Create Embedding with xAI API on New Project Added from FTrack API

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Trigger workflow on

New Project Added from the FTrack API

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Create Embedding with the xAI API

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Getting Started#

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

Select this integration
Configure the New Project Added trigger
1. Connect your FTrack account
2. Configure Polling schedule
Configure the Create Embedding action
1. Connect your xAI account
2. Select a Embedding Models
3. Configure Input
4. Optional- Configure Dimensions
5. Optional- Select a Encoding Format
6. Optional- Configure User
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 Project Added on FTrack

Description:Triggered when a new task is added. [See the documentation](https://help.ftrack.com/en/articles/1040498-operations#query)

Version:0.0.1

Key:ftrack-new-project-added

View on GitHub

FTrack Overview#

The FTrack API is a powerhouse for creative project management, enabling users to automate and integrate a wide array of tasks related to project tracking, asset management, and team collaboration. With Pipedream, you can harness this API to create custom workflows that trigger actions within FTrack or connect it with other apps to streamline your creative operations. It's a toolset for improving efficiency by automating repetitive tasks, syncing data across applications, and orchestrating complex project management activities.

Trigger Code#

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

export default {
  ...common,
  key: "ftrack-new-project-added",
  name: "New Project Added",
  description: "Triggered when a new task is added. [See the documentation](https://help.ftrack.com/en/articles/1040498-operations#query)",
  type: "source",
  version: "0.0.1",
  dedupe: "unique",
  methods: {
    ...common.methods,
    getResourceFn() {
      return this.app.listProjects;
    },
    getResourceFnArgs() {
      const lastCreatedAt = this.getLastCreatedAt();

      if (!lastCreatedAt) {
        return;
      }

      return {
        data: {
          where: `created_at > "${lastCreatedAt}"`,
        },
      };
    },
    generateMeta(resource) {
      return {
        id: resource.id,
        summary: `New Project: ${resource.id}`,
        ts: Date.parse(resource.created_at?.value),
      };
    },
  },
};

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
FTrack	`app`	`app`	This component uses the FTrack app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling schedule	`timer`	`$.interface.timer`	How often to poll the API

Trigger Authentication#

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

To connect FTrack, you will need an FTrack Studio account.

To retrieve your Username and API Key,

Navigate to your ftrack account and sign in
Click the dropdown on the top right

For Username:

Go to “My Account” > “Account”

There are two types of API Keys, Personal and Global.

For Personal API Key:

Go to “My Account” > “Security Settings”

For Global API Key:

Go to “System Settings” > “Security” > “API Keys”

Your subdomain is 1234 if your ftrack URL is https://1234.ftrackapp.com

About FTrack#

ftrack, a Backlight business, is the creator of Emmy and Academy Award-winning team collaboration and media review platforms for the creative industries.

Action#

Create Embedding on xAI

Description:Create an embedding vector representation corresponding to the input text. [See the documentation](https://docs.x.ai/api/endpoints#create-embeddings)

Version:0.0.2

Key:x_ai-create-embeddings

View on GitHub

Action Code#

import app from "../../x_ai.app.mjs";

export default {
  key: "x_ai-create-embeddings",
  name: "Create Embedding",
  description: "Create an embedding vector representation corresponding to the input text. [See the documentation](https://docs.x.ai/api/endpoints#create-embeddings)",
  version: "0.0.2",
  type: "action",
  props: {
    app,
    embeddingModel: {
      propDefinition: [
        app,
        "embeddingModel",
      ],
    },
    input: {
      propDefinition: [
        app,
        "input",
      ],
    },
    dimensions: {
      propDefinition: [
        app,
        "dimensions",
      ],
    },
    encodingFormat: {
      propDefinition: [
        app,
        "encodingFormat",
      ],
    },
    user: {
      propDefinition: [
        app,
        "user",
      ],
    },
  },

  async run({ $ }) {
    const response = await this.app.createEmbeddings({
      $,
      data: {
        dimensions: this.dimensions,
        encoding_format: this.encodingFormat,
        input: this.input,
        model: this.embeddingModel,
        user: this.user,
      },
    });
    $.export("$summary", "Successfully created embedding");
    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
xAI	`app`	`app`	This component uses the xAI app.
Embedding Models	`embeddingModel`	`string`	Select a value from the drop down menu.
Input	`input`	`string[]`	Text input to be converted into an embedding
Dimensions	`dimensions`	`integer`	The number of dimensions the resulting output embeddings should have
Encoding Format	`encodingFormat`	`string`	Select a value from the drop down menu:`floatbase64`
User	`user`	`string`	A unique identifier representing your end-user, which can help xAI to monitor and detect abuse