{
 "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": [
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       "#T_814d2_row2_col2, #T_814d2_row3_col4, #T_814d2_row4_col2, #T_814d2_row7_col2, #T_814d2_row13_col2 {\n",
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       "  background-color: #fca588;\n",
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       "  background-color: #f44d38;\n",
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       "#T_814d2_row7_col6 {\n",
       "  background-color: #005221;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #bb141a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_814d2_row8_col4, #T_814d2_row11_col2, #T_814d2_row11_col7 {\n",
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       "  background-color: #6b010e;\n",
       "  color: #f1f1f1;\n",
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       "  color: #f1f1f1;\n",
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       "#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()"
   ]
  }
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