mirror of
https://github.com/lancedb/lancedb.git
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Brendan Clement
486f6c66bb
This PR is part of the documentation revamp. Once the full set of PRs is merged, we’ll publish the combined site to staging for review before updating docs.lancedb.com. The introductory pages are inconsistent, and the old PR split asked reviewers to review some pages twice. This brings the final quickstart, connection guide, architecture and Concepts pages together. It clarifies OSS and Enterprise, simplifies naming rules and includes the navigation and diagram fixes from #4489. Function and Secret details on the Concepts pages are MDX comments until their release, and CONTRIBUTING.md explains how to write docs before a release. Includes #4532. Ingestion, search and integrations are in #4533; data workflows, SQL and operations are in #4534. Each MDX file has one PR owner. Local assembly, Mint validation and link checks pass. The Function pages stay published from `main` until #4534 hides them, so hold publication until the complete stack is integrated.
67 lines
17 KiB
Plaintext
67 lines
17 KiB
Plaintext
{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
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export const PyQuickstartAddData = "table.add(\n [\n {\n \"id\": \"4\",\n \"name\": \"Morgana\",\n \"role\": \"Sorceress\",\n \"description\": \"Powerful sorceress of Avalon.\",\n \"stats\": {\"strength\": 2, \"magic\": 5, \"leadership\": 4, \"wisdom\": 4},\n \"vector\": [0.3, 0.9, 0.6, 0.8],\n }\n ]\n)\n";
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export const PyQuickstartAddFeature = "table.add_columns(\n {\n \"power_score\": \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\"\n }\n)\n";
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export const PyQuickstartComputedFeature = "table.add_columns(computed={\"description_length\": \"length(description)\"})\ntable.refresh_column(\"description_length\")\n";
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export const PyQuickstartComputedFeatureEnterprise = "table.add_columns(computed={\"description_length\": \"length(description)\"})\njob = table.refresh_column_async(\"description_length\")\njob.wait()\n";
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export const PyQuickstartCreateTable = "table = db.create_table(\"characters\", data=data, mode=\"overwrite\")\n";
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export const PyQuickstartCreateTableAsync = "async_table = await async_db.create_table(\n \"characters\",\n data=data,\n mode=\"overwrite\",\n)\n";
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export const PyQuickstartCurateWithMetadata = "curated = (\n table.search(query_vector)\n .where(\"stats.magic >= 4\")\n .select([\"name\", \"role\", \"description\", \"_distance\"])\n .limit(2)\n .to_polars()\n)\nprint(curated)\n";
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export const PyQuickstartData = "data = [\n {\n \"id\": \"1\",\n \"name\": \"King Arthur\",\n \"role\": \"King\",\n \"description\": \"Leader of Camelot and wielder of Excalibur.\",\n \"stats\": {\"strength\": 4, \"magic\": 1, \"leadership\": 5, \"wisdom\": 4},\n \"vector\": [0.7, 0.1, 0.9, 0.7],\n },\n {\n \"id\": \"2\",\n \"name\": \"Merlin\",\n \"role\": \"Wizard\",\n \"description\": \"Advisor and prophet with deep magical knowledge.\",\n \"stats\": {\"strength\": 2, \"magic\": 5, \"leadership\": 4, \"wisdom\": 5},\n \"vector\": [0.2, 0.9, 0.4, 0.9],\n },\n {\n \"id\": \"3\",\n \"name\": \"Sir Lancelot\",\n \"role\": \"Knight\",\n \"description\": \"Legendary knight known for courage and combat skill.\",\n \"stats\": {\"strength\": 5, \"magic\": 1, \"leadership\": 3, \"wisdom\": 3},\n \"vector\": [0.9, 0.1, 0.5, 0.4],\n },\n]\n";
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export const PyQuickstartMultimodalBytes = "from pathlib import Path\n\nimage_bytes = Path(\"data/sir-lancelot.jpg\").read_bytes()\n\nmultimodal_table = db.create_table(\n \"character_images\",\n data=[\n {\n \"id\": \"lancelot\",\n \"description\": \"Portrait of Sir Lancelot\",\n \"image\": image_bytes,\n \"vector\": [0.9, 0.1, 0.5, 0.4],\n }\n ],\n mode=\"overwrite\",\n)\n";
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export const PyQuickstartOutputPandas = "# Ensure you run `pip install pandas` beforehand\nresult = table.search(query_vector).limit(2).to_pandas()\nprint(result)\n";
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export const PyQuickstartQueryFeature = "features = table.search().select([\"name\", \"role\", \"power_score\"]).to_polars()\nprint(features)\n";
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export const PyQuickstartTraining = "import torch\nfrom lancedb.permutation import Permutation\n\ntable.add_columns(\n {\n \"magic\": \"cast(stats.magic as float)\",\n \"strength\": \"cast(stats.strength as float)\",\n }\n)\ndataset = Permutation.identity(table).select_columns([\"magic\", \"strength\"])\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=1024, shuffle=True)\n";
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export const PyQuickstartVectorSearch1 = "# Search for examples similar to a \"wise magical advisor\"\nquery_vector = [0.2, 0.8, 0.4, 0.9]\n\n# Ensure you run `pip install polars` beforehand\nresult = (\n table.search(query_vector)\n .select([\"name\", \"role\", \"description\", \"_distance\"])\n .limit(2)\n .to_polars()\n)\nprint(result)\n";
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export const PyQuickstartVectorSearch1Async = "# Search for examples similar to a \"wise magical advisor\"\nquery_vector = [0.2, 0.8, 0.4, 0.9]\n\n# Ensure you run `pip install polars` beforehand\nasync_result = await (\n await async_table.search(query_vector)\n).select([\"name\", \"role\", \"description\", \"_distance\"]).limit(2).to_polars()\nprint(async_result)\n";
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export const TsQuickstartAddData = "await table.add([\n {\n id: \"4\",\n name: \"Morgana\",\n role: \"Sorceress\",\n description: \"Powerful sorceress of Avalon.\",\n stats: { strength: 2, magic: 5, leadership: 4, wisdom: 4 },\n vector: [0.3, 0.9, 0.6, 0.8],\n },\n]);\n";
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export const TsQuickstartAddFeature = "await table.addColumns([\n {\n name: \"power_score\",\n valueSql:\n \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\",\n },\n]);\n";
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export const TsQuickstartCreateTable = "const table = await db.createTable(\"characters\", data, { mode: \"overwrite\" });\n";
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export const TsQuickstartCurateWithMetadata = "const curated = await table\n .search(queryVector)\n .where(\"stats.magic >= 4\")\n .select([\"name\", \"role\", \"description\", \"_distance\"])\n .limit(2)\n .toArray();\nconsole.table(curated);\n";
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export const TsQuickstartData = "const data = [\n {\n id: \"1\",\n name: \"King Arthur\",\n role: \"King\",\n description: \"Leader of Camelot and wielder of Excalibur.\",\n stats: { strength: 4, magic: 1, leadership: 5, wisdom: 4 },\n vector: [0.7, 0.1, 0.9, 0.7],\n },\n {\n id: \"2\",\n name: \"Merlin\",\n role: \"Wizard\",\n description: \"Advisor and prophet with deep magical knowledge.\",\n stats: { strength: 2, magic: 5, leadership: 4, wisdom: 5 },\n vector: [0.2, 0.9, 0.4, 0.9],\n },\n {\n id: \"3\",\n name: \"Sir Lancelot\",\n role: \"Knight\",\n description: \"Legendary knight known for courage and combat skill.\",\n stats: { strength: 5, magic: 1, leadership: 3, wisdom: 3 },\n vector: [0.9, 0.1, 0.5, 0.4],\n },\n];\n";
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export const TsQuickstartMultimodalBytes = "const arrow = await import(\"apache-arrow\");\nconst { readFile } = await import(\"node:fs/promises\");\n\nconst imageBytes = await readFile(\"data/sir-lancelot.jpg\");\nconst imageSchema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Utf8()),\n new arrow.Field(\"description\", new arrow.Utf8()),\n new arrow.Field(\"image\", new arrow.Binary()),\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 4,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n]);\nconst imageData = lancedb.makeArrowTable(\n [\n {\n id: \"lancelot\",\n description: \"Portrait of Sir Lancelot\",\n image: imageBytes,\n vector: [0.9, 0.1, 0.5, 0.4],\n },\n ],\n { schema: imageSchema },\n);\nconst multimodalTable = await db.createTable(\n \"character_images\",\n imageData,\n { mode: \"overwrite\" },\n);\n";
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export const TsQuickstartQueryFeature = "const features = await table\n .query()\n .select([\"name\", \"role\", \"power_score\"])\n .toArray();\nconsole.table(features);\n";
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export const TsQuickstartVectorSearch1 = "// Search for examples similar to a \"wise magical advisor\"\nconst queryVector = [0.2, 0.8, 0.4, 0.9];\n\nconst result = await table\n .search(queryVector)\n .select([\"name\", \"role\", \"description\", \"_distance\"])\n .limit(2)\n .toArray();\nconsole.table(result);\n";
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export const RsQuickstartAddData = "let more_data = vec![Character {\n id: \"4\".to_string(),\n name: \"Morgana\".to_string(),\n role: \"Sorceress\".to_string(),\n description: \"Powerful sorceress of Avalon.\".to_string(),\n stats: Stats {\n strength: 2,\n magic: 5,\n leadership: 4,\n wisdom: 4,\n },\n vector: [0.3, 0.9, 0.6, 0.8],\n}];\ntable\n .add(characters_to_reader(schema.clone(), &more_data))\n .execute()\n .await\n .unwrap();\n";
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export const RsQuickstartAddFeature = "table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"power_score\".to_string(),\n \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\"\n .to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n";
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export const RsQuickstartCreateTable = "use lancedb::database::CreateTableMode;\n\nlet schema = characters_schema();\nlet table = db\n .create_table(\"characters\", characters_to_reader(schema.clone(), &data))\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n";
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export const RsQuickstartCurateWithMetadata = "let curated = table\n .query()\n .nearest_to(&query_vector)\n .unwrap()\n .only_if(\"stats.magic >= 4\")\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n \"_distance\".to_string(),\n ]))\n .limit(2)\n .execute()\n .await\n .unwrap()\n .into_polars()\n .await\n .unwrap();\nprintln!(\"{curated:?}\");\n";
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export const RsQuickstartData = "let data = vec![\n Character {\n id: \"1\".to_string(),\n name: \"King Arthur\".to_string(),\n role: \"King\".to_string(),\n description: \"Leader of Camelot and wielder of Excalibur.\".to_string(),\n stats: Stats {\n strength: 4,\n magic: 1,\n leadership: 5,\n wisdom: 4,\n },\n vector: [0.7, 0.1, 0.9, 0.7],\n },\n Character {\n id: \"2\".to_string(),\n name: \"Merlin\".to_string(),\n role: \"Wizard\".to_string(),\n description: \"Advisor and prophet with deep magical knowledge.\".to_string(),\n stats: Stats {\n strength: 2,\n magic: 5,\n leadership: 4,\n wisdom: 5,\n },\n vector: [0.2, 0.9, 0.4, 0.9],\n },\n Character {\n id: \"3\".to_string(),\n name: \"Sir Lancelot\".to_string(),\n role: \"Knight\".to_string(),\n description: \"Legendary knight known for courage and combat skill.\".to_string(),\n stats: Stats {\n strength: 5,\n magic: 1,\n leadership: 3,\n wisdom: 3,\n },\n vector: [0.9, 0.1, 0.5, 0.4],\n },\n];\n";
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export const RsQuickstartDefineStruct = "use std::sync::Arc;\n\nuse lancedb::arrow::arrow_array::types::Float32Type;\nuse lancedb::arrow::arrow_array::{\n FixedSizeListArray, Int8Array, LargeStringArray, RecordBatch, RecordBatchIterator,\n RecordBatchReader, StructArray,\n};\nuse lancedb::arrow::arrow_schema::{DataType, Field, FieldRef, Fields, Schema};\n\n// Define structs representing the data schema\n#[derive(Debug, Clone)]\nstruct Stats {\n strength: i8,\n magic: i8,\n leadership: i8,\n wisdom: i8,\n}\n\n#[derive(Debug, Clone)]\nstruct Character {\n id: String,\n name: String,\n role: String,\n description: String,\n stats: Stats,\n vector: [f32; 4],\n}\n\nfn characters_schema() -> Arc<Schema> {\n Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::LargeUtf8, false),\n Field::new(\"name\", DataType::LargeUtf8, false),\n Field::new(\"role\", DataType::LargeUtf8, false),\n Field::new(\"description\", DataType::LargeUtf8, false),\n Field::new(\n \"stats\",\n DataType::Struct(Fields::from(vec![\n Arc::new(Field::new(\"strength\", DataType::Int8, false)),\n Arc::new(Field::new(\"magic\", DataType::Int8, false)),\n Arc::new(Field::new(\"leadership\", DataType::Int8, false)),\n Arc::new(Field::new(\"wisdom\", DataType::Int8, false)),\n ])),\n false,\n ),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n false,\n ),\n ]))\n}\n\nfn characters_to_reader(\n schema: Arc<Schema>,\n rows: &[Character],\n) -> Box<dyn RecordBatchReader + Send> {\n let ids = LargeStringArray::from_iter_values(rows.iter().map(|row| row.id.as_str()));\n let names = LargeStringArray::from_iter_values(rows.iter().map(|row| row.name.as_str()));\n let roles = LargeStringArray::from_iter_values(rows.iter().map(|row| row.role.as_str()));\n let descriptions =\n LargeStringArray::from_iter_values(rows.iter().map(|row| row.description.as_str()));\n\n let strength = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.strength));\n let magic = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.magic));\n let leadership = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.leadership));\n let wisdom = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.wisdom));\n let stats_fields: Vec<FieldRef> = vec![\n Arc::new(Field::new(\"strength\", DataType::Int8, false)),\n Arc::new(Field::new(\"magic\", DataType::Int8, false)),\n Arc::new(Field::new(\"leadership\", DataType::Int8, false)),\n Arc::new(Field::new(\"wisdom\", DataType::Int8, false)),\n ];\n let stats = StructArray::new(\n stats_fields.into(),\n vec![\n Arc::new(strength),\n Arc::new(magic),\n Arc::new(leadership),\n Arc::new(wisdom),\n ],\n None,\n );\n\n let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n rows.iter()\n .map(|row| Some(row.vector.iter().copied().map(Some).collect::<Vec<_>>())),\n 4,\n );\n\n let batch = RecordBatch::try_new(\n schema.clone(),\n vec![\n Arc::new(ids),\n Arc::new(names),\n Arc::new(roles),\n Arc::new(descriptions),\n Arc::new(stats),\n Arc::new(vectors),\n ],\n )\n .unwrap();\n\n Box::new(RecordBatchIterator::new(\n vec![Ok(batch)].into_iter(),\n schema,\n ))\n}\n";
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export const RsQuickstartMultimodalBytes = "use lancedb::arrow::arrow_array::BinaryArray;\n\nlet image_bytes = std::fs::read(\"data/sir-lancelot.jpg\").unwrap();\n\nlet image_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::LargeUtf8, false),\n Field::new(\"description\", DataType::LargeUtf8, false),\n Field::new(\"image\", DataType::Binary, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n false,\n ),\n]));\nlet image_vectors = [[0.9_f32, 0.1, 0.5, 0.4]];\nlet image_batch = RecordBatch::try_new(\n image_schema.clone(),\n vec![\n Arc::new(LargeStringArray::from_iter_values([\"lancelot\"])),\n Arc::new(LargeStringArray::from_iter_values([\n \"Portrait of Sir Lancelot\",\n ])),\n Arc::new(BinaryArray::from_iter_values([image_bytes.as_slice()])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n image_vectors\n .iter()\n .map(|vector| Some(vector.iter().copied().map(Some).collect::<Vec<_>>())),\n 4,\n ),\n ),\n ],\n)\n.unwrap();\nlet image_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(\n vec![Ok(image_batch)].into_iter(),\n image_schema,\n));\nlet multimodal_table = db\n .create_table(\"character_images\", image_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n";
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export const RsQuickstartOutputArray = "let result = table\n .query()\n .nearest_to(&query_vector)\n .unwrap()\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n \"_distance\".to_string(),\n ]))\n .limit(2)\n .execute()\n .await\n .unwrap()\n .into_polars()\n .await\n .unwrap();\nprintln!(\"{result:?}\");\n";
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export const RsQuickstartQueryFeature = "let features = table\n .query()\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"power_score\".to_string(),\n ]))\n .execute()\n .await\n .unwrap()\n .into_polars()\n .await\n .unwrap();\nprintln!(\"{features:?}\");\n";
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export const RsQuickstartVectorSearch1 = "use lancedb::arrow::IntoPolars;\nuse lancedb::query::{ExecutableQuery, QueryBase, Select};\n\n// Search for examples similar to a \"wise magical advisor\"\nlet query_vector = [0.2, 0.8, 0.4, 0.9];\n\nlet result = table\n .query()\n .nearest_to(&query_vector)\n .unwrap()\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n \"_distance\".to_string(),\n ]))\n .limit(2)\n .execute()\n .await\n .unwrap()\n .into_polars()\n .await\n .unwrap();\nprintln!(\"{result:?}\");\n";
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