← Salesforce + Azure OpenAI Service integrations

Chat with Azure OpenAI Service API on New Updated Record (Instant, of Selectable Type) from Salesforce API

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New Updated Record (Instant, of Selectable Type) from the Salesforce API
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Getting Started

This integration creates a workflow with a Salesforce 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 Updated Record (Instant, of Selectable Type) trigger
    1. Connect your Salesforce account
    2. Configure timer
    3. Select a Object Type
    4. Optional- Select one or more Fields To Watch
  3. 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
  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 record of the selected type is updated. [See the documentation](https://sforce.co/3yPSJZy)
Version:0.2.0
Key:salesforce_rest_api-record-updated-instant

Salesforce Overview

The Salesforce (REST API) provides a powerful platform for creating and managing customer relationships with a wide array of features like data manipulation, querying, and complex automation. With Pipedream's serverless execution, you can create workflows that automate your sales processes, sync data with other platforms, enhance customer engagement, and trigger actions based on specific events. Dive into Salesforce data, streamline lead management, track customer interactions, and push or pull data to or from Salesforce seamlessly.

Trigger Code

import startCase from "lodash/startCase.js";
import common from "../common.mjs";
import constants from "../../common/constants.mjs";
const { salesforce } = common.props;

export default {
  ...common,
  type: "source",
  name: "New Updated Record (Instant, of Selectable Type)",
  key: "salesforce_rest_api-record-updated-instant",
  description: "Emit new event when a record of the selected type is updated. [See the documentation](https://sforce.co/3yPSJZy)",
  version: "0.2.0",
  props: {
    ...common.props,
    fields: {
      propDefinition: [
        salesforce,
        "field",
        ({ objectType }) => ({
          objectType,
          filter: ({ updateable }) => updateable,
        }),
      ],
      label: "Fields To Watch",
      type: "string[]",
      optional: true,
      description: "If specified, events will only be emitted if at least one of the selected fields is updated. This filter is only available when a webhook is created successfully.",
    },
  },
  methods: {
    ...common.methods,
    generateWebhookMeta(data) {
      const nameField = this.getNameField();
      const { New: newObject } = data.body;
      const {
        LastModifiedDate: lastModifiedDate,
        Id: id,
        [nameField]: name,
      } = newObject;
      const entityType = startCase(this.objectType);
      const summary = `${entityType} updated: ${name}`;
      const ts = Date.parse(lastModifiedDate);
      const compositeId = `${id}-${ts}`;
      return {
        id: compositeId,
        summary,
        ts,
      };
    },
    generateTimerMeta(item, fieldName) {
      const { objectType } = this;

      const {
        LastModifiedDate: lastModifiedDate,
        [fieldName]: name,
        Id: id,
      } = item;

      const entityType = startCase(objectType);
      const summary = `${entityType} updated: ${name}`;
      const ts = Date.parse(lastModifiedDate);
      return {
        id: `${id}-${ts}`,
        summary,
        ts,
      };
    },
    getEventType() {
      return "updated";
    },
    isEventRelevant(changedFields) {
      const { fields } = this;
      return fields?.length
        ? Object.keys(changedFields).some((key) => fields.includes(key))
        : true;
    },
    getChangedFields(body) {
      return Object.entries(body.New).filter(([
        key,
        value,
      ]) => {
        const oldValue = body.Old[key];
        return (
          value !== undefined
          && oldValue !== undefined
          && JSON.stringify(value) !== JSON.stringify(oldValue)
        );
      })
        .reduce((obj, [
          key,
          value,
        ]) => {
          obj[key] = {
            old: body.Old[key],
            new: value,
          };
          return obj;
        }, {});
    },
    processWebhookEvent(event) {
      const { body } = event;
      const changedFields = this.getChangedFields(body);
      if (this.isEventRelevant(changedFields)) {
        const meta = this.generateWebhookMeta(event);
        this.$emit({
          ...body,
          changedFields,
        }, meta);
      }
    },
    async processTimerEvent(eventData) {
      const {
        getNameField,
        getObjectTypeColumns,
        paginate,
        objectType,
        setLatestDateCovered,
        generateTimerMeta,
        $emit: emit,
      } = this;

      const {
        startTimestamp,
        endTimestamp,
      } = eventData;

      const fieldName = getNameField();
      const columns = getObjectTypeColumns();

      const events = await paginate({
        objectType,
        startTimestamp,
        endTimestamp,
        columns,
        dateFieldName: constants.FIELD_NAME.LAST_MODIFIED_DATE,
      });

      const [
        latestEvent,
      ] = events;

      if (latestEvent?.LastModifiedDate) {
        const latestDateCovered = new Date(latestEvent.LastModifiedDate);
        latestDateCovered.setSeconds(0);
        setLatestDateCovered(latestDateCovered.toISOString());
      }

      Array.from(events)
        .reverse()
        .forEach((item) => {
          const meta = generateTimerMeta(item, fieldName);
          emit(item, meta);
        });
    },
    async timerActivateHook() {
      const {
        objectType,
        getObjectTypeDescription,
        setObjectTypeColumns,
      } = this;

      const { fields } = await getObjectTypeDescription(objectType);
      const columns = fields.map(({ name }) => name);

      setObjectTypeColumns(columns);
    },
  },
};

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
SalesforcesalesforceappThis component uses the Salesforce app.
N/Adb$.service.dbThis component uses $.service.db to maintain state between executions.
N/Ahttp$.interface.httpThis component uses $.interface.http to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the run() method of the component.
timer$.interface.timer

The timer is only used as a fallback if instant event delivery (webhook) is not available.

Object TypeobjectTypestringSelect a value from the drop down menu.
Fields To Watchfieldsstring[]Select a value from the drop down menu.

Trigger Authentication

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

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

About Salesforce

Web services API for interacting with Salesforce

Action

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

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.

LabelPropTypeDescription
Azure OpenAI ServiceazureOpenAIappThis component uses the Azure OpenAI Service app.
User MessageuserMessagestring

The user messages to provide instructions to the assistant.

System InstructionssystemInstructionsstring

The system message helps set the behavior of the assistant. For example: "You are a helpful assistant."

Prior Message Historymessagesstring[]

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.

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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