commit 5f29d7ef854ad1c14e2ca5b6a082a192a5df2ed8
Author: Egutierrez <egutierrez@dead.dd>
Date:   Mon Apr 6 00:57:17 2026 +0200

    init: estudio_embeddings analysis from fn_registry

diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md
new file mode 100644
index 0000000..36a65c7
--- /dev/null
+++ b/.claude/CLAUDE.md
@@ -0,0 +1,40 @@
+# JUPYTER HABILITADO EN ESTE ANALISIS
+
+## Reglas OBLIGATORIAS para Claude
+
+### 1. CODIGO INMUTABLE — NUNCA MODIFICAR CELDAS EXISTENTES
+- **PROHIBIDO** usar NotebookEdit para reemplazar celdas existentes
+- **SIEMPRE** anadir celdas NUEVAS al final del notebook
+- Si hay un error en una celda, crear celda nueva con la correccion
+- El historial de trabajo debe quedar intacto para trazabilidad
+
+### 2. PROGRAMACION FUNCIONAL OBLIGATORIA
+- **Funciones puras**: sin efectos secundarios, mismo input -> mismo output
+- **Inmutabilidad**: nunca mutar datos, crear copias transformadas
+- **Composicion**: funciones pequenas que se combinan
+- Preferir: `map`, `filter`, `reduce`, list comprehensions
+- Evitar: loops con mutacion, `global`, modificar argumentos in-place
+
+### 3. SIEMPRE usar MCP jupyter para ejecutar codigo Python
+- Las ejecuciones se ven en tiempo real en Jupyter Lab del usuario
+- Compartimos variables y estado del kernel
+- **NUNCA usar bash para ejecutar Python en este analisis**
+
+### 4. Verificar Jupyter activo ANTES de ejecutar
+- Si no esta activo: pedir al usuario que ejecute `./run-jupyter-lab.sh`
+
+### 5. Gestion de notebooks
+- Notebooks en la carpeta `notebooks/` o subcarpetas
+- Si un notebook tiene >50 celdas, crear uno nuevo
+- Nombrar descriptivamente: `01_exploracion.ipynb`, `02_limpieza.ipynb`
+
+### 6. Gestion de Python
+- **SIEMPRE usar `uv`** para gestionar dependencias
+- Anadir paquetes con `uv add nombre_paquete`
+
+### 7. Acceso al fn_registry
+- `FN_REGISTRY_ROOT` apunta a la raiz del registry
+- Para importar funciones Python: `sys.path.insert(0, os.path.join(os.environ["FN_REGISTRY_ROOT"], "python", "functions"))`
+- Para consultar registry.db: `sqlite3` o `import sqlite3` con la ruta `$FN_REGISTRY_ROOT/registry.db`
+
+
diff --git a/.ipython/profile_default/startup/00_fn_registry.py b/.ipython/profile_default/startup/00_fn_registry.py
new file mode 100644
index 0000000..43d611d
--- /dev/null
+++ b/.ipython/profile_default/startup/00_fn_registry.py
@@ -0,0 +1,83 @@
+"""
+fn_registry kernel startup
+Autoconfigura acceso al registry en cada notebook.
+Generado por write_jupyter_registry_kernel (fn_registry).
+"""
+import os
+import sys
+import sqlite3
+from pathlib import Path
+
+# ── FN_REGISTRY_ROOT ────────────────────────────────────────
+FN_REGISTRY_ROOT = Path("/home/lucas/fn_registry")
+os.environ["FN_REGISTRY_ROOT"] = str(FN_REGISTRY_ROOT)
+
+# ── sys.path: importar funciones Python del registry ────────
+_python_functions = FN_REGISTRY_ROOT / "python" / "functions"
+for _domain in sorted(_python_functions.iterdir()) if _python_functions.exists() else []:
+    if _domain.is_dir() and not _domain.name.startswith("_"):
+        _path = str(_domain)
+        if _path not in sys.path:
+            sys.path.insert(0, _path)
+
+# Tambien el directorio padre para imports por dominio: from core import filter_list
+_pf = str(_python_functions)
+if _pf not in sys.path:
+    sys.path.insert(0, _pf)
+
+# ── fn_query: consultar registry.db desde el notebook ───────
+_REGISTRY_DB = FN_REGISTRY_ROOT / "registry.db"
+
+def fn_query(sql, params=()):
+    """Ejecuta una consulta SQL sobre registry.db y retorna las filas.
+
+    Ejemplos:
+        fn_query("SELECT id, description FROM functions WHERE domain = ?", ("finance",))
+        fn_query("SELECT id FROM functions_fts WHERE functions_fts MATCH ?", ("slice*",))
+    """
+    if not _REGISTRY_DB.exists():
+        raise FileNotFoundError(f"registry.db no encontrado en {_REGISTRY_DB}")
+    con = sqlite3.connect(str(_REGISTRY_DB))
+    con.row_factory = sqlite3.Row
+    try:
+        rows = con.execute(sql, params).fetchall()
+        return [dict(r) for r in rows]
+    finally:
+        con.close()
+
+def fn_search(term):
+    """Busca funciones y tipos en el registry por nombre o descripcion.
+
+    Ejemplo:
+        fn_search("slice")
+        fn_search("finance")
+    """
+    fts_term = f"name:{term}* OR description:{term}*"
+    functions = fn_query(
+        "SELECT id, kind, purity, lang, description FROM functions "
+        "WHERE id IN (SELECT id FROM functions_fts WHERE functions_fts MATCH ?) "
+        "ORDER BY name", (fts_term,)
+    )
+    types = fn_query(
+        "SELECT id, algebraic, lang, description FROM types "
+        "WHERE id IN (SELECT id FROM types_fts WHERE types_fts MATCH ?) "
+        "ORDER BY name", (fts_term,)
+    )
+    return {"functions": functions, "types": types}
+
+def fn_code(function_id):
+    """Retorna el codigo fuente de una funcion del registry.
+
+    Ejemplo:
+        print(fn_code("filter_list_py_core"))
+    """
+    rows = fn_query("SELECT code FROM functions WHERE id = ?", (function_id,))
+    if not rows:
+        raise KeyError(f"Funcion no encontrada: {function_id}")
+    return rows[0]["code"]
+
+# ── Mensaje de bienvenida ───────────────────────────────────
+print(f"fn_registry conectado: {FN_REGISTRY_ROOT}")
+print(f"  registry.db: {'OK' if _REGISTRY_DB.exists() else 'NO ENCONTRADO'}")
+print(f"  Python functions: {_pf}")
+print(f"  Helpers: fn_query(), fn_search(), fn_code()")
diff --git a/.jupyter-port b/.jupyter-port
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--- /dev/null
+++ b/.jupyter-port
@@ -0,0 +1 @@
+8888
diff --git a/.jupyter/collaboration_sessions.json b/.jupyter/collaboration_sessions.json
new file mode 100644
index 0000000..c11df8e
--- /dev/null
+++ b/.jupyter/collaboration_sessions.json
@@ -0,0 +1,7 @@
+{
+  "79e34df7-326f-4746-a11b-98ba222ef286": {
+    "version": "2.3.0",
+    "created_at": "2026-04-02T15:37:29.856134+00:00",
+    "document_version": "2.0.0"
+  }
+}
\ No newline at end of file
diff --git a/.jupyter_ystore.db b/.jupyter_ystore.db
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index 0000000..2dd01d3
Binary files /dev/null and b/.jupyter_ystore.db differ
diff --git a/.mcp.json b/.mcp.json
new file mode 100644
index 0000000..1037efc
--- /dev/null
+++ b/.mcp.json
@@ -0,0 +1,12 @@
+{
+  "mcpServers": {
+    "jupyter": {
+      "command": "/home/lucas/fn_registry/analysis/estudio_embeddings/.venv/bin/python",
+      "args": ["-m", "jupyter_mcp_server.server"],
+      "env": {
+        "SERVER_URL": "http://localhost:8888",
+        "TOKEN": ""
+      }
+    }
+  }
+}
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..24ee5b1
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/main.py b/main.py
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index 0000000..e202270
--- /dev/null
+++ b/main.py
@@ -0,0 +1,6 @@
+def main():
+    print("Hello from estudio-embeddings!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/notebooks/.ipynb_checkpoints/01_exploracion_embeddings-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/01_exploracion_embeddings-checkpoint.ipynb
new file mode 100644
index 0000000..8cfed3f
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/01_exploracion_embeddings-checkpoint.ipynb
@@ -0,0 +1,16 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/.ipynb_checkpoints/02_ram_benchmark-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/02_ram_benchmark-checkpoint.ipynb
new file mode 100644
index 0000000..8cfed3f
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/02_ram_benchmark-checkpoint.ipynb
@@ -0,0 +1,16 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/.ipynb_checkpoints/03_modelo_local-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/03_modelo_local-checkpoint.ipynb
new file mode 100644
index 0000000..8cfed3f
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/03_modelo_local-checkpoint.ipynb
@@ -0,0 +1,16 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/.ipynb_checkpoints/04_vector_storage-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/04_vector_storage-checkpoint.ipynb
new file mode 100644
index 0000000..8cfed3f
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/04_vector_storage-checkpoint.ipynb
@@ -0,0 +1,16 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/01_exploracion_embeddings.ipynb b/notebooks/01_exploracion_embeddings.ipynb
new file mode 100644
index 0000000..10b2afd
--- /dev/null
+++ b/notebooks/01_exploracion_embeddings.ipynb
@@ -0,0 +1,1314 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "939ac1fa-c8f0-4285-86aa-892051cd6800",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e11d891",
+   "metadata": {},
+   "source": [
+    "# Exploración de Embeddings: Modelos Livianos para Retrieval\n",
+    "\n",
+    "Comparamos modelos de sentence-transformers enfocándonos en:\n",
+    "- **Tamaño y velocidad** — cuál es el más liviano\n",
+    "- **Calidad de retrieval** — precision@k, recall@k, MRR\n",
+    "- **Trade-off práctico** — el mejor balance peso/rendimiento"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fafce80",
+   "metadata": {},
+   "source": [
+    "## 1. Setup e imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "29508ec1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Modelos a comparar: 6\n",
+      "  MiniLM-L6                 → all-MiniLM-L6-v2                                   (22M - solo inglés (baseline))\n",
+      "  MiniLM-L12                → all-MiniLM-L12-v2                                  (33M - solo inglés)\n",
+      "  paraphrase-multi-L12      → paraphrase-multilingual-MiniLM-L12-v2              (118M - multilingüe 50+ idiomas)\n",
+      "  multi-e5-small            → intfloat/multilingual-e5-small                     (118M - multilingüe, buen retrieval)\n",
+      "  nomic-v1.5                → nomic-ai/nomic-embed-text-v1.5                     (137M - Matryoshka, dim flexible)\n",
+      "  BGE-m3                    → BAAI/bge-m3                                        (568M - multilingüe SOTA, más pesado)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "import faiss\n",
+    "import time\n",
+    "import os\n",
+    "\n",
+    "plt.style.use('seaborn-v0_8-whitegrid')\n",
+    "plt.rcParams['figure.figsize'] = (12, 6)\n",
+    "\n",
+    "# Modelos a comparar: nombre -> (id huggingface, descripcion, prefijo_query, prefijo_doc)\n",
+    "# None = sin prefijo. Los modelos e5 y nomic necesitan prefijos especiales.\n",
+    "MODELS = {\n",
+    "    'MiniLM-L6':           ('all-MiniLM-L6-v2',                      '22M - solo inglés (baseline)',        None, None),\n",
+    "    'MiniLM-L12':          ('all-MiniLM-L12-v2',                     '33M - solo inglés',                   None, None),\n",
+    "    'paraphrase-multi-L12':('paraphrase-multilingual-MiniLM-L12-v2', '118M - multilingüe 50+ idiomas',      None, None),\n",
+    "    'multi-e5-small':      ('intfloat/multilingual-e5-small',        '118M - multilingüe, buen retrieval',  'query: ', 'passage: '),\n",
+    "    'nomic-v1.5':          ('nomic-ai/nomic-embed-text-v1.5',        '137M - Matryoshka, dim flexible',     'search_query: ', 'search_document: '),\n",
+    "    'BGE-m3':              ('BAAI/bge-m3',                           '568M - multilingüe SOTA, más pesado', None, None),\n",
+    "}\n",
+    "\n",
+    "print(f'Modelos a comparar: {len(MODELS)}')\n",
+    "for name, (model_id, desc, _, _) in MODELS.items():\n",
+    "    print(f'  {name:25s} → {model_id:50s} ({desc})')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf402d89",
+   "metadata": {},
+   "source": [
+    "## 2. Corpus de prueba\n",
+    "\n",
+    "Creamos un corpus con documentos de distintas categorías para evaluar retrieval. Cada query tiene documentos relevantes conocidos (ground truth)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3550fb1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Corpus: 25 documentos en español\n",
+      "Queries: 10 en español con ground truth\n",
+      "Categorías: programación, ciencia, cocina, finanzas, geografía\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Corpus: documentos agrupados por tema (en español para evaluar retrieval multilingüe)\n",
+    "corpus = [\n",
+    "    # Programación (0-4)\n",
+    "    'Python es un lenguaje de programación de alto nivel conocido por su legibilidad y versatilidad',\n",
+    "    'JavaScript se ejecuta en el navegador y es esencial para el desarrollo web',\n",
+    "    'Rust proporciona seguridad de memoria sin recolector de basura mediante su sistema de ownership',\n",
+    "    'SQL es el lenguaje estándar para consultar bases de datos relacionales',\n",
+    "    'Git es un sistema de control de versiones distribuido para rastrear cambios en el código',\n",
+    "\n",
+    "    # Ciencia (5-9)\n",
+    "    'La fotosíntesis convierte la luz solar en energía química dentro de las células vegetales',\n",
+    "    'La teoría de la relatividad describe cómo la gravedad deforma el espacio-tiempo',\n",
+    "    'El ADN transporta información genética usando cuatro bases de nucleótidos',\n",
+    "    'El entrelazamiento cuántico vincula partículas sin importar la distancia entre ellas',\n",
+    "    'La evolución ocurre mediante selección natural y deriva genética',\n",
+    "\n",
+    "    # Cocina (10-14)\n",
+    "    'El pan de masa madre requiere un cultivo fermentado de harina y agua',\n",
+    "    'El arroz para sushi se sazona con vinagre, azúcar y sal',\n",
+    "    'La reacción de Maillard crea el dorado y sabor al cocinar carne a alta temperatura',\n",
+    "    'La emulsificación une aceite y agua usando agentes como la yema de huevo',\n",
+    "    'La fermentación preserva alimentos y crea sabores complejos con el tiempo',\n",
+    "\n",
+    "    # Finanzas (15-19)\n",
+    "    'El interés compuesto hace crecer los ahorros exponencialmente a largo plazo',\n",
+    "    'La diversificación reduce el riesgo del portafolio distribuyendo inversiones entre activos',\n",
+    "    'La inflación erosiona el poder adquisitivo y afecta las inversiones de renta fija',\n",
+    "    'La Reserva Federal establece política monetaria ajustando las tasas de interés',\n",
+    "    'Los fondos indexados replican índices de mercado con comisiones mínimas de gestión',\n",
+    "\n",
+    "    # Geografía (20-24)\n",
+    "    'La selva amazónica produce aproximadamente el 20% del oxígeno del mundo',\n",
+    "    'Las placas tectónicas se desplazan causando terremotos y erupciones volcánicas',\n",
+    "    'El desierto del Sahara se extiende por once países del norte de África',\n",
+    "    'Las corrientes oceánicas regulan el clima global y los patrones meteorológicos',\n",
+    "    'La Fosa de las Marianas es el punto más profundo conocido del océano',\n",
+    "]\n",
+    "\n",
+    "# Queries en español con ground truth (índices de documentos relevantes)\n",
+    "queries = [\n",
+    "    ('¿Cómo manejan los lenguajes de programación la memoria?',           [0, 2]),\n",
+    "    ('¿Qué es el control de versiones para código fuente?',               [4]),\n",
+    "    ('¿Cómo producen energía las plantas a partir de la luz?',            [5]),\n",
+    "    ('Cuéntame sobre física de partículas y mecánica cuántica',           [7, 8]),\n",
+    "    ('¿Cómo funciona la fermentación del pan?',                           [10, 14]),\n",
+    "    ('¿Qué hace que la comida sepa mejor al cocinarla a fuego alto?',    [12, 13]),\n",
+    "    ('¿Cómo puedo hacer crecer mis ahorros con el tiempo?',              [15, 16, 19]),\n",
+    "    ('¿Qué afecta la economía y los precios?',                           [17, 18]),\n",
+    "    ('Háblame de las partes más profundas del océano',                    [24]),\n",
+    "    ('¿Cómo cambia la superficie de la Tierra con el tiempo?',           [21, 22]),\n",
+    "]\n",
+    "\n",
+    "print(f'Corpus: {len(corpus)} documentos en español')\n",
+    "print(f'Queries: {len(queries)} en español con ground truth')\n",
+    "print(f'Categorías: programación, ciencia, cocina, finanzas, geografía')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc3857da",
+   "metadata": {},
+   "source": [
+    "## 3. Cargar modelos y generar embeddings\n",
+    "\n",
+    "Cargamos cada modelo, medimos tiempo de carga, tiempo de encoding, y tamaño en memoria."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6470048d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Modelo                    |  Dim  |     Params |   Size |  Load  |  Corpus   | Queries\n",
+      "-------------------------------------------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd228d449f514e07a368b80fd5853c13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/103 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: sentence-transformers/all-MiniLM-L6-v2\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MiniLM-L6                 | dim= 384 | 22.7M params |     87MB | load=  2.4s | corpus=0.264s | queries=0.015s\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43c98b329abd434085175b9decfb1017",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: sentence-transformers/all-MiniLM-L12-v2\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MiniLM-L12                | dim= 384 | 33.4M params |    127MB | load=  1.9s | corpus=0.018s | queries=0.012s\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f90402364c634397ad9b29d5d41c65a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "paraphrase-multi-L12      | dim= 384 | 117.7M params |    449MB | load=  3.5s | corpus=0.041s | queries=0.014s\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c115aee165af46e3b8bd6e2a4e22fb67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "multi-e5-small            | dim= 384 | 117.7M params |    449MB | load=  4.1s | corpus=0.055s | queries=0.017s\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11a55f1c36e34bdf8fdcf7784a283788",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/547M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<All keys matched successfully>\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b68536d70734f0f83201c2feb749e1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b79d8be283d247eb8e2cd522e536ca39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d0f53694616493bbe303a9869972988",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "588f7d602f2342e5916d1b665d0bd941",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/695 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe5eac1948ab48b9a6aa0f7af2d58b00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/286 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nomic-v1.5                | dim= 768 | 136.7M params |    522MB | load= 19.0s | corpus=0.055s | queries=0.018s\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec927998eef14868989f3cf4e19b2a5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6afeac46acb4460a5d609799732f518",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config_sentence_transformers.json:   0%|          | 0.00/123 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adef57db897a41ef89e71fa35145f246",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d7b38439e3346cabb17b8fa66b03774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "sentence_bert_config.json:   0%|          | 0.00/54.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6467c55cb204f67aebde1285b54a64d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/687 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ce485266ee648aea5fb948dcebc0140",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/2.27G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fddf98edd5f4a2aad93f22dff9976b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/391 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef2460bc03a8491693311a460e32e509",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/444 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fbfb4328cab84c83aa5a8f1665df3537",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a2145230a38405bb6d1834e2483dbcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/2.27G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd00e65a5a744979ad58563570c1a945",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cc0f51c1bec4bb08b7fb9b50849f528",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/964 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "381797379d944388b3fbbf707c58e957",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/191 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BGE-m3                    | dim=1024 | 567.8M params |   2166MB | load= 48.0s | corpus=0.069s | queries=0.029s\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gc\n",
+    "\n",
+    "def measure_model(name, model_id, corpus, queries, q_prefix=None, d_prefix=None):\n",
+    "    \"\"\"Carga modelo, genera embeddings, mide tiempos y tamaño.\"\"\"\n",
+    "    gc.collect()\n",
+    "\n",
+    "    # Cargar modelo (nomic necesita trust_remote_code)\n",
+    "    t0 = time.perf_counter()\n",
+    "    kwargs = {'trust_remote_code': True} if 'nomic' in model_id else {}\n",
+    "    model = SentenceTransformer(model_id, **kwargs)\n",
+    "    load_time = time.perf_counter() - t0\n",
+    "\n",
+    "    # Tamaño del modelo en MB (acceder al transformer base)\n",
+    "    try:\n",
+    "        base = model[0].auto_model\n",
+    "    except:\n",
+    "        base = model\n",
+    "    param_size = sum(p.nelement() * p.element_size() for p in base.parameters())\n",
+    "    model_mb = param_size / (1024 * 1024)\n",
+    "    n_params = sum(p.nelement() for p in base.parameters()) / 1e6\n",
+    "\n",
+    "    # Aplicar prefijos si el modelo los requiere\n",
+    "    corpus_input = [f'{d_prefix}{doc}' for doc in corpus] if d_prefix else corpus\n",
+    "    query_texts = [f'{q_prefix}{q}' for q, _ in queries] if q_prefix else [q for q, _ in queries]\n",
+    "\n",
+    "    # Encoding del corpus\n",
+    "    t0 = time.perf_counter()\n",
+    "    corpus_embs = model.encode(corpus_input, normalize_embeddings=True, show_progress_bar=False)\n",
+    "    corpus_time = time.perf_counter() - t0\n",
+    "\n",
+    "    # Encoding de queries\n",
+    "    t0 = time.perf_counter()\n",
+    "    query_embs = model.encode(query_texts, normalize_embeddings=True, show_progress_bar=False)\n",
+    "    query_time = time.perf_counter() - t0\n",
+    "\n",
+    "    dim = corpus_embs.shape[1]\n",
+    "\n",
+    "    print(f'{name:25s} | dim={dim:4d} | {n_params:.1f}M params | {model_mb:6.0f}MB | '\n",
+    "          f'load={load_time:5.1f}s | corpus={corpus_time:.3f}s | queries={query_time:.3f}s')\n",
+    "\n",
+    "    return {\n",
+    "        'name': name,\n",
+    "        'model_id': model_id,\n",
+    "        'dim': dim,\n",
+    "        'n_params_m': round(n_params, 1),\n",
+    "        'model_mb': round(model_mb, 1),\n",
+    "        'load_time_s': round(load_time, 2),\n",
+    "        'corpus_encode_s': round(corpus_time, 4),\n",
+    "        'query_encode_s': round(query_time, 4),\n",
+    "        'corpus_embs': corpus_embs,\n",
+    "        'query_embs': query_embs,\n",
+    "    }\n",
+    "\n",
+    "# Ejecutar benchmark de carga y encoding\n",
+    "results = {}\n",
+    "print(f'{\"Modelo\":25s} | {\"Dim\":>4s}  | {\"Params\":>10s} | {\"Size\":>6s} | {\"Load\":>5s}  | {\"Corpus\":>7s}   | {\"Queries\":>7s}')\n",
+    "print('-' * 115)\n",
+    "for name, (model_id, desc, q_prefix, d_prefix) in MODELS.items():\n",
+    "    results[name] = measure_model(name, model_id, corpus, queries, q_prefix, d_prefix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2598cba",
+   "metadata": {},
+   "source": [
+    "## 4. Evaluación de Retrieval\n",
+    "\n",
+    "Para cada modelo, usamos FAISS para buscar los k documentos más cercanos a cada query y comparamos contra el ground truth.\n",
+    "\n",
+    "**Métricas:**\n",
+    "- **Precision@k**: de los k recuperados, qué fracción es relevante\n",
+    "- **Recall@k**: de los relevantes, qué fracción fue recuperada\n",
+    "- **MRR (Mean Reciprocal Rank)**: inverso de la posición del primer resultado relevante"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "39149ab1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MiniLM-L6       | Precision@5=0.240 | Recall@5=0.733 | MRR=0.778\n",
+      "MiniLM-L12      | Precision@5=0.220 | Recall@5=0.683 | MRR=0.820\n",
+      "paraphrase-multi-L12 | Precision@5=0.320 | Recall@5=0.900 | MRR=0.933\n",
+      "multi-e5-small  | Precision@5=0.300 | Recall@5=0.867 | MRR=0.950\n",
+      "nomic-v1.5      | Precision@5=0.240 | Recall@5=0.733 | MRR=0.900\n",
+      "BGE-m3          | Precision@5=0.300 | Recall@5=0.867 | MRR=1.000\n"
+     ]
+    }
+   ],
+   "source": [
+    "def evaluate_retrieval(corpus_embs, query_embs, queries, k=5):\n",
+    "    \"\"\"Evalúa retrieval con FAISS. Retorna métricas por query y agregadas.\"\"\"\n",
+    "    dim = corpus_embs.shape[1]\n",
+    "\n",
+    "    # Índice FAISS con inner product (embeddings ya normalizados = cosine similarity)\n",
+    "    index = faiss.IndexFlatIP(dim)\n",
+    "    index.add(corpus_embs.astype(np.float32))\n",
+    "\n",
+    "    # Buscar k vecinos para cada query\n",
+    "    scores, indices = index.search(query_embs.astype(np.float32), k)\n",
+    "\n",
+    "    per_query = []\n",
+    "    for i, (query_text, relevant_ids) in enumerate(queries):\n",
+    "        retrieved = indices[i].tolist()\n",
+    "        relevant_set = set(relevant_ids)\n",
+    "        retrieved_relevant = [idx for idx in retrieved if idx in relevant_set]\n",
+    "\n",
+    "        precision_at_k = len(retrieved_relevant) / k\n",
+    "        recall_at_k = len(retrieved_relevant) / len(relevant_set) if relevant_set else 0\n",
+    "\n",
+    "        # MRR: posición del primer relevante\n",
+    "        rr = 0\n",
+    "        for rank, idx in enumerate(retrieved, 1):\n",
+    "            if idx in relevant_set:\n",
+    "                rr = 1.0 / rank\n",
+    "                break\n",
+    "\n",
+    "        per_query.append({\n",
+    "            'query': query_text[:60],\n",
+    "            'retrieved': retrieved,\n",
+    "            'relevant': relevant_ids,\n",
+    "            'precision@k': precision_at_k,\n",
+    "            'recall@k': recall_at_k,\n",
+    "            'mrr': rr,\n",
+    "        })\n",
+    "\n",
+    "    return {\n",
+    "        'per_query': per_query,\n",
+    "        'mean_precision': np.mean([q['precision@k'] for q in per_query]),\n",
+    "        'mean_recall': np.mean([q['recall@k'] for q in per_query]),\n",
+    "        'mrr': np.mean([q['mrr'] for q in per_query]),\n",
+    "    }\n",
+    "\n",
+    "# Evaluar cada modelo\n",
+    "K = 5\n",
+    "eval_results = {}\n",
+    "for name, res in results.items():\n",
+    "    ev = evaluate_retrieval(res['corpus_embs'], res['query_embs'], queries, k=K)\n",
+    "    eval_results[name] = ev\n",
+    "    print(f\"{name:15s} | Precision@{K}={ev['mean_precision']:.3f} | Recall@{K}={ev['mean_recall']:.3f} | MRR={ev['mrr']:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6ae98f7",
+   "metadata": {},
+   "source": [
+    "## 5. Tabla comparativa resumen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "83ce9114",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_6fe24_row0_col1, #T_6fe24_row1_col3 {\n",
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+       "  color: #f1f1f1;\n",
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+       "#T_6fe24_row0_col3 {\n",
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+       "#T_6fe24_row0_col6, #T_6fe24_row4_col6 {\n",
+       "  background-color: #d3eecd;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_6fe24_row0_col7, #T_6fe24_row4_col7 {\n",
+       "  background-color: #ccebc6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_6fe24_row0_col8, #T_6fe24_row1_col6, #T_6fe24_row1_col7 {\n",
+       "  background-color: #f7fcf5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_6fe24_row1_col1 {\n",
+       "  background-color: #71020e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row1_col8 {\n",
+       "  background-color: #d6efd0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_6fe24_row2_col1, #T_6fe24_row3_col1 {\n",
+       "  background-color: #b31218;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row2_col3 {\n",
+       "  background-color: #79040f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row2_col6, #T_6fe24_row2_col7, #T_6fe24_row5_col8 {\n",
+       "  background-color: #00441b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row2_col8 {\n",
+       "  background-color: #2f984f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row3_col3 {\n",
+       "  background-color: #7e0610;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row3_col6, #T_6fe24_row5_col6 {\n",
+       "  background-color: #157f3b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row3_col7, #T_6fe24_row5_col7 {\n",
+       "  background-color: #077331;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row3_col8 {\n",
+       "  background-color: #1c8540;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row4_col1 {\n",
+       "  background-color: #be151a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row4_col3 {\n",
+       "  background-color: #ee3a2c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row4_col8 {\n",
+       "  background-color: #60ba6c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_6fe24_row5_col1, #T_6fe24_row5_col3 {\n",
+       "  background-color: #fff5f0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_6fe24\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_6fe24_level0_col0\" class=\"col_heading level0 col0\" >Params (M)</th>\n",
+       "      <th id=\"T_6fe24_level0_col1\" class=\"col_heading level0 col1\" >Tamaño (MB)</th>\n",
+       "      <th id=\"T_6fe24_level0_col2\" class=\"col_heading level0 col2\" >Dim</th>\n",
+       "      <th id=\"T_6fe24_level0_col3\" class=\"col_heading level0 col3\" >Load (s)</th>\n",
+       "      <th id=\"T_6fe24_level0_col4\" class=\"col_heading level0 col4\" >Encode corpus (s)</th>\n",
+       "      <th id=\"T_6fe24_level0_col5\" class=\"col_heading level0 col5\" >Encode queries (s)</th>\n",
+       "      <th id=\"T_6fe24_level0_col6\" class=\"col_heading level0 col6\" >Precision@5</th>\n",
+       "      <th id=\"T_6fe24_level0_col7\" class=\"col_heading level0 col7\" >Recall@5</th>\n",
+       "      <th id=\"T_6fe24_level0_col8\" class=\"col_heading level0 col8\" >MRR</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Modelo</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6fe24_level0_row0\" class=\"row_heading level0 row0\" >MiniLM-L6</th>\n",
+       "      <td id=\"T_6fe24_row0_col0\" class=\"data row0 col0\" >22.700000</td>\n",
+       "      <td id=\"T_6fe24_row0_col1\" class=\"data row0 col1\" >86.600000</td>\n",
+       "      <td id=\"T_6fe24_row0_col2\" class=\"data row0 col2\" >384</td>\n",
+       "      <td id=\"T_6fe24_row0_col3\" class=\"data row0 col3\" >2.360000</td>\n",
+       "      <td id=\"T_6fe24_row0_col4\" class=\"data row0 col4\" >0.264300</td>\n",
+       "      <td id=\"T_6fe24_row0_col5\" class=\"data row0 col5\" >0.014600</td>\n",
+       "      <td id=\"T_6fe24_row0_col6\" class=\"data row0 col6\" >0.240000</td>\n",
+       "      <td id=\"T_6fe24_row0_col7\" class=\"data row0 col7\" >0.733000</td>\n",
+       "      <td id=\"T_6fe24_row0_col8\" class=\"data row0 col8\" >0.778000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6fe24_level0_row1\" class=\"row_heading level0 row1\" >MiniLM-L12</th>\n",
+       "      <td id=\"T_6fe24_row1_col0\" class=\"data row1 col0\" >33.400000</td>\n",
+       "      <td id=\"T_6fe24_row1_col1\" class=\"data row1 col1\" >127.300000</td>\n",
+       "      <td id=\"T_6fe24_row1_col2\" class=\"data row1 col2\" >384</td>\n",
+       "      <td id=\"T_6fe24_row1_col3\" class=\"data row1 col3\" >1.850000</td>\n",
+       "      <td id=\"T_6fe24_row1_col4\" class=\"data row1 col4\" >0.017600</td>\n",
+       "      <td id=\"T_6fe24_row1_col5\" class=\"data row1 col5\" >0.012400</td>\n",
+       "      <td id=\"T_6fe24_row1_col6\" class=\"data row1 col6\" >0.220000</td>\n",
+       "      <td id=\"T_6fe24_row1_col7\" class=\"data row1 col7\" >0.683000</td>\n",
+       "      <td id=\"T_6fe24_row1_col8\" class=\"data row1 col8\" >0.820000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6fe24_level0_row2\" class=\"row_heading level0 row2\" >paraphrase-multi-L12</th>\n",
+       "      <td id=\"T_6fe24_row2_col0\" class=\"data row2 col0\" >117.700000</td>\n",
+       "      <td id=\"T_6fe24_row2_col1\" class=\"data row2 col1\" >448.800000</td>\n",
+       "      <td id=\"T_6fe24_row2_col2\" class=\"data row2 col2\" >384</td>\n",
+       "      <td id=\"T_6fe24_row2_col3\" class=\"data row2 col3\" >3.500000</td>\n",
+       "      <td id=\"T_6fe24_row2_col4\" class=\"data row2 col4\" >0.040700</td>\n",
+       "      <td id=\"T_6fe24_row2_col5\" class=\"data row2 col5\" >0.013800</td>\n",
+       "      <td id=\"T_6fe24_row2_col6\" class=\"data row2 col6\" >0.320000</td>\n",
+       "      <td id=\"T_6fe24_row2_col7\" class=\"data row2 col7\" >0.900000</td>\n",
+       "      <td id=\"T_6fe24_row2_col8\" class=\"data row2 col8\" >0.933000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6fe24_level0_row3\" class=\"row_heading level0 row3\" >multi-e5-small</th>\n",
+       "      <td id=\"T_6fe24_row3_col0\" class=\"data row3 col0\" >117.700000</td>\n",
+       "      <td id=\"T_6fe24_row3_col1\" class=\"data row3 col1\" >448.800000</td>\n",
+       "      <td id=\"T_6fe24_row3_col2\" class=\"data row3 col2\" >384</td>\n",
+       "      <td id=\"T_6fe24_row3_col3\" class=\"data row3 col3\" >4.100000</td>\n",
+       "      <td id=\"T_6fe24_row3_col4\" class=\"data row3 col4\" >0.055500</td>\n",
+       "      <td id=\"T_6fe24_row3_col5\" class=\"data row3 col5\" >0.016900</td>\n",
+       "      <td id=\"T_6fe24_row3_col6\" class=\"data row3 col6\" >0.300000</td>\n",
+       "      <td id=\"T_6fe24_row3_col7\" class=\"data row3 col7\" >0.867000</td>\n",
+       "      <td id=\"T_6fe24_row3_col8\" class=\"data row3 col8\" >0.950000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6fe24_level0_row4\" class=\"row_heading level0 row4\" >nomic-v1.5</th>\n",
+       "      <td id=\"T_6fe24_row4_col0\" class=\"data row4 col0\" >136.700000</td>\n",
+       "      <td id=\"T_6fe24_row4_col1\" class=\"data row4 col1\" >521.600000</td>\n",
+       "      <td id=\"T_6fe24_row4_col2\" class=\"data row4 col2\" >768</td>\n",
+       "      <td id=\"T_6fe24_row4_col3\" class=\"data row4 col3\" >19.040000</td>\n",
+       "      <td id=\"T_6fe24_row4_col4\" class=\"data row4 col4\" >0.055400</td>\n",
+       "      <td id=\"T_6fe24_row4_col5\" class=\"data row4 col5\" >0.017500</td>\n",
+       "      <td id=\"T_6fe24_row4_col6\" class=\"data row4 col6\" >0.240000</td>\n",
+       "      <td id=\"T_6fe24_row4_col7\" class=\"data row4 col7\" >0.733000</td>\n",
+       "      <td id=\"T_6fe24_row4_col8\" class=\"data row4 col8\" >0.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6fe24_level0_row5\" class=\"row_heading level0 row5\" >BGE-m3</th>\n",
+       "      <td id=\"T_6fe24_row5_col0\" class=\"data row5 col0\" >567.800000</td>\n",
+       "      <td id=\"T_6fe24_row5_col1\" class=\"data row5 col1\" >2165.800000</td>\n",
+       "      <td id=\"T_6fe24_row5_col2\" class=\"data row5 col2\" >1024</td>\n",
+       "      <td id=\"T_6fe24_row5_col3\" class=\"data row5 col3\" >47.990000</td>\n",
+       "      <td id=\"T_6fe24_row5_col4\" class=\"data row5 col4\" >0.069000</td>\n",
+       "      <td id=\"T_6fe24_row5_col5\" class=\"data row5 col5\" >0.028500</td>\n",
+       "      <td id=\"T_6fe24_row5_col6\" class=\"data row5 col6\" >0.300000</td>\n",
+       "      <td id=\"T_6fe24_row5_col7\" class=\"data row5 col7\" >0.867000</td>\n",
+       "      <td id=\"T_6fe24_row5_col8\" class=\"data row5 col8\" >1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f67287be3c0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Tabla resumen: velocidad + retrieval\n",
+    "rows = []\n",
+    "for name in results:\n",
+    "    r = results[name]\n",
+    "    ev = eval_results[name]\n",
+    "    rows.append({\n",
+    "        'Modelo': name,\n",
+    "        'Params (M)': r['n_params_m'],\n",
+    "        'Tamaño (MB)': r['model_mb'],\n",
+    "        'Dim': r['dim'],\n",
+    "        'Load (s)': r['load_time_s'],\n",
+    "        'Encode corpus (s)': r['corpus_encode_s'],\n",
+    "        'Encode queries (s)': r['query_encode_s'],\n",
+    "        'Precision@5': round(ev['mean_precision'], 3),\n",
+    "        'Recall@5': round(ev['mean_recall'], 3),\n",
+    "        'MRR': round(ev['mrr'], 3),\n",
+    "    })\n",
+    "\n",
+    "df_summary = pd.DataFrame(rows).set_index('Modelo')\n",
+    "df_summary.style.background_gradient(subset=['Precision@5', 'Recall@5', 'MRR'], cmap='Greens')     .background_gradient(subset=['Tamaño (MB)', 'Load (s)'], cmap='Reds_r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc5f97a8",
+   "metadata": {},
+   "source": [
+    "## 6. Visualización comparativa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6bf6dd3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
+    "\n",
+    "models_names = list(eval_results.keys())\n",
+    "colors = plt.cm.Set2(np.linspace(0, 1, len(models_names)))\n",
+    "\n",
+    "# Gráfico 1: Métricas de retrieval\n",
+    "metrics = ['mean_precision', 'mean_recall', 'mrr']\n",
+    "labels = ['Precision@5', 'Recall@5', 'MRR']\n",
+    "x = np.arange(len(labels))\n",
+    "width = 0.15\n",
+    "for i, name in enumerate(models_names):\n",
+    "    ev = eval_results[name]\n",
+    "    vals = [ev[m] for m in metrics]\n",
+    "    axes[0].bar(x + i * width, vals, width, label=name, color=colors[i])\n",
+    "axes[0].set_xticks(x + width * (len(models_names) - 1) / 2)\n",
+    "axes[0].set_xticklabels(labels)\n",
+    "axes[0].set_ylim(0, 1.05)\n",
+    "axes[0].set_title('Calidad de Retrieval (español)')\n",
+    "axes[0].legend(fontsize=8)\n",
+    "\n",
+    "# Gráfico 2: Tamaño vs MRR (scatter)\n",
+    "for i, name in enumerate(models_names):\n",
+    "    r = results[name]\n",
+    "    ev = eval_results[name]\n",
+    "    axes[1].scatter(r['model_mb'], ev['mrr'], s=150, color=colors[i], label=name, zorder=5)\n",
+    "    axes[1].annotate(name, (r['model_mb'], ev['mrr']), textcoords='offset points',\n",
+    "                     xytext=(5, 5), fontsize=8)\n",
+    "axes[1].set_xlabel('Tamaño del modelo (MB)')\n",
+    "axes[1].set_ylabel('MRR')\n",
+    "axes[1].set_title('Trade-off: Tamaño vs Calidad de Retrieval')\n",
+    "\n",
+    "# Gráfico 3: Tiempo de encoding del corpus\n",
+    "encode_times = [results[n]['corpus_encode_s'] for n in models_names]\n",
+    "axes[2].barh(models_names, encode_times, color=colors)\n",
+    "axes[2].set_xlabel('Tiempo (s)')\n",
+    "axes[2].set_title('Velocidad de encoding (25 docs)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dafabe0d",
+   "metadata": {},
+   "source": [
+    "## 7. Detalle por query\n",
+    "\n",
+    "Veamos qué documentos recupera cada modelo para cada query, para entender dónde fallan."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e6b6c58e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Q0: \"¿Cómo manejan los lenguajes de programación la memoria?\"\n",
+      "  Relevantes esperados: [0, 2]\n",
+      "  MiniLM-L6                 → [✓0  3  8  16 ✓2]  P=0.40 R=1.00 RR=1.00\n",
+      "  MiniLM-L12                → [✓0  3  15 ✓2  14]  P=0.40 R=1.00 RR=1.00\n",
+      "  paraphrase-multi-L12      → [✓0 ✓2  14  4  7]  P=0.40 R=1.00 RR=1.00\n",
+      "  multi-e5-small            → [✓0  3 ✓2  4  7]  P=0.40 R=1.00 RR=1.00\n",
+      "  nomic-v1.5                → [✓0 ✓2  3  8  18]  P=0.40 R=1.00 RR=1.00\n",
+      "  BGE-m3                    → [✓0 ✓2  18  1  3]  P=0.40 R=1.00 RR=1.00\n",
+      "\n",
+      "Q1: \"¿Qué es el control de versiones para código fuente?\"\n",
+      "  Relevantes esperados: [4]\n",
+      "  MiniLM-L6                 → [✓4  16  15  6  11]  P=0.20 R=1.00 RR=1.00\n",
+      "  MiniLM-L12                → [✓4  15  14  18  11]  P=0.20 R=1.00 RR=1.00\n",
+      "  paraphrase-multi-L12      → [✓4  14  0  2  23]  P=0.20 R=1.00 RR=1.00\n",
+      "  multi-e5-small            → [✓4  0  1  3  2]  P=0.20 R=1.00 RR=1.00\n",
+      "  nomic-v1.5                → [✓4  16  9  12  8]  P=0.20 R=1.00 RR=1.00\n",
+      "  BGE-m3                    → [✓4  1  3  0  2]  P=0.20 R=1.00 RR=1.00\n",
+      "\n",
+      "Q2: \"¿Cómo producen energía las plantas a partir de la luz?\"\n",
+      "  Relevantes esperados: [5]\n",
+      "  MiniLM-L6                 → [✓5  20  14  8  15]  P=0.20 R=1.00 RR=1.00\n",
+      "  MiniLM-L12                → [✓5  20  15  8  14]  P=0.20 R=1.00 RR=1.00\n",
+      "  paraphrase-multi-L12      → [✓5  14  15  9  20]  P=0.20 R=1.00 RR=1.00\n",
+      "  multi-e5-small            → [✓5  8  20  13  10]  P=0.20 R=1.00 RR=1.00\n",
+      "  nomic-v1.5                → [✓5  20  8  10  21]  P=0.20 R=1.00 RR=1.00\n",
+      "  BGE-m3                    → [✓5  9  10  13  12]  P=0.20 R=1.00 RR=1.00\n",
+      "\n",
+      "Q3: \"Cuéntame sobre física de partículas y mecánica cuántica\"\n",
+      "  Relevantes esperados: [7, 8]\n",
+      "  MiniLM-L6                 → [✓8  14  20  21  13]  P=0.20 R=0.50 RR=1.00\n",
+      "  MiniLM-L12                → [✓8  13  14  12  9]  P=0.20 R=0.50 RR=1.00\n",
+      "  paraphrase-multi-L12      → [✓8  6  12  18  14]  P=0.20 R=0.50 RR=1.00\n",
+      "  multi-e5-small            → [✓8  6  21  5  12]  P=0.20 R=0.50 RR=1.00\n",
+      "  nomic-v1.5                → [✓8  21  6  14  5]  P=0.20 R=0.50 RR=1.00\n",
+      "  BGE-m3                    → [✓8  6  0  23  13]  P=0.20 R=0.50 RR=1.00\n",
+      "\n",
+      "Q4: \"¿Cómo funciona la fermentación del pan?\"\n",
+      "  Relevantes esperados: [10, 14]\n",
+      "  MiniLM-L6                 → [✓10 ✓14  12  20  13]  P=0.40 R=1.00 RR=1.00\n",
+      "  MiniLM-L12                → [✓14 ✓10  12  11  15]  P=0.40 R=1.00 RR=1.00\n",
+      "  paraphrase-multi-L12      → [✓10 ✓14  12  13  11]  P=0.40 R=1.00 RR=1.00\n",
+      "  multi-e5-small            → [✓10 ✓14  12  13  7]  P=0.40 R=1.00 RR=1.00\n",
+      "  nomic-v1.5                → [✓10 ✓14  12  13  19]  P=0.40 R=1.00 RR=1.00\n",
+      "  BGE-m3                    → [✓10 ✓14  13  9  5]  P=0.40 R=1.00 RR=1.00\n",
+      "\n",
+      "Q5: \"¿Qué hace que la comida sepa mejor al cocinarla a fuego alto?\"\n",
+      "  Relevantes esperados: [12, 13]\n",
+      "  MiniLM-L6                 → [ 15  14 ✓12  11  10]  P=0.20 R=0.50 RR=0.33\n",
+      "  MiniLM-L12                → [ 15  10  14  11  8]  P=0.00 R=0.00 RR=0.00\n",
+      "  paraphrase-multi-L12      → [✓12  14  10  11 ✓13]  P=0.40 R=1.00 RR=1.00\n",
+      "  multi-e5-small            → [✓12  14 ✓13  10  11]  P=0.40 R=1.00 RR=1.00\n",
+      "  nomic-v1.5                → [✓12  14  8  10  15]  P=0.20 R=0.50 RR=1.00\n",
+      "  BGE-m3                    → [✓12  14  11 ✓13  0]  P=0.40 R=1.00 RR=1.00\n",
+      "\n",
+      "Q6: \"¿Cómo puedo hacer crecer mis ahorros con el tiempo?\"\n",
+      "  Relevantes esperados: [15, 16, 19]\n",
+      "  MiniLM-L6                 → [✓15  6  11  14  20]  P=0.20 R=0.33 RR=1.00\n",
+      "  MiniLM-L12                → [✓15  11  14  6  8]  P=0.20 R=0.33 RR=1.00\n",
+      "  paraphrase-multi-L12      → [✓15  17 ✓16 ✓19  14]  P=0.60 R=1.00 RR=1.00\n",
+      "  multi-e5-small            → [✓15  18 ✓16  17  6]  P=0.40 R=0.67 RR=1.00\n",
+      "  nomic-v1.5                → [✓15  14  6  12  24]  P=0.20 R=0.33 RR=1.00\n",
+      "  BGE-m3                    → [✓15 ✓16  18  17  14]  P=0.40 R=0.67 RR=1.00\n",
+      "\n",
+      "Q7: \"¿Qué afecta la economía y los precios?\"\n",
+      "  Relevantes esperados: [17, 18]\n",
+      "  MiniLM-L6                 → [ 20  15  14 ✓18  6]  P=0.20 R=0.50 RR=0.25\n",
+      "  MiniLM-L12                → [✓18  15  14  20  24]  P=0.20 R=0.50 RR=1.00\n",
+      "  paraphrase-multi-L12      → [✓17  19  16  15  23]  P=0.20 R=0.50 RR=1.00\n",
+      "  multi-e5-small            → [✓17  23  16  9 ✓18]  P=0.40 R=1.00 RR=1.00\n",
+      "  nomic-v1.5                → [✓17  14 ✓18  12  19]  P=0.40 R=1.00 RR=1.00\n",
+      "  BGE-m3                    → [✓17 ✓18  23  21  15]  P=0.40 R=1.00 RR=1.00\n",
+      "\n",
+      "Q8: \"Háblame de las partes más profundas del océano\"\n",
+      "  Relevantes esperados: [24]\n",
+      "  MiniLM-L6                 → [✓24  6  8  23  15]  P=0.20 R=1.00 RR=1.00\n",
+      "  MiniLM-L12                → [✓24  23  8  15  20]  P=0.20 R=1.00 RR=1.00\n",
+      "  paraphrase-multi-L12      → [✓24  23  22  20  13]  P=0.20 R=1.00 RR=1.00\n",
+      "  multi-e5-small            → [✓24  23  20  21  22]  P=0.20 R=1.00 RR=1.00\n",
+      "  nomic-v1.5                → [✓24  23  13  8  21]  P=0.20 R=1.00 RR=1.00\n",
+      "  BGE-m3                    → [✓24  23  21  0  9]  P=0.20 R=1.00 RR=1.00\n",
+      "\n",
+      "Q9: \"¿Cómo cambia la superficie de la Tierra con el tiempo?\"\n",
+      "  Relevantes esperados: [21, 22]\n",
+      "  MiniLM-L6                 → [ 6  15  14  20 ✓21]  P=0.20 R=0.50 RR=0.20\n",
+      "  MiniLM-L12                → [ 15  6  24  14 ✓22]  P=0.20 R=0.50 RR=0.20\n",
+      "  paraphrase-multi-L12      → [ 23  6 ✓21 ✓22  17]  P=0.40 R=1.00 RR=0.33\n",
+      "  multi-e5-small            → [ 6 ✓21  23  8  5]  P=0.20 R=0.50 RR=0.50\n",
+      "  nomic-v1.5                → [ 6  14  8  12  24]  P=0.00 R=0.00 RR=0.00\n",
+      "  BGE-m3                    → [✓21  9  23  6  5]  P=0.20 R=0.50 RR=1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Detalle: para cada query, qué recuperó cada modelo\n",
+    "for qi, (query_text, relevant) in enumerate(queries):\n",
+    "    print(f'\\nQ{qi}: \"{query_text}\"')\n",
+    "    print(f'  Relevantes esperados: {relevant}')\n",
+    "    for name in results:\n",
+    "        ev = eval_results[name]\n",
+    "        pq = ev['per_query'][qi]\n",
+    "        retrieved = pq['retrieved']\n",
+    "        hits = [f'✓{idx}' if idx in set(relevant) else f' {idx}' for idx in retrieved]\n",
+    "        print(f'  {name:25s} → [{\" \".join(hits)}]  P={pq[\"precision@k\"]:.2f} R={pq[\"recall@k\"]:.2f} RR={pq[\"mrr\"]:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f723a45e",
+   "metadata": {},
+   "source": [
+    "## 8. Visualización t-SNE: ¿Cómo agrupa cada modelo los documentos?\n",
+    "\n",
+    "Proyectamos los embeddings del corpus a 2D con t-SNE. Cada color es una categoría. Un buen modelo debería agrupar documentos del mismo tema juntos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d6038c05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xXp+OajYfxbQ3Kvn+GOrtjzSxnhMAAIBzBH8AAACr2Pj8dDw99d04OP1qzCzUzn+93FOIuzftins2vS8qvf0d7BAAAFrXzLJYmBiPbHIimrW5SAp9kQ4MRs9gJZLUoja4VoI/AACAVWp8fjo++8bX4rW5NxZdm1moxZOnX47D1TfikW33Cv8AAFj1FiYnovbCwai9+Hw0q7Pnv54US1G460NR2Ls/egYGO9ghrH2CPwAAgFUoa2bx9NR3Lxn6Xei1uRPxjbe+F5+o7DX2EwBgDev2nXALkxMx86XPR33s6KJrzepszD3z9WiM/U2UH3pY+AfXQPAHAACwCp2an46D06+2VPvMW6/E/v7b48YNm9vcFQAA7dDtO+GaWRa1Fw5eMvS7UH3sSNReeDaK9/9UV4Sd0Al+cgAAAFah8fr0RWf6LWVmoRbj9TNt7ggAgHZ4Zyfc3DN/flHoF/HuTriZL30hFiYnOtThtVuYGI/ai8+3VFt78bnIJsbb3BF0L8EfAADAKlTN5pdZf7ZNnQAA0C7L3QnXzLIV6uz6yiYnFoWal9Oszq7pkBM6TfAHAACwChXT3mXWb2hTJwAAtMt62QnXrM21tR54l+APAABgFark+6PcU2ipttxTiEp+Y5s7AgDgelsvO+GSQl9b64F3Cf4AAABWoaHe/ri7//aWag9s2hVDvf1t7ggAgOttveyESwcGIymWWqpNiqXoGRhsc0fQvQR/AAAAq1CapHHP5jvi1r7tS9bd2rc9PrLpjkgTH+8AANaa9bITrmewEoW7PthSbeGufZEOVtrcEXQvnwwBAABWqUpvfzyy7d54YGDPorGf5Z5CPLBlTzyy7b6o9BrzCQCwFq2XnXBJmkZh792RH925ZF1+dGcU9u6PJBVdwNXKdboBAABWt2bWjJnJWlSnatGoLUSu0BPFzYUoDxQiSZNOtwddr9LbH5/Yujf2b7o9xutnopqdjWK6ISr5jTHU22+nHwDAGvbOTri5Z/78irVrfSdcz8BglB96OGovfCtqL377orMNk2IpCnfti8Le/dEzsKWDXcLaJ/gDAOCyqlO1GHvpZBw7dCrqc43zX8/35WLHnqEY/cBwFDcXlvgTgOshTdK4ccPmuHHD5k63AgDAdfTOTrjG2OtRHzty2bpu2QnXMzAYxfs/HoU7PxQLkxPRrM1FUuiLnoHBSAcra/77g9VA8AcAwCVVp2px6CtHYvL4mUXX6nONOPLciZg8/nbsfnCn8A8AAOAqrbedcEmaRs/WoejZOtTpVqArCf4AAFikmTVj7KWTlwz9LnT6+JkY+87JuO2eEWM/Aa5C1szi1Px0jNeno5rNRzHtjUq+3xhXAFhn7IQDrhfBHwAAi8xM1uLYoVMt1R57+VSM7B6K8mBfm7taH4QAsH6Mz0/H01PfjYPTr8bMQu3818s9hbh70664Z9P7otLb38EOAYCVZCcccD0I/gAAWKQ6VbvoTL+l1OcaUZ06K/i7DoQAsH6Mz0/HZ9/4Wrw298aiazMLtXjy9MtxuPpGPLLtXj/3AABAyywZBgBgkUZtYVn19VprISGX904I8OTkoYtCv4h3Q4DPvvH1GJ+f7lCHwPWSNbN4euq7lwz9LvTa3In4xlvfi6yZrVBnAADAWif4AwBgkVyhZ1n1+YJBEtdCCADry6n56Tg4/WpLtc+89UqcEvgDAAAtEvwBALBIcXMh8n2thXn5vlwUN29oc0fdTQgA68t4fXrRzt7LmVmoxXj9TJs7AgAAuoXgDwCARcoDhdjx/tYOlN+xZyhKA4U2d9TdhACwvlSz+WXWn21TJwAAQLcR/AEAsEiSJjF653BsGdm4ZN2WkY0xeudwJGmyQp11JyEArC/FtHeZ9XZVAwAArRH8AQBwScXNhdj94M7YuW/7orGf+b5c7Ny3PXb/zM4obrLb71oJAWB9qeT7o9zT2ntnuacQlfzSizAAAADe0drBLQAArEvFzYW47Z6RGNk9FNWps1GvNSJfOHemX2mgYKffdfJOCNDKuE8hAKx9Q739cXf/7fHk5KEr1h7YtCuGevtXoCsAAKAbCP6gyzWbWTRnT0RWPRVRn43IlyItDkVS2h5JYtMvAFeWpEmUB/uiPNjX6Va6lhDg+smaWZyan47x+nRUs/kopr1RyffHUG9/pO59WCXSJI17Nt8Rh+f+Nl6bO3HZulv7tsdHNt3h/10AAKBlgj/oYln1ZDTGnojG8a9G1M+8eyG/MXIj90du9GORFoc71yAAEBFCgOtlfH46np76bhycfvWi3ZPlnkLcvWlX3LPpfVERmrJKVHr745Ft98Y3pr4Xz0y/suj/2QObdsVHNt0RlV47fAEAgNYJ/qBLZdWTMX/oscimXl18sX4mGkcfj2zy1ejd/ajwDwBWASHAtRmfn47PvvG1eG3ujUXXZhZq8eTpl+Nw9Y14ZNu9wj9WjUpvf3xi697Yv+n2GK+fiWp2NorphqjkN9qlCgAAXBXBH3ShZjOLxtgTlw79LpBNvhKNsScjf9vDxn4CwCogBLg6WTOLp6e+e8nQ70KvzZ2Ib7z1vfhEZa+/S1aNNEnjxg2b48YNmzvdCgAA0AUEf9CFmrMnzo33bEHj+FORG7kvkvJNbe4KAGiFEGD5Ts1Px8HppRc8veOZt16J/f23+/sFAACgK1nmCl0oq566+Ey/pdTPRFY92d6GAADaaLw+fdFo1KXMLNRivNX7JAAAAFhjBH/Qjeqz7a0HAFhFqtn8MuvPtqkTAAAA6CzBH3SjfKm99QAAq0gx7V1m/YY2dQIAAACdJfiDLpQWhyLyG1srzm+MtDjc3oYAANqoku+Pck+hpdpyTyEqrd4nAQAAwBoj+IMulJS2R27koy3V5kbuj6S0rc0dAQC0z1Bvf9zdf3tLtQc27Yqh3v42dwQAAACdIfiDLpQkaeRGH4h0YNeSdenArsiNPhBJ4q0AAFi70iSNezbfEbf2bV+y7ta+7fGRTXdE6t4HAACALuUTL3SptDgcvbsfjdwtn1w89jO/MXK3fDJ6d3/m3FhQAIA1rtLbH49suzceGNizaOxnuacQD2zZE49suy8qvcZ8AgAA0L1ynW4AaJ+0OBz52x6O3Mh9kVVPRtRnI/KlSIvDkZS22ekHAHSVSm9/fGLr3ti/6fYYr5+JanY2iumGqOQ3xlBvv51+AAAAdD3BH3S5JEkjKd8UafmmTrcCANB2aZLGjRs2x40bNne6FQAAAFhxgj+WpdnMojl7IrLqqQt2jw1FUtpu9xgAAAAAAEAHCf5oWVY9GY2xJ6Jx/KsR9TPvXshvjNzI/ZEb/VikxeHONQgAAAAAALCOCf5oSVY9GfOHHots6tXFF+tnonH08cgmX43e3Y8K/wAAAAAAADrAbEauqNnMojH2xKVDvwtkk69EY+zJaDazFeoMAAAAAACAdwj+uKLm7Ilz4z1b0Dj+VDRn32hzRwAAAAAAAPwwoz65oqx66uIz/ZZSPxNZ9WSk5Zva2xQAAAAAwBU0sywWJsYjm5yIZm0ukkJfpAOD0TNYiSS1LwboPoI/rqw+2956gA7Kmlmcmp+O8fp0VLP5KKa9Ucn3x1Bvf6SJDwAAAACwVi1MTkTthYNRe/H5aFbffWaZFEtRuOtDUdi7P3oGBjvYIcD1J/jjyvKl9tYDdMj4/HQ8PfXdODj9asws1M5/vdxTiLs37Yp7Nr0vKr39HewQAAAAuBoLkxMx86XPR33s6KJrzepszD3z9WiM/U2UH3pY+Ad0FVsZuKK0OBSR39hacX5jpMXh9jYEcB2Mz0/HZ9/4Wjw5eeii0C8iYmahFk+efjk++8bXY3x+ukMdAgAAAFejmWVRe+HgJUO/C9XHjkTthWejmWUr1BlA+wn+uKKktD1yIx9tqTY3cn8kpW1t7gjg2mTNLJ6e+m68NvfGknWvzZ2Ib7z1vciaPgAAAADAWrEwMR61F59vqbb24nORTYy3uSOAlSP444qSJI3c6AORDuxasi4d2BW50QcicSYWsMqdmp+Og9OvtlT7zFuvxCm7/gAAAGDNyCYnLjrTbynN6mwsTE60uSOAlSOhoSVpcTh6dz8auVs+uXjsZ35j5G75ZPTu/sy5saAAq9x4fXrReM/LmVmoxXj9TJs7AgAAAK6XZm2urfUAq1mu0w2wdqTF4cjf9nDkRu6LrHoyoj4bkS9FWhyOpLTNTj9gzahm88usP9umTgAAANa3ZjOL5uyJyKqnLnjWNBRJabtnTVy1pNDX1nqA1Uzwx7IkSRpJ+aZIyzd1uhWAq1ZMe5dZv6FNnQAAAKxfWfVkNMaeiMbxr0ZcOGklvzFyI/dHbvRjkRaHO9cga1Y6MBhJsdTSuM+kWIqegcEV6ApgZVg2A8C6U8n3R7mn0FJtuacQlR8ecQwAAMA1yaonY/7QY9E4+uWLQ7+IiPqZaBx9POb/8rfOTZ2CZeoZrEThrg+2VFu4a1+kg5U2dwSwcgR/AKw7Q739cXf/7S3VHti0K4Z6+9vcEQAAwPrRbGbRGHsisqlXl6zLJl+JxtiT0WxmK9QZ3SJJ0yjsvTvyozuXrMuP7ozC3v2RpB6TA93DOxoA606apHHP5jvi1r7tS9bd2rc9PrLpjkjX+bkSWTOLvz07Fd+bORYvnjkc35s5Fn97dioyH74BAICr0Jw9cW68Zwsax5+K5uwbbe6IbtQzMBjlhx6OvgP3RlIsXXQtKZai78B9UX7oU9EzsKVDHQK0hzP+AFiXKr398ci2e+MbU9+LZ6ZfiZmF2vlr5Z5CHNi0Kz6y6Y6o9K7vMZ/j89Px9NR34+D0q4v+ju7etCvu2fS+qNgRCQAALENWPbV4vOfl1M9EVj0Zafmm9jZFV+oZGIzi/R+Pwp0fioXJiWjW5iIp9EXPwGCkgxU7/YCuJPgDYN2q9PbHJ7bujf2bbo/x+pmoZmejmG6ISn5jDPX2r/udfuPz0/HZN74Wr80tXl07s1CLJ0+/HIerb8Qj2+4V/gGwpmXNZrw504jxaiPm6s3oyydRKeZiazkXaZJ0uj2A7lOfbW89XCBJ0+jZOhQ9W4c63QrAihD8AbCupUkaN27YHDdu2NzpVlaVrJnF01PfvWTod6HX5k7EN976XnyisnfdB6UArE3js4345usz8dyx2ZiZf3eMdbk3jQ/vKMWBm8tRKfnoDHBd5UtXrrmWegBYxzyhAwAWOTU/HQenX22p9pm3XolT89Nt7ggArr/x2UZ87uXT8dXDb18U+kVEzMxn8dTht+NzL5+O8dlGhzoE6E5pcSgi3+KxCvmNkRaH29sQAHQRwR8AsMh4ffqiM/2WMrNQi/FWz+cAgFUiazbjm6/PxOHT80vWHT49HwfHZiJrNleoM4Dul5S2R27koy3V5kbuj6S0rc0dAUD3EPwBAItUs6Ufgi6uP9umTgCgPd6cacRzx1o7M+pbY7Px5oxdfwDXS5KkkRt9INKBXUvWpQO7Ijf6QCSOFQCAlvmtCQAsUkx7l1m/oU2dAEB7jFcbi8Z7Xs7MfBbjVcEfwPWUFoejd/ejkbvlk4vHfuY3Ru6WT0bv7s+cGwsKALTMCeUAwCKVfH+Uewotjfss9xSi0ur5HACwSszVlze6c7n1AFxZWhyO/G0PR27kvsiqJyPqsxH5UqTF4UhK2+z0A4CrIPgDABYZ6u2Pu/tvjycnD12x9sCmXTHU278CXQHA9dOXT9paD0BrkiSNpHxTpOWbOt0KAHQFy2YAgEXSJI17Nt8Rt/ZtX7Lu1r7t8ZFNd0RqJS4Aa0ylmItyb2u/v8q9aVSK1s0CAACrn6d0AMAlVXr745Ft98YDA3ui3FO46Fq5pxAPbNkTj2y7Lyq9xnwCsPZsLedi345SS7X7R0uxtSz4AwAAVj+fXACAy6r09scntu6N/Ztuj/H6mahmZ6OYbohKfmMM9fbb6QfAmpUmSfzEzeU4evpsHD49f9m692zpjQM3lyNNjPoEAABWP8EfALCkNEnjxg2b48YNmzvdCgBcV5VSLj69Z0s8MzYTz47Nxsx8dv5auTeN/aOlOHBzOQaN+QQAANYIn14AAABYtyqlXDz43v748EgpxquNmKs3oy+fRKWYi63lnJ1+AADAmiL4AwAAYF1LkySGb8jH8A35TrcCAABwTRzMAwAAAAAAAF1A8AcAAAAAAABdQPAHAAAAAAAAXUDwBwAAAAAAAF1A8AcAAAAAAABdQPAHAAAAAAAAXUDwBwAAAAAAAF1A8AcAAAAAAABdINfpBuB6ajazaM6eiKx6KqI+G5EvRVociqS0PZJEzr0SvAYAAAAAANAZgj+6RlY9GY2xJ6Jx/KsR9TPvXshvjNzI/ZEb/VikxeHONbgOeA0AAAAAAKBzBH90hax6MuYPPRbZ1KuLL9bPROPo45FNvhq9ux8VPLWJ1wAAAAAAADrL3D3WvGYzi8bYE5cOnC6QTb4SjbEno9nMVqiz9cNrAAAAAAAAnSf4Y81rzp44N1qyBY3jT0Vz9o02d7T+eA0AAAAAAKDzBH+seVn11MXnyS2lfiay6sn2NrQOeQ0AAAAAAKDzBH+sffXZ9tZzZV4DAAAAAADouFynG4Brli+1t54r8xoAAABA2zWbWTRnT/zd5J3ZiHwp0uJQJKXtkSTW9wMAgj+6QFocishvbG3UZH5jpMXh9je1zngNAAAAoL2y6slojD0RjeNfvfjzd35j5Ebuj9zox3zeBgCM+mTtS0rbIzfy0ZZqcyP3R1La1uaO1h+vAQAAALRPVj0Z84cei8bRLy9edFs/E42jj8f8X/5WZNWTnWkQAFg1BH+seUmSRm70gUgHdi1Zlw7sitzoA0ZftIHXAAAAANqj2cyiMfZEZFOvLlmXTb4SjbEno9nMVqgzAGA18vSdrpAWh6N396ORu+WT50ZOXii/MXK3fDJ6d3/m3EhK2sJrAAAAANdfc/bEufGeLWgcfyqas2+0uSMAYDVzxh9dIy0OR/62hyM3ct+50RbnD7kejqS0zS6zFeA1AAAAgOsrq55aPN7zcupnIquejLR8U3ubAgBWLcEfXSVJ0kjKN7nB7SCvAQAAAFxH9dn21gMAXcX2GwAAAABYrfKl9tYDAF3Fjj8AAAAAWKXS4lBEfmNr4z7zGyMtDre/KWBVa2ZZLEyMRzY5Ec3aXCSFvkgHBqNnsBJJai8QdDvBHwAAAACsUklpe+RGPhqNo1++Ym1u5P5ISttWoCtgtVqYnIjaCwej9uLz0ay+O/o3KZaicNeHorB3f/QMDHawQ6DdxPsAAAAAsEolSRq50QciHdi1ZF06sCtyow9EknjcB+vVwuREzHzp8zH3zJ9fFPpFRDSrszH3zNdj5ktfiIXJiQ51CKwEdwIAAAAAsIqlxeHo3f1o5G755LmxnxfKb4zcLZ+M3t2fOTcWFFiXmlkWtRcORn3s6JJ19bEjUXvh2Whm2Qp1Bqw0oz4BAAAAYJVLi8ORv+3hyI3cF1n1ZER9NiJfirQ4HElpm51+sM4tTIxH7cXnW6qtvfhcFO78YPRstVgAupHgDwAAAADWgCRJIynfFGn5pk63Aqwy2eTEovGel9OszsbC5ITgD7qUpUAAAAAAALCGNWtzba0H1g7BHwAAAAAArGFJoa+t9cDaIfgDAAAAAIA1LB0YjKRYaqk2KZaiZ2CwzR0BnSL4AwAAAACANaxnsBKFuz7YUm3hrn2RDlba3BHQKblONwAAnZY1szg1Px3j9emoZvNRTHujku+Pod7+SBNrZFYbrxcAAABcLEnTKOy9Oxpjr0d97Mhl6/KjO6Owd38kqc/P0K0EfwCsa+Pz0/H01Hfj4PSrMbNQO//1ck8h7t60K+7Z9L6o9PZ3sEMu5PUCAACAS+sZGIzyQw9H7YVvRe3Fb0ezOnv+WlIsReGufVHYuz96BrZ0sEug3QR/AKxb4/PT8dk3vhavzb2x6NrMQi2ePP1yHK6+EY9su1eYtAp4vQAAAGBpPQODUbz/41G480OxMDkRzdpcJIW+6BkYjHSwYqcfrAN+ygFYl7JmFk9PffeSIdKFXps7Ed9463uRNbMV6oxL8XoBAABAa5I0jZ6tQ9H7994bG370/dH7994bPVuHhH6wTvhJB2BdOjU/HQenX22p9pm3XolT89Nt7oileL0AAAAA4MoEfwCsS+P16YvOiFvKzEItxutn2twRS/F6AQAAAMCVCf4AWJeq2fwy68+2qRNa4fUCAAAAgCsT/AGwLhXT3mXWb2hTJ7TC6wUAAAAAVyb4A2BdquT7o9xTaKm23FOISn5jmztiKV4vAAAAALgywR8A69JQb3/c3X97S7UHNu2Kod7+NnfEUrxeAAAAAHBlgj8A1qU0SeOezXfErX3bl6y7tW97fGTTHZEmfmV2ktcLAAAAAK7MUzEA1q1Kb388su3eeGBgz6IxkuWeQjywZU88su2+qPQaG7kaeL0AAAAAYGm5TjcAAJ1U6e2PT2zdG/s33R7j9TNRzc5GMd0QlfzGGOrtt3NslfF6AQAAAMDlCf4AWPfSJI0bN2yOGzds7nQrtMDrBQAAAACXZlk8AAAAAAAAdAHBHwAAAAAAAHQBwR8AAAAAAAB0AcEfAAAAAAAAdAHBHwAAAAAAAHQBwR8AAAAAAAB0AcEfAAAAAAAAdAHBHwAAAAAAAHSBXKcbAAAA1oZmM4vm7InIqqci6rMR+VKkxaFIStsjSawpBAAAgE4T/AEAAFeUVU9GY+yJaBz/akT9zLsX8hsjN3J/5EY/FmlxuHMNAgAAAII/AABgaVn1ZMwfeiyyqVcXX6yficbRxyObfDV6dz8q/AMAAIAOMo8HAAC4rGYzi8bYE5cO/S6QTb4SjbEno9nMVqgzAAAA4IcJ/gAAgMtqzp44N96zBY3jT0Vz9o02dwQAAABcjlGfAADAZWXVUxef6beU+pnIqicjLd/U3qYAgDWr2cyiOXvi7+4xZiPypUiLQ5GUtkeS2KMAANdK8AcAAFxefba99QDAupFVT0Zj7Ilz0wQuXFiU3xi5kfsjN/ox5wUDwDUS/AEAAJeXL7W3nuuimWWxMDEe2eRENGtzkRT6Ih0YjJ7BSiSp3RMAdF5WPRnzhx679LnB9TPROPp4ZJOvRu/uR4V/AHANBH8AAMBlpcWhiPzG1sZ95jd6UNcBC5MTUXvhYNRefD6a1Xd3XCbFUhTu+lAU9u6PnoHBDnYIwHrXbGbRGHvi0qHfBbLJV6Ix9mTkb3vY2E8AuEp+gwIAAJeVlLZHbuSjLdXmRu6PpLStzR1xoYXJiZj50udj7pk/vyj0i4hoVmdj7pmvx8yXvhALkxMd6hAAIpqzJ86N92xB4/hT0Zx9o80dAUD3EvwBAACXlSRp5EYfiHRg15J16cCuyI0+YHX+CmpmWdReOBj1saNL1tXHjkTthWejmWUr1BkAXCyrnmptekBERP1MZNWT7W0IALqYT+UAAMCS0uJw9O5+NHK3fPLc2M8L5TdG7pZPRu/uz5wbC8qKWZgYj9qLz7dUW3vxucgmxtvcEQBcRn32yjXXUg8AnOeMPwAA4IrS4nDkb3s4ciP3nVuFX5+NyJciLQ5HUtpmp18HZJMTi8Z7Xk6zOhsLkxPRs1U4C0AH5EvtrQcAzhP8AQAALUmSNJLyTZGWb+p0K0REszbX1noAuF7S4tC5qQGtjPvMb4y0ONz+pgCgS1mWCwAAsAYlhb621gPA9ZKUtkdu5KMt1eZG7o+ktK3NHQFA9xL8AQAArEHpwGAkxdZGoSXFUvQMDLa5IwC4tCRJIzf6QKQDu5asSwd2RW70ASPEAeAa+C0KAACwBvUMVqJw1wdbqi3ctS/SwUqbOwKAy0uLw9G7+9HI3fLJc2M/L5TfGLlbPhm9uz9zbiwoAHDVnPEHAACwBiVpGoW9d0dj7PWojx25bF1+dGcU9u6PJLXuE4DOSovDkb/t4ciN3BdZ9WREfTYiX4q0OBxJaZudfgBwHQj+AAAA1qiegcEoP/Rw1F74VtRe/HY0q7PnryXFUhTu2heFvfujZ2BLB7sEgHclSRp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+      "text/plain": [
+       "<Figure size 1800x1200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "categories = ['Programación'] * 5 + ['Ciencia'] * 5 + ['Cocina'] * 5 + ['Finanzas'] * 5 + ['Geografía'] * 5\n",
+    "cat_colors = {'Programación': '#e74c3c', 'Ciencia': '#3498db', 'Cocina': '#2ecc71', 'Finanzas': '#f39c12', 'Geografía': '#9b59b6'}\n",
+    "\n",
+    "n_models = len(results)\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, (name, res) in enumerate(results.items()):\n",
+    "    embs = res['corpus_embs']\n",
+    "    tsne = TSNE(n_components=2, random_state=42, perplexity=8)\n",
+    "    coords = tsne.fit_transform(embs)\n",
+    "\n",
+    "    ax = axes[i]\n",
+    "    for cat in cat_colors:\n",
+    "        mask = [c == cat for c in categories]\n",
+    "        ax.scatter(coords[np.array(mask), 0], coords[np.array(mask), 1],\n",
+    "                   c=cat_colors[cat], label=cat, s=80, alpha=0.8, edgecolors='white', linewidth=0.5)\n",
+    "    ax.set_title(f'{name} ({res[\"dim\"]}d, {res[\"n_params_m\"]}M)', fontsize=11)\n",
+    "    ax.set_xticks([]); ax.set_yticks([])\n",
+    "    if i == 0:\n",
+    "        ax.legend(fontsize=8, loc='upper left')\n",
+    "\n",
+    "# Ocultar ejes sobrantes\n",
+    "for j in range(i + 1, len(axes)):\n",
+    "    axes[j].set_visible(False)\n",
+    "\n",
+    "plt.suptitle('t-SNE: Agrupación de documentos por categoría (corpus español)', fontsize=14, y=1.01)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5093816f",
+   "metadata": {},
+   "source": [
+    "## 9. Conclusiones\n",
+    "\n",
+    "Ejecuta todas las celdas arriba y compara:\n",
+    "- **Modelos solo-inglés** (MiniLM-L6, L12): muy rápidos y livianos, pero ¿entienden español?\n",
+    "- **Multilingüe ligero** (paraphrase-multi-L12, multi-e5-small): buen balance\n",
+    "- **Nomic v1.5**: más grande pero con Matryoshka — ¿compensa el tamaño extra?\n",
+    "- **BGE-m3**: el más pesado — ¿la diferencia en retrieval justifica 4x el tamaño?\n",
+    "\n",
+    "La tabla resumen y los gráficos dan la respuesta concreta para **tu** caso de uso."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/02_ram_benchmark.ipynb b/notebooks/02_ram_benchmark.ipynb
new file mode 100644
index 0000000..d1b4fa0
--- /dev/null
+++ b/notebooks/02_ram_benchmark.ipynb
@@ -0,0 +1,804 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "daf8e241-af34-4594-b406-08fe56191cf6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "raw",
+   "id": "51191f74-f5fc-46bc-abb9-72db3941c796",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa5ab0bb-f8ae-499e-8d9e-7d76c9a9b415",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdd06db5",
+   "metadata": {},
+   "source": [
+    "# Benchmark de RAM: BGE-m3 vs multilingual-e5-small\n",
+    "\n",
+    "Medimos consumo real de memoria en cada fase:\n",
+    "1. **Baseline** — RAM antes de cargar nada\n",
+    "2. **Carga del modelo** — cuánta RAM consume tener el modelo en memoria\n",
+    "3. **Encoding** — pico de RAM durante encoding a distintos tamaños de corpus\n",
+    "4. **Scaling** — cómo escala la RAM con corpus de 100, 500, 1000, 5000 documentos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3d149af",
+   "metadata": {},
+   "source": [
+    "## 1. Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9ee663ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sistema: 31970 MB total, 21550 MB disponible (32.6% usado)\n",
+      "Proceso actual: 179 MB\n",
+      "Modelos a probar: ['multi-e5-small', 'BGE-m3']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, gc, time, tracemalloc\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import psutil\n",
+    "\n",
+    "plt.style.use('seaborn-v0_8-whitegrid')\n",
+    "plt.rcParams['figure.figsize'] = (14, 6)\n",
+    "\n",
+    "MODELS = {\n",
+    "    'multi-e5-small': {\n",
+    "        'id': 'intfloat/multilingual-e5-small',\n",
+    "        'q_prefix': 'query: ',\n",
+    "        'd_prefix': 'passage: ',\n",
+    "    },\n",
+    "    'BGE-m3': {\n",
+    "        'id': 'BAAI/bge-m3',\n",
+    "        'q_prefix': None,\n",
+    "        'd_prefix': None,\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "def get_process_ram_mb():\n",
+    "    \"\"\"RAM del proceso actual en MB (RSS).\"\"\"\n",
+    "    return psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\n",
+    "\n",
+    "def get_system_ram():\n",
+    "    \"\"\"RAM del sistema: total, usada, disponible en MB.\"\"\"\n",
+    "    m = psutil.virtual_memory()\n",
+    "    return {'total_mb': m.total / (1024**2), 'used_mb': m.used / (1024**2), 'available_mb': m.available / (1024**2), 'percent': m.percent}\n",
+    "\n",
+    "sys_ram = get_system_ram()\n",
+    "print(f'Sistema: {sys_ram[\"total_mb\"]:.0f} MB total, {sys_ram[\"available_mb\"]:.0f} MB disponible ({sys_ram[\"percent\"]}% usado)')\n",
+    "print(f'Proceso actual: {get_process_ram_mb():.0f} MB')\n",
+    "print(f'Modelos a probar: {list(MODELS.keys())}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f1571c1",
+   "metadata": {},
+   "source": [
+    "## 2. Corpus sintético escalable\n",
+    "\n",
+    "Generamos corpus de distintos tamaños a partir de frases semilla en español."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f5a78c0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Corpus   100 docs → ejemplo: \"Python es un lenguaje de programación de alto nivel conocido por su le...\"\n",
+      "Corpus   500 docs → ejemplo: \"Python es un lenguaje de programación de alto nivel conocido por su le...\"\n",
+      "Corpus  1000 docs → ejemplo: \"Python es un lenguaje de programación de alto nivel conocido por su le...\"\n",
+      "Corpus  5000 docs → ejemplo: \"Python es un lenguaje de programación de alto nivel conocido por su le...\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "# Frases semilla para generar corpus de cualquier tamaño\n",
+    "SEEDS = [\n",
+    "    'Python es un lenguaje de programación de alto nivel conocido por su legibilidad',\n",
+    "    'La fotosíntesis convierte la luz solar en energía química en las células vegetales',\n",
+    "    'El pan de masa madre requiere un cultivo fermentado de harina y agua',\n",
+    "    'El interés compuesto hace crecer los ahorros exponencialmente a largo plazo',\n",
+    "    'La selva amazónica produce aproximadamente el 20 por ciento del oxígeno mundial',\n",
+    "    'Las redes neuronales profundas aprenden representaciones jerárquicas de los datos',\n",
+    "    'La criptografía asimétrica usa pares de claves pública y privada',\n",
+    "    'Los contenedores Docker encapsulan aplicaciones con todas sus dependencias',\n",
+    "    'La teoría de grafos modela relaciones entre entidades como nodos y aristas',\n",
+    "    'Las bases de datos columnares optimizan consultas analíticas sobre grandes volúmenes',\n",
+    "    'El protocolo TCP garantiza la entrega ordenada y confiable de paquetes',\n",
+    "    'La transformada de Fourier descompone señales en sus frecuencias componentes',\n",
+    "    'Los microservicios dividen una aplicación en servicios pequeños e independientes',\n",
+    "    'El álgebra lineal es fundamental para machine learning y procesamiento de señales',\n",
+    "    'La reacción de Maillard crea el dorado y sabor al cocinar proteínas a alta temperatura',\n",
+    "    'Los índices invertidos permiten búsqueda full-text eficiente en documentos',\n",
+    "    'Las corrientes oceánicas regulan el clima global y los patrones meteorológicos',\n",
+    "    'El garbage collector libera memoria de objetos que ya no son referenciados',\n",
+    "    'La diversificación reduce el riesgo del portafolio distribuyendo inversiones',\n",
+    "    'Los embeddings representan texto como vectores densos en un espacio semántico',\n",
+    "]\n",
+    "\n",
+    "def generate_corpus(n):\n",
+    "    \"\"\"Genera corpus de n documentos variando las frases semilla.\"\"\"\n",
+    "    random.seed(42)\n",
+    "    suffixes = [\n",
+    "        '', ' en sistemas modernos', ' según investigaciones recientes',\n",
+    "        ' de manera eficiente', ' con aplicaciones prácticas',\n",
+    "        ' en el contexto actual', ' para usuarios avanzados',\n",
+    "        ' combinando múltiples enfoques', ' optimizando recursos',\n",
+    "        ' a gran escala',\n",
+    "    ]\n",
+    "    corpus = []\n",
+    "    for i in range(n):\n",
+    "        base = SEEDS[i % len(SEEDS)]\n",
+    "        suffix = suffixes[i % len(suffixes)]\n",
+    "        corpus.append(f'{base}{suffix}')\n",
+    "    return corpus\n",
+    "\n",
+    "CORPUS_SIZES = [100, 500, 1000, 5000]\n",
+    "corpora = {n: generate_corpus(n) for n in CORPUS_SIZES}\n",
+    "for n, c in corpora.items():\n",
+    "    print(f'Corpus {n:5d} docs → ejemplo: \"{c[0][:70]}...\"')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aca2f5ce",
+   "metadata": {},
+   "source": [
+    "## 3. Benchmark de RAM por modelo\n",
+    "\n",
+    "Para cada modelo medimos:\n",
+    "- **RAM al cargar** — delta RSS después de instanciar el modelo\n",
+    "- **RAM pico encoding** — tracemalloc captura el pico durante encode\n",
+    "- **Tiempo de encoding** — por cada tamaño de corpus\n",
+    "- **RAM de embeddings** — cuánto pesan los arrays numpy resultantes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a09ef594",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abe7fa6799534cb89e3cc572e00969fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================================================\n",
+      "multi-e5-small (117.7M params, dim=384)\n",
+      "======================================================================\n",
+      "  RAM baseline:        881 MB\n",
+      "  RAM después de carga: 1565 MB (modelo = +684 MB)\n",
+      "  Tiempo de carga:     5.5s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    100 docs | encode=  0.28s (  356.2 docs/s) | RAM delta=+ 191 MB | peak(tracemalloc)=   0.2 MB | embeddings=0.15 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    500 docs | encode=  0.28s ( 1791.3 docs/s) | RAM delta=+   2 MB | peak(tracemalloc)=   0.9 MB | embeddings=0.73 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   1000 docs | encode=  0.55s ( 1807.4 docs/s) | RAM delta=+   2 MB | peak(tracemalloc)=   1.8 MB | embeddings=1.46 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   5000 docs | encode=  2.61s ( 1915.6 docs/s) | RAM delta=+  15 MB | peak(tracemalloc)=   9.1 MB | embeddings=7.32 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  RAM después de cleanup: 1757 MB (liberados ~-192 MB)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fe30614e002401f94625c0b152e3696",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/391 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================================================\n",
+      "BGE-m3 (567.8M params, dim=1024)\n",
+      "======================================================================\n",
+      "  RAM baseline:        1756 MB\n",
+      "  RAM después de carga: 1866 MB (modelo = +110 MB)\n",
+      "  Tiempo de carga:     10.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    100 docs | encode=  0.52s (  192.5 docs/s) | RAM delta=+   0 MB | peak(tracemalloc)=   0.5 MB | embeddings=0.39 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    500 docs | encode=  1.00s (  501.7 docs/s) | RAM delta=+   0 MB | peak(tracemalloc)=   2.2 MB | embeddings=1.95 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   1000 docs | encode=  1.87s (  535.7 docs/s) | RAM delta=+   0 MB | peak(tracemalloc)=   4.3 MB | embeddings=3.91 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   5000 docs | encode=  9.01s (  554.9 docs/s) | RAM delta=+  24 MB | peak(tracemalloc)=  21.3 MB | embeddings=19.53 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  RAM después de cleanup: 1833 MB (liberados ~33 MB)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sentence_transformers import SentenceTransformer\n",
+    "\n",
+    "def benchmark_model(name, config, corpora):\n",
+    "    \"\"\"Benchmark completo de RAM para un modelo.\"\"\"\n",
+    "    gc.collect()\n",
+    "    ram_before = get_process_ram_mb()\n",
+    "\n",
+    "    # Cargar modelo\n",
+    "    t0 = time.perf_counter()\n",
+    "    model = SentenceTransformer(config['id'], trust_remote_code=True)\n",
+    "    load_time = time.perf_counter() - t0\n",
+    "    ram_after_load = get_process_ram_mb()\n",
+    "    ram_model = ram_after_load - ram_before\n",
+    "\n",
+    "    # Parámetros del modelo\n",
+    "    n_params = sum(p.nelement() for p in model[0].auto_model.parameters()) / 1e6\n",
+    "    dim = model.get_sentence_embedding_dimension()\n",
+    "\n",
+    "    print(f'\\n{\"=\" * 70}')\n",
+    "    print(f'{name} ({n_params:.1f}M params, dim={dim})')\n",
+    "    print(f'{\"=\" * 70}')\n",
+    "    print(f'  RAM baseline:        {ram_before:.0f} MB')\n",
+    "    print(f'  RAM después de carga: {ram_after_load:.0f} MB (modelo = +{ram_model:.0f} MB)')\n",
+    "    print(f'  Tiempo de carga:     {load_time:.1f}s')\n",
+    "\n",
+    "    results = []\n",
+    "    prefix = config['d_prefix']\n",
+    "\n",
+    "    for corpus_size, corpus in corpora.items():\n",
+    "        gc.collect()\n",
+    "        texts = [f'{prefix}{doc}' for doc in corpus] if prefix else corpus\n",
+    "\n",
+    "        # Medir pico de RAM con tracemalloc\n",
+    "        tracemalloc.start()\n",
+    "        ram_pre_encode = get_process_ram_mb()\n",
+    "        t0 = time.perf_counter()\n",
+    "\n",
+    "        embeddings = model.encode(texts, normalize_embeddings=True,\n",
+    "                                  show_progress_bar=False, batch_size=32)\n",
+    "\n",
+    "        encode_time = time.perf_counter() - t0\n",
+    "        ram_post_encode = get_process_ram_mb()\n",
+    "        current, peak = tracemalloc.get_traced_memory()\n",
+    "        tracemalloc.stop()\n",
+    "\n",
+    "        # Tamaño de los embeddings en MB\n",
+    "        emb_size_mb = embeddings.nbytes / (1024 * 1024)\n",
+    "\n",
+    "        result = {\n",
+    "            'model': name,\n",
+    "            'corpus_size': corpus_size,\n",
+    "            'dim': dim,\n",
+    "            'ram_model_mb': round(ram_model),\n",
+    "            'ram_pre_encode_mb': round(ram_pre_encode),\n",
+    "            'ram_post_encode_mb': round(ram_post_encode),\n",
+    "            'ram_delta_encode_mb': round(ram_post_encode - ram_pre_encode),\n",
+    "            'tracemalloc_peak_mb': round(peak / (1024 * 1024), 1),\n",
+    "            'emb_size_mb': round(emb_size_mb, 2),\n",
+    "            'encode_time_s': round(encode_time, 3),\n",
+    "            'docs_per_sec': round(corpus_size / encode_time, 1),\n",
+    "        }\n",
+    "        results.append(result)\n",
+    "\n",
+    "        print(f'  {corpus_size:5d} docs | encode={encode_time:6.2f}s ({result[\"docs_per_sec\"]:7.1f} docs/s) | '\n",
+    "              f'RAM delta=+{result[\"ram_delta_encode_mb\"]:4d} MB | peak(tracemalloc)={result[\"tracemalloc_peak_mb\"]:6.1f} MB | '\n",
+    "              f'embeddings={emb_size_mb:.2f} MB')\n",
+    "\n",
+    "    # Limpiar modelo\n",
+    "    del model\n",
+    "    gc.collect()\n",
+    "    ram_after_cleanup = get_process_ram_mb()\n",
+    "    print(f'  RAM después de cleanup: {ram_after_cleanup:.0f} MB (liberados ~{ram_after_load - ram_after_cleanup:.0f} MB)')\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "# Ejecutar benchmarks secuencialmente\n",
+    "all_results = []\n",
+    "for name, config in MODELS.items():\n",
+    "    all_results.extend(benchmark_model(name, config, corpora))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6d6679a",
+   "metadata": {},
+   "source": [
+    "## 4. Tabla resumen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7b447fa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_2d890_row0_col3, #T_2d890_row0_col4, #T_2d890_row1_col3, #T_2d890_row2_col3, #T_2d890_row3_col3 {\n",
+       "  background-color: #67000d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_2d890_row0_col8 {\n",
+       "  background-color: #e9f7e5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2d890_row1_col4, #T_2d890_row2_col4 {\n",
+       "  background-color: #fff4ee;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2d890_row1_col8 {\n",
+       "  background-color: #005b25;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_2d890_row2_col8 {\n",
+       "  background-color: #005924;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_2d890_row3_col4 {\n",
+       "  background-color: #fee8dd;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2d890_row3_col8 {\n",
+       "  background-color: #00441b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_2d890_row4_col3, #T_2d890_row4_col4, #T_2d890_row5_col3, #T_2d890_row5_col4, #T_2d890_row6_col3, #T_2d890_row6_col4, #T_2d890_row7_col3 {\n",
+       "  background-color: #fff5f0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2d890_row4_col8 {\n",
+       "  background-color: #f7fcf5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2d890_row5_col8 {\n",
+       "  background-color: #d9f0d3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2d890_row6_col8 {\n",
+       "  background-color: #d4eece;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2d890_row7_col4 {\n",
+       "  background-color: #fee0d2;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_2d890_row7_col8 {\n",
+       "  background-color: #d1edcb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_2d890\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_2d890_level0_col0\" class=\"col_heading level0 col0\" >Modelo</th>\n",
+       "      <th id=\"T_2d890_level0_col1\" class=\"col_heading level0 col1\" >Docs</th>\n",
+       "      <th id=\"T_2d890_level0_col2\" class=\"col_heading level0 col2\" >Dim</th>\n",
+       "      <th id=\"T_2d890_level0_col3\" class=\"col_heading level0 col3\" >RAM modelo (MB)</th>\n",
+       "      <th id=\"T_2d890_level0_col4\" class=\"col_heading level0 col4\" >RAM delta encode (MB)</th>\n",
+       "      <th id=\"T_2d890_level0_col5\" class=\"col_heading level0 col5\" >Peak tracemalloc (MB)</th>\n",
+       "      <th id=\"T_2d890_level0_col6\" class=\"col_heading level0 col6\" >Embeddings (MB)</th>\n",
+       "      <th id=\"T_2d890_level0_col7\" class=\"col_heading level0 col7\" >Tiempo (s)</th>\n",
+       "      <th id=\"T_2d890_level0_col8\" class=\"col_heading level0 col8\" >Docs/s</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d890_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_2d890_row0_col0\" class=\"data row0 col0\" >multi-e5-small</td>\n",
+       "      <td id=\"T_2d890_row0_col1\" class=\"data row0 col1\" >100</td>\n",
+       "      <td id=\"T_2d890_row0_col2\" class=\"data row0 col2\" >384</td>\n",
+       "      <td id=\"T_2d890_row0_col3\" class=\"data row0 col3\" >684</td>\n",
+       "      <td id=\"T_2d890_row0_col4\" class=\"data row0 col4\" >191</td>\n",
+       "      <td id=\"T_2d890_row0_col5\" class=\"data row0 col5\" >0.200000</td>\n",
+       "      <td id=\"T_2d890_row0_col6\" class=\"data row0 col6\" >0.150000</td>\n",
+       "      <td id=\"T_2d890_row0_col7\" class=\"data row0 col7\" >0.281000</td>\n",
+       "      <td id=\"T_2d890_row0_col8\" class=\"data row0 col8\" >356.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d890_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_2d890_row1_col0\" class=\"data row1 col0\" >multi-e5-small</td>\n",
+       "      <td id=\"T_2d890_row1_col1\" class=\"data row1 col1\" >500</td>\n",
+       "      <td id=\"T_2d890_row1_col2\" class=\"data row1 col2\" >384</td>\n",
+       "      <td id=\"T_2d890_row1_col3\" class=\"data row1 col3\" >684</td>\n",
+       "      <td id=\"T_2d890_row1_col4\" class=\"data row1 col4\" >2</td>\n",
+       "      <td id=\"T_2d890_row1_col5\" class=\"data row1 col5\" >0.900000</td>\n",
+       "      <td id=\"T_2d890_row1_col6\" class=\"data row1 col6\" >0.730000</td>\n",
+       "      <td id=\"T_2d890_row1_col7\" class=\"data row1 col7\" >0.279000</td>\n",
+       "      <td id=\"T_2d890_row1_col8\" class=\"data row1 col8\" >1791.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d890_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_2d890_row2_col0\" class=\"data row2 col0\" >multi-e5-small</td>\n",
+       "      <td id=\"T_2d890_row2_col1\" class=\"data row2 col1\" >1000</td>\n",
+       "      <td id=\"T_2d890_row2_col2\" class=\"data row2 col2\" >384</td>\n",
+       "      <td id=\"T_2d890_row2_col3\" class=\"data row2 col3\" >684</td>\n",
+       "      <td id=\"T_2d890_row2_col4\" class=\"data row2 col4\" >2</td>\n",
+       "      <td id=\"T_2d890_row2_col5\" class=\"data row2 col5\" >1.800000</td>\n",
+       "      <td id=\"T_2d890_row2_col6\" class=\"data row2 col6\" >1.460000</td>\n",
+       "      <td id=\"T_2d890_row2_col7\" class=\"data row2 col7\" >0.553000</td>\n",
+       "      <td id=\"T_2d890_row2_col8\" class=\"data row2 col8\" >1807.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d890_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_2d890_row3_col0\" class=\"data row3 col0\" >multi-e5-small</td>\n",
+       "      <td id=\"T_2d890_row3_col1\" class=\"data row3 col1\" >5000</td>\n",
+       "      <td id=\"T_2d890_row3_col2\" class=\"data row3 col2\" >384</td>\n",
+       "      <td id=\"T_2d890_row3_col3\" class=\"data row3 col3\" >684</td>\n",
+       "      <td id=\"T_2d890_row3_col4\" class=\"data row3 col4\" >15</td>\n",
+       "      <td id=\"T_2d890_row3_col5\" class=\"data row3 col5\" >9.100000</td>\n",
+       "      <td id=\"T_2d890_row3_col6\" class=\"data row3 col6\" >7.320000</td>\n",
+       "      <td id=\"T_2d890_row3_col7\" class=\"data row3 col7\" >2.610000</td>\n",
+       "      <td id=\"T_2d890_row3_col8\" class=\"data row3 col8\" >1915.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d890_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_2d890_row4_col0\" class=\"data row4 col0\" >BGE-m3</td>\n",
+       "      <td id=\"T_2d890_row4_col1\" class=\"data row4 col1\" >100</td>\n",
+       "      <td id=\"T_2d890_row4_col2\" class=\"data row4 col2\" >1024</td>\n",
+       "      <td id=\"T_2d890_row4_col3\" class=\"data row4 col3\" >110</td>\n",
+       "      <td id=\"T_2d890_row4_col4\" class=\"data row4 col4\" >0</td>\n",
+       "      <td id=\"T_2d890_row4_col5\" class=\"data row4 col5\" >0.500000</td>\n",
+       "      <td id=\"T_2d890_row4_col6\" class=\"data row4 col6\" >0.390000</td>\n",
+       "      <td id=\"T_2d890_row4_col7\" class=\"data row4 col7\" >0.520000</td>\n",
+       "      <td id=\"T_2d890_row4_col8\" class=\"data row4 col8\" >192.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d890_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_2d890_row5_col0\" class=\"data row5 col0\" >BGE-m3</td>\n",
+       "      <td id=\"T_2d890_row5_col1\" class=\"data row5 col1\" >500</td>\n",
+       "      <td id=\"T_2d890_row5_col2\" class=\"data row5 col2\" >1024</td>\n",
+       "      <td id=\"T_2d890_row5_col3\" class=\"data row5 col3\" >110</td>\n",
+       "      <td id=\"T_2d890_row5_col4\" class=\"data row5 col4\" >0</td>\n",
+       "      <td id=\"T_2d890_row5_col5\" class=\"data row5 col5\" >2.200000</td>\n",
+       "      <td id=\"T_2d890_row5_col6\" class=\"data row5 col6\" >1.950000</td>\n",
+       "      <td id=\"T_2d890_row5_col7\" class=\"data row5 col7\" >0.997000</td>\n",
+       "      <td id=\"T_2d890_row5_col8\" class=\"data row5 col8\" >501.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d890_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_2d890_row6_col0\" class=\"data row6 col0\" >BGE-m3</td>\n",
+       "      <td id=\"T_2d890_row6_col1\" class=\"data row6 col1\" >1000</td>\n",
+       "      <td id=\"T_2d890_row6_col2\" class=\"data row6 col2\" >1024</td>\n",
+       "      <td id=\"T_2d890_row6_col3\" class=\"data row6 col3\" >110</td>\n",
+       "      <td id=\"T_2d890_row6_col4\" class=\"data row6 col4\" >0</td>\n",
+       "      <td id=\"T_2d890_row6_col5\" class=\"data row6 col5\" >4.300000</td>\n",
+       "      <td id=\"T_2d890_row6_col6\" class=\"data row6 col6\" >3.910000</td>\n",
+       "      <td id=\"T_2d890_row6_col7\" class=\"data row6 col7\" >1.867000</td>\n",
+       "      <td id=\"T_2d890_row6_col8\" class=\"data row6 col8\" >535.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d890_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_2d890_row7_col0\" class=\"data row7 col0\" >BGE-m3</td>\n",
+       "      <td id=\"T_2d890_row7_col1\" class=\"data row7 col1\" >5000</td>\n",
+       "      <td id=\"T_2d890_row7_col2\" class=\"data row7 col2\" >1024</td>\n",
+       "      <td id=\"T_2d890_row7_col3\" class=\"data row7 col3\" >110</td>\n",
+       "      <td id=\"T_2d890_row7_col4\" class=\"data row7 col4\" >24</td>\n",
+       "      <td id=\"T_2d890_row7_col5\" class=\"data row7 col5\" >21.300000</td>\n",
+       "      <td id=\"T_2d890_row7_col6\" class=\"data row7 col6\" >19.530000</td>\n",
+       "      <td id=\"T_2d890_row7_col7\" class=\"data row7 col7\" >9.011000</td>\n",
+       "      <td id=\"T_2d890_row7_col8\" class=\"data row7 col8\" >554.900000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb9335216a0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(all_results)\n",
+    "display_cols = ['model', 'corpus_size', 'dim', 'ram_model_mb', 'ram_delta_encode_mb',\n",
+    "                'tracemalloc_peak_mb', 'emb_size_mb', 'encode_time_s', 'docs_per_sec']\n",
+    "df_display = df[display_cols].copy()\n",
+    "df_display.columns = ['Modelo', 'Docs', 'Dim', 'RAM modelo (MB)', 'RAM delta encode (MB)',\n",
+    "                       'Peak tracemalloc (MB)', 'Embeddings (MB)', 'Tiempo (s)', 'Docs/s']\n",
+    "df_display.style.background_gradient(subset=['RAM modelo (MB)', 'RAM delta encode (MB)'], cmap='Reds')     .background_gradient(subset=['Docs/s'], cmap='Greens')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0265a6ae",
+   "metadata": {},
+   "source": [
+    "## 5. Visualizaciones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bf8954d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
+    "colors = {'multi-e5-small': '#3498db', 'BGE-m3': '#e74c3c'}\n",
+    "\n",
+    "# Gráfico 1: RAM pico vs tamaño de corpus\n",
+    "ax = axes[0]\n",
+    "for name in MODELS:\n",
+    "    subset = df[df['model'] == name]\n",
+    "    ax.plot(subset['corpus_size'], subset['tracemalloc_peak_mb'], 'o-',\n",
+    "            color=colors[name], label=name, linewidth=2, markersize=8)\n",
+    "ax.set_xlabel('Documentos en corpus')\n",
+    "ax.set_ylabel('Peak RAM tracemalloc (MB)')\n",
+    "ax.set_title('Pico de RAM durante encoding')\n",
+    "ax.legend()\n",
+    "ax.set_xscale('log')\n",
+    "\n",
+    "# Gráfico 2: Throughput (docs/s) vs tamaño de corpus\n",
+    "ax = axes[1]\n",
+    "for name in MODELS:\n",
+    "    subset = df[df['model'] == name]\n",
+    "    ax.plot(subset['corpus_size'], subset['docs_per_sec'], 's-',\n",
+    "            color=colors[name], label=name, linewidth=2, markersize=8)\n",
+    "ax.set_xlabel('Documentos en corpus')\n",
+    "ax.set_ylabel('Docs/segundo')\n",
+    "ax.set_title('Velocidad de encoding')\n",
+    "ax.legend()\n",
+    "ax.set_xscale('log')\n",
+    "\n",
+    "# Gráfico 3: Tamaño de embeddings vs corpus\n",
+    "ax = axes[2]\n",
+    "for name in MODELS:\n",
+    "    subset = df[df['model'] == name]\n",
+    "    ax.bar([f'{name}\\n{n}' for n in subset['corpus_size']],\n",
+    "           subset['emb_size_mb'], color=colors[name], alpha=0.8)\n",
+    "ax.set_ylabel('Tamaño embeddings (MB)')\n",
+    "ax.set_title('Almacenamiento de embeddings')\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48eb2b03",
+   "metadata": {},
+   "source": [
+    "## 6. Comparación directa: coste por documento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3d9dd845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Métrica                        |  multi-e5-small |          BGE-m3 |    Ratio\n",
+      "------------------------------------------------------------------------------\n",
+      "RAM modelo (MB)                |           684.0 |           110.0 |     0.2x\n",
+      "Peak RAM encode (MB)           |             9.1 |            21.3 |     2.3x\n",
+      "Embeddings 5000 docs (MB)      |             7.3 |            19.5 |     2.7x\n",
+      "Encode 5000 docs (s)           |             2.6 |             9.0 |     3.5x\n",
+      "Throughput (docs/s)            |          1915.6 |           554.9 |     0.3x\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Comparación lado a lado para el corpus más grande\n",
+    "biggest = df[df['corpus_size'] == max(CORPUS_SIZES)]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 5))\n",
+    "metrics = ['ram_model_mb', 'tracemalloc_peak_mb', 'emb_size_mb']\n",
+    "labels = ['RAM modelo\\n(MB)', 'Peak RAM encode\\n(MB)', 'Embeddings\\n(MB)']\n",
+    "x = np.arange(len(labels))\n",
+    "width = 0.35\n",
+    "\n",
+    "for i, (_, row) in enumerate(biggest.iterrows()):\n",
+    "    vals = [row[m] for m in metrics]\n",
+    "    offset = -width/2 + i * width\n",
+    "    bars = ax.bar(x + offset, vals, width, label=row['model'], color=list(colors.values())[i])\n",
+    "    for bar, val in zip(bars, vals):\n",
+    "        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5,\n",
+    "                f'{val:.0f}', ha='center', va='bottom', fontsize=10, fontweight='bold')\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(labels)\n",
+    "ax.set_ylabel('MB')\n",
+    "ax.set_title(f'Comparación de consumo de memoria ({max(CORPUS_SIZES)} documentos)')\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Resumen numérico\n",
+    "print(f'\\n{\"Métrica\":30s} | {\"multi-e5-small\":>15s} | {\"BGE-m3\":>15s} | {\"Ratio\":>8s}')\n",
+    "print('-' * 78)\n",
+    "for _, row_e5 in biggest[biggest['model'] == 'multi-e5-small'].iterrows():\n",
+    "    for _, row_bge in biggest[biggest['model'] == 'BGE-m3'].iterrows():\n",
+    "        for metric, label in [('ram_model_mb', 'RAM modelo (MB)'),\n",
+    "                               ('tracemalloc_peak_mb', 'Peak RAM encode (MB)'),\n",
+    "                               ('emb_size_mb', f'Embeddings {max(CORPUS_SIZES)} docs (MB)'),\n",
+    "                               ('encode_time_s', f'Encode {max(CORPUS_SIZES)} docs (s)'),\n",
+    "                               ('docs_per_sec', 'Throughput (docs/s)')]:\n",
+    "            v_e5 = row_e5[metric]\n",
+    "            v_bge = row_bge[metric]\n",
+    "            ratio = v_bge / v_e5 if v_e5 > 0 else float('inf')\n",
+    "            print(f'{label:30s} | {v_e5:>15.1f} | {v_bge:>15.1f} | {ratio:>7.1f}x')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/03_modelo_local.ipynb b/notebooks/03_modelo_local.ipynb
new file mode 100644
index 0000000..ed7e53e
--- /dev/null
+++ b/notebooks/03_modelo_local.ipynb
@@ -0,0 +1,713 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b64ed95-f2d5-440e-b90b-bfd083d63716",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b488857",
+   "metadata": {},
+   "source": [
+    "# Guardar multilingual-e5-small en local y benchmark de carga\n",
+    "\n",
+    "Guardamos el modelo en `.local/models/` (gitignored) y comparamos:\n",
+    "- Carga desde HuggingFace cache vs carga desde path local\n",
+    "- Tiempo de primer encoding tras carga\n",
+    "- Verificación de que los embeddings son idénticos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "22b75c11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FN_REGISTRY_ROOT: /home/lucas/fn_registry\n",
+      "Destino local:    /home/lucas/fn_registry/.local/models/multilingual-e5-small\n",
+      "RAM actual:       833 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, time, gc\n",
+    "import numpy as np\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "import psutil\n",
+    "\n",
+    "FN_ROOT = os.environ.get('FN_REGISTRY_ROOT', '/home/lucas/fn_registry')\n",
+    "MODEL_ID = 'intfloat/multilingual-e5-small'\n",
+    "LOCAL_PATH = os.path.join(FN_ROOT, '.local', 'models', 'multilingual-e5-small')\n",
+    "\n",
+    "def ram_mb():\n",
+    "    return psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\n",
+    "\n",
+    "print(f'FN_REGISTRY_ROOT: {FN_ROOT}')\n",
+    "print(f'Destino local:    {LOCAL_PATH}')\n",
+    "print(f'RAM actual:       {ram_mb():.0f} MB')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00e8cb16",
+   "metadata": {},
+   "source": [
+    "## 1. Guardar modelo en .local/models/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "90df0aac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cargando modelo desde HuggingFace cache...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c21758811d8c41f28cec7ecf11df90ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Cargado en 6.43s\n",
+      "Guardando en /home/lucas/fn_registry/.local/models/multilingual-e5-small...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb775051d2b64cfcad0f9f0a4302a2c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Guardado en 2.33s\n",
+      "  Tamaño en disco: 465.6 MB\n",
+      "  multilingual-e5-small/\n",
+      "    README.md (0.5 MB)\n",
+      "    config.json (0.0 MB)\n",
+      "    config_sentence_transformers.json (0.0 MB)\n",
+      "    model.safetensors (448.8 MB)\n",
+      "    modules.json (0.0 MB)\n",
+      "    sentence_bert_config.json (0.0 MB)\n",
+      "    tokenizer.json (16.3 MB)\n",
+      "    tokenizer_config.json (0.0 MB)\n",
+      "    1_Pooling/\n",
+      "      config.json (0.0 MB)\n",
+      "    2_Normalize/\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "4063"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cargar desde HF cache y guardar localmente\n",
+    "print('Cargando modelo desde HuggingFace cache...')\n",
+    "t0 = time.perf_counter()\n",
+    "model = SentenceTransformer(MODEL_ID)\n",
+    "hf_load_time = time.perf_counter() - t0\n",
+    "print(f'  Cargado en {hf_load_time:.2f}s')\n",
+    "\n",
+    "# Guardar en .local/models/\n",
+    "print(f'Guardando en {LOCAL_PATH}...')\n",
+    "t0 = time.perf_counter()\n",
+    "model.save(LOCAL_PATH)\n",
+    "save_time = time.perf_counter() - t0\n",
+    "print(f'  Guardado en {save_time:.2f}s')\n",
+    "\n",
+    "# Verificar tamaño en disco\n",
+    "total_size = 0\n",
+    "for dirpath, dirnames, filenames in os.walk(LOCAL_PATH):\n",
+    "    for f in filenames:\n",
+    "        fp = os.path.join(dirpath, f)\n",
+    "        total_size += os.path.getsize(fp)\n",
+    "print(f'  Tamaño en disco: {total_size / (1024*1024):.1f} MB')\n",
+    "\n",
+    "# Listar archivos guardados\n",
+    "for dirpath, dirnames, filenames in os.walk(LOCAL_PATH):\n",
+    "    level = dirpath.replace(LOCAL_PATH, '').count(os.sep)\n",
+    "    indent = '  ' * level\n",
+    "    subdir = os.path.basename(dirpath)\n",
+    "    print(f'  {indent}{subdir}/')\n",
+    "    for f in sorted(filenames):\n",
+    "        size = os.path.getsize(os.path.join(dirpath, f))\n",
+    "        print(f'  {indent}  {f} ({size/(1024*1024):.1f} MB)')\n",
+    "\n",
+    "del model\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "876b8c22",
+   "metadata": {},
+   "source": [
+    "## 2. Benchmark: carga local vs HF cache\n",
+    "\n",
+    "Cargamos 5 veces de cada fuente para tener estadísticas estables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "42346646",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benchmark de carga (5 iteraciones cada uno)...\n",
+      "\n",
+      "→ Cargando desde HuggingFace cache...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0859bcd797a94dca8afdc0341ed73044",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39c9cc105c904dd39fcf08889b33ba98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c777e538f764bc99cca408f6242eeda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16d63aeb9fcb4d5f8e8d330931ff7d4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b7b24c69e87464ea2efd25156b77170",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Media: 4.134s ± 0.211s (min: 3.797s)\n",
+      "→ Cargando desde .local/models/...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c12421a330124488ad628e6175f3229a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a36175de726e4c13ba1cadb89186af2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0507871bb0648a7910f9521856ece72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f5aa78b20d34492a2e924445d4ed769",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb904177545448d5b83aea15f8f15b40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Media: 1.821s ± 0.138s (min: 1.563s)\n",
+      "\n",
+      "Speedup local vs HF cache: 2.27x\n"
+     ]
+    }
+   ],
+   "source": [
+    "N_RUNS = 5\n",
+    "\n",
+    "def bench_load(source, path, runs=N_RUNS):\n",
+    "    \"\"\"Carga modelo N veces, retorna tiempos y RAM.\"\"\"\n",
+    "    times = []\n",
+    "    rams = []\n",
+    "    for i in range(runs):\n",
+    "        gc.collect()\n",
+    "        ram_before = ram_mb()\n",
+    "        t0 = time.perf_counter()\n",
+    "        m = SentenceTransformer(path)\n",
+    "        elapsed = time.perf_counter() - t0\n",
+    "        ram_after = ram_mb()\n",
+    "        times.append(elapsed)\n",
+    "        rams.append(ram_after - ram_before)\n",
+    "        del m\n",
+    "        gc.collect()\n",
+    "    return {\n",
+    "        'source': source,\n",
+    "        'times': times,\n",
+    "        'mean_time': np.mean(times),\n",
+    "        'std_time': np.std(times),\n",
+    "        'min_time': np.min(times),\n",
+    "        'mean_ram_delta': np.mean(rams),\n",
+    "    }\n",
+    "\n",
+    "print(f'Benchmark de carga ({N_RUNS} iteraciones cada uno)...')\n",
+    "print()\n",
+    "\n",
+    "# Carga desde HF cache\n",
+    "print('→ Cargando desde HuggingFace cache...')\n",
+    "hf_bench = bench_load('HF cache', MODEL_ID)\n",
+    "print(f'  Media: {hf_bench[\"mean_time\"]:.3f}s ± {hf_bench[\"std_time\"]:.3f}s (min: {hf_bench[\"min_time\"]:.3f}s)')\n",
+    "\n",
+    "# Carga desde path local\n",
+    "print('→ Cargando desde .local/models/...')\n",
+    "local_bench = bench_load('Local path', LOCAL_PATH)\n",
+    "print(f'  Media: {local_bench[\"mean_time\"]:.3f}s ± {local_bench[\"std_time\"]:.3f}s (min: {local_bench[\"min_time\"]:.3f}s)')\n",
+    "\n",
+    "speedup = hf_bench['mean_time'] / local_bench['mean_time']\n",
+    "print(f'\\nSpeedup local vs HF cache: {speedup:.2f}x')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06291006",
+   "metadata": {},
+   "source": [
+    "## 3. Verificación: embeddings idénticos\n",
+    "\n",
+    "Confirmamos que el modelo local produce exactamente los mismos embeddings que el de HF cache."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8e6eaa4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a469b638479a45deb836f54d1edaa056",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: intfloat/multilingual-e5-small\n",
+      "Key                     | Status     |  | \n",
+      "------------------------+------------+--+-\n",
+      "embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2da4a0a304ba40a0a4748941dbe4ea9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max diferencia absoluta:  0.00e+00\n",
+      "Mean diferencia absoluta: 0.00e+00\n",
+      "¿Son idénticos (atol=1e-6)? ✓ SÍ\n",
+      "  [0] cosine=1.0000000000 | passage: La inteligencia artificial transforma la industria \n",
+      "  [1] cosine=1.0000000000 | passage: Los mercados financieros reaccionan a las decisione\n",
+      "  [2] cosine=1.0000000000 | passage: El aprendizaje profundo requiere grandes cantidades\n",
+      "  [3] cosine=1.0000000000 | query: ¿Cómo funciona el machine learning?\n",
+      "  [4] cosine=1.0000000000 | query: ¿Qué afecta los precios de las acciones?\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "6961"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_texts = [\n",
+    "    'passage: La inteligencia artificial transforma la industria tecnológica',\n",
+    "    'passage: Los mercados financieros reaccionan a las decisiones de política monetaria',\n",
+    "    'passage: El aprendizaje profundo requiere grandes cantidades de datos etiquetados',\n",
+    "    'query: ¿Cómo funciona el machine learning?',\n",
+    "    'query: ¿Qué afecta los precios de las acciones?',\n",
+    "]\n",
+    "\n",
+    "# Cargar ambos modelos\n",
+    "model_hf = SentenceTransformer(MODEL_ID)\n",
+    "model_local = SentenceTransformer(LOCAL_PATH)\n",
+    "\n",
+    "# Generar embeddings\n",
+    "emb_hf = model_hf.encode(test_texts, normalize_embeddings=True)\n",
+    "emb_local = model_local.encode(test_texts, normalize_embeddings=True)\n",
+    "\n",
+    "# Comparar\n",
+    "max_diff = np.max(np.abs(emb_hf - emb_local))\n",
+    "mean_diff = np.mean(np.abs(emb_hf - emb_local))\n",
+    "are_equal = np.allclose(emb_hf, emb_local, atol=1e-6)\n",
+    "\n",
+    "print(f'Max diferencia absoluta:  {max_diff:.2e}')\n",
+    "print(f'Mean diferencia absoluta: {mean_diff:.2e}')\n",
+    "print(f'¿Son idénticos (atol=1e-6)? {\"✓ SÍ\" if are_equal else \"✗ NO\"}')\n",
+    "\n",
+    "# Cosine similarity entre pares correspondientes\n",
+    "for i, text in enumerate(test_texts):\n",
+    "    sim = np.dot(emb_hf[i], emb_local[i])\n",
+    "    print(f'  [{i}] cosine={sim:.10f} | {text[:60]}')\n",
+    "\n",
+    "del model_hf, model_local\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c139a193",
+   "metadata": {},
+   "source": [
+    "## 4. Visualización de tiempos de carga"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "53664d2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_18429/1763775165.py:10: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
+      "  bp = axes[0].boxplot(data, labels=labels, patch_artist=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Box plot de tiempos\n",
+    "data = [hf_bench['times'], local_bench['times']]\n",
+    "labels = ['HF cache', 'Local (.local/)']\n",
+    "colors = ['#e74c3c', '#2ecc71']\n",
+    "\n",
+    "bp = axes[0].boxplot(data, labels=labels, patch_artist=True)\n",
+    "for patch, color in zip(bp['boxes'], colors):\n",
+    "    patch.set_facecolor(color)\n",
+    "    patch.set_alpha(0.7)\n",
+    "axes[0].set_ylabel('Tiempo de carga (s)')\n",
+    "axes[0].set_title('Distribución de tiempos de carga')\n",
+    "\n",
+    "# Bar chart comparativo\n",
+    "means = [hf_bench['mean_time'], local_bench['mean_time']]\n",
+    "stds = [hf_bench['std_time'], local_bench['std_time']]\n",
+    "bars = axes[1].bar(labels, means, yerr=stds, color=colors, alpha=0.8, capsize=10)\n",
+    "for bar, mean in zip(bars, means):\n",
+    "    axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.05,\n",
+    "                 f'{mean:.3f}s', ha='center', fontsize=12, fontweight='bold')\n",
+    "axes[1].set_ylabel('Tiempo medio (s)')\n",
+    "axes[1].set_title(f'Carga media ({N_RUNS} runs) — speedup: {speedup:.2f}x')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/04_vector_storage.ipynb b/notebooks/04_vector_storage.ipynb
new file mode 100644
index 0000000..16e3ec3
--- /dev/null
+++ b/notebooks/04_vector_storage.ipynb
@@ -0,0 +1,1126 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46e94147-fc78-4423-8142-024587261562",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7edb270b",
+   "metadata": {},
+   "source": [
+    "# Comparativa: almacenamiento y recuperación local de embeddings\n",
+    "\n",
+    "Buscamos el equivalente a SQLite/DuckDB para vectores: **embebido, sin servidor, archivo local**.\n",
+    "\n",
+    "Candidatos:\n",
+    "- **FAISS** — Meta, gold standard ANN, sin metadata nativa\n",
+    "- **sqlite-vec** — Extensión SQLite pura en C, vectores junto a datos SQL\n",
+    "- **LanceDB** — DB columnar para vectores, persiste en directorio\n",
+    "- **ChromaDB** — Popular, metadata rica, modo local\n",
+    "- **USearch** — Ultra ligero, rivaliza FAISS en velocidad\n",
+    "\n",
+    "Medimos: inserción, búsqueda top-10, persistencia a disco, carga desde disco, tamaño en disco."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8a2144f",
+   "metadata": {},
+   "source": [
+    "## 1. Setup y corpus de embeddings pre-generados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "36e79f24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dimensión: 384\n",
+      "Tamaños de corpus: [1000, 10000, 50000]\n",
+      "Queries: 100\n",
+      "Embeddings pre-generados (normalizados, float32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import time, os, shutil, json\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.style.use('seaborn-v0_8-whitegrid')\n",
+    "\n",
+    "# Generamos embeddings sintéticos (dim=384 como e5-small) para no depender del modelo\n",
+    "# Esto aísla el benchmark de storage del benchmark de encoding\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "SIZES = [1_000, 10_000, 50_000]\n",
+    "DIM = 384\n",
+    "DATA_DIR = 'data/vector_bench'\n",
+    "os.makedirs(DATA_DIR, exist_ok=True)\n",
+    "\n",
+    "# Pre-generar embeddings normalizados (simulan output de e5-small)\n",
+    "datasets = {}\n",
+    "for n in SIZES:\n",
+    "    vecs = np.random.randn(n, DIM).astype(np.float32)\n",
+    "    # Normalizar como haría sentence-transformers\n",
+    "    norms = np.linalg.norm(vecs, axis=1, keepdims=True)\n",
+    "    vecs = vecs / norms\n",
+    "    datasets[n] = vecs\n",
+    "\n",
+    "# Queries (100 vectores)\n",
+    "N_QUERIES = 100\n",
+    "queries = np.random.randn(N_QUERIES, DIM).astype(np.float32)\n",
+    "queries = queries / np.linalg.norm(queries, axis=1, keepdims=True)\n",
+    "\n",
+    "# Metadata simulada\n",
+    "def make_metadata(n):\n",
+    "    categories = ['programación', 'ciencia', 'cocina', 'finanzas', 'geografía']\n",
+    "    return [{'id': i, 'category': categories[i % len(categories)], 'text': f'documento_{i}'} for i in range(n)]\n",
+    "\n",
+    "print(f'Dimensión: {DIM}')\n",
+    "print(f'Tamaños de corpus: {SIZES}')\n",
+    "print(f'Queries: {N_QUERIES}')\n",
+    "print(f'Embeddings pre-generados (normalizados, float32)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbadd0ba",
+   "metadata": {},
+   "source": [
+    "## 2. Benchmark framework\n",
+    "\n",
+    "Cada backend implementa: insert, search, save, load, size_on_disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f187cb44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Framework de benchmark listo\n"
+     ]
+    }
+   ],
+   "source": [
+    "K = 10  # top-k para búsqueda\n",
+    "\n",
+    "def bench_backend(name, insert_fn, search_fn, save_fn, load_search_fn, size_fn, cleanup_fn):\n",
+    "    \"\"\"Benchmark completo de un backend de vectores.\"\"\"\n",
+    "    results = []\n",
+    "    for n in SIZES:\n",
+    "        vecs = datasets[n]\n",
+    "        meta = make_metadata(n)\n",
+    "        path = os.path.join(DATA_DIR, f'{name}_{n}')\n",
+    "\n",
+    "        # Limpiar estado previo\n",
+    "        cleanup_fn(path)\n",
+    "\n",
+    "        # INSERT\n",
+    "        t0 = time.perf_counter()\n",
+    "        state = insert_fn(vecs, meta, path)\n",
+    "        insert_time = time.perf_counter() - t0\n",
+    "\n",
+    "        # SEARCH (100 queries)\n",
+    "        t0 = time.perf_counter()\n",
+    "        results_search = search_fn(state, queries, K)\n",
+    "        search_time = time.perf_counter() - t0\n",
+    "        search_per_query = search_time / N_QUERIES\n",
+    "\n",
+    "        # SAVE / persist\n",
+    "        t0 = time.perf_counter()\n",
+    "        save_fn(state, path)\n",
+    "        save_time = time.perf_counter() - t0\n",
+    "\n",
+    "        # SIZE on disk\n",
+    "        disk_mb = size_fn(path)\n",
+    "\n",
+    "        # LOAD from disk + search\n",
+    "        t0 = time.perf_counter()\n",
+    "        loaded_results = load_search_fn(path, queries[:1], K)\n",
+    "        load_search_time = time.perf_counter() - t0\n",
+    "\n",
+    "        results.append({\n",
+    "            'backend': name,\n",
+    "            'n_vectors': n,\n",
+    "            'insert_s': round(insert_time, 4),\n",
+    "            'search_100q_s': round(search_time, 4),\n",
+    "            'per_query_ms': round(search_per_query * 1000, 3),\n",
+    "            'save_s': round(save_time, 4),\n",
+    "            'load_and_search_s': round(load_search_time, 4),\n",
+    "            'disk_mb': round(disk_mb, 2),\n",
+    "        })\n",
+    "\n",
+    "        print(f'  {name:12s} | {n:6d} vecs | insert={insert_time:.3f}s | '\n",
+    "              f'search={search_per_query*1000:.2f}ms/q | save={save_time:.3f}s | '\n",
+    "              f'load+search={load_search_time:.3f}s | disk={disk_mb:.1f}MB')\n",
+    "\n",
+    "        cleanup_fn(path)\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "def dir_size_mb(path):\n",
+    "    total = 0\n",
+    "    if os.path.isfile(path):\n",
+    "        return os.path.getsize(path) / (1024*1024)\n",
+    "    if not os.path.exists(path):\n",
+    "        return 0\n",
+    "    for dp, dn, fns in os.walk(path):\n",
+    "        for f in fns:\n",
+    "            total += os.path.getsize(os.path.join(dp, f))\n",
+    "    return total / (1024*1024)\n",
+    "\n",
+    "def cleanup_path(path):\n",
+    "    if os.path.isfile(path):\n",
+    "        os.remove(path)\n",
+    "    elif os.path.isdir(path):\n",
+    "        shutil.rmtree(path, ignore_errors=True)\n",
+    "    # También limpiar archivos con sufijos\n",
+    "    for suffix in ['.faiss', '.ids.npy', '.usearch', '.meta.json', '.db']:\n",
+    "        p = path + suffix\n",
+    "        if os.path.exists(p):\n",
+    "            os.remove(p)\n",
+    "\n",
+    "print('Framework de benchmark listo')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47e89c3c",
+   "metadata": {},
+   "source": [
+    "## 3. Implementaciones por backend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bc86400d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ FAISS listo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── FAISS ──────────────────────────────────────────────────────\n",
+    "import faiss\n",
+    "\n",
+    "def faiss_insert(vecs, meta, path):\n",
+    "    index = faiss.IndexFlatIP(DIM)\n",
+    "    index.add(vecs)\n",
+    "    return index\n",
+    "\n",
+    "def faiss_search(index, queries, k):\n",
+    "    scores, ids = index.search(queries, k)\n",
+    "    return ids\n",
+    "\n",
+    "def faiss_save(index, path):\n",
+    "    faiss.write_index(index, path + '.faiss')\n",
+    "\n",
+    "def faiss_load_search(path, queries, k):\n",
+    "    index = faiss.read_index(path + '.faiss')\n",
+    "    scores, ids = index.search(queries, k)\n",
+    "    return ids\n",
+    "\n",
+    "def faiss_size(path):\n",
+    "    return dir_size_mb(path + '.faiss')\n",
+    "\n",
+    "print('✓ FAISS listo')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "30a64cf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ sqlite-vec listo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── sqlite-vec ─────────────────────────────────────────────────\n",
+    "import sqlite3\n",
+    "import sqlite_vec\n",
+    "\n",
+    "def sqlvec_insert(vecs, meta, path):\n",
+    "    db = sqlite3.connect(path + '.db')\n",
+    "    db.enable_load_extension(True)\n",
+    "    sqlite_vec.load(db)\n",
+    "    db.execute(f'CREATE VIRTUAL TABLE IF NOT EXISTS vec_items USING vec0(embedding float[{DIM}])')\n",
+    "    # Insert en batches\n",
+    "    batch_size = 500\n",
+    "    for i in range(0, len(vecs), batch_size):\n",
+    "        batch = [(j, vecs[j].tobytes()) for j in range(i, min(i + batch_size, len(vecs)))]\n",
+    "        db.executemany('INSERT INTO vec_items(rowid, embedding) VALUES (?, ?)', batch)\n",
+    "    db.commit()\n",
+    "    return db\n",
+    "\n",
+    "def sqlvec_search(db, queries, k):\n",
+    "    results = []\n",
+    "    for q in queries:\n",
+    "        rows = db.execute(\n",
+    "            'SELECT rowid, distance FROM vec_items WHERE embedding MATCH ? ORDER BY distance LIMIT ?',\n",
+    "            (q.tobytes(), k)\n",
+    "        ).fetchall()\n",
+    "        results.append([r[0] for r in rows])\n",
+    "    return results\n",
+    "\n",
+    "def sqlvec_save(db, path):\n",
+    "    db.close()  # Ya está persistido en el .db\n",
+    "\n",
+    "def sqlvec_load_search(path, queries, k):\n",
+    "    db = sqlite3.connect(path + '.db')\n",
+    "    db.enable_load_extension(True)\n",
+    "    sqlite_vec.load(db)\n",
+    "    q = queries[0]\n",
+    "    rows = db.execute(\n",
+    "        'SELECT rowid, distance FROM vec_items WHERE embedding MATCH ? ORDER BY distance LIMIT ?',\n",
+    "        (q.tobytes(), k)\n",
+    "    ).fetchall()\n",
+    "    db.close()\n",
+    "    return [r[0] for r in rows]\n",
+    "\n",
+    "def sqlvec_size(path):\n",
+    "    return dir_size_mb(path + '.db')\n",
+    "\n",
+    "print('✓ sqlite-vec listo')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "74d1bb27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ LanceDB listo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── LanceDB ────────────────────────────────────────────────────\n",
+    "import lancedb\n",
+    "import pyarrow as pa\n",
+    "\n",
+    "def lance_insert(vecs, meta, path):\n",
+    "    db = lancedb.connect(path)\n",
+    "    data = pa.table({\n",
+    "        'id': list(range(len(vecs))),\n",
+    "        'category': [meta[i]['category'] for i in range(len(vecs))],\n",
+    "        'vector': [v.tolist() for v in vecs],\n",
+    "    })\n",
+    "    tbl = db.create_table('vectors', data, mode='overwrite')\n",
+    "    return tbl\n",
+    "\n",
+    "def lance_search(tbl, queries, k):\n",
+    "    results = []\n",
+    "    for q in queries:\n",
+    "        r = tbl.search(q.tolist()).limit(k).to_list()\n",
+    "        results.append([row['id'] for row in r])\n",
+    "    return results\n",
+    "\n",
+    "def lance_save(tbl, path):\n",
+    "    pass  # LanceDB persiste automáticamente\n",
+    "\n",
+    "def lance_load_search(path, queries, k):\n",
+    "    db = lancedb.connect(path)\n",
+    "    tbl = db.open_table('vectors')\n",
+    "    q = queries[0]\n",
+    "    r = tbl.search(q.tolist()).limit(k).to_list()\n",
+    "    return [row['id'] for row in r]\n",
+    "\n",
+    "def lance_size(path):\n",
+    "    return dir_size_mb(path)\n",
+    "\n",
+    "print('✓ LanceDB listo')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1541cef1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ ChromaDB listo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── ChromaDB ───────────────────────────────────────────────────\n",
+    "import chromadb\n",
+    "\n",
+    "def chroma_insert(vecs, meta, path):\n",
+    "    client = chromadb.PersistentClient(path=path)\n",
+    "    col = client.get_or_create_collection('vectors', metadata={'hnsw:space': 'ip'})\n",
+    "    # Chroma tiene límite de batch, insertar en chunks\n",
+    "    batch_size = 5000\n",
+    "    for i in range(0, len(vecs), batch_size):\n",
+    "        end = min(i + batch_size, len(vecs))\n",
+    "        col.add(\n",
+    "            ids=[str(j) for j in range(i, end)],\n",
+    "            embeddings=[v.tolist() for v in vecs[i:end]],\n",
+    "            metadatas=[meta[j] for j in range(i, end)],\n",
+    "        )\n",
+    "    return (client, col)\n",
+    "\n",
+    "def chroma_search(state, queries, k):\n",
+    "    _, col = state\n",
+    "    results = col.query(query_embeddings=[q.tolist() for q in queries], n_results=k)\n",
+    "    return results['ids']\n",
+    "\n",
+    "def chroma_save(state, path):\n",
+    "    pass  # PersistentClient persiste automáticamente\n",
+    "\n",
+    "def chroma_load_search(path, queries, k):\n",
+    "    client = chromadb.PersistentClient(path=path)\n",
+    "    col = client.get_collection('vectors')\n",
+    "    results = col.query(query_embeddings=[queries[0].tolist()], n_results=k)\n",
+    "    return results['ids']\n",
+    "\n",
+    "def chroma_size(path):\n",
+    "    return dir_size_mb(path)\n",
+    "\n",
+    "print('✓ ChromaDB listo')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6bec353d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ USearch listo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── USearch ────────────────────────────────────────────────────\n",
+    "from usearch.index import Index\n",
+    "\n",
+    "def usearch_insert(vecs, meta, path):\n",
+    "    index = Index(ndim=DIM, metric='ip', dtype='f32')\n",
+    "    keys = np.arange(len(vecs), dtype=np.uint64)\n",
+    "    index.add(keys, vecs)\n",
+    "    return index\n",
+    "\n",
+    "def usearch_search(index, queries, k):\n",
+    "    results = index.search(queries, k)\n",
+    "    return results.keys\n",
+    "\n",
+    "def usearch_save(index, path):\n",
+    "    index.save(path + '.usearch')\n",
+    "\n",
+    "def usearch_load_search(path, queries, k):\n",
+    "    index = Index(ndim=DIM, metric='ip', dtype='f32')\n",
+    "    index.load(path + '.usearch')\n",
+    "    results = index.search(queries[:1], k)\n",
+    "    return results.keys\n",
+    "\n",
+    "def usearch_size(path):\n",
+    "    return dir_size_mb(path + '.usearch')\n",
+    "\n",
+    "print('✓ USearch listo')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c82f6260",
+   "metadata": {},
+   "source": [
+    "## 4. Ejecutar benchmarks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e02de33b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "── FAISS ──\n",
+      "  FAISS        |   1000 vecs | insert=0.003s | search=0.23ms/q | save=0.006s | load+search=0.003s | disk=1.5MB\n",
+      "  FAISS        |  10000 vecs | insert=0.020s | search=0.48ms/q | save=0.030s | load+search=0.034s | disk=14.6MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  FAISS        |  50000 vecs | insert=0.240s | search=2.77ms/q | save=0.169s | load+search=0.211s | disk=73.2MB\n",
+      "\n",
+      "── sqlite-vec ──\n",
+      "  sqlite-vec   |   1000 vecs | insert=0.053s | search=0.35ms/q | save=0.001s | load+search=0.001s | disk=1.6MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  sqlite-vec   |  10000 vecs | insert=0.293s | search=4.87ms/q | save=0.000s | load+search=0.006s | disk=15.3MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  sqlite-vec   |  50000 vecs | insert=1.275s | search=19.50ms/q | save=0.000s | load+search=0.018s | disk=74.7MB\n",
+      "\n",
+      "── LanceDB ──\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[90m[\u001b[0m2026-04-02T15:27:41Z \u001b[33mWARN \u001b[0m lance::dataset::write::insert\u001b[90m]\u001b[0m No existing dataset at /home/lucas/fn_registry/analysis/estudio_embeddings/notebooks/data/vector_bench/LanceDB_1000/vectors.lance, it will be created\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  LanceDB      |   1000 vecs | insert=0.040s | search=3.32ms/q | save=0.000s | load+search=0.007s | disk=1.5MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[90m[\u001b[0m2026-04-02T15:27:42Z \u001b[33mWARN \u001b[0m lance::dataset::write::insert\u001b[90m]\u001b[0m No existing dataset at /home/lucas/fn_registry/analysis/estudio_embeddings/notebooks/data/vector_bench/LanceDB_10000/vectors.lance, it will be created\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  LanceDB      |  10000 vecs | insert=0.297s | search=12.30ms/q | save=0.000s | load+search=0.011s | disk=14.7MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[90m[\u001b[0m2026-04-02T15:27:47Z \u001b[33mWARN \u001b[0m lance::dataset::write::insert\u001b[90m]\u001b[0m No existing dataset at /home/lucas/fn_registry/analysis/estudio_embeddings/notebooks/data/vector_bench/LanceDB_50000/vectors.lance, it will be created\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  LanceDB      |  50000 vecs | insert=3.740s | search=28.88ms/q | save=0.000s | load+search=0.030s | disk=73.7MB\n",
+      "\n",
+      "── ChromaDB ──\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  ChromaDB     |   1000 vecs | insert=0.492s | search=0.30ms/q | save=0.000s | load+search=0.006s | disk=4.1MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  ChromaDB     |  10000 vecs | insert=2.778s | search=0.53ms/q | save=0.000s | load+search=0.006s | disk=29.6MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  ChromaDB     |  50000 vecs | insert=19.123s | search=1.23ms/q | save=0.000s | load+search=0.010s | disk=108.6MB\n",
+      "\n",
+      "── USearch ──\n",
+      "  ERROR: usearch.index.Index() got multiple values for keyword argument 'ndim'\n",
+      "\n",
+      "Benchmark completo\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_results = []\n",
+    "\n",
+    "backends = [\n",
+    "    ('FAISS',      faiss_insert,   faiss_search,   faiss_save,   faiss_load_search,   faiss_size,   cleanup_path),\n",
+    "    ('sqlite-vec', sqlvec_insert,  sqlvec_search,   sqlvec_save,  sqlvec_load_search,  sqlvec_size,  cleanup_path),\n",
+    "    ('LanceDB',    lance_insert,   lance_search,   lance_save,   lance_load_search,   lance_size,   cleanup_path),\n",
+    "    ('ChromaDB',   chroma_insert,  chroma_search,  chroma_save,  chroma_load_search,  chroma_size,  cleanup_path),\n",
+    "    ('USearch',    usearch_insert, usearch_search, usearch_save, usearch_load_search, usearch_size, cleanup_path),\n",
+    "]\n",
+    "\n",
+    "for name, *fns in backends:\n",
+    "    print(f'\\n── {name} ──')\n",
+    "    try:\n",
+    "        res = bench_backend(name, *fns)\n",
+    "        all_results.extend(res)\n",
+    "    except Exception as e:\n",
+    "        print(f'  ERROR: {e}')\n",
+    "\n",
+    "df = pd.DataFrame(all_results)\n",
+    "print('\\nBenchmark completo')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f126167a",
+   "metadata": {},
+   "source": [
+    "## 5. Tabla resumen y visualizaciones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "311f88fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_814d2_row0_col2, #T_814d2_row0_col7, #T_814d2_row1_col2, #T_814d2_row3_col2, #T_814d2_row3_col7, #T_814d2_row6_col2, #T_814d2_row6_col7, #T_814d2_row12_col2, #T_814d2_row12_col4, #T_814d2_row12_col7, #T_814d2_row13_col4 {\n",
+       "  background-color: #67000d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row0_col4, #T_814d2_row14_col4 {\n",
+       "  background-color: #69000d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row0_col6 {\n",
+       "  background-color: #00451c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row1_col4, #T_814d2_row10_col4 {\n",
+       "  background-color: #6f020e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row1_col6 {\n",
+       "  background-color: #087432;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row1_col7, #T_814d2_row7_col7 {\n",
+       "  background-color: #a30f15;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row2_col2, #T_814d2_row3_col4, #T_814d2_row4_col2, #T_814d2_row7_col2, #T_814d2_row13_col2 {\n",
+       "  background-color: #6d010e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row2_col4 {\n",
+       "  background-color: #960b13;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row2_col6 {\n",
+       "  background-color: #f7fcf5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_814d2_row2_col7 {\n",
+       "  background-color: #fca183;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_814d2_row3_col6, #T_814d2_row12_col6 {\n",
+       "  background-color: #00441b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row4_col4 {\n",
+       "  background-color: #b21218;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row4_col6, #T_814d2_row9_col6, #T_814d2_row10_col6 {\n",
+       "  background-color: #004a1e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row4_col7 {\n",
+       "  background-color: #a50f15;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row5_col2 {\n",
+       "  background-color: #880811;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row5_col4, #T_814d2_row8_col7 {\n",
+       "  background-color: #fca285;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_814d2_row5_col6 {\n",
+       "  background-color: #005e26;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row5_col7 {\n",
+       "  background-color: #fca588;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_814d2_row6_col4 {\n",
+       "  background-color: #9f0e14;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row6_col6 {\n",
+       "  background-color: #004d1f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row7_col4 {\n",
+       "  background-color: #f44d38;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row7_col6 {\n",
+       "  background-color: #005221;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row8_col2 {\n",
+       "  background-color: #bb141a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row8_col4, #T_814d2_row11_col2, #T_814d2_row11_col7 {\n",
+       "  background-color: #fff5f0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_814d2_row8_col6 {\n",
+       "  background-color: #03702e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row9_col2, #T_814d2_row9_col7 {\n",
+       "  background-color: #73030f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row9_col4 {\n",
+       "  background-color: #6b010e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row10_col2 {\n",
+       "  background-color: #ab1016;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row10_col7 {\n",
+       "  background-color: #cf1c1f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row11_col4 {\n",
+       "  background-color: #7a0510;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row11_col6 {\n",
+       "  background-color: #005120;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row13_col6 {\n",
+       "  background-color: #005f26;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row13_col7 {\n",
+       "  background-color: #a81016;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row14_col2 {\n",
+       "  background-color: #dc2924;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_814d2_row14_col6 {\n",
+       "  background-color: #e8f6e3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_814d2_row14_col7 {\n",
+       "  background-color: #fcb79c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_814d2\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_814d2_level0_col0\" class=\"col_heading level0 col0\" >Backend</th>\n",
+       "      <th id=\"T_814d2_level0_col1\" class=\"col_heading level0 col1\" >Vectors</th>\n",
+       "      <th id=\"T_814d2_level0_col2\" class=\"col_heading level0 col2\" >Insert (s)</th>\n",
+       "      <th id=\"T_814d2_level0_col3\" class=\"col_heading level0 col3\" >Search 100q (s)</th>\n",
+       "      <th id=\"T_814d2_level0_col4\" class=\"col_heading level0 col4\" >Per query (ms)</th>\n",
+       "      <th id=\"T_814d2_level0_col5\" class=\"col_heading level0 col5\" >Save (s)</th>\n",
+       "      <th id=\"T_814d2_level0_col6\" class=\"col_heading level0 col6\" >Load+Search (s)</th>\n",
+       "      <th id=\"T_814d2_level0_col7\" class=\"col_heading level0 col7\" >Disk (MB)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_814d2_row0_col0\" class=\"data row0 col0\" >FAISS</td>\n",
+       "      <td id=\"T_814d2_row0_col1\" class=\"data row0 col1\" >1000</td>\n",
+       "      <td id=\"T_814d2_row0_col2\" class=\"data row0 col2\" >0.002700</td>\n",
+       "      <td id=\"T_814d2_row0_col3\" class=\"data row0 col3\" >0.022600</td>\n",
+       "      <td id=\"T_814d2_row0_col4\" class=\"data row0 col4\" >0.226000</td>\n",
+       "      <td id=\"T_814d2_row0_col5\" class=\"data row0 col5\" >0.006000</td>\n",
+       "      <td id=\"T_814d2_row0_col6\" class=\"data row0 col6\" >0.002600</td>\n",
+       "      <td id=\"T_814d2_row0_col7\" class=\"data row0 col7\" >1.460000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_814d2_row1_col0\" class=\"data row1 col0\" >FAISS</td>\n",
+       "      <td id=\"T_814d2_row1_col1\" class=\"data row1 col1\" >10000</td>\n",
+       "      <td id=\"T_814d2_row1_col2\" class=\"data row1 col2\" >0.020000</td>\n",
+       "      <td id=\"T_814d2_row1_col3\" class=\"data row1 col3\" >0.048000</td>\n",
+       "      <td id=\"T_814d2_row1_col4\" class=\"data row1 col4\" >0.480000</td>\n",
+       "      <td id=\"T_814d2_row1_col5\" class=\"data row1 col5\" >0.030200</td>\n",
+       "      <td id=\"T_814d2_row1_col6\" class=\"data row1 col6\" >0.034000</td>\n",
+       "      <td id=\"T_814d2_row1_col7\" class=\"data row1 col7\" >14.650000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_814d2_row2_col0\" class=\"data row2 col0\" >FAISS</td>\n",
+       "      <td id=\"T_814d2_row2_col1\" class=\"data row2 col1\" >50000</td>\n",
+       "      <td id=\"T_814d2_row2_col2\" class=\"data row2 col2\" >0.240500</td>\n",
+       "      <td id=\"T_814d2_row2_col3\" class=\"data row2 col3\" >0.277200</td>\n",
+       "      <td id=\"T_814d2_row2_col4\" class=\"data row2 col4\" >2.772000</td>\n",
+       "      <td id=\"T_814d2_row2_col5\" class=\"data row2 col5\" >0.169200</td>\n",
+       "      <td id=\"T_814d2_row2_col6\" class=\"data row2 col6\" >0.210900</td>\n",
+       "      <td id=\"T_814d2_row2_col7\" class=\"data row2 col7\" >73.240000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_814d2_row3_col0\" class=\"data row3 col0\" >sqlite-vec</td>\n",
+       "      <td id=\"T_814d2_row3_col1\" class=\"data row3 col1\" >1000</td>\n",
+       "      <td id=\"T_814d2_row3_col2\" class=\"data row3 col2\" >0.053100</td>\n",
+       "      <td id=\"T_814d2_row3_col3\" class=\"data row3 col3\" >0.035500</td>\n",
+       "      <td id=\"T_814d2_row3_col4\" class=\"data row3 col4\" >0.355000</td>\n",
+       "      <td id=\"T_814d2_row3_col5\" class=\"data row3 col5\" >0.000600</td>\n",
+       "      <td id=\"T_814d2_row3_col6\" class=\"data row3 col6\" >0.001300</td>\n",
+       "      <td id=\"T_814d2_row3_col7\" class=\"data row3 col7\" >1.550000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_814d2_row4_col0\" class=\"data row4 col0\" >sqlite-vec</td>\n",
+       "      <td id=\"T_814d2_row4_col1\" class=\"data row4 col1\" >10000</td>\n",
+       "      <td id=\"T_814d2_row4_col2\" class=\"data row4 col2\" >0.292600</td>\n",
+       "      <td id=\"T_814d2_row4_col3\" class=\"data row4 col3\" >0.486800</td>\n",
+       "      <td id=\"T_814d2_row4_col4\" class=\"data row4 col4\" >4.868000</td>\n",
+       "      <td id=\"T_814d2_row4_col5\" class=\"data row4 col5\" >0.000300</td>\n",
+       "      <td id=\"T_814d2_row4_col6\" class=\"data row4 col6\" >0.006000</td>\n",
+       "      <td id=\"T_814d2_row4_col7\" class=\"data row4 col7\" >15.260000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_814d2_row5_col0\" class=\"data row5 col0\" >sqlite-vec</td>\n",
+       "      <td id=\"T_814d2_row5_col1\" class=\"data row5 col1\" >50000</td>\n",
+       "      <td id=\"T_814d2_row5_col2\" class=\"data row5 col2\" >1.274600</td>\n",
+       "      <td id=\"T_814d2_row5_col3\" class=\"data row5 col3\" >1.949800</td>\n",
+       "      <td id=\"T_814d2_row5_col4\" class=\"data row5 col4\" >19.498000</td>\n",
+       "      <td id=\"T_814d2_row5_col5\" class=\"data row5 col5\" >0.000300</td>\n",
+       "      <td id=\"T_814d2_row5_col6\" class=\"data row5 col6\" >0.018300</td>\n",
+       "      <td id=\"T_814d2_row5_col7\" class=\"data row5 col7\" >74.690000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_814d2_row6_col0\" class=\"data row6 col0\" >LanceDB</td>\n",
+       "      <td id=\"T_814d2_row6_col1\" class=\"data row6 col1\" >1000</td>\n",
+       "      <td id=\"T_814d2_row6_col2\" class=\"data row6 col2\" >0.040000</td>\n",
+       "      <td id=\"T_814d2_row6_col3\" class=\"data row6 col3\" >0.332400</td>\n",
+       "      <td id=\"T_814d2_row6_col4\" class=\"data row6 col4\" >3.324000</td>\n",
+       "      <td id=\"T_814d2_row6_col5\" class=\"data row6 col5\" >0.000000</td>\n",
+       "      <td id=\"T_814d2_row6_col6\" class=\"data row6 col6\" >0.007100</td>\n",
+       "      <td id=\"T_814d2_row6_col7\" class=\"data row6 col7\" >1.480000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_814d2_row7_col0\" class=\"data row7 col0\" >LanceDB</td>\n",
+       "      <td id=\"T_814d2_row7_col1\" class=\"data row7 col1\" >10000</td>\n",
+       "      <td id=\"T_814d2_row7_col2\" class=\"data row7 col2\" >0.296800</td>\n",
+       "      <td id=\"T_814d2_row7_col3\" class=\"data row7 col3\" >1.230500</td>\n",
+       "      <td id=\"T_814d2_row7_col4\" class=\"data row7 col4\" >12.305000</td>\n",
+       "      <td id=\"T_814d2_row7_col5\" class=\"data row7 col5\" >0.000000</td>\n",
+       "      <td id=\"T_814d2_row7_col6\" class=\"data row7 col6\" >0.010700</td>\n",
+       "      <td id=\"T_814d2_row7_col7\" class=\"data row7 col7\" >14.740000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_814d2_row8_col0\" class=\"data row8 col0\" >LanceDB</td>\n",
+       "      <td id=\"T_814d2_row8_col1\" class=\"data row8 col1\" >50000</td>\n",
+       "      <td id=\"T_814d2_row8_col2\" class=\"data row8 col2\" >3.740100</td>\n",
+       "      <td id=\"T_814d2_row8_col3\" class=\"data row8 col3\" >2.888400</td>\n",
+       "      <td id=\"T_814d2_row8_col4\" class=\"data row8 col4\" >28.884000</td>\n",
+       "      <td id=\"T_814d2_row8_col5\" class=\"data row8 col5\" >0.000000</td>\n",
+       "      <td id=\"T_814d2_row8_col6\" class=\"data row8 col6\" >0.030100</td>\n",
+       "      <td id=\"T_814d2_row8_col7\" class=\"data row8 col7\" >73.670000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_814d2_row9_col0\" class=\"data row9 col0\" >ChromaDB</td>\n",
+       "      <td id=\"T_814d2_row9_col1\" class=\"data row9 col1\" >1000</td>\n",
+       "      <td id=\"T_814d2_row9_col2\" class=\"data row9 col2\" >0.491600</td>\n",
+       "      <td id=\"T_814d2_row9_col3\" class=\"data row9 col3\" >0.029500</td>\n",
+       "      <td id=\"T_814d2_row9_col4\" class=\"data row9 col4\" >0.295000</td>\n",
+       "      <td id=\"T_814d2_row9_col5\" class=\"data row9 col5\" >0.000000</td>\n",
+       "      <td id=\"T_814d2_row9_col6\" class=\"data row9 col6\" >0.006000</td>\n",
+       "      <td id=\"T_814d2_row9_col7\" class=\"data row9 col7\" >4.090000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_814d2_row10_col0\" class=\"data row10 col0\" >ChromaDB</td>\n",
+       "      <td id=\"T_814d2_row10_col1\" class=\"data row10 col1\" >10000</td>\n",
+       "      <td id=\"T_814d2_row10_col2\" class=\"data row10 col2\" >2.778100</td>\n",
+       "      <td id=\"T_814d2_row10_col3\" class=\"data row10 col3\" >0.053500</td>\n",
+       "      <td id=\"T_814d2_row10_col4\" class=\"data row10 col4\" >0.535000</td>\n",
+       "      <td id=\"T_814d2_row10_col5\" class=\"data row10 col5\" >0.000000</td>\n",
+       "      <td id=\"T_814d2_row10_col6\" class=\"data row10 col6\" >0.005500</td>\n",
+       "      <td id=\"T_814d2_row10_col7\" class=\"data row10 col7\" >29.570000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_814d2_row11_col0\" class=\"data row11 col0\" >ChromaDB</td>\n",
+       "      <td id=\"T_814d2_row11_col1\" class=\"data row11 col1\" >50000</td>\n",
+       "      <td id=\"T_814d2_row11_col2\" class=\"data row11 col2\" >19.122500</td>\n",
+       "      <td id=\"T_814d2_row11_col3\" class=\"data row11 col3\" >0.122800</td>\n",
+       "      <td id=\"T_814d2_row11_col4\" class=\"data row11 col4\" >1.228000</td>\n",
+       "      <td id=\"T_814d2_row11_col5\" class=\"data row11 col5\" >0.000000</td>\n",
+       "      <td id=\"T_814d2_row11_col6\" class=\"data row11 col6\" >0.009800</td>\n",
+       "      <td id=\"T_814d2_row11_col7\" class=\"data row11 col7\" >108.640000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_814d2_row12_col0\" class=\"data row12 col0\" >USearch</td>\n",
+       "      <td id=\"T_814d2_row12_col1\" class=\"data row12 col1\" >1000</td>\n",
+       "      <td id=\"T_814d2_row12_col2\" class=\"data row12 col2\" >0.010800</td>\n",
+       "      <td id=\"T_814d2_row12_col3\" class=\"data row12 col3\" >0.001300</td>\n",
+       "      <td id=\"T_814d2_row12_col4\" class=\"data row12 col4\" >0.013000</td>\n",
+       "      <td id=\"T_814d2_row12_col5\" class=\"data row12 col5\" >0.001100</td>\n",
+       "      <td id=\"T_814d2_row12_col6\" class=\"data row12 col6\" >0.001500</td>\n",
+       "      <td id=\"T_814d2_row12_col7\" class=\"data row12 col7\" >1.610000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_814d2_row13_col0\" class=\"data row13 col0\" >USearch</td>\n",
+       "      <td id=\"T_814d2_row13_col1\" class=\"data row13 col1\" >10000</td>\n",
+       "      <td id=\"T_814d2_row13_col2\" class=\"data row13 col2\" >0.289400</td>\n",
+       "      <td id=\"T_814d2_row13_col3\" class=\"data row13 col3\" >0.002500</td>\n",
+       "      <td id=\"T_814d2_row13_col4\" class=\"data row13 col4\" >0.025000</td>\n",
+       "      <td id=\"T_814d2_row13_col5\" class=\"data row13 col5\" >0.013400</td>\n",
+       "      <td id=\"T_814d2_row13_col6\" class=\"data row13 col6\" >0.018800</td>\n",
+       "      <td id=\"T_814d2_row13_col7\" class=\"data row13 col7\" >16.070000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_814d2_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_814d2_row14_col0\" class=\"data row14 col0\" >USearch</td>\n",
+       "      <td id=\"T_814d2_row14_col1\" class=\"data row14 col1\" >50000</td>\n",
+       "      <td id=\"T_814d2_row14_col2\" class=\"data row14 col2\" >5.965700</td>\n",
+       "      <td id=\"T_814d2_row14_col3\" class=\"data row14 col3\" >0.013500</td>\n",
+       "      <td id=\"T_814d2_row14_col4\" class=\"data row14 col4\" >0.135000</td>\n",
+       "      <td id=\"T_814d2_row14_col5\" class=\"data row14 col5\" >0.111700</td>\n",
+       "      <td id=\"T_814d2_row14_col6\" class=\"data row14 col6\" >0.188300</td>\n",
+       "      <td id=\"T_814d2_row14_col7\" class=\"data row14 col7\" >80.320000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f23d0fd1940>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_display = df.copy()\n",
+    "df_display.columns = ['Backend', 'Vectors', 'Insert (s)', 'Search 100q (s)', 'Per query (ms)',\n",
+    "                       'Save (s)', 'Load+Search (s)', 'Disk (MB)']\n",
+    "df_display.style.background_gradient(subset=['Per query (ms)', 'Insert (s)', 'Disk (MB)'], cmap='Reds_r')     .background_gradient(subset=['Load+Search (s)'], cmap='Greens_r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6b183baa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "transient": {}
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "colors = {'FAISS': '#e74c3c', 'sqlite-vec': '#3498db', 'LanceDB': '#2ecc71', 'ChromaDB': '#f39c12', 'USearch': '#9b59b6'}\n",
+    "\n",
+    "# 1: Search latency per query\n",
+    "ax = axes[0, 0]\n",
+    "for name in df['backend'].unique():\n",
+    "    sub = df[df['backend'] == name]\n",
+    "    ax.plot(sub['n_vectors'], sub['per_query_ms'], 'o-', color=colors.get(name, 'gray'),\n",
+    "            label=name, linewidth=2, markersize=8)\n",
+    "ax.set_xlabel('Vectores en índice')\n",
+    "ax.set_ylabel('Latencia por query (ms)')\n",
+    "ax.set_title('Velocidad de búsqueda (top-10)')\n",
+    "ax.legend()\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')\n",
+    "\n",
+    "# 2: Insert time\n",
+    "ax = axes[0, 1]\n",
+    "for name in df['backend'].unique():\n",
+    "    sub = df[df['backend'] == name]\n",
+    "    ax.plot(sub['n_vectors'], sub['insert_s'], 's-', color=colors.get(name, 'gray'),\n",
+    "            label=name, linewidth=2, markersize=8)\n",
+    "ax.set_xlabel('Vectores')\n",
+    "ax.set_ylabel('Tiempo de inserción (s)')\n",
+    "ax.set_title('Velocidad de inserción')\n",
+    "ax.legend()\n",
+    "ax.set_xscale('log')\n",
+    "\n",
+    "# 3: Disk usage\n",
+    "ax = axes[1, 0]\n",
+    "for name in df['backend'].unique():\n",
+    "    sub = df[df['backend'] == name]\n",
+    "    ax.plot(sub['n_vectors'], sub['disk_mb'], 'D-', color=colors.get(name, 'gray'),\n",
+    "            label=name, linewidth=2, markersize=8)\n",
+    "ax.set_xlabel('Vectores')\n",
+    "ax.set_ylabel('Tamaño en disco (MB)')\n",
+    "ax.set_title('Almacenamiento en disco')\n",
+    "ax.legend()\n",
+    "ax.set_xscale('log')\n",
+    "\n",
+    "# 4: Load from disk + search\n",
+    "ax = axes[1, 1]\n",
+    "for name in df['backend'].unique():\n",
+    "    sub = df[df['backend'] == name]\n",
+    "    ax.plot(sub['n_vectors'], sub['load_and_search_s'], '^-', color=colors.get(name, 'gray'),\n",
+    "            label=name, linewidth=2, markersize=8)\n",
+    "ax.set_xlabel('Vectores')\n",
+    "ax.set_ylabel('Load + 1 búsqueda (s)')\n",
+    "ax.set_title('Cold start: cargar de disco y buscar')\n",
+    "ax.legend()\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')\n",
+    "\n",
+    "plt.suptitle('Comparativa de backends de vectores (dim=384, inner product)', fontsize=14)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75c7c573",
+   "metadata": {},
+   "source": [
+    "## 6. Resumen: pros y contras para fn_registry\n",
+    "\n",
+    "| Backend | Pro | Contra | Ideal para |\n",
+    "|---|---|---|---|\n",
+    "| **FAISS** | Más rápido, battle-tested | Sin metadata nativa, hay que gestionar IDs aparte | Índices de solo vectores donde la velocidad importa |\n",
+    "| **sqlite-vec** | Mismo archivo SQLite, SQL completo, metadata con JOINs | Búsqueda más lenta en brute-force | Integrar vectores en registry.db o operations.db |\n",
+    "| **LanceDB** | Vector-native, filtros, columnar | Directorio no archivo único, API puede cambiar | Datasets medianos-grandes con filtros |\n",
+    "| **ChromaDB** | Metadata rica, popular | Más overhead, más lento, directorio grande | Prototipado rápido con metadata compleja |\n",
+    "| **USearch** | Ultra ligero, casi tan rápido como FAISS | Sin metadata, API minimalista | Índice puro de vectores, mínima dependencia |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ccb97d3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "── USearch ──\n",
+      "  USearch      |   1000 vecs | insert=0.011s | search=0.01ms/q | save=0.001s | load+search=0.002s | disk=1.6MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  USearch      |  10000 vecs | insert=0.289s | search=0.02ms/q | save=0.013s | load+search=0.019s | disk=16.1MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  USearch      |  50000 vecs | insert=5.966s | search=0.13ms/q | save=0.112s | load+search=0.188s | disk=80.3MB\n",
+      "USearch añadido al dataframe\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Re-run solo USearch\n",
+    "print('── USearch ──')\n",
+    "try:\n",
+    "    usearch_res = bench_backend('USearch', usearch_insert, usearch_search, usearch_save, usearch_load_search, usearch_size, cleanup_path)\n",
+    "    all_results.extend(usearch_res)\n",
+    "    df = pd.DataFrame(all_results)\n",
+    "    print('USearch añadido al dataframe')\n",
+    "except Exception as e:\n",
+    "    import traceback\n",
+    "    traceback.print_exc()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/pdf/01_exploracion_embeddings.pdf b/notebooks/pdf/01_exploracion_embeddings.pdf
new file mode 100644
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diff --git a/notebooks/pdf/02_ram_benchmark.pdf b/notebooks/pdf/02_ram_benchmark.pdf
new file mode 100644
index 0000000..0b45f56
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diff --git a/notebooks/pdf/03_modelo_local.pdf b/notebooks/pdf/03_modelo_local.pdf
new file mode 100644
index 0000000..1b4e317
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diff --git a/notebooks/pdf/04_vector_storage.pdf b/notebooks/pdf/04_vector_storage.pdf
new file mode 100644
index 0000000..4bfe78a
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..9e05036
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,20 @@
+[project]
+name = "estudio-embeddings"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "faiss-cpu>=1.13.2",
+    "jupyter>=1.1.1",
+    "jupyter-collaboration>=4.3.0",
+    "jupyter-mcp-server>=0.4.0",
+    "jupyterlab>=4.5.6",
+    "matplotlib>=3.10.8",
+    "nbconvert>=7.17.0",
+    "numpy>=2.4.4",
+    "pandas>=3.0.2",
+    "playwright>=1.58.0",
+    "scikit-learn>=1.8.0",
+    "sentence-transformers>=5.3.0",
+]
diff --git a/run-jupyter-lab.sh b/run-jupyter-lab.sh
new file mode 100755
index 0000000..cda2bb8
--- /dev/null
+++ b/run-jupyter-lab.sh
@@ -0,0 +1,45 @@
+#!/bin/bash
+# Jupyter Lab — modo colaborativo con autodeteccion de puerto
+# Generado por write_jupyter_launcher (fn_registry)
+
+find_free_port() {
+    for port in 8888 8889 8890 8891 8892 8893 8894 8895 8896 8897 8898 8899; do
+        if ! ss -tln 2>/dev/null | grep -q ":${port} " && \
+           ! lsof -i:"$port" >/dev/null 2>&1; then
+            echo $port
+            return
+        fi
+    done
+    echo 8888
+}
+
+PORT=${1:-$(find_free_port)}
+cd "$(dirname "$0")"
+
+echo $PORT > .jupyter-port
+
+source .venv/bin/activate 2>/dev/null || true
+
+if ! python -c "import jupyter_collaboration" 2>/dev/null; then
+    echo "ERROR: jupyter-collaboration no esta instalado"
+    echo "Instala con: uv add jupyter-collaboration"
+    exit 1
+fi
+
+echo "════════════════════════════════════════════════"
+echo "  Jupyter Lab + Colaboracion en puerto $PORT"
+echo "════════════════════════════════════════════════"
+echo ""
+echo "  Abre: http://localhost:$PORT"
+echo "  Ctrl+C para detener"
+echo ""
+
+jupyter lab \
+    --port=$PORT \
+    --no-browser \
+    --ServerApp.token='' \
+    --ServerApp.password='' \
+    --ServerApp.disable_check_xsrf=True \
+    --ServerApp.allow_origin='*' \
+    --ServerApp.root_dir="$(pwd)" \
+    --collaborative
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..e4d028e
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,3244 @@
+version = 1
+revision = 3
+requires-python = ">=3.13"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
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+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
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+[[package]]
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