**feat: add examples demonstrating simplified augmentations and streamlined pipelines**
### Changes: - Introduced a new `examples/simplified-augmentations.js` file showcasing comprehensive usage examples: - Example 1: Creating and using a simple memory augmentation. - Example 2: Adding WebSocket support to a conduit augmentation. - Example 3: Processing static data through a reusable pipeline. - Example 4: Setting up and executing streaming data pipelines. - Example 5: Dynamically loading augmentations from a module. - Demonstrated augmentation factories, streamlined pipelines, dynamic loading, and reusable workflows through practical scenarios. ### Purpose: Added detailed examples to assist developers in understanding and implementing augmentation factories and streamlined pipelines. These examples provide a hands-on reference for building static and streaming workflows, improving usability and accessibility for new and existing users.
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examples/simplified-augmentations.js
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examples/simplified-augmentations.js
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/**
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* Simplified Augmentations Example
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*
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* This example demonstrates how to use the simplified augmentation factory and streamlined pipeline
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* to create, import, and execute augmentations for both static and streaming data.
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*/
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import {
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// Augmentation factory
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createMemoryAugmentation,
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createConduitAugmentation,
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createSenseAugmentation,
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addWebSocketSupport,
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loadAugmentationModule,
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// Streamlined pipeline
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processStaticData,
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processStreamingData,
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createPipeline,
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createStreamingPipeline,
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executeStreamlined,
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executeByType,
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executeSingle,
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StreamlinedExecutionMode,
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// Core types
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AugmentationType
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} from '../src/index.js'
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/**
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* Example 1: Creating a simple memory augmentation
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*/
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async function example1() {
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console.log('Example 1: Creating a simple memory augmentation')
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// Create a memory augmentation with the factory
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const memoryAug = createMemoryAugmentation({
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name: 'simple-memory',
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description: 'A simple in-memory storage augmentation',
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autoRegister: true,
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autoInitialize: true,
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// Implement the required methods
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storeData: async (key, data) => {
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console.log(`Storing data for key: ${key}`)
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// In a real implementation, you would store the data somewhere
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return {
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success: true,
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data: true
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}
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},
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retrieveData: async (key) => {
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console.log(`Retrieving data for key: ${key}`)
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// In a real implementation, you would retrieve the data from storage
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return {
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success: true,
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data: { example: 'data', key }
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}
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}
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})
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// Use the augmentation directly
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const storeResult = await executeSingle(memoryAug, 'storeData', 'test-key', {
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value: 'test-value'
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})
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console.log('Store result:', storeResult)
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const retrieveResult = await executeSingle(
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memoryAug,
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'retrieveData',
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'test-key'
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)
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console.log('Retrieve result:', retrieveResult)
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console.log('-----------------------------------')
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}
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/**
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* Example 2: Creating a WebSocket-enabled conduit augmentation
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*/
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async function example2() {
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console.log('Example 2: Creating a WebSocket-enabled conduit augmentation')
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// Create a basic conduit augmentation
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const conduitAug = createConduitAugmentation({
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name: 'simple-conduit',
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description: 'A simple conduit augmentation',
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// Implement the required methods
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readData: async (query) => {
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console.log(`Reading data with query:`, query)
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return {
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success: true,
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data: { result: 'some data' }
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}
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},
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writeData: async (data) => {
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console.log(`Writing data:`, data)
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return {
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success: true,
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data: { written: true }
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}
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}
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})
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// Add WebSocket support to the conduit augmentation
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const wsConduitAug = addWebSocketSupport(conduitAug, {
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connectWebSocket: async (url) => {
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console.log(`Connecting to WebSocket at ${url}`)
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// In a real implementation, you would establish a WebSocket connection
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return {
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connectionId: 'ws-1',
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url,
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status: 'connected'
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}
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},
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sendWebSocketMessage: async (connectionId, data) => {
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console.log(`Sending message on connection ${connectionId}:`, data)
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// In a real implementation, you would send the message over the WebSocket
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}
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})
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// Use the WebSocket-enabled augmentation
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const connectResult = await executeSingle(
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wsConduitAug,
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'connectWebSocket',
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'wss://example.com'
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)
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console.log('Connect result:', connectResult)
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await executeSingle(wsConduitAug, 'sendWebSocketMessage', 'ws-1', {
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type: 'hello'
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})
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console.log('-----------------------------------')
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}
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/**
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* Example 3: Processing static data through a pipeline
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*/
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async function example3() {
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console.log('Example 3: Processing static data through a pipeline')
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// Create a sense augmentation for processing raw data
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const senseAug = createSenseAugmentation({
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name: 'text-processor',
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description: 'Processes text into nouns and verbs',
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processRawData: (rawData, dataType) => {
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if (dataType !== 'text') {
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return {
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success: false,
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data: { nouns: [], verbs: [] },
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error: `Unsupported data type: ${dataType}`
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}
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}
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const text = rawData.toString()
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console.log(`Processing text: ${text}`)
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// Simple example - in a real implementation, you would use NLP
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const words = text.split(' ')
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const nouns = words.filter((w) => w.length > 4)
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const verbs = words.filter((w) => w.length <= 4)
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return {
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success: true,
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data: { nouns, verbs }
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}
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}
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})
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// Create a perception augmentation for interpreting the processed data
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const perceptionAug = createMemoryAugmentation({
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name: 'text-interpreter',
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description: 'Interprets processed text data',
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storeData: async (key, data) => {
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console.log(`Interpreting data:`, data)
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// Simple example - in a real implementation, you would do more sophisticated interpretation
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return {
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success: true,
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data: {
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interpreted: true,
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nounCount: data.nouns.length,
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verbCount: data.verbs.length,
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summary: `Found ${data.nouns.length} nouns and ${data.verbs.length} verbs`
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}
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}
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}
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})
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// Process static data through a pipeline
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const result = await processStaticData(
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'This is an example text for processing through the pipeline',
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[
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{
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augmentation: senseAug,
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method: 'processRawData',
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transformArgs: (data) => [data, 'text']
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},
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{
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augmentation: perceptionAug,
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method: 'storeData',
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transformArgs: (data) => ['processed-text', data]
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}
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]
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)
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console.log('Pipeline result:', result)
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// Create a reusable pipeline
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const textPipeline = createPipeline([
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{
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augmentation: senseAug,
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method: 'processRawData',
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transformArgs: (data) => [data, 'text']
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},
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{
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augmentation: perceptionAug,
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method: 'storeData',
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transformArgs: (data) => ['processed-text', data]
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}
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])
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// Use the reusable pipeline
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const result2 = await textPipeline(
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'Another example text for the reusable pipeline'
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)
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console.log('Reusable pipeline result:', result2)
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console.log('-----------------------------------')
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}
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/**
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* Example 4: Processing streaming data
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*/
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async function example4() {
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console.log('Example 4: Processing streaming data')
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// Create a sense augmentation that can listen to a data feed
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const streamingSenseAug = createSenseAugmentation({
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name: 'stream-processor',
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description: 'Processes streaming data',
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listenToFeed: async (feedUrl, callback) => {
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console.log(`Listening to feed at ${feedUrl}`)
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// Simulate streaming data with setInterval
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const interval = setInterval(() => {
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const timestamp = new Date().toISOString()
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console.log(`Received data from feed at ${timestamp}`)
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// Send data to the callback
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callback({
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nouns: [`data-${Date.now()}`, 'stream', 'example'],
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verbs: ['is', 'runs', 'processes']
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})
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}, 2000)
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// In a real implementation, you would return a way to stop the stream
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// For this example, we'll stop after 3 iterations
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setTimeout(() => {
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clearInterval(interval)
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console.log('Stream ended')
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}, 7000)
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}
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})
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// Create a perception augmentation for processing the streaming data
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const streamingPerceptionAug = createMemoryAugmentation({
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name: 'stream-interpreter',
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description: 'Interprets streaming data',
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storeData: async (key, data) => {
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console.log(`Processing streaming data:`, data)
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return {
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success: true,
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data: {
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processed: true,
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timestamp: new Date().toISOString(),
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nounCount: data.nouns.length,
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verbCount: data.verbs.length
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}
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}
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}
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})
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// Set up a streaming pipeline
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await processStreamingData(
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streamingSenseAug,
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'listenToFeed',
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['http://example.com/data-feed'],
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[
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{
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augmentation: streamingPerceptionAug,
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method: 'storeData',
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transformArgs: (data) => [`stream-${Date.now()}`, data]
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}
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],
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(result) => {
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console.log('Streaming pipeline result:', result)
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}
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)
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// Wait for the streaming example to complete
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await new Promise((resolve) => setTimeout(resolve, 8000))
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console.log('-----------------------------------')
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}
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/**
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* Example 5: Dynamic loading of augmentations
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*/
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async function example5() {
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console.log('Example 5: Dynamic loading of augmentations')
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// Simulate dynamic loading of a module
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// In a real application, you would use dynamic import()
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const mockModulePromise = Promise.resolve({
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dynamicMemoryAug: createMemoryAugmentation({
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name: 'dynamic-memory',
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description: 'Dynamically loaded memory augmentation',
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storeData: async (key, data) => {
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console.log(`[Dynamic] Storing data for key: ${key}`)
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return {
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success: true,
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data: true
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}
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},
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retrieveData: async (key) => {
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console.log(`[Dynamic] Retrieving data for key: ${key}`)
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return {
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success: true,
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data: { dynamic: true, key }
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}
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}
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})
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})
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// Load the augmentations from the module
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const loadedAugmentations = await loadAugmentationModule(mockModulePromise, {
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autoRegister: true,
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autoInitialize: true
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})
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console.log(`Loaded ${loadedAugmentations.length} augmentations dynamically`)
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// Use the dynamically loaded augmentation
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if (loadedAugmentations.length > 0) {
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const dynamicAug = loadedAugmentations[0]
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console.log(`Using dynamically loaded augmentation: ${dynamicAug.name}`)
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const retrieveResult = await executeSingle(
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dynamicAug,
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'retrieveData',
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'dynamic-key'
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)
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console.log('Dynamic retrieve result:', retrieveResult)
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}
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console.log('-----------------------------------')
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}
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/**
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* Run all examples
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*/
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async function runExamples() {
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try {
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await example1()
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await example2()
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await example3()
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await example4()
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await example5()
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console.log('All examples completed successfully!')
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} catch (error) {
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console.error('Error running examples:', error)
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}
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}
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// Run the examples
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runExamples()
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