
Mathematical intuition
Parses structured inputs into AST node trees, normalizes formatting whitespace, and prunes low-entropy array elements while validating schema structure.
- 01Compressing JSON or XML via raw text token truncation causes syntax errors and parse failures.
- 02LLMSlim format optimizers validate AST integrity before and after structural reduction.
- 03Safely compresses large JSON API payloads passed into function-calling LLMs.
1. Structural JSON Compression Pattern
Using format-specific modes in LLMSlim:
json_opt_example.py
from llmslim import compress
json_prompt = """{
"request_id": "req_99281a",
"instructions": "Extract entities from payload",
"schema": {
"type": "object",
"properties": {
"user_name": {"type": "string"},
"user_id": {"type": "integer"}
}
}
}"""
# Perform AST syntax-aware normalization and compression
result = compress(json_prompt, mode="json", target_ratio=0.5)
print(f"Original Tokens: {result.original_tokens}")
print(f"Compressed Tokens: {result.compressed_tokens}")
print(result.compressed_text)