Chat with OpenAI Passthrough API on New Scheduled Tasks from AWS API

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New Scheduled Tasks from the AWS API

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Getting Started#

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

Select this integration
Configure the New Scheduled Tasks trigger
1. Connect your AWS account
2. Select a AWS Region
3. Optional- Configure Secret
Configure the Chat action
1. Connect your OpenAI Passthrough account
2. Select a Model ID
3. Configure User Message
4. Optional- Configure System Instructions
5. Optional- Configure Prior Message History
6. Optional- Configure Temperature
7. Optional- Configure Maximum Length
8. Optional- Configure Top P
9. Optional- Configure Frequency Penalty
10. Optional- Configure Presence Penalty
11. Optional- Select a Response Format
12. Optional- Configure JSON Schema
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 Scheduled Tasks on AWS

Description:Creates a Step Function State Machine to publish a message to an SNS topic at a specific timestamp. The SNS topic delivers the message to this Pipedream source, and the source emits it as a new event.

Version:0.4.2

Key:aws-new-scheduled-tasks

View on GitHub

AWS Overview#

The AWS API unlocks endless possibilities for automation with Pipedream. With this powerful combo, you can manage your AWS services and resources, automate deployment workflows, process data, and react to events across your AWS infrastructure. Pipedream offers a serverless platform for creating workflows triggered by various events that can execute AWS SDK functions, making it an efficient tool to integrate, automate, and orchestrate tasks across AWS services and other apps.

Trigger Code#

import base from "../common/scheduled.mjs";
import { toSingleLineString } from "../../common/utils.mjs";

export default {
  ...base,
  key: "aws-new-scheduled-tasks",
  name: "New Scheduled Tasks",
  description: toSingleLineString(`
    Creates a Step Function State Machine to publish a message
    to an SNS topic at a specific timestamp. The SNS topic delivers
    the message to this Pipedream source, and the source emits it as a new event.
  `),
  version: "0.4.2",
  type: "source",
  dedupe: "unique", // Dedupe on SNS message ID
  methods: {
    ...base.methods,
    getStateMachineDefinition() {
      const { PD_COMPONENT: componentId } = process.env;
      const topicArn = this.getTopicArn();
      return {
        Comment: `Task Scheduler for Pipedream component ${componentId}`,
        StartAt: "Wait",
        States: {
          Wait: {
            Comment: "Wait until specified timestamp",
            Type: "Wait",
            TimestampPath: "$.timestamp",
            Next: "SendMessageToSNS",
          },
          SendMessageToSNS: {
            Type: "Task",
            Resource: "arn:aws:states:::sns:publish",
            Parameters: {
              "TopicArn": topicArn,
              "Message.$": "$",
            },
            End: true,
          },
        },
      };
    },
    getStateMachinePermissions() {
      const topicArn = this.getTopicArn();
      const document = {
        Version: "2012-10-17",
        Statement: [
          {
            Effect: "Allow",
            Action: [
              "sns:Publish",
            ],
            Resource: [
              topicArn,
            ],
          },
        ],
      };
      const name = "publish-messages-to-pipedream-sns-topic";
      return {
        document,
        name,
      };
    },
  },
};

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
AWS	`aws`	`app`	This component uses the AWS app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
N/A	`http`	`$.interface.http`	This component uses `$.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.
AWS Region	`region`	`string`	Select a value from the drop down menu.
Secret	`secret`	`string`	Optional but recommended: if you enter a secret here, you must pass this value in the `secret` parameter of each HTTP POST request

Trigger Authentication#

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

Follow the AWS Instructions for creating an IAM user with an associated access and secret key.

As a best practice, attach the minimum set of IAM permissions necessary to perform the specific task in Pipedream. If your workflow only needs to perform a single API call, you should create a user and associate an IAM group / policy with permission to do only that task. You can create as many linked AWS accounts in Pipedream as you'd like.

Enter your access and secret key below.

About AWS#

Amazon Web Services (AWS) offers reliable, scalable, and inexpensive cloud computing services.

Action#

Chat on OpenAI Passthrough

Description:The Chat API, using the `gpt-3.5-turbo` or `gpt-4` model. [See the documentation](https://platform.openai.com/docs/api-reference/chat)

Version:0.0.2

Key:openai-passthrough-chat

View on GitHub

Action Code#

const CHAT_RESPONSE_FORMAT = {
  TEXT: {
    label: "Text",
    value: "text",
  },
  JSON_OBJECT: {
    label: "JSON Object",
    value: "json_object",
  },
  JSON_SCHEMA: {
    label: "JSON Schema",
    value: "json_schema",
  },
}

export default {
  name: "Chat",
  version: "0.0.2",
  key: "openai-passthrough-chat",
  description:
    "The Chat API, using the `gpt-3.5-turbo` or `gpt-4` model. [See the documentation](https://platform.openai.com/docs/api-reference/chat)",
  type: "action",
  props: {
    app: {
      type: "app",
      app: "openai_passthrough",
    },
    modelId: {
      label: "Model ID",
      type: "string",
      options: ["gpt-4.1", "o4-mini", "o3", "gpt-3.5-turbo"],
    },
    userMessage: {
      label: "User Message",
      type: "string",
      description:
        "The user messages provide instructions to the assistant. They can be generated by the end users of an application, or set by a developer as an instruction.",
    },
    systemInstructions: {
      label: "System Instructions",
      type: "string",
      description:
        'The system message helps set the behavior of the assistant. For example: "You are a helpful assistant." [See these docs](https://platform.openai.com/docs/guides/chat/instructing-chat-models) for tips on writing good instructions.',
      optional: true,
    },
    messages: {
      label: "Prior Message History",
      type: "string[]",
      description:
        "_Advanced_. Because [the models have no memory of past chat requests](https://platform.openai.com/docs/guides/chat/introduction), all relevant information must be supplied via the conversation. You can provide [an array of messages](https://platform.openai.com/docs/guides/chat/introduction) from prior conversations here. If this param is set, the action ignores the values passed to **System Instructions** and **Assistant Response**, appends the new **User Message** to the end of this array, and sends it to the API.",
      optional: true,
    },
    temperature: {
      type: "string",
      label: "Temperature",
      description:
        "Controls randomness: 0 is deterministic, higher values (up to 2) make output more random",
      default: "1",
      optional: true,
    },
    maxTokens: {
      type: "integer",
      label: "Maximum Length",
      description: "The maximum number of tokens to generate in the completion",
      optional: true,
    },
    topP: {
      type: "string",
      label: "Top P",
      description:
        "Alternative to temperature, nucleus sampling considers the results of the tokens with top_p probability mass",
      default: "1",
      optional: true,
    },
    frequencyPenalty: {
      type: "string",
      label: "Frequency Penalty",
      description:
        "Decreases the likelihood of repeating the same line verbatim",
      default: "0",
      optional: true,
    },
    presencePenalty: {
      type: "string",
      label: "Presence Penalty",
      description: "Increases the likelihood of talking about new topics",
      default: "0",
      optional: true,
    },
    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: Object.values(CHAT_RESPONSE_FORMAT),
      default: CHAT_RESPONSE_FORMAT.TEXT.value,
      optional: true,
    },
    jsonSchema: {
      type: "string",
      label: "JSON Schema",
      description:
        "If responseFormat is jsonSchema: Define the schema that the model's output must adhere to. [See the documentation here](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas).",
      optional: true,
    },
  },
  async run({ $ }) {
    const messages = []

    // Add system message if provided
    if (this.systemInstructions) {
      messages.push({
        role: "system",
        content: this.systemInstructions,
      })
    }

    // Use provided message history or create a new conversation
    if (this.messages && this.messages.length > 0) {
      messages.push(...this.messages)
    }

    // Add the user message
    messages.push({
      role: "user",
      content: this.userMessage,
    })

    // Prepare request parameters
    const params = {
      model: this.modelId,
      messages: messages,
    }

    // Add optional parameters if provided
    if (this.temperature !== undefined) params.temperature = +this.temperature
    if (this.maxTokens !== undefined) params.max_tokens = this.maxTokens
    if (this.topP !== undefined) params.top_p = +this.topP
    if (this.frequencyPenalty !== undefined)
      params.frequency_penalty = +this.frequencyPenalty
    if (this.presencePenalty !== undefined)
      params.presence_penalty = +this.presencePenalty

    // Handle response format if specified
    if (
      this.responseFormat === CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value &&
      this.jsonSchema
    ) {
      params.response_format = {
        type: "json_object",
        schema: JSON.parse(this.jsonSchema),
      }
    } else if (this.responseFormat === CHAT_RESPONSE_FORMAT.JSON_OBJECT.value) {
      params.response_format = { type: "json_object" }
    }

    // Call the OpenAI API using the Pipedream service
    const response = await $.services.openai.completions.create(params)

    // Extract and process the response
    const assistantMessage = response.choices[0].message.content

    // Export a summary for the user
    $.export("$summary", "Generated response from OpenAI")

    // Return the assistant message and the full response
    return {
      message: assistantMessage,
      raw_response: 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.

Label	Prop	Type	Description
OpenAI Passthrough	`app`	`app`	This component uses the OpenAI Passthrough app.
Model ID	`modelId`	`string`	Select a value from the drop down menu:`gpt-4.1o4-minio3gpt-3.5-turbo`
User Message	`userMessage`	`string`	The user messages provide instructions to the assistant. They can be generated by the end users of an application, or set by a developer as an instruction.
System Instructions	`systemInstructions`	`string`	The system message helps set the behavior of the assistant. For example: "You are a helpful assistant." See these docs for tips on writing good instructions.
Prior Message History	`messages`	`string[]`	Advanced. Because the models have no memory of past chat requests, all relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here. If this param is set, the action ignores the values passed to System Instructions and Assistant Response, appends the new User Message to the end of this array, and sends it to the API.
Temperature	`temperature`	`string`	Controls randomness: 0 is deterministic, higher values (up to 2) make output more random
Maximum Length	`maxTokens`	`integer`	The maximum number of tokens to generate in the completion
Top P	`topP`	`string`	Alternative to temperature, nucleus sampling considers the results of the tokens with top_p probability mass
Frequency Penalty	`frequencyPenalty`	`string`	Decreases the likelihood of repeating the same line verbatim
Presence Penalty	`presencePenalty`	`string`	Increases the likelihood of talking about new topics
Response Format	`responseFormat`	`string`	Select a value from the drop down menu:`{ "label": "Text", "value": "text" }{ "label": "JSON Object", "value": "json_object" }{ "label": "JSON Schema", "value": "json_schema" }`
JSON Schema	`jsonSchema`	`string`	If responseFormat is jsonSchema: Define the schema that the model's output must adhere to. See the documentation here

Action Authentication#

The OpenAI Passthrough API does not require authentication.

About OpenAI Passthrough#

OpenAI Passthrough

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Pipedream makes it easy to connect APIs for OpenAI Passthrough, AWS and 2,700+ other apps remarkably fast.

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

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