Primer commit

2025-05-05 02:21:55 +02:00
commit 7b6f525809
62 changed files with 78661 additions and 0 deletions
@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "36832aa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.2.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import duckdb\n",
+    "print(duckdb.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c357d895",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<duckdb.duckdb.DuckDBPyConnection at 0x1cda135ac30>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conn.execute(\"INSTALL vss; LOAD vss;\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4a7a3ffb",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "BinderException",
+     "evalue": "Binder Error: HNSW indexes can only be created in in-memory databases, or when the configuration option 'hnsw_enable_experimental_persistence' is set to true.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mBinderException\u001b[39m                           Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 38\u001b[39m\n\u001b[32m     35\u001b[39m     conn.execute(\u001b[33m\"\u001b[39m\u001b[33mINSERT INTO textos VALUES (?, ?, ?)\u001b[39m\u001b[33m\"\u001b[39m, (i, texto, vector))\n\u001b[32m     37\u001b[39m \u001b[38;5;66;03m# Crear el índice HNSW en la columna embedding\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m38\u001b[39m \u001b[43mconn\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mCREATE INDEX vss_idx ON textos USING HNSW (embedding);\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m     40\u001b[39m \u001b[38;5;66;03m# Realizar una búsqueda de los 3 textos más similares a una consulta\u001b[39;00m\n\u001b[32m     41\u001b[39m consulta = \u001b[33m\"\u001b[39m\u001b[33minteligencia artificial en Python\u001b[39m\u001b[33m\"\u001b[39m\n",
+      "\u001b[31mBinderException\u001b[39m: Binder Error: HNSW indexes can only be created in in-memory databases, or when the configuration option 'hnsw_enable_experimental_persistence' is set to true."
+     ]
+    }
+   ],
+   "source": [
+    "import duckdb\n",
+    "import numpy as np\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "\n",
+    "# Conectar a la base de datos\n",
+    "conn = duckdb.connect(\"textos_vss.duckdb\")\n",
+    "\n",
+    "# Instalar y cargar la extensión VSS\n",
+    "conn.execute(\"INSTALL vss;\")\n",
+    "conn.execute(\"LOAD vss;\")\n",
+    "\n",
+    "# Modelo de embeddings\n",
+    "model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
+    "\n",
+    "# Crear la tabla con una columna de embeddings de tipo FLOAT[]\n",
+    "conn.execute(\"\"\"\n",
+    "CREATE TABLE IF NOT EXISTS textos (\n",
+    "    id INTEGER PRIMARY KEY,\n",
+    "    texto TEXT,\n",
+    "    embedding FLOAT[]\n",
+    ");\n",
+    "\"\"\")\n",
+    "\n",
+    "# Insertar textos y sus embeddings\n",
+    "extractos = [\n",
+    "    \"El rápido zorro marrón salta sobre el perro perezoso.\",\n",
+    "    \"La inteligencia artificial está revolucionando el mundo.\",\n",
+    "    \"DuckDB permite análisis embebidos rápidos en Python.\",\n",
+    "    \"Los modelos de lenguaje pueden entender consultas humanas.\",\n",
+    "    \"Python es un lenguaje poderoso para ciencia de datos.\"\n",
+    "]\n",
+    "\n",
+    "for i, texto in enumerate(extractos):\n",
+    "    vector = model.encode(texto).astype(np.float32).tolist()\n",
+    "    conn.execute(\"INSERT INTO textos VALUES (?, ?, ?)\", (i, texto, vector))\n",
+    "\n",
+    "# Crear el índice HNSW en la columna embedding\n",
+    "conn.execute(\"CREATE INDEX vss_idx ON textos USING HNSW (embedding);\")\n",
+    "\n",
+    "# Realizar una búsqueda de los 3 textos más similares a una consulta\n",
+    "consulta = \"inteligencia artificial en Python\"\n",
+    "vector_consulta = model.encode(consulta).astype(np.float32).tolist()\n",
+    "resultados = conn.execute(\"\"\"\n",
+    "    SELECT id, texto, array_distance(embedding, ?) AS distancia\n",
+    "    FROM textos\n",
+    "    ORDER BY distancia ASC\n",
+    "    LIMIT 3;\n",
+    "\"\"\", (vector_consulta,)).fetchall()\n",
+    "\n",
+    "# Mostrar los resultados\n",
+    "for id_, texto, distancia in resultados:\n",
+    "    print(f\"[ID: {id_}] Distancia: {distancia:.4f} → {texto}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2f6059bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<duckdb.duckdb.DuckDBPyConnection at 0x1cda32d9730>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conn = duckdb.connect(\"textos_vss.duckdb\")\n",
+    "conn.execute(\"SET hnsw_enable_experimental_persistence=true;\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "449bebc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<duckdb.duckdb.DuckDBPyConnection at 0x1cda322a5b0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conn.execute(\"DELETE FROM textos;\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}