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Oracle Drags Its Feet in the JavaScript Trademark Dispute
Oracle seeks to dismiss fraud claims in the JavaScript trademark dispute, delaying the case and avoiding questions about its right to the name.
In-memory vector embeddings database using embeddings for efficient querying text documents
This project provides an in-memory vector embeddings database using embeddings for efficient querying and searching of text documents.
The project allows you to create, query, and manage an in-memory vector embeddings database. It uses embeddings to represent text documents as vectors, enabling efficient similarity searches.
To install the package, use the following command:
npm install @j-o-r/vdb
Here is an example of how to use the Vdb
class to create a database, perform searches, and manage the database:
import path from 'path';
import Vdb from '@j-o-r/vdb';
const db = new Vdb('path/to/storage');
// Create a database from a text document
// You only have to do this ones.
// It may take some time
if (!db.list().includes('readme')) {
const file = path.resolve('README.md');
await db.create(file, 'readme');
}
// Perform a search in the database
let str = await db.search('readme', 'How to create a database', { treshhold: 0.86, results: 4, preRead: 1, postRead: 10 });
console.log(str);
// -- Delete a database
// db.delete('readme');
new Vdb(storagePath)
storagePath
(string): Path to the storage folder.list()
: Returns a list of available databases.delete(dbName)
: Deletes the specified database.
dbName
(string): Name of the database to delete.create(file, dbName, batchSize)
: Creates or overwrites an embeddings database from a text document.
file
(string): Path to the text document.dbName
(string): Name of the database.batchSize
(number, optional): Batch size for processing (default is 256).search(dbName, query, selector)
: Searches the database and returns formatted results.
dbName
(string): Name of the database.query
(string): Search query.selector
(object, optional): Selector options.
results
(number): Number of results to return.preRead
(number): Number of lines to return before the found index.postRead
(number): Number of lines to return after the found index.getResult(dbName, query, results)
: Gets raw search results from the database.
dbName
(string): Name of the database.query
(string): Search query.results
(number, optional): Number of results to return (default is 5).This project is licensed under the APACHE 2.0 License. See the LICENSE file for details.
FAQs
In-memory vector embeddings database using embeddings for efficient querying text documents
The npm package @j-o-r/vdb receives a total of 5 weekly downloads. As such, @j-o-r/vdb popularity was classified as not popular.
We found that @j-o-r/vdb demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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