Getting Started5 min read•Updated July 2026
Quick Start Guide
Start compressing system prompts and RAG contexts in under 60 seconds.
Integrating with OpenAI SDK
Wrap long system instructions or multi-document RAG context before dispatching your OpenAI API request:
Guaranteed Instruction Retention
System directives containing 'must', 'never', or formatted constraints are automatically retained by the priority tier engine.
openai_app.py
from openai import OpenAI
from llmslim import compress
client = OpenAI()
long_rag_context = """... 4,000 tokens of unstructured document context ..."""
# Compress to 40% of original token count while preserving key facts
compressed_context = compress(long_rag_context, target_ratio=0.4).compressed_text
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "value": "You are a helpful research assistant."},
{"role": "user", "content": f"Context: {compressed_context}\n\nQuestion: Summarize key revenue metrics."},
]
)
print(response.choices[0].message.content)Frequently Asked Questions
How do I wrap an OpenAI chat completion call?
Pass your system or user content through compress() before constructing the messages array in client.chat.completions.create().