RAG Pipelines

The primary use case for Laconic: compress retrieved documents before injecting them into an LLM’s context window.

The Problem

RAG pipelines retrieve markdown documents and stuff them into prompts. But markdown carries a lot of decorative weight:

Badge images that mean nothing to an LLM
Padded table formatting that wastes tokens
HTML wrappers from CMS exports
Redundant whitespace

Every wasted token is money spent and context window consumed.

Basic Integration

# Compress all retrieved docs before feeding to the LLM
for doc in retrieved_docs/*.md; do
  laconic compress -f "$doc" > "context/$(basename "$doc")"
done

Python Integration

Call the CLI from Python:

import subprocess

def compress_markdown(text: str) -> str:
    result = subprocess.run(
        ["laconic", "compress", "-f", "-"],
        input=text,
        capture_output=True,
        text=True,
    )
    return result.stdout

# In your RAG pipeline
retrieved_doc = vector_store.query("How do I configure auth?")
compressed = compress_markdown(retrieved_doc.content)
prompt = f"Given this context:\n\n{compressed}\n\nAnswer the question..."

With Token Stats

If you want to track savings:

import subprocess
import json

def compress_with_stats(text: str) -> dict:
    result = subprocess.run(
        ["laconic", "compress", "-j", "-"],
        input=text,
        capture_output=True,
        text=True,
    )
    return json.loads(result.stdout)

stats = compress_with_stats(doc.content)
print(f"Saved {stats['tokens_saved']} tokens ({stats['savings_pct']}%)")
compressed_text = stats["text"]

Rust Integration

If your pipeline is in Rust:

#![allow(unused)]
fn main() {
use laconic_core::{compress_text, CompressConfig};

fn prepare_context(docs: &[String]) -> String {
    let config = CompressConfig::default();
    docs.iter()
        .map(|doc| compress_text(doc, &config))
        .collect::<Vec<_>>()
        .join("\n---\n")
}
}

Decision: When to Compress

Not every document benefits from compression. Use estimate to decide:

laconic estimate docs/*.md

If a document shows 0% savings (pure prose), skip it. Focus compression on structure-heavy documents where savings are 10%+.

The MCP server’s estimate_savings tool lets agents make this decision autonomously.

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