From 8df5d79619458eb6a56c9d8a63faa73c4e087fbf Mon Sep 17 00:00:00 2001 From: Jeffrey Date: Wed, 1 May 2024 13:59:07 +0200 Subject: [PATCH] add pages to docs --- .../docs/collecting-data/adding-tasks.mdx | 30 +++ .../collecting-data/create-a-campaign.mdx | 43 ++++ .../collecting-data/create-a-template.mdx | 57 ++++++ .../docs/collecting-data/introduction.mdx | 0 ...gettingStarted.mdx => getting-started.mdx} | 2 - docs/pages/docs/guides/createACampaign.mdx | 90 --------- docs/pages/docs/introduction.mdx | 57 +----- ...lDevelopment.mdx => local-development.mdx} | 0 docs/pages/docs/templates/introduction.mdx | 6 + docs/pages/docs/templates/placeholders.mdx | 0 docs/pages/index.mdx | 11 +- docs/public/preview-bg.png | Bin 0 -> 1336086 bytes docs/sidebar.ts | 105 ++++++---- ....timestamp-1714557963488-3f044118497c9.mjs | 189 ++++++++++++++++++ ....timestamp-1714557963488-3f9070a6527d8.mjs | 189 ++++++++++++++++++ src/@generated/types/effecttasks2.ts | 2 + src/actions/tasks/batch/createBatch.ts | 2 +- src/actions/tasks/campaigns/createCampaign.ts | 11 +- 18 files changed, 602 insertions(+), 192 deletions(-) create mode 100644 docs/pages/docs/collecting-data/adding-tasks.mdx create mode 100644 docs/pages/docs/collecting-data/create-a-campaign.mdx create mode 100644 docs/pages/docs/collecting-data/create-a-template.mdx create mode 100644 docs/pages/docs/collecting-data/introduction.mdx rename docs/pages/docs/{gettingStarted.mdx => getting-started.mdx} (99%) delete mode 100644 docs/pages/docs/guides/createACampaign.mdx rename docs/pages/docs/{localDevelopment.mdx => local-development.mdx} (100%) create mode 100644 docs/pages/docs/templates/introduction.mdx create mode 100644 docs/pages/docs/templates/placeholders.mdx create mode 100644 docs/public/preview-bg.png create mode 100644 docs/vocs.config.tsx.timestamp-1714557963488-3f044118497c9.mjs create mode 100644 docs/vocs.config.tsx.timestamp-1714557963488-3f9070a6527d8.mjs diff --git a/docs/pages/docs/collecting-data/adding-tasks.mdx b/docs/pages/docs/collecting-data/adding-tasks.mdx new file mode 100644 index 00000000..4c6e43a2 --- /dev/null +++ b/docs/pages/docs/collecting-data/adding-tasks.mdx @@ -0,0 +1,30 @@ +## Adding tasks + +Adding tasks to a campaign is done through batches batches are a collection of tasks that are added to a campaign. Each batch can contain multiple tasks. +Let's start by creating a batch with 3 tasks to our newly created image classification campaign. + +```ts [example.ts] +import { createBatch } from '@effectai/effect-js' + +const { actor } = client.session + +const batch = await createBatch({ + client, + batch: { + campaign_id: '<..>', // the id of the campaign + repetitions: 1 // number of times each task should be repeated + payer: actor // the actor that will pay the workers for the tasks + } + data: [ + { + image: 'https://example.com/image.jpg', //task 1 image placeholder + }, + { + image: 'https://example.com/image2.jpg', //task 2 image placeholder + }, + { + image: 'https://example.com/image3.jpg', // task 3 image placeholder + } + ] +}) +``` \ No newline at end of file diff --git a/docs/pages/docs/collecting-data/create-a-campaign.mdx b/docs/pages/docs/collecting-data/create-a-campaign.mdx new file mode 100644 index 00000000..106d96d7 --- /dev/null +++ b/docs/pages/docs/collecting-data/create-a-campaign.mdx @@ -0,0 +1,43 @@ +## Creating Your First Campaign + +Campaigns are the main way to collect data on Effect Network. +A campaign is a collection of tasks that need to be completed by workers. Each task is a small piece of work that needs to be done. For example, a task could be to label an image, transcribe a piece of audio, or answer a question. + They are created by the data requester (you) and contain information like the reward for the workers, the template for the tasks, the instructions for the workers etc. + +### Creating a campaign + +```ts [example.ts] +import { createCampaign } from '@effectai/effect-js' + +// get the actor from the session +const { actor } = client.session + +// example template +const template = '...' + +const campaign = await createCampaign({ + client, + data: { + title: 'My first campaign', + category: 'Image Labeling', // Category of the campaign e.g. Image Labeling, Audio Transcription etc. + description: 'This is a description of my first campaign', // Description of the campaign + estimated_time: 3600, // Estimated time to complete a task in seconds + example_task: 'This is an example task', + image: 'https://example.com/image.png', + instructions: 'This is the instructions for the workers', + input_schema: null, + output_schema: null, + template, + version: 1, + }, + campaign: { + owner: actor, + reward: { quantity: '1', contract: 'effecttokens' }, + max_task_time: 3600, + qualis: [], + payer: actor, + }, +}) +``` + +Wooooho! You have created your first campaign. You can now view the campaign on the [Effect Network](https://app.effect.network/campaigns) before you start collecting data, you need to add tasks to the campaign. You can do this by following the [Adding Tasks](/docs/collecting-data/add-tasks) guide. \ No newline at end of file diff --git a/docs/pages/docs/collecting-data/create-a-template.mdx b/docs/pages/docs/collecting-data/create-a-template.mdx new file mode 100644 index 00000000..8353a261 --- /dev/null +++ b/docs/pages/docs/collecting-data/create-a-template.mdx @@ -0,0 +1,57 @@ + +# Create a Template +Before we can create a data collection campaign, we need to create a **template**. + +A template is a blueprint for the tasks in a campaign. It defines the structure of the tasks, the input and output data. +Templates are created by the data requester (you) and can be reused across multiple campaigns. + +A typical template will be a HTML file that contains the structure of the tasks. The template can contain placeholders for the input data, output data, and other dynamic content. + +For this guide, we will create a simple template for an image labeling campaign. The template contains a title, an image, and a question for the workers to answer. The image is loaded from IPFS using the `ipfs` placeholder. + +```html + + + My Image labeling Campaign + + +
+
+

Classify The Image

+ + + +
+
+ +

How much does this resemble the original one?

+ + +
+ + + + + + +
+ +
+ + + +
+ + + + + +``` + +You can find examples for different types of templates on our [github](https://github.com/effectai/effect-force-templates) +We highly recommended to follow the [template guide](/docs/templates/introduction) when you are creating your own template. + + +Now that we have created a template for our campaign, we can move on to the next step: [Creating a Campaign](/docs/collecting-data/create-a-campaign) \ No newline at end of file diff --git a/docs/pages/docs/collecting-data/introduction.mdx b/docs/pages/docs/collecting-data/introduction.mdx new file mode 100644 index 00000000..e69de29b diff --git a/docs/pages/docs/gettingStarted.mdx b/docs/pages/docs/getting-started.mdx similarity index 99% rename from docs/pages/docs/gettingStarted.mdx rename to docs/pages/docs/getting-started.mdx index 6cc33084..71093f34 100644 --- a/docs/pages/docs/gettingStarted.mdx +++ b/docs/pages/docs/getting-started.mdx @@ -1,7 +1,5 @@ # Getting Started [Get started with the SDK in just a few lines of code.] -## Overview - ## Installation Use your favorite package manager to install the SDK. diff --git a/docs/pages/docs/guides/createACampaign.mdx b/docs/pages/docs/guides/createACampaign.mdx deleted file mode 100644 index e27149ea..00000000 --- a/docs/pages/docs/guides/createACampaign.mdx +++ /dev/null @@ -1,90 +0,0 @@ -## Creating Your First Campaign - -To create a campaign, you need to have a few things in place: - - -# Create and upload campaign - -## Usage - -```ts [example.ts] -import { createClient, createCampaign } from '@effectai/effect-js' - -const campaign: Mkcampaign = { - owner: ["name", "efxefxefxefx"], - content: { - field_0: 0, - field_1: "QmVKwq3bYM6cPW6kstpiq4WYckWRtdfJnzAmms2iMyGqQg", - }, - max_task_time: 100, - reward: { quantity: "42.1234, EFX", contract: "efxtoken1112" }, - qualis: [], - payer: "efxefxefxefx", -}; - -const data = { - version: 1.1, - title: "Labelstudio OCR (LAION)", - description: - "You are contributing to a dataset for conversational style chatbots.", - instructions: "Some instructions here", - template: "

Template here

", - input_schema: null, - output_schema: null, - image: "", - category: "", - example_task: "", - estimated_time: 10, -}; - -const client = await testClientSession({ testEnvNetwork: jungle4 }); -const result = await createCampaign({ client, campaign, data }); -``` - -## Returns - -Result is a transaction response object. - -```json -response: { - transaction_id: "9d321af28b7354c5cbee6ee956ea3e6590228b48539a9f0cafc6a8ca5ffe0ca2", - processed: { - id: "9d321af28b7354c5cbee6ee956ea3e6590228b48539a9f0cafc6a8ca5ffe0ca2", - block_num: 137520447, - block_time: "2024-05-01T03:55:31.500", - producer_block_id: null, - receipt: [Object ...], - elapsed: 4854, - net_usage: 176, - scheduled: false, - action_traces: [ - [Object ...] - ], - account_ram_delta: null, - except: null, - error_code: null, - }, -} -``` - - -- **Type:** [`Promise>`](/docs/glossary/types#campaign) -- **Description:** A list of campaigns. -- **Properties:** - - **rows:** An array of campaigns. - - **more:** A boolean indicating if there are more campaigns to fetch. - - **next_key:** A string that can be used to fetch the next page of campaigns. - -## Parameters - -### client -- **Type:** `Client` - -### limit (optional) -- **Type:** `number` -- **Default:** `10` - -### page (optional) -- **Type:** `number` -- **Default:** `1` - diff --git a/docs/pages/docs/introduction.mdx b/docs/pages/docs/introduction.mdx index 6728b05b..7397ce75 100644 --- a/docs/pages/docs/introduction.mdx +++ b/docs/pages/docs/introduction.mdx @@ -1,57 +1,14 @@ # Why Effect AI -## Introduction - -Effect AI was built to solve the problem of AI development. -It's a platform that connects AI developers with AI trainers to create data to train AI models. - ## Problems -The problem that Effect AI is trying to solve is the lack of training data for AI models. -AI models are only as good as the data they are trained on. -The more data you have, the better the model will be. - -There is an issue though that now we are facing, AI models are being trained with data that ws created by other AI models. -This is a problem because the data is not always accurate, the model will learn and exacerbate mistakes from the training data. -Leading to unreliable AI models. +AI will be the pivotal technology of the future that will transcend us into a new era of efficiency and innovation. +However, the development of AI models is contingent upon the availability of high-quality training data. +This poses a significant challenge, as the creation of such data is a labor-intensive and time-consuming process. +Moreover, this data is often gathered & developed behind closed doors, leading to a lack of transparency and accountability. ## Solution -The soluiton is to create high quality training data. -This is where Effect AI comes in. - -Effect AI is a platform that connects AI developers with AI trainers. - -AI developers can post tasks that they need training data for. -Ai trainers can then complete these tasks and get paid for their work. - ---- - - -In the rapidly evolving landscape of artificial intelligence (AI), the quest for optimal development and performance remains paramount. -Effect AI emerges as a beacon in this realm, offering a solution to a pressing dilemma: the scarcity of high-quality training data for AI models. - -At its core, Effect AI addresses the fundamental challenge of AI development—the insufficiency of robust datasets. -As the adage goes, "garbage in, garbage out"; AI models are only as proficient as the data they are trained upon. -Consequently, the quest for superior data becomes imperative for fostering the accuracy and reliability of AI technologies. - -However, a disconcerting trend has emerged in the AI domain—models being trained with data generated by other AI models. -This recursive process, while ostensibly efficient, precipitates a cascade of issues. -Foremost among these concerns is the potential for error amplification: inaccuracies within the training data can be perpetuated and magnified within the AI model, resulting in flawed outcomes and unreliable performance. - -Enter Effect AI, a platform meticulously crafted to address these challenges. -Serving as a nexus between AI developers and AI trainers, Effect AI facilitates the creation of high-quality training data, thereby fortifying the foundations of AI development. - -The modus operandi of Effect AI is elegantly simple yet profoundly impactful. -AI developers leverage the platform to articulate their data requirements through the formulation of tasks. -These tasks, ranging from image classification to natural language processing, encapsulate the diverse array of challenges encountered in AI development. - -In response, AI trainers—individuals proficient in data annotation and curation—step forward to fulfill these tasks. -Through their collective efforts, they meticulously craft datasets that adhere to the highest standards of quality and accuracy. -In doing so, they contribute not merely to the advancement of AI technologies but to the cultivation of a virtuous cycle wherein superior data begets superior models. - -Crucially, Effect AI's platform incentivizes participation and excellence. -AI trainers are duly compensated for their contributions, thereby fostering a symbiotic relationship wherein both developers and trainers are mutually benefited. - -In conclusion, Effect AI stands as a testament to the power of innovation in addressing complex challenges. -By championing the creation of high-quality training data, it not only mitigates the pitfalls of current AI development practices but also charts a course towards a future where AI technologies are synonymous with reliability, accuracy, and societal benefit. +The solution is Effect AI, a decentralized network that connects AI developers with a global workforce of AI trainers. +Leading the charge in the democratization of AI, Effect AI enables developers to create high-quality training data through a transparent and collaborative process. +By leveraging the power of the crowd, Effect AI ensures that AI models are trained on diverse and reliable datasets, thereby enhancing their accuracy and performance. diff --git a/docs/pages/docs/localDevelopment.mdx b/docs/pages/docs/local-development.mdx similarity index 100% rename from docs/pages/docs/localDevelopment.mdx rename to docs/pages/docs/local-development.mdx diff --git a/docs/pages/docs/templates/introduction.mdx b/docs/pages/docs/templates/introduction.mdx new file mode 100644 index 00000000..94a2826b --- /dev/null +++ b/docs/pages/docs/templates/introduction.mdx @@ -0,0 +1,6 @@ +# Introduction + + +## What is a template? + +## Creating Your own template \ No newline at end of file diff --git a/docs/pages/docs/templates/placeholders.mdx b/docs/pages/docs/templates/placeholders.mdx new file mode 100644 index 00000000..e69de29b diff --git a/docs/pages/index.mdx b/docs/pages/index.mdx index 27cc5b06..68ba2a6d 100644 --- a/docs/pages/index.mdx +++ b/docs/pages/index.mdx @@ -10,7 +10,14 @@ logo: false import { HomePage } from 'vocs/components' -
+
@@ -19,7 +26,7 @@ import { HomePage } from 'vocs/components' Effect logo
Effect AI is the data network for training next-gen transparent AI models. Integrate the SDK into your app and tap into a global, decentralized workforce.
- Get started + Get started GitHub
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Mkcampaign; - data: Record; + campaign: Omit; + data: CampaignInfo; }; export const createCampaign = async ({ @@ -27,6 +31,7 @@ export const createCampaign = async ({ const { tasks, token } = useEFXContracts(client); try { + // Upload Campaign data to IPFS const hash = await uploadIpfsResource({ client, data }); const response = await transact({

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