← 302.AI

Classify Items with 302.AI API

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Classify Items with the 302.AI API
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Getting Started

Create a workflow to Classify Items with the 302.AI API. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

  1. Configure the Classify Items action
    1. Connect your 302.AI account
    2. Select a Model
    3. Optional- Configure Max Tokens
    4. Optional- Configure Temperature
    5. Optional- Configure Top P
    6. Optional- Configure N
    7. Optional- Configure Stop
    8. Optional- Configure Presence Penalty
    9. Optional- Configure Frequency Penalty
    10. Optional- Configure User
    11. Configure info
    12. Configure Items
    13. Configure Categories
  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

Classify Items with 302.AI API on New Requests (Payload Only) from HTTP / Webhook API
HTTP / Webhook + 302.AI
 
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Classify Items with 302.AI API on New Submission from Typeform API
Typeform + 302.AI
 
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Classify Items with 302.AI API on New Submission (Instant) from Jotform API
Jotform + 302.AI
 
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Classify Items with 302.AI API on New Scheduled Tasks from Pipedream API
Pipedream + 302.AI
 
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Classify Items with 302.AI API on New Download Counts from npm API
npm + 302.AI
 
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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 on 302.AI
Description:Classify input items into predefined categories using 302.AI models. Perfect for tagging, segmentation, and automated organization. [See documentation](https://doc.302.ai/147522039e0)
Version:0.0.1
Key:_302_ai-classify-items

Code

import common from "../common/common-helper.mjs";

export default {
  ...common,
  name: "Classify Items",
  version: "0.0.1",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: true,
  },
  key: "_302_ai-classify-items",
  description: "Classify input items into predefined categories using 302.AI models. Perfect for tagging, segmentation, and automated organization. [See documentation](https://doc.302.ai/147522039e0)",
  type: "action",
  props: {
    ...common.props,
    info: {
      type: "alert",
      alertType: "info",
      content: `Provide a list of **items** and a list of **categories**. The output will contain an array of objects, each with properties \`item\` and \`category\`
        \nExample:
        \nIf **Categories** is \`["people", "pets"]\`, and **Items** is \`["dog", "George Washington"]\`
        \n The output will contain the following categorizations:
        \n \`[{"item":"dog","category":"pets"},{"item":"George Washington","category":"people"}]\`
      `,
    },
    items: {
      label: "Items",
      description: "Items to categorize",
      type: "string[]",
    },
    categories: {
      label: "Categories",
      description: "Categories to classify items into",
      type: "string[]",
    },
  },
  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 responses = response.choices?.map(({ message }) => message.content);
      const categorizations = [];
      for (const response of responses) {
        try {
          categorizations.push(JSON.parse(response));
        } catch (err) {
          console.log("Failed to parse output, assistant returned malformed JSON");
        }
      }
      const output = {
        messages,
      };
      const n = this.n
        ? parseInt(this.n)
        : 1;
      if (n > 1) {
        output.categorizations = categorizations;
      } else {
        output.categorizations = categorizations[0];
      }
      return output;
    },
  },
};

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
302.AI_302_aiappThis component uses the 302.AI app.
ModelmodelIdstringSelect a value from the drop down menu.
Max TokensmaxTokensstring

The maximum number of tokens to generate in the completion.

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.

Top PtopPstring

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

Nnstring

How many completions to generate for each prompt

Stopstopstring[]

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

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 monitor and detect abuse.

Itemsitemsstring[]

Items to categorize

Categoriescategoriesstring[]

Categories to classify items into

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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