Chat with Azure OpenAI Service API on New Item Updated (Instant) from Microsoft Excel API

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New Item Updated (Instant) from the Microsoft Excel API

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Getting Started#

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

Select this integration
Configure the New Item Updated (Instant) trigger
1. Connect your Microsoft Excel account
Configure the Chat action
1. Connect your Azure OpenAI Service 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 Item Updated (Instant) on Microsoft Excel

Description:Emit new event when an item is updated.

Version:0.0.2

Key:microsoft_excel-new-item-updated

View on GitHub

Microsoft Excel Overview#

The Microsoft Excel API on Pipedream allows you to automate spreadsheet tasks, manipulate data, and integrate Excel with other apps and services. Through Pipedream, you can create serverless workflows that react to events, perform operations on Excel files like reading, writing, updating cells, and managing worksheets. This API turns Excel into a powerful tool for data analysis, report generation, and task automation without manual intervention.

Trigger Code#

import moment from "moment";
import microsoftExcel from "../../microsoft_excel.app.mjs";
import sampleEmit from "./test-event.mjs";

export default {
  dedupe: "unique",
  key: "microsoft_excel-new-item-updated",
  name: "New Item Updated (Instant)",
  description: "Emit new event when an item is updated.",
  version: "0.0.2",
  type: "source",
  props: {
    microsoftExcel,
    http: {
      type: "$.interface.http",
      customResponse: true,
    },
    db: "$.service.db",
  },
  hooks: {
    async activate() {
      const response = await this.microsoftExcel.createHook({
        data: {
          changeType: "updated",
          notificationUrl: this.http.endpoint,
          resource: "me/drive/root",
          expirationDateTime: moment().add(30, "days"),
        },
      });

      this._setHookId(response.id);
    },
    async deactivate() {
      const id = this._getHookId("hookId");
      await this.microsoftExcel.deleteHook(id);
    },
  },
  methods: {
    emitEvent(body) {
      const meta = this.generateMeta(body);
      this.$emit(body, meta);
    },
    _getHookId() {
      return this.db.get("hookId");
    },
    _setHookId(hookId) {
      this.db.set("hookId", hookId);
    },
    generateMeta(body) {
      const { value } = body;

      const ts = new Date();
      return {
        id: `${value[0].tenantId}${ts}`,
        summary: `The item ${value[0].tenantId
          ? `with TenantId: ${value[0].tenantId} `
          : " "}was updated!`,
        ts: ts,
      };
    },
    async updateSubscription() {
      const hookId = this._getHookId();
      await this.microsoftExcel.updateSubscription({
        hookId,
        data: {
          expirationDateTime: moment().add(30, "days"),
        },
      });
    },
  },
  async run({
    body, query,
  }) {
    if (query.validationToken) {
      this.http.respond({
        status: 200,
        body: query.validationToken,
        headers: {
          "content-type": "text/plan",
        },
      });
      return;
    }

    this.emitEvent(body);
    await this.updateSubscription();
  },
  sampleEmit,
};

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
Microsoft Excel	`microsoftExcel`	`app`	This component uses the Microsoft Excel app.
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.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.

Trigger Authentication#

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

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

User.Reademailoffline_accessopenidprofileFiles.ReadWriteFiles.Read.AllUser.ReadCrossTenantInformation.ReadBasic.All

About Microsoft Excel#

You can use Microsoft Graph to allow web and mobile applications to read and modify Excel workbooks stored in OneDrive for Business, SharePoint site or Group drive.

Action#

Chat on Azure OpenAI Service

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 Service 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 Service	`azureOpenAI`	`app`	This component uses the Azure OpenAI Service 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.

Action Authentication#

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

Before you start, you'll need to deploy a model in the Azure OpenAI Service.

Once that's done, enter the name of your Azure OpenAI resource, the deployment name you chose when you deployed the model, and your Azure OpenAI key below.