← Microsoft Teams + Azure OpenAI Service integrations

Classify Items Into Categories with Azure OpenAI Service API on New Chat from Microsoft Teams API

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New Chat from the Microsoft Teams API
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Classify Items Into Categories with the Azure OpenAI Service API
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Getting Started

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

  1. Select this integration
  2. Configure the New Chat trigger
    1. Connect your Microsoft Teams account
    2. Configure Polling interval
  3. Configure the Classify Items Into Categories action
    1. Connect your Azure OpenAI Service account
    2. Configure Items
    3. Configure Categories
    4. Optional- Configure Temperature
    5. Optional- Configure N
    6. Optional- Configure Stream
    7. Optional- Configure Stop
    8. Optional- Configure Max Tokens
    9. Optional- Configure Presence Penalty
    10. Optional- Configure Frequency Penalty
    11. Optional- Configure User
  4. Deploy the workflow
  5. Send a test event to validate your setup
  6. 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

Description:Emit new event when a new chat is created
Version:0.0.9
Key:microsoft_teams-new-chat

Microsoft Teams Overview

The Microsoft Teams API on Pipedream allows you to automate tasks, streamline communication, and integrate with other services to enhance the functionality of Teams as a collaboration hub. With this API, you can send messages to channels, orchestrate complex workflows based on Teams events, and manage Teams' settings programmatically.

Trigger Code

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

export default {
  ...base,
  key: "microsoft_teams-new-chat",
  name: "New Chat",
  description: "Emit new event when a new chat is created",
  version: "0.0.9",
  type: "source",
  dedupe: "unique",
  methods: {
    ...base.methods,
    async getResources(lastCreated) {
      return this.getNewPaginatedResources(
        this.microsoftTeams.listChats,
        {},
        lastCreated,
      );
    },
    generateMeta(chat) {
      return {
        id: chat.id,
        summary: chat.topic ?? `Chat ID ${chat.id}`,
        ts: Date.parse(chat.createdDateTime),
      };
    },
  },
};

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.
LabelPropTypeDescription
Microsoft TeamsmicrosoftTeamsappThis component uses the Microsoft Teams app.
N/Adb$.service.dbThis component uses $.service.db to maintain state between executions.
Polling intervaltimer$.interface.timer

Pipedream will poll the YouTube API on this schedule

Trigger Authentication

Microsoft Teams uses OAuth authentication. When you connect your Microsoft Teams account, Pipedream will open a popup window where you can sign into Microsoft Teams 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 Teams API.

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

User.Reademailoffline_accessopenidprofileChat.ReadChat.ReadWriteChatMessage.SendChannel.ReadBasic.AllChannelMessage.Read.AllChannelMessage.SendTeam.ReadBasic.AllSchedule.Read.All

About Microsoft Teams

Microsoft Teams has communities, events, chats, channels, meetings, storage, tasks, and calendars in one place.

Action

Description:Classify items into specific categories. [See the documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions)
Version:0.0.1
Key:azure_openai_service-classify-items-into-categories

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-helper.mjs";

export default {
  ...common,
  key: "azure_openai_service-classify-items-into-categories",
  name: "Classify Items Into Categories",
  description: "Classify items into specific categories. [See the documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions)",
  version: "0.0.1",
  type: "action",
  props: {
    azureOpenAI,
    items: {
      label: "Items",
      description: "Items to categorize",
      type: "string[]",
    },
    categories: {
      label: "Categories",
      description: "Categories to classify items into",
      type: "string[]",
    },
    ...common.props,
  },
  methods: {
    ...common.methods,
    systemInstructions() {
      return "Your goal is to categorize items into specific categories and produce ONLY JSON. The user will provide both the items and categories. Please only categorize items into the specific categories, and no others, and output ONLY JSON.";
    },
    outputFormat() {
      return "Please only categorize items into the specific categories, and no others. Output a valid JSON string — an array of objects, where each object has the following properties: item, category. Do not return any English text other than the JSON, either before or after the JSON. I need to parse the response as JSON, and parsing will fail if you return any English before or after the JSON";
    },
    userMessage() {
      return `Categorize each of the following items:\n\n${this.items.join("\n")}\n\ninto one of the following categories:\n\n${this.categories.join("\n")}\n\n${this.outputFormat()}}`;
    },
    summary() {
      return `Categorized ${this.items.length} items into ${this.categories.length} categories.`;
    },
    formatOutput({
      messages, response,
    }) {
      if (!messages || !response) {
        throw new Error("Invalid API output, please reach out to https://pipedream.com/support");
      }
      const assistantResponse = response.choices?.[0]?.message?.content;
      let categorizations = assistantResponse;
      try {
        const categorizationJSON = assistantResponse.substring(
          assistantResponse.indexOf("```json") + 7,
          assistantResponse.lastIndexOf("```"),
        ).trim();
        categorizations = JSON.parse(categorizationJSON);
      } catch (err) {
        console.log("Failed to parse output, assistant returned malformed JSON");
      }
      return {
        categorizations,
        messages,
      };
    },
  },
};

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.

LabelPropTypeDescription
Azure OpenAI ServiceazureOpenAIappThis component uses the Azure OpenAI Service app.
Itemsitemsstring[]

Items to categorize

Categoriescategoriesstring[]

Categories to classify items into

Temperaturetemperaturestring

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.

Nninteger

How many completions to generate

Streamstreamboolean

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.

Stopstopstring

Up to 4 sequences where the API will stop generating further tokens.

Max TokensmaxTokensinteger

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 PenaltypresencePenaltystring

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 PenaltyfrequencyPenaltystring

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.

Useruserstring

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.

About Azure OpenAI Service

Apply large language models and generative AI to a variety of use cases

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