{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "source": [
    "! pip install ipywidgets \"transformers>=4.38.0,<5.0.0\" \"torch>=2.1.1,<3.0.0\" \"guardrails-jsonformer>=0.13.1,<1.0.0\" -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dtam/dev/guardrails/guardrails/validator_service/__init__.py:85: UserWarning: Could not obtain an event loop. Falling back to synchronous validation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"dogs\": [\n",
      "    {\n",
      "      \"name\": \"Max\",\n",
      "      \"color\": \"red\",\n",
      "      \"weight_kg\": 20.555\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"Lola\",\n",
      "      \"color\": \"blue\",\n",
      "      \"weight_kg\": 15.234\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"Sarah\",\n",
      "      \"color\": \"green\",\n",
      "      \"weight_kg\": 10.567\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from guardrails import Guard\n",
    "from pydantic import BaseModel\n",
    "\n",
    "# JSONFormer is only compatible with HF Pipelines and HF Models:\n",
    "from transformers import pipeline\n",
    "\n",
    "\n",
    "class Dog(BaseModel):\n",
    "    name: str\n",
    "    color: str\n",
    "    weight_kg: float\n",
    "\n",
    "\n",
    "class NewFriends(BaseModel):\n",
    "    dogs: list[Dog]\n",
    "\n",
    "\n",
    "guard = Guard.for_pydantic(NewFriends, output_formatter=\"jsonformer\")\n",
    "\n",
    "\n",
    "tiny_llama_pipeline = pipeline(\"text-generation\", \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\")\n",
    "\n",
    "# Inference is straightforward:\n",
    "response = guard(\n",
    "    tiny_llama_pipeline,\n",
    "    messages=[{\"role\": \"user\", \"content\": \"Please enjoy this list of good dogs:\"}],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# `out` is a dict.  Format it as JSON for readability:\n",
    "import json\n",
    "\n",
    "print(json.dumps(response.validated_output, indent=2))"
   ]
  }
 ],
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