Create Embeddings with 302.AI API on New Requests (Payload Only) from HTTP / Webhook API

Pipedream makes it easy to connect APIs for 302.AI, HTTP / Webhook and 2,900+ other apps remarkably fast.

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New Requests (Payload Only) from the HTTP / Webhook API

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Create Embeddings with the 302.AI API

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Getting Started#

This integration creates a workflow with a HTTP / Webhook trigger and 302.AI action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Requests (Payload Only) trigger
1. Connect your HTTP / Webhook account
Configure the Create Embeddings action
1. Connect your 302.AI account
2. Select a Model
3. Configure Input
4. Optional- Configure User
5. Optional- Select a Encoding Format
6. Optional- Configure Dimensions
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 Requests (Payload Only) on HTTP / Webhook

Description:Get a URL and emit the HTTP body as an event on every request

Version:0.1.1

Key:http-new-requests-payload-only

View on GitHub

HTTP / Webhook Overview#

Build, test, and send HTTP requests without code using your Pipedream workflows. The HTTP / Webhook action is a tool to build HTTP requests with a Postman-like graphical interface.

Point and click HTTP requests

Define the target URL, HTTP verb, headers, query parameters, and payload body without writing custom code.

$A screenshot of Pipedream's HTTP Request Configuration interface with a GET request type selected. The request URL is set to 'https://api.openai.com/v1/models'. The 'Auth' tab is highlighted, indicating that authentication is required for this request. In the headers section, there are two headers configured: 'User-Agent' is set to 'pipedream/1', and 'Authorization' is set to 'Bearer {{openai_api_key}}', showing how the OpenAI account's API key is dynamically inserted into the headers to handle authentication automatically.$

Here's an example workflow that uses the HTTP / Webhook action to send an authenticated API request to OpenAI.

Focus on integrating, not authenticating

This action can also use your connected accounts with third-party APIs. Selecting an integrated app will automatically update the request’s headers to authenticate with the app properly, and even inject your token dynamically.

Pipedream integrates with thousands of APIs, but if you can’t find a Pipedream integration simply use Environment Variables in your request headers to authenticate with.

Compatible with no code actions or Node.js and Python

The HTTP/Webhook action exports HTTP response data for use in subsequent workflow steps, enabling easy data transformation, further API calls, database storage, and more.

Response data is available for both coded (Node.js, Python) and no-code steps within your workflow.

Trigger Code#

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

// Core HTTP component
// Returns a 200 OK response, emits the HTTP payload as an event
export default {
  key: "http-new-requests-payload-only",
  name: "New Requests (Payload Only)",
  // eslint-disable-next-line
  description: "Get a URL and emit the HTTP body as an event on every request",
  version: "0.1.1",
  type: "source",
  props: {
    // eslint-disable-next-line
    httpInterface: {
      type: "$.interface.http",
      customResponse: true,
    },
    http,
  },
  async run(event) {
    const { body } = event;
    this.httpInterface.respond({
      status: 200,
      body,
    });
    // Emit the HTTP payload
    this.$emit({
      body,
    });
  },
};

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
N/A	`httpInterface`	`$.interface.http`	This component uses `$.interface.http` to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the `run()` method of the component.
HTTP / Webhook	`http`	`app`	This component uses the HTTP / Webhook app.

Trigger Authentication#

The HTTP / Webhook API does not require authentication.

About HTTP / Webhook#

Get a unique URL where you can send HTTP or webhook requests

Action#

Create Embeddings on 302.AI

Description:Generate vector embeddings from text using the 302.AI Embeddings API. Useful for semantic search, clustering, and vector store indexing. [See documentation](https://doc.302.ai/147522048e0)

Version:0.0.1

Key:_302_ai-create-embeddings

View on GitHub

Action Code#

import { ConfigurationError } from "@pipedream/platform";
import _302_ai from "../../_302_ai.app.mjs";

export default {
  name: "Create Embeddings",
  version: "0.0.1",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: true,
  },
  key: "_302_ai-create-embeddings",
  description: "Generate vector embeddings from text using the 302.AI Embeddings API. Useful for semantic search, clustering, and vector store indexing. [See documentation](https://doc.302.ai/147522048e0)",
  type: "action",
  props: {
    _302_ai,
    modelId: {
      propDefinition: [
        _302_ai,
        "embeddingsModelId",
      ],
    },
    input: {
      label: "Input",
      description: "Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs in a single request, pass an array of strings or array of token arrays. Each input must not exceed 8192 tokens in length.",
      type: "string[]",
    },
    user: {
      label: "User",
      description: "A unique identifier representing your end-user, which can help monitor and detect abuse.",
      type: "string",
      optional: true,
    },
    encodingFormat: {
      label: "Encoding Format",
      description: "The format to return the embeddings in. Can be either `float` or `base64`.",
      type: "string",
      optional: true,
      options: [
        "float",
        "base64",
      ],
      default: "float",
    },
    dimensions: {
      label: "Dimensions",
      description: "The number of dimensions the resulting output embeddings should have. Only supported in some models.",
      type: "string",
      optional: true,
    },
  },
  async run({ $ }) {
    // Confirm no element is more than 8192 tokens in length
    for (const [
      i,
      element,
    ] of this.input.entries()) {
      if (element.length > 8192) {
        throw new ConfigurationError(`Element #${i} is more than 8192 tokens in length. Each input must not exceed 8192 tokens in length.`);
      }
    }

    const data = {
      model: this.modelId,
      input: this.input,
    };

    if (this.user) {
      data.user = this.user;
    }

    if (this.encodingFormat) {
      data.encoding_format = this.encodingFormat;
    }

    if (this.dimensions) {
      data.dimensions = parseInt(this.dimensions);
    }

    const response = await this._302_ai.createEmbeddings({
      $,
      data,
    });

    if (response) {
      $.export("$summary", "Successfully created embeddings");
    }

    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
302.AI	`_302_ai`	`app`	This component uses the 302.AI app.
Model	`modelId`	`string`	Select a value from the drop down menu.
Input	`input`	`string[]`	Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs in a single request, pass an array of strings or array of token arrays. Each input must not exceed 8192 tokens in length.
User	`user`	`string`	A unique identifier representing your end-user, which can help monitor and detect abuse.
Encoding Format	`encodingFormat`	`string`	Select a value from the drop down menu:`floatbase64`
Dimensions	`dimensions`	`string`	The number of dimensions the resulting output embeddings should have. Only supported in some models.

Action Authentication#

302.AI uses API keys for authentication. When you connect your 302.AI account, Pipedream securely stores the keys so you can easily authenticate to 302.AI APIs in both code and no-code steps.

About 302.AI#

302.AI is a pay-as-you-go enterprise AI resource hub that offers the latest and most comprehensive AI models and APIs on the market, along with a variety of ready-to-use online AI applications.

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