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## Summary - Enhanced vector column detection to use substring matching instead of exact matching - Now detects columns with names containing "vector" or "embedding" (case-insensitive) - Added integer vector support to Node.js implementation (matching Python) - Comprehensive test coverage for both float and integer vector types ## Changes ### Python (`python/python/lancedb/table.py`) - Updated `_infer_target_schema()` to use substring matching with helper function `_is_vector_column()` - Preserved original field names instead of forcing "vector" - Consolidated duplicate logic for better maintainability ### Node.js (`nodejs/lancedb/arrow.ts`) - Enhanced type inference with `nameSuggestsVectorColumn()` helper function - Added `isAllIntegers()` function with performance optimization (checks first 10 elements) - Implemented integer vector support using `Uint8` type (matching Python) - Improved type safety by removing `any` usage ### Tests - **Python**: Added `test_infer_target_schema_with_vector_embedding_names()` in `test_util.py` - **Node.js**: Added comprehensive test case in `arrow.test.ts` - Both test suites cover various naming patterns and integer/float vector types ## Examples of newly supported column names: - `user_vector`, `text_embedding`, `doc_embeddings` - `my_vector_field`, `embedding_model` - `VECTOR_COL`, `Vector_Mixed` (case-insensitive) - Both float and integer arrays are properly converted to fixed-size lists ## Test plan - [x] All existing tests pass (backward compatibility maintained) - [x] New tests pass for both Python and Node.js implementations - [x] Integer vector detection works correctly in Node.js - [x] Code passes linting and formatting checks - [x] Performance optimized for large vector arrays Fixes #2546 🤖 Generated with [Claude Code](https://claude.ai/code) --------- Co-authored-by: Claude <noreply@anthropic.com>
LanceDB JavaScript SDK
A JavaScript library for LanceDB.
Installation
npm install @lancedb/lancedb
This will download the appropriate native library for your platform. We currently support:
- Linux (x86_64 and aarch64 on glibc and musl)
- MacOS (Intel and ARM/M1/M2)
- Windows (x86_64 and aarch64)
Usage
Basic Example
import * as lancedb from "@lancedb/lancedb";
const db = await lancedb.connect("data/sample-lancedb");
const table = await db.createTable("my_table", [
{ id: 1, vector: [0.1, 1.0], item: "foo", price: 10.0 },
{ id: 2, vector: [3.9, 0.5], item: "bar", price: 20.0 },
]);
const results = await table.vectorSearch([0.1, 0.3]).limit(20).toArray();
console.log(results);
The quickstart contains a more complete example.
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.