Database API
Manage data storage and retrieval using the built-in database functionality.
const db = new DatabaseImpl();
await db.initialize({
type: 'indexeddb',
config: {
databaseName: 'MyApp-DB'
}
});Initialize Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| options | DatabaseOptions | Yes | Database configuration options |
DatabaseOptions
| Option | Type | Default | Description |
|---|---|---|---|
| type | ’indexeddb’ | ‘sqlite' | 'indexeddb’ | Database type to use |
| config | DatabaseConfig | - | Configuration object |
DatabaseConfig
| Option | Type | Default | Description |
|---|---|---|---|
| databaseName | string | ’BrowserAI-DB’ | Database name |
| version | number | 1 | Database version |
| storeName | string | ’BrowserAIStore’ | Store name |
| stores | object | - | Store configurations |
Core Operations
store()
Store a document in the database.
await db.store({
id: 'doc1',
text: 'Sample document',
embedding: [0.1, 0.2, 0.3]
});Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| document | Document | Yes | Document to store |
| options | StoreOptions | No | Storage options |
get()
Retrieve a document by ID.
const doc = await db.get('doc1');Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| id | string | Yes | Document ID |
Returns
Promise<DatabaseDocument | null>
update()
Update an existing document.
await db.update({
id: 'doc1',
text: 'Updated content'
});Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| document | DatabaseDocument | Yes | Updated document fields |
delete()
Delete a document by ID.
await db.delete('doc1');Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| id | string | Yes | Document ID to delete |
Vector Operations
setVectorStore()
Initialize vector store functionality.
await db.setVectorStore('default');Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| name | string | Yes | Vector store name |
| options | VectorStoreOptions | No | Vector store configuration |
addVector()
Add a vector embedding for a document.
await db.addVector('doc1', [0.1, 0.2, 0.3]);Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| id | string | Yes | Document ID |
| vector | number[] | Yes | Vector embedding |
| metadata | object | No | Additional metadata |
searchVector()
Search for similar documents using vector similarity.
const results = await db.searchVector(
[0.1, 0.2, 0.3],
5,
{ threshold: 0.8 }
);Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| vector | number[] | Yes | Query vector |
| k | number | Yes | Number of results |
| options | SearchOptions | No | Search configuration |
Returns
Promise<Array<{ id: string, score: number }>>
Types
interface Document {
id: string;
text?: string;
embedding?: number[];
[key: string]: any;
}
interface DatabaseConfig {
databaseName?: string;
version?: number;
storeName?: string;
stores?: Record<string, StoreConfig>;
}
interface StoreConfig {
keyPath?: string;
indexes?: Array<string>;
}
interface SearchOptions {
threshold?: number;
filter?: (doc: Document) => boolean;
}