Chat with Azure OpenAI API on New Scraping Completed from WebScraper.IO API

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New Scraping Completed from the WebScraper.IO API

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Chat with the Azure OpenAI API

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Getting Started#

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

Select this integration
Configure the New Scraping Completed trigger
1. Connect your WebScraper.IO account
2. Configure timer
Configure the Chat action
1. Connect your Azure OpenAI account
2. Configure User Message
3. Optional- Configure System Instructions
4. Optional- Configure Prior Message History
5. Optional- Configure Temperature
6. Optional- Configure N
7. Optional- Configure Stream
8. Optional- Configure Stop
9. Optional- Configure Max Tokens
10. Optional- Configure Presence Penalty
11. Optional- Configure Frequency Penalty
12. 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#

New Scraping Completed on WebScraper.IO

Description:Emit new event when a page scraping job has completed. [See the docs here](https://webscraper.io/documentation/web-scraper-cloud/api)

Version:0.0.1

Key:webscraper_io-new-scraping-completed

View on GitHub

WebScraper.IO Overview#

The WebScraper.IO API allows you to programmatically perform web scraping tasks, extracting structured data from websites. With the API, you can automate the gathering of web content for analysis, monitoring, and integration with other data sources. In Pipedream, you can leverage this API to build workflows that process, analyze, and act on the data you scrape without writing code for backend infrastructure.

Trigger Code#

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

export default {
  ...common,
  key: "webscraper_io-new-scraping-completed",
  name: "New Scraping Completed",
  description: "Emit new event when a page scraping job has completed. [See the docs here](https://webscraper.io/documentation/web-scraper-cloud/api)",
  version: "0.0.1",
  type: "source",
  dedupe: "unique",
  methods: {
    ...common.methods,
    async emitHistoricalEvents({ limit }) {
      const jobs = await this.getScrapingJobs();
      if (!(jobs?.length > 0)) {
        return;
      }
      jobs.reverse().slice(0, limit)
        .forEach((job) => this.emitEvent(job));
    },
    isRelevant(job, previousIds) {
      return job.status === "finished" && !previousIds[job.id];
    },
    emitEvent(job) {
      const meta = this.generateMeta(job);
      this.$emit(job, meta);
    },
    generateMeta(job) {
      return {
        id: job.id,
        summary: job.sitemap_name,
        ts: job.time_created,
      };
    },
    async getScrapingJobs() {
      const jobs = [];
      const previousIds = this._getPreviousIds();

      const results = await this.webscraper.paginate(this.webscraper.getScrapingJobs);
      for (const job of results) {
        if (this.isRelevant(job, previousIds)) {
          previousIds[job.id] = true;
          jobs.push(job);
        }
      }

      this._setPreviousIds(previousIds);

      return jobs;
    },
  },
  async run() {
    const jobs = await this.getScrapingJobs();
    jobs.forEach((job) => this.emitEvent(job));
  },
};

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
WebScraper.IO	`webscraper`	`app`	This component uses the WebScraper.IO app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`

Trigger Authentication#

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

" To retrieve your API token,

Navigate to your Web Scraper account and sign in
Go to “Account Info” > “API settings”"

About WebScraper.IO#

Making web data extraction easy and accessible for everyone.

Action#

Chat on Azure OpenAI

Description:Create completions for chat messages with the GPT-35-Turbo and GPT-4 models. [See the documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions)

Version:0.0.1

Key:azure_openai_service-chat

View on GitHub

Azure OpenAI Overview#

The Azure OpenAI Service API provides access to powerful AI models that can understand and generate human-like text. With Pipedream, you can harness this capability to create a variety of serverless workflows, automating tasks like content creation, code generation, and language translation. By integrating the API with other apps on Pipedream, you can streamline processes, analyze sentiment, and even automate customer support.

Action Code#

import azureOpenAI from "../../azure_openai_service.app.mjs";
import common from "../common/common.mjs";

export default {
  ...common,
  key: "azure_openai_service-chat",
  name: "Chat",
  description: "Create completions for chat messages with the GPT-35-Turbo and GPT-4 models. [See the documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions)",
  version: "0.0.1",
  type: "action",
  props: {
    azureOpenAI,
    userMessage: {
      type: "string",
      label: "User Message",
      description: "The user messages to provide instructions to the assistant.",
    },
    systemInstructions: {
      label: "System Instructions",
      type: "string",
      description: "The system message helps set the behavior of the assistant. For example: \"You are a helpful assistant.\"",
      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 data = this._getChatArgs();
    const response = await this.azureOpenAI.createChatCompletion({
      data,
      $,
    });

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

    const { messages } = data;
    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
Azure OpenAI	`azureOpenAI`	`app`	This component uses the Azure OpenAI app.
User Message	`userMessage`	`string`	The user messages to provide instructions to the assistant.
System Instructions	`systemInstructions`	`string`	The system message helps set the behavior of the assistant. For example: "You are a helpful assistant."
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`	What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
N	`n`	`integer`	How many completions to generate
Stream	`stream`	`boolean`	If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.
Stop	`stop`	`string`	Up to 4 sequences where the API will stop generating further tokens.
Max Tokens	`maxTokens`	`integer`	The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens).
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 Azure OpenAI to monitor and detect abuse.