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5deb26bc8b
metadata{filename=xyz} filename would be there structurally, but ALWAYS
null.
I didn't include this as a file but it may be useful for understanding
the problem for people searching on this issue so I'm including it here
as documentation. Before this patch any field that is more than 1 deep
is accepted but returns null values for subfields when queried.
```js
const lancedb = require('@lancedb/lancedb');
// Debug logger
function debug(message, data) {
console.log(`[TEST] ${message}`, data !== undefined ? data : '');
}
// Log when our unwrapArrowObject is called
const kParent = Symbol.for("parent");
const kRowIndex = Symbol.for("rowIndex");
// Override console.log for our test
const originalConsoleLog = console.log;
console.log = function() {
// Filter out noisy logs
if (arguments[0] && typeof arguments[0] === 'string' && arguments[0].includes('[INFO] [LanceDB]')) {
originalConsoleLog.apply(console, arguments);
}
originalConsoleLog.apply(console, arguments);
};
async function main() {
debug('Starting test...');
// Connect to the database
debug('Connecting to database...');
const db = await lancedb.connect('./.lancedb');
// Try to open an existing table, or create a new one if it doesn't exist
let table;
try {
table = await db.openTable('test_nested_fields');
debug('Opened existing table');
} catch (e) {
debug('Creating new table...');
// Create test data with nested metadata structure
const data = [
{
id: 'test1',
vector: [1, 2, 3],
metadata: {
filePath: "/path/to/file1.ts",
startLine: 10,
endLine: 20,
text: "function test() { return true; }"
}
},
{
id: 'test2',
vector: [4, 5, 6],
metadata: {
filePath: "/path/to/file2.ts",
startLine: 30,
endLine: 40,
text: "function test2() { return false; }"
}
}
];
debug('Data to be inserted:', JSON.stringify(data, null, 2));
// Create the table
table = await db.createTable('test_nested_fields', data);
debug('Table created successfully');
}
// Query the table and get results
debug('Querying table...');
const results = await table.search([1, 2, 3]).limit(10).toArray();
// Log the results
debug('Number of results:', results.length);
if (results.length > 0) {
const firstResult = results[0];
debug('First result properties:', Object.keys(firstResult));
// Check if metadata is accessible and what properties it has
if (firstResult.metadata) {
debug('Metadata properties:', Object.keys(firstResult.metadata));
debug('Metadata filePath:', firstResult.metadata.filePath);
debug('Metadata startLine:', firstResult.metadata.startLine);
// Destructure to see if that helps
const { filePath, startLine, endLine, text } = firstResult.metadata;
debug('Destructured values:', { filePath, startLine, endLine, text });
// Check if it's a proxy object
debug('Result is proxy?', Object.getPrototypeOf(firstResult) === Object.prototype ? false : true);
debug('Metadata is proxy?', Object.getPrototypeOf(firstResult.metadata) === Object.prototype ? false : true);
} else {
debug('Metadata is not accessible!');
}
}
// Close the database
await db.close();
}
main().catch(e => {
console.error('Error:', e);
});
```
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Summary by CodeRabbit
- **Bug Fixes**
- Improved handling of nested struct fields to ensure accurate
preservation of values during serialization and deserialization.
- Enhanced robustness when accessing nested object properties, reducing
errors with missing or null values.
- **Tests**
- Added tests to verify correct handling of nested struct fields through
serialization and deserialization.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Search More, Manage Less
LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
The key features of LanceDB include:
-
Production-scale vector search with no servers to manage.
-
Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
-
Support for vector similarity search, full-text search and SQL.
-
Native Python and Javascript/Typescript support.
-
Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
-
GPU support in building vector index(*).
-
Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.
Quick Start
Javascript
npm install @lancedb/lancedb
import * as lancedb from "@lancedb/lancedb";
const db = await lancedb.connect("data/sample-lancedb");
const table = await db.createTable("vectors", [
{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
], {mode: 'overwrite'});
const query = table.vectorSearch([0.1, 0.3]).limit(2);
const results = await query.toArray();
// You can also search for rows by specific criteria without involving a vector search.
const rowsByCriteria = await table.query().where("price >= 10").toArray();
Python
pip install lancedb
import lancedb
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.create_table("my_table",
data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
result = table.search([100, 100]).limit(2).to_pandas()