Context Pruning & Prompt Compression: The LLMLingua Guide

Large context windows allow developers to pass full code repositories into LLMs, but they rack up massive token costs and cause speed degradation. This guide explains how to use LLMLingua to compress prompts by up to 20x without losing semantic details.

1. What is Prompt Compression?

LLMs are highly redundant in natural language and code. Prompt compression algorithms utilize small, fast models (like GPT-2 or LLaMA-3B) to calculate the perplexity of tokens in a prompt. Tokens with low information density (redundant words, boilerplate descriptions) are removed, leaving a compressed prompt with identical reasoning performance.

2. Getting Started with LLMLingua

LLMLingua is a library developed by Microsoft for prompt compression. Here is a simple python setup:

from llmlingua import PromptCompressor

# Initialize compressor with a small local model
compressor = PromptCompressor("microsoft/llmlingua-2-bert-base-multilingual-cased-meeting")

# Compress your large prompt
results = compressor.compress_prompt(
    context=["Your huge codebase text or API documentation..."],
    instruction="Write a script to clean database records.",
    target_token=800
)

print(results["compressed_prompt"])