{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HAG: Hopfield-Augmented Generation Demo\n",
    "\n",
    "This notebook demonstrates the core Hopfield retrieval mechanism with synthetic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from hag.config import HopfieldConfig\n",
    "from hag.hopfield import HopfieldRetrieval\n",
    "from hag.energy import compute_energy_curve, verify_monotonic_decrease, compute_attention_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create synthetic memory bank and query\n",
    "torch.manual_seed(42)\n",
    "d, N = 64, 200\n",
    "memory = F.normalize(torch.randn(d, N), dim=0)\n",
    "query = F.normalize(torch.randn(1, d), dim=-1)\n",
    "\n",
    "# Run Hopfield retrieval with different beta values\n",
    "for beta in [0.5, 1.0, 2.0, 5.0]:\n",
    "    config = HopfieldConfig(beta=beta, max_iter=20, conv_threshold=1e-6)\n",
    "    hopfield = HopfieldRetrieval(config)\n",
    "    result = hopfield.retrieve(query, memory, return_energy=True)\n",
    "    curve = compute_energy_curve(result)\n",
    "    entropy = compute_attention_entropy(result.attention_weights)\n",
    "    print(f'beta={beta}: steps={result.num_steps}, entropy={entropy:.4f}, monotonic={verify_monotonic_decrease(curve)}')"
   ]
  }
 ],
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