← Drata + OpenAI (ChatGPT) integrations

Chat with OpenAI (ChatGPT) API on Employee Terminated from Drata API

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Employee Terminated from the Drata API
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Getting Started

This integration creates a workflow with a Drata trigger and OpenAI (ChatGPT) 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 Employee Terminated trigger
    1. Connect your Drata account
    2. Configure timer
  3. 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 Images
    7. Optional- Select a Response Format
    8. Optional- Configure Max Tokens
    9. Optional- Configure Temperature
    10. Optional- Configure Top P
    11. Optional- Configure N
    12. Optional- Configure Stop
    13. Optional- Configure Presence Penalty
    14. Optional- Configure Frequency Penalty
    15. 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 a new event when an employee is terminated. [See the documentation](https://developers.drata.com/docs/openapi/reference/operation/PersonnelPublicController_listPersonnel/).
Version:0.0.3
Key:drata-terminated-employee

Drata Overview

Drata is a compliance automation platform. Drata’s platform is built by compliance and security experts so you don’t have to be one. With 75+ native integrations, you can easily connect your tech stack and automate evidence collection and testing.

Pipedream customers get 25% off Drata, with implementation fees waived. Visit https://drata.com/partner/pipedream for more information.

Trigger Code

import drata from "../../drata.app.mjs";
import constants from "../../common/constants.mjs";
import { DEFAULT_POLLING_SOURCE_TIMER_INTERVAL } from "@pipedream/platform";

const docsLink = "https://developers.drata.com/docs/openapi/reference/operation/PersonnelPublicController_listPersonnel/";

export default {
  key: "drata-terminated-employee",
  name: "Employee Terminated",
  description: `Emit a new event when an employee is terminated. [See the documentation](${docsLink}).`,
  type: "source",
  version: "0.0.3",
  dedupe: "unique",
  props: {
    drata,
    db: "$.service.db",
    timer: {
      type: "$.interface.timer",
      default: {
        intervalSeconds: DEFAULT_POLLING_SOURCE_TIMER_INTERVAL,
      },
    },
  },
  hooks: {
    async deploy() {
      const { data: lastCurrentEmployees } = await this.drata.listPersonnel({
        paginate: true,
        params: {
          sort: constants.SORT_CRITERIA.START_DATE, // this endpoint does not support sorting by creation date
          employmentStatuses: [
            "CURRENT_EMPLOYEE",
            "CURRENT_CONTRACTOR",
          ],
        },
      });

      this._setLastCurrentEmployees(lastCurrentEmployees.map((personnel) => personnel.id));

      const response = await this.drata.listPersonnel({
        params: {
          limit: constants.DEPLOY_LIMIT,
          sort: constants.SORT_CRITERIA.SEPARATION_DATE,
          sortDir: "DESC",
          employmentStatuses: [
            "FORMER_EMPLOYEE",
            "FORMER_CONTRACTOR",
            "SPECIAL_FORMER_EMPLOYEE",
            "SPECIAL_FORMER_CONTRACTOR",
          ],
        },
      });

      for (const personnel of response.data.reverse()) {
        this.$emit(personnel, {
          id: personnel.id,
          summary: `Historical terminated employee added event: ${this.drata.getPersonnelName(personnel)}`,
          ts: personnel.updatedAt,
        });
      }
    },
  },
  methods: {
    _getLastCurrentEmployees() {
      return new Set(this.db.get("lastCurrentEmployees"));
    },
    _setLastCurrentEmployees(lastCurrentEmployees) {
      this.db.set("lastCurrentEmployees", Array.from(lastCurrentEmployees));
    },
    getDifference(setA, setB) {
      return new Set(
        Array.from(setA).filter((element) => !setB.has(element)),
      );
    },
    async getTerminatedEmployees(terminatedEmployees) {
      if (!Array.isArray(terminatedEmployees)) {
        terminatedEmployees = Array.from(terminatedEmployees);
      }
      const employees = await Promise.all(
        terminatedEmployees.map((id) => this.drata.getPersonnelById({
          id,
        })),
      );
      return employees.filter(({ employmentStatus }) => employmentStatus.includes("FORMER"));
    },
    addNewEmployees(newEmployees, lastCurrentEmployees) {
      newEmployees.forEach((id) => lastCurrentEmployees.add(id));
    },
  },
  async run() {
    const lastCurrentEmployees = this._getLastCurrentEmployees();

    const response = await this.drata.listPersonnel({
      paginate: true,
      params: {
        limit: constants.PAGINATION_LIMIT,
        sort: constants.SORT_CRITERIA.START_DATE,
        employmentStatuses: [
          "CURRENT_EMPLOYEE",
          "CURRENT_CONTRACTOR",
        ],
      },
    });

    const currentEmployees = new Set(response.data.map((personnel) => personnel.id));
    const terminatedEmployees = this.getDifference(lastCurrentEmployees, currentEmployees);
    const newEmployees = this.getDifference(currentEmployees, lastCurrentEmployees);

    const terminated = await this.getTerminatedEmployees(Array.from(terminatedEmployees));
    for (const employee of terminated) {
      lastCurrentEmployees.delete(employee.id);
      this.$emit(employee, {
        id: employee.id,
        summary: `Employee terminated: ${this.drata.getPersonnelName(employee)}`,
        ts: employee.updatedAt,
      });
    }

    this.addNewEmployees(newEmployees, lastCurrentEmployees);
    this._setLastCurrentEmployees(lastCurrentEmployees);
  },
};

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
DratadrataappThis component uses the Drata app.
N/Adb$.service.dbThis component uses $.service.db to maintain state between executions.
timer$.interface.timer

Trigger Authentication

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

Make sure you're logged into the Drata UI, then see these docs to create a Drata API key. Enter it below.

About Drata

Drata automates your compliance journey from start to audit-ready and beyond and provides support from the security and compliance experts who built it.

Action

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:0.1.7
Key:openai-chat

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

export default {
  ...common,
  name: "Chat",
  version: "0.1.7",
  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,
    },
    images: {
      label: "Images",
      type: "string[]",
      description: "Provide one or more images to [OpenAI's vision model](https://platform.openai.com/docs/guides/vision). Accepts URLs or base64 encoded strings. Compatible with the `gpt4-vision-preview model`",
      optional: true,
    },
    responseFormat: {
      type: "string",
      label: "Response Format",
      description: "Specify the format that the model must output. [Setting to `json_object` guarantees the message the model generates is valid JSON](https://platform.openai.com/docs/api-reference/chat/create#chat-create-response_format). Defaults to `text`",
      options: [
        "text",
        "json_object",
      ],
      optional: true,
      default: "text",
    },
    ...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.

LabelPropTypeDescription
OpenAI (ChatGPT)openaiappThis component uses the OpenAI (ChatGPT) app.
ModelmodelIdstringSelect a value from the drop down menu.
User MessageuserMessagestring

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 InstructionssystemInstructionsstring

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 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.

Imagesimagesstring[]

Provide one or more images to OpenAI's vision model. Accepts URLs or base64 encoded strings. Compatible with the gpt4-vision-preview model

Response FormatresponseFormatstringSelect a value from the drop down menu:textjson_object
Max TokensmaxTokensinteger

The maximum number of tokens to generate in the completion.

Temperaturetemperaturestring

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 PtopPstring

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.

Nninteger

How many completions to generate for each prompt

Stopstopstring[]

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

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 OpenAI to monitor and detect abuse. Learn more here.

Action Authentication

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

About OpenAI (ChatGPT)

OpenAI is an AI research and deployment company with the mission to ensure that artificial general intelligence benefits all of humanity. They are the makers of popular models like ChatGPT, DALL-E, and Whisper.

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