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Various minor tweaks & cleanups for 2.0 (#84)
* updated serdes * changed tokne provider a bit * minor linting fix * fixed couple import/typing issues + tables readme * add shorthand datatype functions * update readme w/ shorthand datatype fns * marked mroe internal values * removed deeppartial & strict filters/sorts/projs * tiny bit of renaming
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‎README.md

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@@ -22,7 +22,7 @@ Use your preferred package manager to install `@datastax/astra-db-ts`. Note that
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Get the *API endpoint* and your *application token* for your Astra DB instance @ [astra.datastax.com](https://astra.datastax.com).
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Try the following code after setting the following environment variables:
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### Collections
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```typescript
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import { DataAPIClient, VectorDoc, UUID, ObjectId } from '@datastax/astra-db-ts';
@@ -37,68 +37,146 @@ const client = new DataAPIClient('*TOKEN*');
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const db = client.db('*ENDPOINT*', { namespace: '*NAMESPACE*' });
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(async () => {
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try {
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// Creates collections, or gets it if it already exists with same options
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const collection = await db.createCollection<Idea>('vector_5_collection', {
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vector: {
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dimension: 5,
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metric: 'cosine',
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},
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checkExists: false,
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});
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// Insert many ideas into the collections
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const ideas = [
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{
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idea: 'An AI quilt to help you sleep forever',
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$vector: [0.1, 0.15, 0.3, 0.12, 0.05],
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},
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{
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_id: new UUID('e7f1f3a0-7e3d-11eb-9439-0242ac130002'),
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idea: 'Vision Vector Frame—A deep learning display that controls your mood',
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$vector: [0.1, 0.05, 0.08, 0.3, 0.6],
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},
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{
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idea: 'A smartwatch that tells you what to eat based on your mood',
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$vector: [0.2, 0.3, 0.1, 0.4, 0.15],
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},
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];
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await collection.insertMany(ideas);
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// Insert a specific idea into the collections
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const sneakersIdea = {
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_id: new ObjectId('507f191e810c19729de860ea'),
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idea: 'ChatGPT-integrated sneakers that talk to you',
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$vector: [0.45, 0.09, 0.01, 0.2, 0.11],
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}
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await collection.insertOne(sneakersIdea);
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// Actually, let's change that idea
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await collection.updateOne(
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{ _id: sneakersIdea._id },
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{ $set: { idea: 'Gemini-integrated sneakers that talk to you' } },
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);
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// Get similar results as desired
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const cursor = collection.find({}, {
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vector: [0.1, 0.15, 0.3, 0.12, 0.05],
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includeSimilarity: true,
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limit: 2,
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});
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for await (const doc of cursor) {
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// Prints the following:
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// - An AI quilt to help you sleep forever: 1
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// - A smartwatch that tells you what to eat based on your mood: 0.85490346
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console.log(`${doc.idea}: ${doc.$similarity}`);
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}
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// Cleanup (if desired)
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await collection.drop();
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} finally {
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// Cleans up all open http sessions
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await client.close();
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// Creates collections, or gets it if it already exists with same options
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const collection = await db.createCollection<Idea>('vector_5_collection', {
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vector: {
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dimension: 5,
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metric: 'cosine',
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},
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checkExists: false,
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});
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// Insert many ideas into the collections
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const ideas = [
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{
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idea: 'An AI quilt to help you sleep forever',
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$vector: [0.1, 0.15, 0.3, 0.12, 0.05],
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},
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{
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_id: new UUID('e7f1f3a0-7e3d-11eb-9439-0242ac130002'),
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idea: 'Vision Vector Frame—A deep learning display that controls your mood',
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$vector: [0.1, 0.05, 0.08, 0.3, 0.6],
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},
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{
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idea: 'A smartwatch that tells you what to eat based on your mood',
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$vector: [0.2, 0.3, 0.1, 0.4, 0.15],
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},
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];
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await collection.insertMany(ideas);
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// Insert a specific idea into the collections
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const sneakersIdea = {
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_id: new ObjectId('507f191e810c19729de860ea'),
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idea: 'ChatGPT-integrated sneakers that talk to you',
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$vector: [0.45, 0.09, 0.01, 0.2, 0.11],
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}
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await collection.insertOne(sneakersIdea);
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// Actually, let's change that idea
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await collection.updateOne(
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{ _id: sneakersIdea._id },
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{ $set: { idea: 'Gemini-integrated sneakers that talk to you' } },
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);
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// Get similar results as desired
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const cursor = collection.find({}, {
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vector: [0.1, 0.15, 0.3, 0.12, 0.05],
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includeSimilarity: true,
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limit: 2,
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});
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for await (const doc of cursor) {
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// Prints the following:
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// - An AI quilt to help you sleep forever: 1
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// - A smartwatch that tells you what to eat based on your mood: 0.85490346
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console.log(`${doc.idea}: ${doc.$similarity}`);
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}
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// Cleanup (if desired)
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await collection.drop();
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})();
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```
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### Tables
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```typescript
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import { CreateTableDefinition, DataAPIClient, InferTableSchema, vector } from '@datastax/astra-db-ts';
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// Connect to the db
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const client = new DataAPIClient();
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const db = client.db('https://178cf75b-ec19-4d13-9ca7-b6dcfa613157-us-west-2.apps.astra-dev.datastax.com', { token: 'AstraCS:EFZEBUXuMzwyFntFYcroNsWQ:e473c9b244253e3c873346cc8584d4767789d93e54c6c5453c996fbd5ab4605d' });
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// Example table schema using bespoke Data API table definition syntax
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const TableDefinition = <const>{
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columns: {
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id: 'int',
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summary: 'text',
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tags: { type: 'set', valueType: 'text' },
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vector: { type: 'vector', dimension: 3 },
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},
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primaryKey: {
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partitionBy: ['id'],
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},
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} satisfies CreateTableDefinition;
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// Infer the TS-equivalent type from the table definition (like zod or arktype). Equivalent to:
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//
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// interface TableSchema extends Row<'id'> { // Types 'id' as a primary key for insertion command return types
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// summary?: string | null; // Not a primary key, so it's optional and may return as null when found
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// tags?: Set<string>; // Sets/maps/lists are optional to insert, but will actually be returned as empty collections instead of null
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// vector?: DataAPIVector | null; // Vectors, however, may be null.
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// }
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type TableSchema = InferTableSchema<typeof TableDefinition>;
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(async () => {
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// Create the table if it doesn't alredy exist
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const table = await db.createTable('dreams', {
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definition: TableDefinition,
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ifNotExists: true,
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});
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// Enables vector search on the table (on the 'vector' column)
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await table.createVectorIndex('dreams_vector_idx', 'vector', {
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options: { metric: 'cosine' },
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ifNotExists: true,
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});
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// Insert some rows into the table
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const rows: TableSchema[] = [{
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id: 102,
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summary: 'A dinner on the Moon',
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vector: vector([0.2, -0.3, -0.5]), // Shorthand for `new DataAPIVector([0.2, -0.3, -0.5])`
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}, {
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id: 103,
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summary: 'Riding the waves',
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tags: new Set(['sport']),
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vector: vector([0, 0.2, 1]),
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}, {
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id: 37,
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summary: 'Meeting Beethoven at the dentist',
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vector: vector([0.2, 0.6, 0]),
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}];
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await table.insertMany(rows);
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// Hm, changed my mind
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await table.updateOne({ id: 103 }, { $set: { summary: 'Surfers\' paradise' } });
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// Let's see what we've got
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const cursor = table.find({}, {
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sort: { vector: vector([0, 0.2, 0.4]) },
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includeSimilarity: true,
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limit: 2,
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});
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// This would print:
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// - Surfers' paradise: 0.98238194
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// - Friendly aliens in town: 0.91873914
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for await (const result of cursor) {
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console.log(`${result.summary}: ${result.$similarity}`);
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}
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// Cleanup (if desired)
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await table.drop();
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})();
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```
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