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Classify Items Into Categories with Azure OpenAI Service API

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Classify Items Into Categories with the Azure OpenAI Service API
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Getting Started

Create a workflow to Classify Items Into Categories with the Azure OpenAI Service API. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

  1. 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
  2. Select a trigger to run your workflow on HTTP requests, schedules or app events
  3. Deploy the workflow
  4. Send a test event to validate your setup
  5. Turn on the trigger

Integrations

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Details

This is a pre-built, source-available component from Pipedream's GitHub repo. The component is 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.

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

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,
      };
    },
  },
};

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.
LabelPropTypeDescription
Azure OpenAI ServiceazureOpenAIappThis component uses the Azure OpenAI Service app.
Itemsitemsstring[]

Items to categorize

Categoriescategoriesstring[]

Categories to classify items into

Temperaturetemperaturestring

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.

Nninteger

How many completions to generate

Streamstreamboolean

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.

Stopstopstring

Up to 4 sequences where the API will stop generating further tokens.

Max TokensmaxTokensinteger

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 PenaltypresencePenaltystring

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 PenaltyfrequencyPenaltystring

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.

Useruserstring

A unique identifier representing your end-user, which can help Azure OpenAI to monitor and detect abuse.

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

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