{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "902bff53",
   "metadata": {},
   "outputs": [],
   "source": [
    "import { load } from \"jsr:@std/dotenv\";\n",
    "import OpenAI from \"jsr:@openai/openai\";\n",
    "\n",
    "const env = await load({\n",
    "    export: true,\n",
    "});\n",
    "\n",
    "const openai = new OpenAI();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b0acbc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import { createClient } from \"@supabase/supabase-js\";\n",
    "const supabaseUrl = Deno.env.get(\"SUPABASE_URL\") || env.SUPABASE_URL;\n",
    "const supabaseKey = Deno.env.get(\"SUPABASE_ANON_KEY\") || env.SUPABASE_ANON_KEY;\n",
    "const supabase = createClient(supabaseUrl, supabaseKey);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dae842ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  object: \"list\",\n",
      "  data: [\n",
      "    {\n",
      "      object: \"embedding\",\n",
      "      index: 0,\n",
      "      embedding: [\n",
      "          0.017360063269734383,   0.022553985938429832,   0.025286488234996796,\n",
      "         -0.009742671623826027,  -0.016438385471701622,  -0.014725150540471077,\n",
      "         -0.020840751007199287,    0.02215278521180153,   -0.02024437114596367,\n",
      "          0.007834257557988167,  0.0029981620609760284,   -0.04109596461057663,\n",
      "          0.030165957286953926,   -0.03402615711092949,  0.0008369643473997712,\n",
      "        0.00044016868923790753,  -0.027129843831062317,   -0.04248390346765518,\n",
      "         -0.058683738112449646,   0.034655068069696426,   0.013738414272665977,\n",
      "           0.02060219831764698,   0.016687780618667603,     0.0059637944214046,\n",
      "         -0.010306521318852901,   0.014898642897605896,  -0.008826415985822678,\n",
      "          0.042136918753385544,   0.009477011859416962,   -0.03953453525900841,\n",
      "         -0.012198670767247677,  -0.040055014193058014,  -0.019409440457820892,\n",
      "           -0.0210576169192791,    -0.0392959825694561,  -0.005996324121952057,\n",
      "        -0.0022486215457320213,    0.05209103599190712,  0.0009325206046923995,\n",
      "          0.013163721188902855,     0.0505296029150486,   0.014540815725922585,\n",
      "         0.0027921402361243963,    0.03190087899565697, 0.00004917589103570208,\n",
      "           0.03209605813026428,  -0.038363464176654816,   -0.02424553595483303,\n",
      "          0.014096241444349289,    0.02559009939432144,   -0.06089576333761215,\n",
      "         -0.011016755364835262,  -0.026696112006902695,   0.050659723579883575,\n",
      "          0.019561246037483215,   0.050833214074373245,   0.014529972337186337,\n",
      "         -0.023204581812024117,  -0.014399852603673935,  -0.009292676113545895,\n",
      "          0.003149967873468995,    0.02007087878882885,   0.025503354147076607,\n",
      "            0.0230094026774168,    0.04471761733293533,    0.01479020994156599,\n",
      "          -0.06692461669445038,       0.03591288626194,  -0.037127330899238586,\n",
      "          0.029558734968304634,  -0.017576929181814194,    0.01953955926001072,\n",
      "         -0.027888871729373932, -0.0041095963679254055,  0.0004940461367368698,\n",
      "         -0.011266149580478668,   -0.04200679808855057,    0.03537072241306305,\n",
      "        -0.0015058581484481692,   -0.05551750585436821,   -0.05287174880504608,\n",
      "          0.016373327001929283,   0.005573437083512545,      -0.01235047634691,\n",
      "         -0.009005329571664333,  -0.034047845751047134,  -0.018368486315011978,\n",
      "          -0.00271217105910182,   0.007731246296316385,  -0.020981714129447937,\n",
      "         -0.040900785475969315,   -0.03727913647890091,  -0.059030722826719284,\n",
      "          -0.04536821320652962,    0.02257567271590233,    0.08579189330339432,\n",
      "          0.014605875127017498,  -0.030686434358358383, -0.0019084142986685038,\n",
      "           0.02216362953186035,\n",
      "        ... 1436 more items\n",
      "      ]\n",
      "    }\n",
      "  ],\n",
      "  model: \"text-embedding-3-small\",\n",
      "  usage: { prompt_tokens: 13, total_tokens: 13 }\n",
      "}\n",
      "✅ Insertados textos con embeddings\n"
     ]
    }
   ],
   "source": [
    "const textos = [\n",
    "  \"Los vectores se usan con el cliente de OPENAI embeddings\",\n",
    "];\n",
    "\n",
    "for (const contenido of textos) {\n",
    "  const emb = await openai.embeddings.create({\n",
    "    model: \"text-embedding-3-small\",\n",
    "    input: contenido,\n",
    "  });\n",
    "\n",
    "  const embedding = emb.data[0].embedding;\n",
    "  console.log(emb);\n",
    "  \n",
    "\n",
    "  const { error } = await supabase.from(\"documentos\").insert({\n",
    "    contenido,\n",
    "    embedding,\n",
    "  });\n",
    "\n",
    "  if (error) throw error;\n",
    "}\n",
    "\n",
    "console.log(\"✅ Insertados textos con embeddings\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d8fa9a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔎 Consulta: ¿Cómo se usan vectores?\n",
      "\n",
      "- Los vectores se usan con el cliente de OPENAI embeddings\n",
      "  Similaridad: 0.6407\n",
      "\n",
      "- Supabase permite almacenar vectores usando pgvector.\n",
      "  Similaridad: 0.5367\n",
      "\n",
      "- Los embeddings convierten texto en representaciones numéricas.\n",
      "  Similaridad: 0.4706\n",
      "\n"
     ]
    }
   ],
   "source": [
    "const consulta = \"¿Cómo se usan vectores?\";\n",
    "\n",
    "const emb = await openai.embeddings.create({\n",
    "  model: \"text-embedding-3-small\",\n",
    "  input: consulta,\n",
    "});\n",
    "\n",
    "const query_embedding = emb.data[0].embedding;\n",
    "\n",
    "const { data, error } = await supabase.rpc(\"buscar_documentos\", {\n",
    "  query_embedding,\n",
    "  match_count: 3,\n",
    "});\n",
    "\n",
    "if (error) throw error;\n",
    "\n",
    "console.log(`🔎 Consulta: ${consulta}\\n`);\n",
    "for (const r of data ?? []) {\n",
    "  console.log(`- ${r.contenido}`);\n",
    "  console.log(`  Similaridad: ${Number(r.similarity).toFixed(4)}\\n`);\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "791a94d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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