Chat with OpenAI (ChatGPT) API on Watch Folder from pCloud API

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Watch Folder from the pCloud API

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Chat with the OpenAI (ChatGPT) API

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Getting Started#

This integration creates a workflow with a pCloud 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 Watch Folder trigger
1. Connect your pCloud account
2. Configure Polling schedule
3. Select a Folder ID
4. Select a Folder Event
5. Optional- Configure Show Deleted?
Configure the Chat action
1. Connect your OpenAI (ChatGPT) account
2. Select a Model
3. Configure User Message
4. Optional- Configure System Instructions
5. Optional- Configure Prior Message History
6. Optional- Configure Max Tokens
7. Optional- Configure Temperature
8. Optional- Configure Top P
9. Optional- Configure N
10. Optional- Configure Stop
11. Optional- Configure Presence Penalty
12. Optional- Configure Frequency Penalty
13. Optional- Configure User
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#

Watch Folder on pCloud

Description:Emit new event when a file is created or modified in the specified folder.

Version:0.0.4

Key:pcloud-watch-folder

View on GitHub

pCloud Overview#

The pCloud API allows you to build a variety of products and applications that
integrate with the pCloud service. With the pCloud API, you can get access to a
wide range of features, including:

Storage for cloud-based file sharing
Automated synchronization of content
Content and account management
Automation of user-related tasks
Programmatic access to all pCloud services

With pCloud, you can easily build:

Web and mobile applications
Cloud-based backup & storage services
Content management solutions
User authentication & identities
File collaboration & sharing
Automated media streaming
Photo & video sharing services
Online Photo & Video Editor
Business process automation
Business intelligence & analytics

Trigger Code#

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

export default {
  key: "pcloud-watch-folder",
  name: "Watch Folder",
  description: "Emit new event when a file is created or modified in the specified folder.",
  version: "0.0.4",
  type: "source",
  dedupe: "unique",
  props: {
    pcloud,
    db: "$.service.db",
    timer: {
      label: "Polling schedule",
      description: "Pipedream polls pCloud for events on this schedule.",
      type: "$.interface.timer",
      default: {
        intervalSeconds: DEFAULT_POLLING_SOURCE_TIMER_INTERVAL,
      },
    },
    folderId: {
      propDefinition: [
        pcloud,
        "folderId",
      ],
      description: "Select a **Folder** to watch for changes. Alternatively, you can provide a custom *Folder ID*.",
    },
    event: {
      type: "string",
      label: "Folder Event",
      options: [
        "Created",
        "Modified",
      ],
      description: "Specify when to emit an event related to a given folder.",
      default: "Created",
    },
    showDeleted: {
      propDefinition: [
        pcloud,
        "showDeleted",
      ],
    },
  },
  hooks: {
    async deploy() {
      const files = [];
      const { contents } = await this.getContents();
      if (!contents) {
        return;
      }
      for (const folderItem of contents) {
        if (this.isRelevant(folderItem)) {
          files.push(folderItem);
          if (files.length == 10) {
            break;
          }
        }
      }
      files.forEach(this.emitpCloudEvent);
      this._setPreviousFiles(contents);
    },
  },
  methods: {
    _getPreviousFiles() {
      return this.db.get("previousFiles");
    },
    _setPreviousFiles(files) {
      const key = this.getFileKey();
      const previousFiles = files.filter((file) => file[key]).map((file) => ({
        [file[key]]: true,
      }));
      this.db.set("previousFiles", previousFiles);
    },
    getFileKey() {
      return this.event === "Created"
        ? "fileid"
        : "hash";
    },
    async getContents() {
      return this.pcloud._withRetries(() =>
        this.pcloud.listContents(
          this.folderId,
          false,
          this.showDeleted,
          false,
          false,
        ));
    },
    emitpCloudEvent(pCloudEvent) {
      const metadata = this.getEventData(pCloudEvent);
      this.$emit(pCloudEvent, metadata);
    },
    getEventData(pcloudEvent) {
      const key = this.getFileKey();
      return {
        id: pcloudEvent[key],
        summary: `${this.event} file "${pcloudEvent.name}"`,
        ts: Date.now(),
      };
    },
    isRelevant(folderItem, previousFiles = []) {
      const key = this.getFileKey();
      return !folderItem.isFolder && !previousFiles[folderItem[key]];
    },
  },
  async run() {
    const previousFiles = this._getPreviousFiles();
    const files = [];
    const { contents } = await this.getContents();
    if (!contents) {
      console.log("No data available, skipping iteration");
      return;
    }
    for (const folderItem of contents) {
      if (this.isRelevant(folderItem, previousFiles)) {
        files.push(folderItem);
      }
    }
    files.forEach(this.emitpCloudEvent);
    this._setPreviousFiles(contents);
  },
};

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
pCloud	`pcloud`	`app`	This component uses the pCloud app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling schedule	`timer`	`$.interface.timer`	Pipedream polls pCloud for events on this schedule.
Folder ID	`folderId`	`integer`	Select a value from the drop down menu.
Folder Event	`event`	`string`	Select a value from the drop down menu:`CreatedModified`
Show Deleted?	`showDeleted`	`boolean`	If true, deleted files and folders that can be undeleted will be displayed.

Trigger Authentication#

pCloud uses OAuth authentication. When you connect your pCloud account, Pipedream will open a popup window where you can sign into pCloud and grant Pipedream permission to connect to your account. Pipedream securely stores and automatically refreshes the OAuth tokens so you can easily authenticate any pCloud API.

Pipedream requests the following authorization scopes when you connect your account:

About pCloud#

Secure encrypted cloud storage

Action#

Chat on OpenAI (ChatGPT)

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

Version:0.1.2

Key:openai-chat

View on GitHub

OpenAI (ChatGPT) Overview#

The OpenAI API is a powerful tool that provides access to a range of
high-powered machine learning models. With the OpenAI API, developers can
create products, services, and tools that enable humanizing AI experiences, and
build applications that extend the power of human language.

Using the OpenAI API, developers can create language-driven applications such
as:

Natural language understanding and sentiment analysis
Text-based search
Question answering
Dialogue systems and conversation agents
Intelligent text completion
Text summarization
Text classification
Machine translation
Language generation
Multi-factor authentication
Anomaly detection
Text analysis

Action Code#

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

export default {
  ...common,
  name: "Chat",
  version: "0.1.2",
  key: "openai-chat",
  description: "The Chat API, using the `gpt-3.5-turbo` or `gpt-4` model. [See docs here](https://platform.openai.com/docs/api-reference/chat)",
  type: "action",
  props: {
    openai,
    modelId: {
      propDefinition: [
        openai,
        "chatCompletionModelId",
      ],
    },
    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,
    },
    ...common.props,
  },
  async run({ $ }) {
    const args = this._getChatArgs();
    const response = await this.openai.createChatCompletion({
      $,
      args,
    });

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

    const { messages } = args;
    return {
      original_messages: messages,
      original_messages_with_assistant_response: messages.concat(response.choices[0]?.message),
      ...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 (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
Model	`modelId`	`string`	Select a value from the drop down menu.
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.
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.