Post Chat Completion with xAI API on Weekly schedule from Schedule API

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Trigger workflow on

Weekly schedule from the Schedule API

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Post Chat Completion with the xAI API

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Getting Started#

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

Select this integration
Configure the Weekly schedule trigger
1. Connect your Schedule account
2. Configure Cron Schedule
Configure the Post Chat Completion action
1. Connect your xAI account
2. Select a Model
3. Configure Message
4. Optional- Configure Frequency Penalty
5. Optional- Configure Log Probabilities
6. Optional- Configure Max Tokens
7. Optional- Configure Completion Number
8. Optional- Configure Presence Penalty
9. Optional- Configure Seed
10. Optional- Configure Stream
11. Optional- Configure Temperature
12. Optional- Configure Nucleus Sampling
13. Optional- Configure User
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#

Weekly schedule on Schedule

Description:Trigger your workflow on one or more days each week at a specific time (with timezone support).

Version:0.0.1

Key:schedule-weekly

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: "Weekly schedule",
  version: "0.0.1",
  key: "schedule-weekly",
  type: "source",
  description: "Trigger your workflow on one or more days each week at a specific time (with timezone support).",
  props: {
    schedule,
    cron: {
      propDefinition: [
        schedule,
        "cron",
      ],
      default: {
        cron: "0 10 * * 1",
      },
    },
  },
};

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#

Post Chat Completion on xAI

Description:Create a language model response for a chat conversation. [See the documentation](https://docs.x.ai/api/endpoints#chat-completions)

Version:0.0.2

Key:x_ai-post-chat-completion

View on GitHub

Action Code#

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

export default {
  key: "x_ai-post-chat-completion",
  name: "Post Chat Completion",
  description: "Create a language model response for a chat conversation. [See the documentation](https://docs.x.ai/api/endpoints#chat-completions)",
  version: "0.0.2",
  type: "action",
  props: {
    app,
    model: {
      propDefinition: [
        app,
        "model",
      ],
    },
    message: {
      propDefinition: [
        app,
        "message",
      ],
    },
    frequencyPenalty: {
      propDefinition: [
        app,
        "frequencyPenalty",
      ],
    },
    logprobs: {
      propDefinition: [
        app,
        "logprobs",
      ],
    },
    maxTokens: {
      propDefinition: [
        app,
        "maxTokens",
      ],
    },
    n: {
      propDefinition: [
        app,
        "n",
      ],
    },
    presencePenalty: {
      propDefinition: [
        app,
        "presencePenalty",
      ],
    },
    seed: {
      propDefinition: [
        app,
        "seed",
      ],
    },
    stream: {
      propDefinition: [
        app,
        "stream",
      ],
    },
    temperature: {
      propDefinition: [
        app,
        "temperature",
      ],
    },
    topP: {
      propDefinition: [
        app,
        "topP",
      ],
    },
    user: {
      propDefinition: [
        app,
        "user",
      ],
    },
  },

  async run({ $ }) {
    const response = await this.app.postChatCompletion({
      $,
      data: {
        model: this.model,
        messages: [
          {
            role: "user",
            content: this.message,
          },
        ],
        frequency_penalty: Number(this.frequencyPenalty),
        logprobs: this.logprobs,
        max_tokens: this.maxTokens,
        n: this.n,
        presence_penalty: Number(this.presencePenalty),
        seed: this.seed,
        stream: this.stream,
        temperature: Number(this.temperature),
        top_p: Number(this.topP),
        user: this.user,
      },
    });
    $.export("$summary", `Successfully sent message to the model '${this.model}'`);
    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
xAI	`app`	`app`	This component uses the xAI app.
Model	`model`	`string`	Select a value from the drop down menu.
Message	`message`	`string`	Message for the chat completion
Frequency Penalty	`frequencyPenalty`	`string`	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
Log Probabilities	`logprobs`	`boolean`	Include the log probabilities on the `logprobs` most likely output tokens, as well the chosen tokens
Max Tokens	`maxTokens`	`integer`	Limits the number of tokens that can be produced in the output
Completion Number	`n`	`integer`	Determines how many completion sequences to produce for each prompt. Be cautious with its use due to high token consumption
Presence Penalty	`presencePenalty`	`string`	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
Seed	`seed`	`integer`	If specified, our system will make a best effort to sample deterministically
Stream	`stream`	`boolean`	Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available
Temperature	`temperature`	`string`	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
Nucleus Sampling	`topP`	`string`	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
User	`user`	`string`	A unique identifier representing your end-user, which can help xAI to monitor and detect abuse