Add Guardrails section to README with service table, usage instructions, configuration details, and extensibility guide.

Author: Paweł Kędzia

README.md

@@ -20,6 +20,50 @@ The services are **stateless**; they load their models once at start‑up and th
 
 ---
 
+## 🛡️ Guardrails
+
+The **guardrail** sub‑package implements safety‑checking services that can be queried via HTTP:
+
+| Service                  | Model                               | Endpoint                           | Description                                                                                                                                    |
+|--------------------------|-------------------------------------|------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
+| **NASK‑PIB Guard**       | `NASK‑PIB/HerBERT‑PL‑Guard`         | `POST /api/guardrails/nask_guard`  | Polish‑language safety classifier detecting unsafe content (e.g., hate, violence). Returns a `safe` flag and per‑chunk classification details. |
+| **Sojka Guard**          | `speakleash/Bielik‑Guard‑0.1B‑v1.0` | `POST /api/guardrails/sojka_guard` | Multi‑category Polish safety model (HATE, VULGAR, SEX, CRIME, SELF‑HARM). Returns detailed scores per category and an overall `safe` flag.     |
+| **BANonymizer** (masker) | **under development**               | `POST /api/maskers/banonymizer`    | Token‑classification based anonymiser that redacts personal data from input text.                                                              |
+
+### How to use
+
+1. **Start the service** – run the provided shell script (`run_*_guardrail.sh` or `run_*_masker.sh`) or invoke the Flask
+   module directly (e.g., `python -m llm_router_services.guardrails.speakleash.sojka_guard_app`).
+2. **Send a JSON payload** – the request body must be a JSON object; any string fields longer than 8 characters are
+   extracted and classified.
+3. **Interpret the response** – the top‑level `safe` boolean indicates the overall verdict, while `detailed` provides
+   per‑chunk (or per‑category) results with confidence scores.
+
+### Configuration
+
+All guardrail services read configuration from environment variables prefixed with:
+
+* `LLM_ROUTER_NASK_PIB_GUARD_` – for the NASK‑PIB guardrail.
+* `LLM_ROUTER_SOJKA_GUARD_` – for the Sojka guardrail.
+* `LLM_ROUTER_BANONYMIZER_` – for the masker.
+
+Key variables include:
+
+* `MODEL_PATH` – path or Hugging‑Face hub identifier of the model.
+* `DEVICE` – `-1` for CPU or CUDA device index for GPU inference.
+* `FLASK_HOST` / `FLASK_PORT` – network binding for the Flask server.
+
+### Extensibility
+
+The guardrail architecture is built around the **`GuardrailBase`** abstract class and a **factory** (
+`GuardrailClassifierModelFactory`). To add a new safety model:
+
+1. Implement a concrete subclass of `GuardrailBase` (or reuse `TextClassificationGuardrail`).
+2. Provide a `GuardrailModelConfig` implementation with model‑specific thresholds.
+3. Register the model type in the factory if a new identifier is required.
+
+---
+
 ## 📜 License
 
 See the [LICENSE](LICENSE) file.