Classify Items into Categories with OpenAI (ChatGPT) API on New Check-In Created from Badger Maps API

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New Check-In Created from the Badger Maps API

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Classify Items into Categories with the OpenAI (ChatGPT) API

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Getting Started#

This integration creates a workflow with a Badger Maps trigger and OpenAI (ChatGPT) action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Check-In Created trigger
1. Connect your Badger Maps account
2. Configure Polling schedule
3. Select a Account ID
Configure the Classify Items into Categories action
1. Connect your OpenAI (ChatGPT) account
2. Optional- Configure Max Tokens
3. Optional- Configure Temperature
4. Optional- Configure Top P
5. Optional- Configure N
6. Optional- Configure Stop
7. Optional- Configure Presence Penalty
8. Optional- Configure Frequency Penalty
9. Optional- Configure User
10. Configure Items
11. Configure Categories
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 Check-In Created on Badger Maps

Description:Emit new event each time a new check-in is created.

Version:0.0.1

Key:badger_maps-new-check-in-created

View on GitHub

Badger Maps Overview#

The Badger Maps API offers interaction with a platform designed for field sales; it allows you to optimize routes, manage territories, and explore customer locations. Combining the Badger Maps API with Pipedream's capabilities enables the automation of sales processes, integration with CRM systems, and enhancement of data analytics. You can build workflows that respond to events in real-time, sync data across multiple platforms, and create custom triggers and actions that cater specifically to your sales strategies.

Trigger Code#

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

export default {
  ...common,
  key: "badger_maps-new-check-in-created",
  name: "New Check-In Created",
  description: "Emit new event each time a new check-in is created.",
  type: "source",
  version: "0.0.1",
  dedupe: "unique",
  props: {
    ...common.props,
    accountId: {
      propDefinition: [
        common.props.app,
        "accountId",
      ],
    },
  },
  methods: {
    ...common.methods,
    getResourceFn() {
      return this.app.listCheckIns;
    },
    getResourceFnArgs() {
      return {
        params: {
          customer_id: this.accountId,
        },
      };
    },
    generateMeta(resource) {
      return {
        id: resource.id,
        summary: `New check-in ${resource.id}`,
        ts: Date.parse(resource.log_datetime),
      };
    },
  },
};

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
Badger Maps	`app`	`app`	This component uses the Badger Maps 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
Account ID	`accountId`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

To connect Pipedream to Badger Maps, first make sure you've requested API access from the Badger Maps Support team. Once they've enabled API access,

Sign in to your Badger Maps account
Navigate to your "Settings" at the bottom left
Copy your "Developer API Key" and paste it below

About Badger Maps#

The #1 Route Planner for Field Sales.

Action#

Classify Items into Categories on OpenAI (ChatGPT)

Description:Classify items into specific categories using the Chat API

Version:0.0.10

Key:openai-classify-items-into-categories

View on GitHub

OpenAI (ChatGPT) Overview#

OpenAI provides a suite of powerful AI models through its API, enabling developers to integrate advanced natural language processing and generative capabilities into their applications. Here’s an overview of the services offered by OpenAI's API:

Use Python or Node.js code to make fully authenticated API requests with your OpenAI account:

Action Code#

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

export default {
  ...common,
  name: "Classify Items into Categories",
  version: "0.0.10",
  key: "openai-classify-items-into-categories",
  description: "Classify items into specific categories using the Chat API",
  type: "action",
  props: {
    ...common.props,
    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 assistantResponse = response.choices?.[0]?.message?.content;
      let categorizations = assistantResponse;
      try {
        categorizations = JSON.parse(assistantResponse);
      } 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
OpenAI (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
Max Tokens	`maxTokens`	`integer`	The maximum number of tokens to generate in the completion.
Temperature	`temperature`	`string`	Optional. What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
Top P	`topP`	`string`	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. We generally recommend altering this or `temperature` but not both.
N	`n`	`integer`	How many completions to generate for each prompt
Stop	`stop`	`string[]`	Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
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 OpenAI to monitor and detect abuse. Learn more here.
Items	`items`	`string[]`	Items to categorize
Categories	`categories`	`string[]`	Categories to classify items into