← Rejoiner + OpenAI (ChatGPT) integrations

Chat using Functions with OpenAI (ChatGPT) API on New Contact in List from Rejoiner API

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New Contact in List from the Rejoiner API
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Chat using Functions with the OpenAI (ChatGPT) API
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Getting Started

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

  1. Select this integration
  2. Configure the New Contact in List trigger
    1. Connect your Rejoiner account
    2. Configure timer
    3. Select a List ID
  3. Configure the Chat using Functions action
    1. Connect your OpenAI (ChatGPT) account
    2. Configure alert
    3. Select a Model
    4. Configure Chat Input
    5. Configure Functions
    6. Optional- Configure Instructions
    7. Optional- Select a Tool Choice
    8. Optional- Configure Parallel Function Calling
    9. Optional- Configure Previous Response ID
    10. Optional- Select a Truncation
    11. Optional- Select a Response Format
    12. Optional- Configure Skip This Step
  4. Deploy the workflow
  5. Send a test event to validate your setup
  6. 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

Description:Emit new event when a contact is added to the specified list. [See the documentation](https://docs.rejoiner.com/reference/retrieve-list-contacts).
Version:0.0.1
Key:rejoiner-new-contact-in-list

Trigger Code

import { DEFAULT_POLLING_SOURCE_TIMER_INTERVAL } from "@pipedream/platform";
import rejoiner from "../../rejoiner.app.mjs";

export default {
  key: "rejoiner-new-contact-in-list",
  name: "New Contact in List",
  description: "Emit new event when a contact is added to the specified list. [See the documentation](https://docs.rejoiner.com/reference/retrieve-list-contacts).",
  version: "0.0.1",
  type: "source",
  dedupe: "unique",
  props: {
    rejoiner,
    timer: {
      type: "$.interface.timer",
      default: {
        intervalSeconds: DEFAULT_POLLING_SOURCE_TIMER_INTERVAL,
      },
    },
    listId: {
      propDefinition: [
        rejoiner,
        "listId",
      ],
    },
  },
  methods: {
    generateMeta(contact) {
      return {
        id: contact.id,
        summary: contact.email,
        ts: Date.now(),
      };
    },
  },
  async run() {
    const results = this.rejoiner.paginate({
      fn: this.rejoiner.listListContacts,
      args: {
        listId: this.listId,
      },
    });

    for await (const item of results) {
      const contact = item.customer;
      const meta = this.generateMeta(contact);
      this.$emit(item, meta);
    }
  },
};

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.
LabelPropTypeDescription
RejoinerrejoinerappThis component uses the Rejoiner app.
timer$.interface.timer
List IDlistIdstringSelect a value from the drop down menu.

Trigger Authentication

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

About Rejoiner

Retention Marketing Software for Ecommerce

Action

Description:Chat with your models and allow them to invoke functions. Optionally, you can build and invoke workflows as functions. [See the documentation](https://platform.openai.com/docs/guides/function-calling)
Version:0.0.4
Key:openai-chat-using-functions

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 openai from "../../openai.app.mjs";
import common from "../common/common.mjs";
import constants from "../../common/constants.mjs";

export default {
  ...common,
  name: "Chat using Functions",
  version: "0.0.4",
  key: "openai-chat-using-functions",
  description: "Chat with your models and allow them to invoke functions. Optionally, you can build and invoke workflows as functions. [See the documentation](https://platform.openai.com/docs/guides/function-calling)",
  type: "action",
  props: {
    openai,
    alert: {
      type: "alert",
      alertType: "info",
      content: "Provide function names and parameters, and the model will either answer the question directly or decide to invoke one of the functions, returning a function call that adheres to your specified schema. Add a custom code step that includes all available functions which can be invoked based on the model's response - [you can even build an entire workflow as a function](https://pipedream.com/docs/workflows/building-workflows/code/nodejs/#invoke-another-workflow)! Once the appropriate function or workflow is executed, continue the overall execution or pass the result back to the model for further analysis. For more details, [see this guide](https://platform.openai.com/docs/guides/function-calling?api-mode=responses#overview) and this [walkthrough](https://pipedream.com/blog/introducing-enhanced-openai-chat-actions-for-pipedream/#using-pipedream-workflows-as-functions).",
    },
    modelId: {
      propDefinition: [
        openai,
        "chatCompletionModelId",
      ],
    },
    input: {
      type: "string",
      label: "Chat Input",
      description: "Text inputs to the model used to generate a response",
    },
    functions: {
      type: "string",
      label: "Functions",
      description: "A valid JSON array of functions using OpenAI's function schema definition. [See guide here](https://platform.openai.com/docs/guides/function-calling?api-mode=responses&example=search-knowledge-base#defining-functions).",
      default:
`[
  {
    "type": "function",
    "name": "your_function_name",
    "description": "Details on when and how to use the function",
    "strict": true,
    "parameters": {
      "type": "object",
      "properties": {
        "property_name": {
          "type": "property_type",
          "description": "A description for this property"
        },
        "another_property_name": {
          "type": "property_type",
          "description": "A description for this property"
        }
      },
      "required": [
        "list",
        "of",
        "required",
        "properties",
        "for",
        "this",
        "object"
      ],
      "additionalProperties": false
    }
  }
]`,
    },
    instructions: {
      type: "string",
      label: "Instructions",
      description: "Inserts a system (or developer) message as the first item in the model's context",
      optional: true,
    },
    toolChoice: {
      type: "string",
      label: "Tool Choice",
      description: "- **auto**: The model decides whether and how many functions to call.\n- **required**: The model must call one or more functions.\n- **function_name**: Enter a custom expression to force the model to call this specific function.",
      optional: true,
      default: "auto",
      options: [
        "auto",
        "required",
      ],
    },
    parallelToolCalls: {
      type: "boolean",
      label: "Parallel Function Calling",
      description: "Allow or prevent the model to call multiple functions in a single turn",
      optional: true,
      default: true,
    },
    previousResponseId: {
      type: "string",
      label: "Previous Response ID",
      description: "The unique ID of the previous response to the model. Use this to create multi-turn conversations",
      optional: true,
    },
    truncation: {
      type: "string",
      label: "Truncation",
      description: "Specifies the truncation mode for the response if it's larger than the context window size",
      optional: true,
      default: "auto",
      options: [
        "auto",
        "disabled",
      ],
    },
    responseFormat: {
      type: "string",
      label: "Response Format",
      description: "- **Text**: Returns unstructured text output.\n- **JSON Schema**: Enables you to define a [specific structure for the model's output using a JSON schema](https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses).",
      options: [
        "text",
        "json_schema",
      ],
      default: "text",
      optional: true,
      reloadProps: true,
    },
    skipThisStep: {
      type: "boolean",
      label: "Skip This Step",
      description: "Pass in a boolean custom expression to skip this step's execution at runtime",
      optional: true,
      default: false,
    },
  },
  additionalProps() {
    const {
      modelId,
      responseFormat,
    } = this;
    const props = {};

    if (this.openai.isReasoningModel(modelId)) {
      props.reasoningEffort = {
        type: "string",
        label: "Reasoning Effort",
        description: "Constrains effort on reasoning for reasoning models",
        optional: true,
        options: [
          "low",
          "medium",
          "high",
        ],
      };

      // aparrently not supported yet as of 12/march/2025
      // props.generateSummary = {
      //   type: "string",
      //   label: "Generate Reasoning Summary",
      //   description: "A summary of the reasoning performed by the model",
      //   optional: true,
      //   options: [
      //     "concise",
      //     "detailed",
      //   ],
      // };
    }

    if (responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) {
      props.jsonSchema = {
        type: "string",
        label: "JSON Schema",
        description: "Define the schema that the model's output must adhere to. [Generate one here](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas).",
      };
    }

    return props;
  },
  methods: {
    ...common.methods,
  },
  async run({ $ }) {
    if (this.skipThisStep) {
      $.export("$summary", "Step execution skipped");
      return;
    }

    const data = {
      model: this.modelId,
      input: this.input,
      instructions: this.instructions,
      previous_response_id: this.previousResponseId,
      truncation: this.truncation,
      parallel_tool_calls: this.parallelToolCalls,
      tools: [],
    };

    let functions = this.functions;
    if (typeof functions === "string") {
      try {
        functions = JSON.parse(functions);
      } catch (error) {
        throw new Error("Invalid JSON format in the provided Functions Schema");
      }
    }

    if (Array.isArray(functions)) {
      data.tools.push(...functions);
    } else {
      data.tools.push(functions);
    }

    if (this.toolChoice) {
      if (this.toolChoice === "auto" || this.toolChoice === "required") {
        data.tool_choice = this.toolChoice;
      } else {
        data.tool_choice = {
          type: "function",
          name: this.toolChoice,
        };
      }
    }

    if (this.openai.isReasoningModel(this.modelId) && this.reasoningEffort) {
      data.reasoning = {
        ...data.reasoning,
        effort: this.reasoningEffort,
      };
    }

    if (this.openai.isReasoningModel(this.modelId) && this.generateSummary) {
      data.reasoning = {
        ...data.reasoning,
        generate_summary: this.generateSummary,
      };
    }

    if (this.responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) {
      try {
        data.text = {
          format: {
            type: this.responseFormat,
            ...JSON.parse(this.jsonSchema),
          },
        };
      } catch (error) {
        throw new Error("Invalid JSON format in the provided JSON Schema");
      }
    }

    const response = await this.openai.responses({
      $,
      data,
    });

    if (response) {
      $.export("$summary", `Successfully sent chat with id ${response.id}`);
      $.export("chat_responses", response.output);
    }

    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.

LabelPropTypeDescription
OpenAI (ChatGPT)openaiappThis component uses the OpenAI (ChatGPT) app.
ModelmodelIdstringSelect a value from the drop down menu.
Chat Inputinputstring

Text inputs to the model used to generate a response

Functionsfunctionsstring

A valid JSON array of functions using OpenAI's function schema definition. See guide here

Instructionsinstructionsstring

Inserts a system (or developer) message as the first item in the model's context

Tool ChoicetoolChoicestringSelect a value from the drop down menu:autorequired
Parallel Function CallingparallelToolCallsboolean

Allow or prevent the model to call multiple functions in a single turn

Previous Response IDpreviousResponseIdstring

The unique ID of the previous response to the model. Use this to create multi-turn conversations

TruncationtruncationstringSelect a value from the drop down menu:autodisabled
Response FormatresponseFormatstringSelect a value from the drop down menu:textjson_schema
Skip This StepskipThisStepboolean

Pass in a boolean custom expression to skip this step's execution at runtime

Action Authentication

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

About OpenAI (ChatGPT)

OpenAI is an AI research and deployment company with the mission to ensure that artificial general intelligence benefits all of humanity. They are the makers of popular models like ChatGPT, DALL-E, and Whisper.

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