Request Task with Runware API on New Field in Document from MongoDB API

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Trigger workflow on

New Field in Document from the MongoDB API

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Request Task with the Runware API

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Getting Started#

This integration creates a workflow with a MongoDB trigger and Runware action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Field in Document trigger
1. Connect your MongoDB account
2. Configure Polling Interval
3. Select a Database
4. Select a Collection
5. Select a Document
Configure the Request Task action
1. Connect your Runware account
2. Select a Task Type
3. Optional- Select a Output Type
4. Optional- Select a Output Format
5. Optional- Configure Upload Endpoint
6. Optional- Configure Check NSFW
7. Optional- Configure Include Cost
8. Configure Positive Prompt
9. Optional- Configure Negative Prompt
10. Optional- Configure Seed Image
11. Optional- Configure Mask Image
12. Optional- Configure Strength
13. Configure Height
14. Configure Width
15. Configure Model
16. Optional- Configure Steps
17. Optional- Configure Scheduler
18. Optional- Configure Seed
19. Optional- Configure Number Of Results
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Field in Document on MongoDB

Description:Emit new an event when a new field is added to a document

Version:0.0.7

Key:mongodb-new-field-in-document

View on GitHub

MongoDB Overview#

The MongoDB API provides powerful capabilities to interact with a MongoDB database, allowing you to perform CRUD (Create, Read, Update, Delete) operations, manage databases, and execute sophisticated queries. With Pipedream, you can harness these abilities to automate tasks, sync data across various apps, and react to events in real-time. It’s a combo that’s particularly potent for managing data workflows, syncing application states, or triggering actions based on changes to your data.

Trigger Code#

import common from "../common/base.mjs";

export default {
  ...common,
  key: "mongodb-new-field-in-document",
  name: "New Field in Document",
  description: "Emit new an event when a new field is added to a document",
  version: "0.0.7",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    database: {
      propDefinition: [
        common.props.mongodb,
        "database",
      ],
    },
    collection: {
      propDefinition: [
        common.props.mongodb,
        "collection",
        (c) => ({
          database: c.database,
        }),
      ],
    },
    document: {
      propDefinition: [
        common.props.mongodb,
        "document",
        (c) => ({
          database: c.database,
          collection: c.collection,
        }),
      ],
    },
  },
  methods: {
    ...common.methods,
    _getFields() {
      return this.db.get("fields");
    },
    _setFields(fields) {
      this.db.set("fields", fields);
    },
    isRelevant(key, fields) {
      return !fields.includes(key);
    },
    async processEvent(client, ts) {
      const fields = this._getFields() || [];
      const collection = this.mongodb.getCollection(client, this.database, this.collection);
      const documents = await this.mongodb.listDocuments(collection);
      const doc = documents.find((obj) => {
        return obj._id == this.document;
      });
      const keys = Object.keys(doc);
      for (const key of keys) {
        if (!this.isRelevant(key, fields)) {
          continue;
        }
        fields.push(key);
        this.emitEvent(key, ts);
      }
      this._setFields(fields);
    },
    generateMeta(key, ts) {
      return {
        id: key,
        summary: `New Field ${key}`,
        ts,
      };
    },
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
MongoDB	`mongodb`	`app`	This component uses the MongoDB app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling Interval	`timer`	`$.interface.timer`	Pipedream will poll the API on this schedule
Database	`database`	`string`	Select a value from the drop down menu.
Collection	`collection`	`string`	Select a value from the drop down menu.
Document	`document`	`string`	Select a value from the drop down menu.

Trigger Authentication#

MongoDB uses API keys for authentication. When you connect your MongoDB account, Pipedream securely stores the keys so you can easily authenticate to MongoDB APIs in both code and no-code steps.

Before you connect to your Mongo database from Pipedream, please make sure your database is accessible from the public internet. You may need to add a firewall rule allowing connections from 0.0.0.0/0.

Then, enter the details of your Mongo host / database below.

About MongoDB#

MongoDB is an open source NoSQL database management program.

Action#

Request Task on Runware

Description:Request one task to be processed by the Runware API. [See the documentation](https://docs.runware.ai/en/image-inference/api-reference).

Version:0.0.1

Key:runware-request-task

View on GitHub

Action Code#

import { v4 as uuid } from "uuid";
import app from "../../runware.app.mjs";

export default {
  key: "runware-request-task",
  name: "Request Task",
  description: "Request one task to be processed by the Runware API. [See the documentation](https://docs.runware.ai/en/image-inference/api-reference).",
  version: "0.0.1",
  type: "action",
  props: {
    app,
    taskType: {
      propDefinition: [
        app,
        "taskType",
      ],
    },
    outputType: {
      propDefinition: [
        app,
        "outputType",
      ],
    },
    outputFormat: {
      propDefinition: [
        app,
        "outputFormat",
      ],
    },
    uploadEndpoint: {
      propDefinition: [
        app,
        "uploadEndpoint",
      ],
    },
    checkNSFW: {
      propDefinition: [
        app,
        "checkNSFW",
      ],
    },
    includeCost: {
      propDefinition: [
        app,
        "includeCost",
      ],
    },
    positivePrompt: {
      propDefinition: [
        app,
        "positivePrompt",
      ],
    },
    negativePrompt: {
      propDefinition: [
        app,
        "negativePrompt",
      ],
    },
    seedImage: {
      propDefinition: [
        app,
        "seedImage",
      ],
    },
    maskImage: {
      propDefinition: [
        app,
        "maskImage",
      ],
    },
    strength: {
      propDefinition: [
        app,
        "strength",
      ],
    },
    height: {
      propDefinition: [
        app,
        "height",
      ],
    },
    width: {
      propDefinition: [
        app,
        "width",
      ],
    },
    model: {
      propDefinition: [
        app,
        "model",
      ],
    },
    steps: {
      propDefinition: [
        app,
        "steps",
      ],
    },
    scheduler: {
      propDefinition: [
        app,
        "scheduler",
      ],
    },
    seed: {
      propDefinition: [
        app,
        "seed",
      ],
    },
    numberResults: {
      propDefinition: [
        app,
        "numberResults",
      ],
    },
  },
  async run({ $ }) {
    const {
      app,
      taskType,
      outputType,
      outputFormat,
      uploadEndpoint,
      checkNSFW,
      includeCost,
      positivePrompt,
      negativePrompt,
      seedImage,
      maskImage,
      strength,
      height,
      width,
      model,
      steps,
      scheduler,
      seed,
      numberResults,
    } = this;

    const response = await app.post({
      $,
      data: [
        {
          taskUUID: uuid(),
          taskType,
          outputType,
          outputFormat,
          uploadEndpoint,
          checkNSFW,
          includeCost,
          positivePrompt,
          negativePrompt,
          seedImage,
          maskImage,
          strength,
          height,
          width,
          model,
          steps,
          scheduler,
          seed: seed
            ? parseInt(seed)
            : undefined,
          numberResults,
        },
      ],
    });

    $.export("$summary", `Successfully requested task with UUID \`${response.data[0].taskUUID}\`.`);
    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Runware	`app`	`app`	This component uses the Runware app.
Task Type	`taskType`	`string`	Select a value from the drop down menu:`{ "value": "imageInference", "label": "Image Inference" }{ "value": "imageControlNetPreProcess", "label": "Image Control Net Preprocess" }{ "value": "imageUpscale", "label": "Image Upscale" }{ "value": "imageBackgroundRemoval", "label": "Image Background Removal" }{ "value": "imageCaption", "label": "Image Caption" }{ "value": "promptEnhance", "label": "Prompt Enhance" }`
Output Type	`outputType`	`string`	Select a value from the drop down menu:`base64DatadataURIURL`
Output Format	`outputFormat`	`string`	Select a value from the drop down menu:`PNGJPGWEBP`
Upload Endpoint	`uploadEndpoint`	`string`	This parameter allows you to specify a URL to which the generated image will be uploaded as binary image data using the HTTP PUT method. For example, an S3 bucket URL can be used as the upload endpoint. When the image is ready, it will be uploaded to the specified URL.
Check NSFW	`checkNSFW`	`boolean`	This parameter is used to enable or disable the NSFW check. When enabled, the API will check if the image contains NSFW (not safe for work) content. This check is done using a pre-trained model that detects adult content in images. When the check is enabled, the API will return `NSFWContent: true` in the response object if the image is flagged as potentially sensitive content. If the image is not flagged, the API will return `NSFWContent: false`. If this parameter is not used, the parameter `NSFWContent` will not be included in the response object. Adds `0.1` seconds to image inference time and incurs additional costs. The NSFW filter occasionally returns false positives and very rarely false negatives.
Include Cost	`includeCost`	`boolean`	If set to `true`, the cost to perform the task will be included in the response object. Defaults to `false`.
Positive Prompt	`positivePrompt`	`string`	A positive prompt is a text instruction to guide the model on generating the image. It is usually a sentence or a paragraph that provides positive guidance for the task. This parameter is essential to shape the desired results. For example, if the positive prompt is `dragon drinking coffee`, the model will generate an image of a dragon drinking coffee. The more detailed the prompt, the more accurate the results. The length of the prompt must be between 4 and 2000 characters.
Negative Prompt	`negativePrompt`	`string`	A negative prompt is a text instruction to guide the model on generating the image. It is usually a sentence or a paragraph that provides negative guidance for the task. This parameter helps to avoid certain undesired results. For example, if the negative prompt is `red dragon, cup`, the model will follow the positive prompt but will avoid generating an image of a red dragon or including a cup. The more detailed the prompt, the more accurate the results. The length of the prompt must be between 4 and 2000 characters.
Seed Image	`seedImage`	`string`	When doing Image-to-Image, Inpainting or Outpainting, this parameter is required. Specifies the seed image to be used for the diffusion process. The image can be specified in one of the following formats: An UUID v4 string of a previously uploaded image or a generated image. A data URI string representing the image. The data URI must be in the format `data:<mediaType>;base64,` followed by the base64-encoded image. For example: `data:image/png;base64,iVBORw0KGgo...`. A base64 encoded image without the data URI prefix. For example: `iVBORw0KGgo...`. A URL pointing to the image. The image must be accessible publicly. Supported formats are: PNG, JPG and WEBP.
Mask Image	`maskImage`	`string`	When doing Inpainting or Outpainting, this parameter is required. Specifies the mask image to be used for the inpainting process. The image can be specified in one of the following formats: An UUID v4 string of a previously uploaded image or a generated image. A data URI string representing the image. The data URI must be in the format `data:<mediaType>;base64,` followed by the base64-encoded image. For example: `data:image/png;base64,iVBORw0KGgo...`. A base64 encoded image without the data URI prefix. For example: `iVBORw0KGgo...`. A URL pointing to the image. The image must be accessible publicly. Supported formats are: PNG, JPG and WEBP.
Strength	`strength`	`string`	When doing Image-to-Image, Inpainting or Outpainting, this parameter is used to determine the influence of the Seed Image image in the generated output. A higher value results in more influence from the original image, while a lower value allows more creative deviation. Min: `0` Max: `1` and Default: `0.8`.
Height	`height`	`integer`	Used to define the height dimension of the generated image. Certain models perform better with specific dimensions. The value must be divisible by 64, eg: `512`, `576`, `640` ... `2048`.
Width	`width`	`integer`	Used to define the width dimension of the generated image. Certain models perform better with specific dimensions. The value must be divisible by 64, eg: `512`, `576`, `640` ... `2048`.
Model	`model`	`string`	This identifier is a unique string that represents a specific model. You can find the AIR identifier of the model you want to use in our Model Explorer, which is a tool that allows you to search for models based on their characteristics. More information about the AIR system can be found in the Models page. Eg. `civitai:78605@83390`.
Steps	`steps`	`integer`	The number of steps is the number of iterations the model will perform to generate the image. The higher the number of steps, the more detailed the image will be. However, increasing the number of steps will also increase the time it takes to generate the image and may not always result in a better image (some schedulers work differently). When using your own models you can specify a new default value for the number of steps. Defaults to `20`.
Scheduler	`scheduler`	`string`	An scheduler is a component that manages the inference process. Different schedulers can be used to achieve different results like more detailed images, faster inference, or more accurate results. The default scheduler is the one that the model was trained with, but you can choose a different one to get different results. Schedulers are explained in more detail in the Schedulers page.
Seed	`seed`	`string`	A seed is a value used to randomize the image generation. If you want to make images reproducible (generate the same image multiple times), you can use the same seed value. When requesting multiple images with the same seed, the seed will be incremented by 1 (+1) for each image generated. Min: `0` Max: `9223372036854776000`. Defaults to `Random`.
Number Of Results	`numberResults`	`integer`	The number of images to generate from the specified prompt. If Seed is set, it will be incremented by 1 (+1) for each image generated.