Chat with Cohere API on New Events (Instant) from Twilio SendGrid API

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New Events (Instant) from the Twilio SendGrid API

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Chat with the Cohere API

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Getting Started#

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

Select this integration
Configure the New Events (Instant) trigger
1. Connect your Twilio SendGrid account
2. Select one or more Event Types
Configure the Chat action
1. Connect your Cohere account
2. Configure Message
3. Optional- Select a Model
4. Optional- Configure Temperature
5. Optional- Configure Max Tokens
6. Optional- Configure K
7. Optional- Configure P
8. Optional- Configure Stop Sequences
9. Optional- Configure Frequency Penalty
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 Events (Instant) on Twilio SendGrid

Description:Emit new event when any of the specified SendGrid events is received

Version:0.0.5

Key:sendgrid-events

View on GitHub

Twilio SendGrid Overview#

The Twilio SendGrid API opens up a world of possibilities for email automation, enabling you to send emails efficiently and track their performance. With this API, you can programmatically create and send personalized email campaigns, manage contacts, and parse inbound emails for data extraction. When you harness the power of Pipedream, you can connect SendGrid to hundreds of other apps to automate workflows, such as triggering email notifications based on specific actions, syncing email stats with your analytics, or handling incoming emails to create tasks or tickets.

Trigger Code#

import common from "../common/http-based.mjs";
import deliveryEventTypes from "./delivery-event-types.mjs";
import engagementEventTypes from "./engagement-event-types.mjs";

export default {
  ...common,
  key: "sendgrid-events",
  name: "New Events (Instant)",
  description: "Emit new event when any of the specified SendGrid events is received",
  version: "0.0.5",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    eventTypes: {
      type: "string[]",
      label: "Event Types",
      description: "The type of events to listen to",
      options(context) {
        const { page } = context;
        if (page !== 0) {
          return {
            options: [],
          };
        }

        const options = [
          ...deliveryEventTypes,
          ...engagementEventTypes,
        ];
        return {
          options,
        };
      },
    },
  },
  methods: {
    ...common.methods,
    baseWebhookSettings() {
      // The list of events that a webhook can listen to. This method returns an
      // exhaustive list of all such flags disabled, and each event source can
      // then override the flags that are relevant to the event they handle.
      //
      // See the docs for more information:
      // https://sendgrid.com/docs/api-reference/
      const eventTypesData = [
        ...deliveryEventTypes,
        ...engagementEventTypes,
      ];
      return eventTypesData.reduce((accum, eventTypeData) => ({
        ...accum,
        [eventTypeData.value]: false,
      }), {});
    },
    webhookEventFlags() {
      return this.eventTypes.reduce((accum, eventType) => ({
        ...accum,
        [eventType]: true,
      }), {});
    },
    generateMeta(data) {
      const {
        event: eventType,
        sg_event_id: id,
        timestamp: ts,
      } = data;
      const summary = `New event: ${eventType}`;
      return {
        id,
        summary,
        ts,
      };
    },
  },
};

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
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Twilio SendGrid	`sendgrid`	`app`	This component uses the Twilio SendGrid 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.
Event Types	`eventTypes`	`string[]`	Select a value from the drop down menu.

Trigger Authentication#

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

About Twilio SendGrid#

Send marketing and transactional email through the Twilio SendGrid platform with the Email API, proprietary mail transfer agent, and infrastructure for scalable delivery.

Action#

Chat on Cohere

Description:Generates a text response to a user message. [See the documentation](https://docs.cohere.com/reference/chat)

Version:0.0.1

Key:cohere_platform-chat

View on GitHub

Cohere Overview#

The Cohere API enables the development of apps with advanced natural language understanding capabilities. Utilizing machine learning, it can help with tasks like text generation, summarization, sentiment analysis, and more. On Pipedream, you can seamlessly integrate Cohere's features into serverless workflows, triggering actions based on text input, processing large volumes of data, or even enhancing chatbots with more human-like responses.

Action Code#

import app from "../../cohere_platform.app.mjs";
import { clearObj } from "../../common/utils.mjs";

export default {
  key: "cohere_platform-chat",
  name: "Chat",
  version: "0.0.1",
  description: "Generates a text response to a user message. [See the documentation](https://docs.cohere.com/reference/chat)",
  type: "action",
  props: {
    app,
    message: {
      propDefinition: [
        app,
        "message",
      ],
    },
    model: {
      propDefinition: [
        app,
        "model",
      ],
    },
    temperature: {
      propDefinition: [
        app,
        "temperature",
      ],
    },
    maxTokens: {
      propDefinition: [
        app,
        "maxTokens",
      ],
    },
    k: {
      propDefinition: [
        app,
        "k",
      ],
    },
    p: {
      propDefinition: [
        app,
        "p",
      ],
    },
    stopSequences: {
      propDefinition: [
        app,
        "stopSequences",
      ],
    },
    frequencyPenalty: {
      propDefinition: [
        app,
        "frequencyPenalty",
      ],
    },
  },
  methods: {
    chat(data) {
      return this.app.client().chat(data);
    },
  },
  async run({ $ }) {
    const {
      chat,
      message,
      model,
      temperature,
      maxTokens,
      k,
      p,
      stopSequences,
      frequencyPenalty,

    } = this;
    const response = await chat(clearObj({
      message,
      model,
      ...(temperature && {
        temperature: parseFloat(temperature),
      }),
      maxTokens,
      k,
      ...(p && {
        p: parseFloat(p),
      }),
      stopSequences,
      ...(frequencyPenalty && {
        frequencyPenalty: parseFloat(frequencyPenalty),
      }),
    }));

    $.export("$summary", `The message was successfully responded with ID \`${response.response_id}\``);
    return 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
Cohere	`app`	`app`	This component uses the Cohere app.
Message	`message`	`string`	Text input for the model to respond to. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker, Private Deployments
Model	`model`	`string`	Select a value from the drop down menu:`command-r-pluscommand-rcommandcommand-nightlycommand-lightcommand-light-nightlyembed-english-v3.0embed-english-light-v3.0embed-multilingual-v3.0embed-multilingual-light-v3.0embed-english-v2.0embed-english-light-v2.0embed-multilingual-v2.0rerank-english-v3.0rerank-multilingual-v3.0rerank-english-v2.0rerank-multilingual-v2.0`
Temperature	`temperature`	`string`	Must be between 0 and 1.0 inclusive that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations. Randomness can be further maximized by increasing the value of the P parameter. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker, Private Deployments See Temperature for more details.
Max Tokens	`maxTokens`	`integer`	The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker, Private Deployments. See BPE Tokens for more details.
K	`k`	`integer`	Ensures only the top K most likely tokens are considered for generation at each step. Defaults to `0`, min value of `0`, max value of `500`. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker, Private Deployments
P	`p`	`string`	Ensures that only the most likely tokens, with total probability mass of P, are considered for generation at each step. If both K and P are enabled, P acts after K. Defaults to `0.75`. min value of `0.01`, max value of `0.99`. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker, Private Deployments
Stop Sequences	`stopSequences`	`string[]`	A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker, Private Deployments
Frequency Penalty	`frequencyPenalty`	`string`	Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker, Private Deployments