{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🧪 WFGY e_resonance Demo (v0.1)\n",
    "Measure how a prompt + answer jointly resonate with a given semantic field."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formula\n",
    "For a set of anchor vectors **D = {d₁ … dₙ}** representing a domain:\n",
    "$$ e_{res} = \\frac{1}{n}\\sum_{i=1}^{n}\\bigl[\\cos(I,d_i)\\;\\times\\;\\cos(G,d_i)\\bigr] $$\n",
    "Where *I* = prompt intent, *G* = generated answer."
   ]
  },
  {
   "cell_type": "code",
   "metadata": { "id": "install" },
   "source": [
    "!pip -q install sentence-transformers --upgrade"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": { "id": "imports" },
   "source": [
    "from sentence_transformers import SentenceTransformer, util"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": { "id": "model" },
   "source": [
    "model = SentenceTransformer('all-MiniLM-L6-v2')"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anchors\n",
    "A minimal 3-sentence anchor set for the **Buddhism** field (edit as you like)."
   ]
  },
  {
   "cell_type": "code",
   "metadata": { "id": "anchors" },
   "source": [
    "anchors = [\n",
    "    \"All compounded things are impermanent.\",\n",
    "    \"Suffering arises from attachment and craving.\",\n",
    "    \"The mind is everything; what you think you become.\"\n",
    "]\n",
    "anchor_vecs = model.encode(anchors, convert_to_tensor=True)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ✏️ Edit & run\n",
    "Replace `prompt` / `answer`, then ▶️."
   ]
  },
  {
   "cell_type": "code",
   "metadata": { "id": "user" },
   "source": [
    "prompt  = \"How can one reduce daily anxiety?\"\n",
    "answer  = \"By recognising thoughts as fleeting and practising mindful breathing.\"\n",
    "\n",
    "p_vec = model.encode(prompt, convert_to_tensor=True)\n",
    "a_vec = model.encode(answer, convert_to_tensor=True)\n",
    "\n",
    "scores = util.cos_sim(p_vec, anchor_vecs)[0] * util.cos_sim(a_vec, anchor_vecs)[0]\n",
    "e_res  = scores.mean().item()\n",
    "\n",
    "print(f\"e_resonance with Buddhism anchors : {e_res:.3f}\\n\")\n",
    "for s, txt in sorted(zip(scores, anchors), reverse=True):\n",
    "    print(f\"  {s:.3f}  |  {txt}\")"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Next Steps\n",
    "* Swap in your own anchor set: philosophy, physics, pop-culture, etc.  \n",
    "* Compare multiple answers — choose the highest **e_resonance**.  \n",
    "* For full failure taxonomy see **Problem Map 1.0 / 2.0**.\n"
   ]
  }
 ],
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