Chat with Cohere API on Custom Interval from Schedule API

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Custom Interval from the Schedule API

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Chat with the Cohere API

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Getting Started#

This integration creates a workflow with a Schedule 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 Custom Interval trigger
1. Connect your Schedule account
2. Configure Cron Schedule
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#

Custom Interval on Schedule

Description:Trigger your workflow every N hours, minutes or seconds.

Version:0.0.2

Key:schedule-custom-interval

View on GitHub

Schedule Overview#

The Schedule app in Pipedream is a powerful tool that allows you to trigger workflows at regular intervals, ranging from every minute to once a year. This enables the automation of repetitive tasks and the scheduling of actions to occur without manual intervention. By leveraging this API, you can execute code, run integrations, and process data on a reliable schedule, all within Pipedream's serverless environment.

Trigger Code#

import schedule from "../../schedule.app.mjs";

export default {
  name: "Custom Interval",
  version: "0.0.2",
  key: "schedule-custom-interval",
  type: "source",
  description: "Trigger your workflow every N hours, minutes or seconds.",
  props: {
    schedule,
    cron: {
      propDefinition: [
        schedule,
        "cron",
      ],
    },
  },
};

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
Schedule	`schedule`	`app`	This component uses the Schedule app.
Cron Schedule	`cron`	`$.interface.timer`	Enter a cron expression

Trigger Authentication#

The Schedule API does not require authentication.

About Schedule#

Trigger workflows on an interval or cron schedule.

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