Classify Items Into Categories with Azure OpenAI Service API on New Requests (Payload Only) from HTTP / Webhook API

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

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Classify Items Into Categories with the Azure OpenAI Service API

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Getting Started#

This integration creates a workflow with a HTTP / Webhook trigger and Azure OpenAI Service 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 Classify Items Into Categories action
1. Connect your Azure OpenAI Service account
2. Configure Items
3. Configure Categories
4. Optional- Configure Temperature
5. Optional- Configure N
6. Optional- Configure Stream
7. Optional- Configure Stop
8. Optional- Configure Max Tokens
9. Optional- Configure Presence Penalty
10. Optional- Configure Frequency Penalty
11. 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 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#

Classify Items Into Categories on Azure OpenAI Service

Description:Classify items into specific categories. [See the documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions)

Version:0.0.1

Key:azure_openai_service-classify-items-into-categories

View on GitHub

Azure OpenAI Service Overview#

The Azure OpenAI Service API provides access to powerful AI models that can understand and generate human-like text. With Pipedream, you can harness this capability to create a variety of serverless workflows, automating tasks like content creation, code generation, and language translation. By integrating the API with other apps on Pipedream, you can streamline processes, analyze sentiment, and even automate customer support.

Action Code#

import azureOpenAI from "../../azure_openai_service.app.mjs";
import common from "../common/common-helper.mjs";

export default {
  ...common,
  key: "azure_openai_service-classify-items-into-categories",
  name: "Classify Items Into Categories",
  description: "Classify items into specific categories. [See the documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions)",
  version: "0.0.1",
  type: "action",
  props: {
    azureOpenAI,
    items: {
      label: "Items",
      description: "Items to categorize",
      type: "string[]",
    },
    categories: {
      label: "Categories",
      description: "Categories to classify items into",
      type: "string[]",
    },
    ...common.props,
  },
  methods: {
    ...common.methods,
    systemInstructions() {
      return "Your goal is to categorize items into specific categories and produce ONLY JSON. The user will provide both the items and categories. Please only categorize items into the specific categories, and no others, and output ONLY JSON.";
    },
    outputFormat() {
      return "Please only categorize items into the specific categories, and no others. Output a valid JSON string — an array of objects, where each object has the following properties: item, category. Do not return any English text other than the JSON, either before or after the JSON. I need to parse the response as JSON, and parsing will fail if you return any English before or after the JSON";
    },
    userMessage() {
      return `Categorize each of the following items:\n\n${this.items.join("\n")}\n\ninto one of the following categories:\n\n${this.categories.join("\n")}\n\n${this.outputFormat()}}`;
    },
    summary() {
      return `Categorized ${this.items.length} items into ${this.categories.length} categories.`;
    },
    formatOutput({
      messages, response,
    }) {
      if (!messages || !response) {
        throw new Error("Invalid API output, please reach out to https://pipedream.com/support");
      }
      const assistantResponse = response.choices?.[0]?.message?.content;
      let categorizations = assistantResponse;
      try {
        const categorizationJSON = assistantResponse.substring(
          assistantResponse.indexOf("```json") + 7,
          assistantResponse.lastIndexOf("```"),
        ).trim();
        categorizations = JSON.parse(categorizationJSON);
      } catch (err) {
        console.log("Failed to parse output, assistant returned malformed JSON");
      }
      return {
        categorizations,
        messages,
      };
    },
  },
};

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
Azure OpenAI Service	`azureOpenAI`	`app`	This component uses the Azure OpenAI Service app.
Items	`items`	`string[]`	Items to categorize
Categories	`categories`	`string[]`	Categories to classify items into
Temperature	`temperature`	`string`	What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
N	`n`	`integer`	How many completions to generate
Stream	`stream`	`boolean`	If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.
Stop	`stop`	`string`	Up to 4 sequences where the API will stop generating further tokens.
Max Tokens	`maxTokens`	`integer`	The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens).
Presence Penalty	`presencePenalty`	`string`	Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
Frequency Penalty	`frequencyPenalty`	`string`	Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
User	`user`	`string`	A unique identifier representing your end-user, which can help Azure OpenAI to monitor and detect abuse.

Action Authentication#

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

Before you start, you'll need to deploy a model in the Azure OpenAI Service.

Once that's done, enter the name of your Azure OpenAI resource, the deployment name you chose when you deployed the model, and your Azure OpenAI key below.

About Azure OpenAI Service#

Apply large language models and generative AI to a variety of use cases