{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}

export const PyFilterSearch = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n                \"vector\": [1.0, 0.0],\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n                \"vector\": [0.9, 0.1],\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n                \"vector\": [0.0, 1.0],\n            },\n        ],\n    )\n    rows = (\n        table.search([0.0, 1.0])\n        .where(\"category = 'animals'\", prefilter=True)\n        .select([\"id\", \"text\", \"category\", \"_distance\"])\n        .limit(2)\n        .to_list()\n    )\n    print(rows)\n";

export const PyHybridSearch = "from tempfile import TemporaryDirectory\n\nimport lancedb\nfrom lancedb.index import FTS\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n                \"vector\": [1.0, 0.0],\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n                \"vector\": [0.9, 0.1],\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n                \"vector\": [0.0, 1.0],\n            },\n        ],\n    )\n    from lancedb.rerankers import RRFReranker\n\n    table.create_index(\"text\", config=FTS())\n    rows = (\n        table.search(\n            query_type=\"hybrid\", vector_column_name=\"vector\", fts_columns=\"text\"\n        )\n        .vector([1.0, 0.0])\n        .text(\"puppy\")\n        .rerank(RRFReranker())\n        .limit(2)\n        .to_list()\n    )\n    print(rows)\n";

export const PyQueryPlan = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n                \"vector\": [1.0, 0.0],\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n                \"vector\": [0.9, 0.1],\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n                \"vector\": [0.0, 1.0],\n            },\n        ],\n    )\n    query = table.search([1.0, 0.0]).select([\"id\", \"text\", \"_distance\"]).limit(2)\n    print(\"Plan:\", query.explain_plan())\n    print(\"Measured execution:\", query.analyze_plan())\n    rows = query.to_list()\n    print(rows)\n";

export const PyTextSearch = "from tempfile import TemporaryDirectory\n\nimport lancedb\nfrom lancedb.index import FTS\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n            },\n        ],\n    )\n    table.create_index(\"text\", config=FTS())\n    rows = (\n        table.search(\"puppy\", query_type=\"fts\", fts_columns=\"text\")\n        .select([\"id\", \"text\", \"_score\"])\n        .limit(2)\n        .to_list()\n    )\n    print(rows)\n";

export const PyVectorSearch = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n                \"vector\": [1.0, 0.0],\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n                \"vector\": [0.9, 0.1],\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n                \"vector\": [0.0, 1.0],\n            },\n        ],\n    )\n    rows = (\n        table.search([1.0, 0.0])\n        .distance_type(\"l2\")\n        .select([\"id\", \"text\", \"_distance\"])\n        .limit(2)\n        .to_list()\n    )\n    print(rows)\n";

export const TsFilterSearchBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    {\n      id: 1,\n      text: \"A puppy playing in the park\",\n      category: \"animals\",\n      vector: [1.0, 0.0],\n    },\n    {\n      id: 2,\n      text: \"A dog resting at home\",\n      category: \"animals\",\n      vector: [0.9, 0.1],\n    },\n    {\n      id: 3,\n      text: \"A guide to growing tomatoes\",\n      category: \"gardening\",\n      vector: [0.0, 1.0],\n    },\n  ]);\n  const rows = await table\n    .vectorSearch([0.0, 1.0])\n    .where(\"category = 'animals'\")\n    .select([\"id\", \"text\", \"category\", \"_distance\"])\n    .limit(2)\n    .toArray();\n  console.log(rows);\n";

export const TsFilterSearchTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";

export const TsHybridSearchBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    {\n      id: 1,\n      text: \"A puppy playing in the park\",\n      category: \"animals\",\n      vector: [1.0, 0.0],\n    },\n    {\n      id: 2,\n      text: \"A dog resting at home\",\n      category: \"animals\",\n      vector: [0.9, 0.1],\n    },\n    {\n      id: 3,\n      text: \"A guide to growing tomatoes\",\n      category: \"gardening\",\n      vector: [0.0, 1.0],\n    },\n  ]);\n  await table.createIndex(\"text\", { config: lancedb.Index.fts() });\n  const reranker = await lancedb.rerankers.RRFReranker.create();\n  const rows = await table\n    .vectorSearch([1.0, 0.0])\n    .fullTextSearch(\"puppy\", { columns: [\"text\"] })\n    .rerank(reranker)\n    .limit(2)\n    .toArray();\n  console.log(rows);\n";

export const TsHybridSearchTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";

export const TsQueryPlanBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    {\n      id: 1,\n      text: \"A puppy playing in the park\",\n      category: \"animals\",\n      vector: [1.0, 0.0],\n    },\n    {\n      id: 2,\n      text: \"A dog resting at home\",\n      category: \"animals\",\n      vector: [0.9, 0.1],\n    },\n    {\n      id: 3,\n      text: \"A guide to growing tomatoes\",\n      category: \"gardening\",\n      vector: [0.0, 1.0],\n    },\n  ]);\n  const query = table\n    .vectorSearch([1.0, 0.0])\n    .select([\"id\", \"text\", \"_distance\"])\n    .limit(2);\n  console.log(\"Plan:\", await query.explainPlan());\n  console.log(\"Measured execution:\", await query.analyzePlan());\n  const rows = await query.toArray();\n  console.log(rows);\n";

export const TsQueryPlanTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";

export const TsTextSearchBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    { id: 1, text: \"A puppy playing in the park\", category: \"animals\" },\n    { id: 2, text: \"A dog resting at home\", category: \"animals\" },\n    { id: 3, text: \"A guide to growing tomatoes\", category: \"gardening\" },\n  ]);\n  await table.createIndex(\"text\", { config: lancedb.Index.fts() });\n  const rows = await table\n    .query()\n    .fullTextSearch(\"puppy\", { columns: [\"text\"] })\n    .select([\"id\", \"text\", \"_score\"])\n    .limit(2)\n    .toArray();\n  console.log(rows);\n";

export const TsTextSearchTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";

export const TsVectorSearchBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    {\n      id: 1,\n      text: \"A puppy playing in the park\",\n      category: \"animals\",\n      vector: [1.0, 0.0],\n    },\n    {\n      id: 2,\n      text: \"A dog resting at home\",\n      category: \"animals\",\n      vector: [0.9, 0.1],\n    },\n    {\n      id: 3,\n      text: \"A guide to growing tomatoes\",\n      category: \"gardening\",\n      vector: [0.0, 1.0],\n    },\n  ]);\n  const rows = await table\n    .vectorSearch([1.0, 0.0])\n    .distanceType(\"l2\")\n    .select([\"id\", \"text\", \"_distance\"])\n    .limit(2)\n    .toArray();\n  console.log(rows);\n";

export const TsVectorSearchTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";