Create Chatbot with gpt-trainer API on New SNS Messages from AWS API

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New SNS Messages from the AWS API

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Create Chatbot with the gpt-trainer API

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Getting Started#

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

Select this integration
Configure the New SNS Messages trigger
1. Connect your AWS account
2. Select a AWS Region
3. Optional- Select a SNS Topic
4. Optional- Configure SNS Topic Name
Configure the Create Chatbot action
1. Connect your gpt-trainer account
2. Configure Name
3. Optional- Configure Prompt
4. Optional- Configure Temperature
5. Optional- Select a Model
6. Optional- Select a Visibility
7. Optional- Configure Rate Limit Message
8. Optional- Configure Show Citations
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 SNS Messages on AWS

Description:Creates an SNS topic in your AWS account. Messages published to this topic are emitted from the Pipedream source.

Version:0.4.2

Key:aws-new-sns-messages

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/sns.mjs";
import { toSingleLineString } from "../../common/utils.mjs";
import commonSNS from "../../common/common-sns.mjs";
import { ConfigurationError } from "@pipedream/platform";

export default {
  ...base,
  key: "aws-new-sns-messages",
  name: "New SNS Messages",
  description: toSingleLineString(`
    Creates an SNS topic in your AWS account.
    Messages published to this topic are emitted from the Pipedream source.
  `),
  version: "0.4.2",
  type: "source",
  dedupe: "unique", // Dedupe on SNS message ID
  props: {
    ...base.props,
    topicArn: {
      ...commonSNS.props.topic,
      optional: true,
    },
    topic: {
      label: "SNS Topic Name",
      description: toSingleLineString(`
        **Pipedream will create an SNS topic with this name in your account**,
        converting it to a [valid SNS topic
        name](https://docs.aws.amazon.com/sns/latest/api/API_CreateTopic.html).
      `),
      type: "string",
      optional: true,
    },
  },
  methods: {
    ...base.methods,
    getTopicName() {
      return this.convertNameToValidSNSTopicName(this.topic);
    },
  },
  async run(event) {
    if (!this.topicArn && !this.topic) {
      throw new ConfigurationError("Must specify either an existing topic or a new topic name");
    }

    if (this._isSubscriptionConfirmationEvent(event)) {
      const { body } = event;
      const subscriptionArn = await this._confirmSubscription(body);
      this._setSubscriptionArn(subscriptionArn);
      return;
    }

    await this.processEvent(event);
  },
};

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.
SNS Topic	`topicArn`	`string`	Select a value from the drop down menu.
SNS Topic Name	`topic`	`string`	Pipedream will create an SNS topic with this name in your account, converting it to a valid SNS topic name.

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#

Create Chatbot on gpt-trainer

Description:Creates a new chatbot that belongs to the authenticated user. [See the documentation](https://guide.gpt-trainer.com/api-reference/chatbots/create)

Version:0.0.1

Key:gpt_trainer-create-chatbot

View on GitHub

gpt-trainer Overview#

The gpt-trainer API is a tool designed to train, run, and manage custom GPT-2 and GPT-3 models. It provides endpoints for submitting training data, starting the training process, and generating text from the trained model. With Pipedream's serverless integration platform, you can automate workflows that interact with the gpt-trainer API. You can trigger workflows using webhooks, schedule them, or even run them in response to events from other apps. Integrate the gpt-trainer API with other services on Pipedream to create powerful applications such as automated content creation, personalized messaging, or AI-driven data analysis.

Action Code#

import app from "../../gpt_trainer.app.mjs";

export default {
  key: "gpt_trainer-create-chatbot",
  name: "Create Chatbot",
  description: "Creates a new chatbot that belongs to the authenticated user. [See the documentation](https://guide.gpt-trainer.com/api-reference/chatbots/create)",
  version: "0.0.1",
  type: "action",
  props: {
    app,
    name: {
      type: "string",
      label: "Name",
      description: "The name of the chatbot",
    },
    prompt: {
      type: "string",
      label: "Prompt",
      description: "The prompt for the chatbot",
      optional: true,
    },
    temperature: {
      type: "string",
      label: "Temperature",
      description: "The temperature setting for the chatbot",
      optional: true,
    },
    model: {
      type: "string",
      label: "Model",
      description: "The model of the chatbot",
      optional: true,
      default: "gpt-3.5-turbo",
      options: [
        "gpt-3.5-turbo",
        "gpt-3.5-turbo-16k",
        "gpt-4",
      ],
    },
    visibility: {
      type: "string",
      label: "Visibility",
      description: "The visibility of the chatbot",
      optional: true,
      default: "private",
      options: [
        "public",
        "private",
        "hybrid",
      ],
    },
    rateLimitMessage: {
      type: "string",
      label: "Rate Limit Message",
      description: "The rate limit message for the chatbot. Eg. `Too many messages in a row.`",
      optional: true,
    },
    showCitations: {
      type: "boolean",
      label: "Show Citations",
      description: "Whether the chatbot should show citations",
      optional: true,
    },
  },
  methods: {
    parseFloat(value) {
      const parsed = parseFloat(value);
      return isFinite(parsed)
        ? parsed
        : 0;
    },
    createChatbot(args = {}) {
      return this.app.post({
        path: "/chatbot/create",
        ...args,
      });
    },
  },
  async run({ $ }) {
    const {
      createChatbot,
      parseFloat,
      name,
      prompt,
      temperature,
      model,
      visibility,
      rateLimitMessage,
      showCitations,
    } = this;

    const response = await createChatbot({
      $,
      data: {
        name,
        temperature: parseFloat(temperature),
        prompt,
        model,
        visibility,
        show_citations: showCitations,
        rate_limit_message: rateLimitMessage,
      },
    });

    $.export("$summary", `Successfully created chatbot with UUID \`${response.uuid}\`.`);
    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.

Label	Prop	Type	Description
gpt-trainer	`app`	`app`	This component uses the gpt-trainer app.
Name	`name`	`string`	The name of the chatbot
Prompt	`prompt`	`string`	The prompt for the chatbot
Temperature	`temperature`	`string`	The temperature setting for the chatbot
Model	`model`	`string`	Select a value from the drop down menu:`gpt-3.5-turbogpt-3.5-turbo-16kgpt-4`
Visibility	`visibility`	`string`	Select a value from the drop down menu:`publicprivatehybrid`
Rate Limit Message	`rateLimitMessage`	`string`	The rate limit message for the chatbot. Eg. `Too many messages in a row.`
Show Citations	`showCitations`	`boolean`	Whether the chatbot should show citations

Action Authentication#

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

About gpt-trainer#

Create a personalized AI chatbot using your own data

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Create Chatbot with gpt-trainer API on New SNS Messages from AWS API

Pipedream makes it easy to connect APIs for gpt-trainer, AWS and 2,400+ other apps remarkably fast.

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

Developers ♥ Pipedream

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