Skip to content

More snippets in TS/Rust for table ops #182

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
prrao87 merged 5 commits into from
Mar 7, 2026
Merged

More snippets in TS/Rust for table ops #182

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
0268d93
Normalize subheaders to simple present tense for consistent heading s…
prrao87 Mar 6, 2026
85646ea
Add ts/rs snippets for table updates and fix deprecation warnings in …
prrao87 Mar 6, 2026
c6ab00a
Modernize versioning docs and add new py/ts/rs snippets
prrao87 Mar 6, 2026
b5e3a3c
Add new snippets and update consistency
prrao87 Mar 7, 2026
20936f4
Update multimodal examples and snippets
prrao87 Mar 7, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 32 additions & 0 deletions docs/snippets/multimodal.mdx
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,3 +16,35 @@ export const PyProcessResults = "# Convert back to PIL Image\nfor _, row in resu

export const PySearchData = "# Search for similar images\nquery_vector = np.random.rand(128).astype(np.float32)\nresults = tbl.search(query_vector).limit(1).to_pandas()\n";

export const TsBlobApiIngest = "const blobData = lancedb.makeArrowTable(\n [\n { id: 1, video: Buffer.from(\"fake_video_bytes_1\") },\n { id: 2, video: Buffer.from(\"fake_video_bytes_2\") },\n ],\n { schema: blobSchema },\n);\nconst blobTable = await db.createTable(\"videos\", blobData, {\n mode: \"overwrite\",\n});\n";

export const TsBlobApiSchema = "const blobSchema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int64()),\n new arrow.Field(\n \"video\",\n new arrow.LargeBinary(),\n true,\n new Map([[\"lance-encoding:blob\", \"true\"]]),\n ),\n]);\n";

export const TsCreateDummyData = "const createDummyImage = (color: string): Uint8Array => {\n const pngHeader = Uint8Array.from([137, 80, 78, 71, 13, 10, 26, 10]);\n return Buffer.concat([Buffer.from(pngHeader), Buffer.from(color, \"utf8\")]);\n};\n\nconst data = [\n {\n id: 1,\n filename: \"red_square.png\",\n vector: Array.from({ length: 128 }, (_, i) => (i % 16) / 16),\n image_blob: createDummyImage(\"red\"),\n label: \"red\",\n },\n {\n id: 2,\n filename: \"blue_square.png\",\n vector: Array.from({ length: 128 }, (_, i) => ((i + 8) % 16) / 16),\n image_blob: createDummyImage(\"blue\"),\n label: \"blue\",\n },\n];\n";

export const TsDefineSchema = "const schema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int32()),\n new arrow.Field(\"filename\", new arrow.Utf8()),\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 128,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n new arrow.Field(\"image_blob\", new arrow.Binary()),\n new arrow.Field(\"label\", new arrow.Utf8()),\n]);\n";

export const TsIngestData = "const multimodalData = lancedb.makeArrowTable(data, { schema });\nconst tbl = await db.createTable(\"images\", multimodalData, {\n mode: \"overwrite\",\n});\n";

export const TsMultimodalImports = "import * as arrow from \"apache-arrow\";\nimport { Buffer } from \"node:buffer\";\nimport * as lancedb from \"@lancedb/lancedb\";\n";

export const TsProcessResults = "for (const row of results) {\n const imageBytes = row.image_blob as Uint8Array;\n console.log(\n `Retrieved image: ${row.filename}, Byte length: ${imageBytes.length}`,\n );\n}\n";

export const TsSearchData = "const queryVector = Array.from({ length: 128 }, (_, i) => (i % 16) / 16);\nconst results = await tbl.search(queryVector).limit(1).toArray();\n";

export const RsBlobApiIngest = "let blob_rows = vec![\n (1_i64, b\"fake_video_bytes_1\".to_vec()),\n (2_i64, b\"fake_video_bytes_2\".to_vec()),\n];\n\nlet blob_schema = Arc::new(blob_schema);\nlet blob_batch = RecordBatch::try_new(\n blob_schema.clone(),\n vec![\n Arc::new(Int64Array::from_iter_values(blob_rows.iter().map(|row| row.0))),\n Arc::new(LargeBinaryArray::from_iter_values(\n blob_rows.iter().map(|row| row.1.as_slice()),\n )),\n ],\n)\n.unwrap();\nlet blob_reader = RecordBatchIterator::new(vec![Ok(blob_batch)].into_iter(), blob_schema);\nlet blob_table = db\n .create_table(\"videos\", blob_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n";

export const RsBlobApiSchema = "let blob_metadata = HashMap::from([(\n \"lance-encoding:blob\".to_string(),\n \"true\".to_string(),\n)]);\nlet blob_schema = Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"video\", DataType::LargeBinary, true).with_metadata(blob_metadata),\n]);\n";

export const RsCreateDummyData = "let create_dummy_image = |color: u8| -> Vec<u8> {\n let mut png_like = vec![137, 80, 78, 71, 13, 10, 26, 10];\n png_like.push(color);\n png_like\n};\n\nlet data = vec![\n (\n 1_i32,\n \"red_square.png\",\n vec![0.1_f32; 128],\n create_dummy_image(1),\n \"red\",\n ),\n (\n 2_i32,\n \"blue_square.png\",\n vec![0.2_f32; 128],\n create_dummy_image(2),\n \"blue\",\n ),\n];\n";

export const RsDefineSchema = "let schema = Schema::new(vec![\n Field::new(\"id\", DataType::Int32, false),\n Field::new(\"filename\", DataType::Utf8, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n false,\n ),\n Field::new(\"image_blob\", DataType::Binary, false),\n Field::new(\"label\", DataType::Utf8, false),\n]);\n";

export const RsIngestData = "let schema = Arc::new(schema);\nlet image_batch = RecordBatch::try_new(\n schema.clone(),\n vec![\n Arc::new(Int32Array::from_iter_values(data.iter().map(|row| row.0))),\n Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.1))),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n data.iter()\n .map(|row| Some(row.2.iter().copied().map(Some).collect::<Vec<_>>())),\n 128,\n ),\n ),\n Arc::new(BinaryArray::from_iter_values(\n data.iter().map(|row| row.3.as_slice()),\n )),\n Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.4))),\n ],\n)\n.unwrap();\nlet image_reader = RecordBatchIterator::new(vec![Ok(image_batch)].into_iter(), schema.clone());\nlet table = db\n .create_table(\"images\", image_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n";

export const RsMultimodalImports = "use std::collections::HashMap;\nuse std::sync::Arc;\n\nuse arrow_array::types::Float32Type;\nuse arrow_array::{\n BinaryArray, FixedSizeListArray, Int32Array, Int64Array, LargeBinaryArray, RecordBatch,\n RecordBatchIterator, StringArray,\n};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures_util::TryStreamExt;\nuse lancedb::connect;\nuse lancedb::database::CreateTableMode;\nuse lancedb::query::{ExecutableQuery, QueryBase};\n";

export const RsProcessResults = "for batch in &results {\n let filenames = batch\n .column_by_name(\"filename\")\n .unwrap()\n .as_any()\n .downcast_ref::<StringArray>()\n .unwrap();\n let images = batch\n .column_by_name(\"image_blob\")\n .unwrap()\n .as_any()\n .downcast_ref::<BinaryArray>()\n .unwrap();\n\n for row in 0..batch.num_rows() {\n let image_bytes = images.value(row);\n println!(\n \"Retrieved image: {}, Byte length: {}\",\n filenames.value(row),\n image_bytes.len()\n );\n }\n}\n";

export const RsSearchData = "let query_vector = vec![0.1_f32; 128];\nlet results = table\n .query()\n .nearest_to(query_vector)\n .unwrap()\n .limit(1)\n .execute()\n .await\n .unwrap()\n .try_collect::<Vec<_>>()\n .await\n .unwrap();\n";

Loading