# LLMLingua-2 token-pruning sidecar

Prunes low-perplexity tokens from freeform prose before it hits the local LLM, so more usable
context fits a bounded window. Implements pipeline stage 3 (`TOKEN_PRUNE`, level 3+) — see
`docs/plans/correx-compression-pipeline.md` §4.

Python-only because LLMLingua-2 is a torch/BERT classifier with no JVM equivalent. correx calls
it over localhost HTTP via `HttpTokenPruner`, which **fails open**: if this sidecar is down, the
kernel passes context through uncompressed. Nothing breaks; you just don't get token pruning.

## Run

```bash
cd sidecars/llmlingua
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn server:app --host 127.0.0.1 --port 8199
```

First `/prune` call downloads the model (~1-2 GB) and loads torch; `/health` responds immediately.

## Wire into correx

Set compression level ≥ 3 for the workflow and point the kernel at the sidecar:

```toml
[compression]
level = 4
token_pruner_url = "http://127.0.0.1:8199"
```

## API

- `GET /health` → `{"status":"ok"}`
- `POST /prune` `{"text": str, "protected": [str], "rate": 0.55}` → `{"compressed": str}`
  - `rate` = fraction of tokens to **keep** (0.55 ≈ 45% compression)
  - `protected` substrings (IDs, numbers, paths, code) are kept verbatim