Chat with Google PaLM API on Monthly Schedule from Schedule API

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Trigger workflow on

Monthly Schedule from the Schedule API

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Chat with the Google PaLM API

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Getting Started#

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

Select this integration
Configure the Monthly Schedule trigger
1. Connect your Schedule account
2. Configure Cron Schedule
Configure the Chat action
1. Connect your Google PaLM account
2. Configure Prompt Text
3. Optional- Configure Previous Messages
4. Optional- Configure Temperature
5. Optional- Configure Context
6. Optional- Configure Candidate Count
7. Optional- Configure Top K
8. Optional- Configure Top P
9. Optional- Configure Max Output Tokens
10. Optional- Configure Stop Sequences
11. Optional- Select one or more Harm Categories
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#

Monthly Schedule on Schedule

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

Version:0.0.2

Key:schedule-monthly

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: "Monthly Schedule",
  version: "0.0.2",
  key: "schedule-monthly",
  type: "source",
  description: "Trigger your workflow on one or more days each month 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#

Chat on Google PaLM

Description:Chat using Google PaLM. [See the docs here](https://developers.generativeai.google/api/python/google/generativeai/chat)

Version:0.0.2

Key:google_palm_api-chat

View on GitHub

Google PaLM Overview#

The Google PaLM API is a cutting-edge language model that allows developers to integrate advanced natural language understanding into their applications. On Pipedream, you can harness this power to create serverless workflows that react to various triggers and perform actions based on the insights and outputs from PaLM. Whether it's generating content, summarizing text, or understanding user intent, PaLM's capabilities can be integrated into Pipedream workflows to automate complex tasks involving language.

Action Code#

import app from "../../google_palm_api.app.mjs";
import constants from "../../common/constants.mjs";

export default {
  key: "google_palm_api-chat",
  name: "Chat",
  description: "Chat using Google PaLM. [See the docs here](https://developers.generativeai.google/api/python/google/generativeai/chat)",
  version: "0.0.2",
  type: "action",
  props: {
    app,
    promptText: {
      type: "string",
      label: "Prompt Text",
      description: "The text to be used as a prompt for the chat",
    },
    previousMessages: {
      type: "string[]",
      label: "Previous Messages",
      description: "The previous messages in the chat. If provided, will override the chat history",
      optional: true,
    },
    temperature: {
      type: "string",
      label: "Temperature",
      description: `The temperature to use for the chat. Values can range from [0.0,1.0], inclusive.
        A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in more straightforward responses from the model.
        Defaults to \`0.5\``,
      optional: true,
    },
    context: {
      type: "string",
      label: "Context",
      description: "Text that should be provided to the model first, to ground the response",
      optional: true,
    },
    candidateCount: {
      type: "integer",
      label: "Candidate Count",
      description: "The maximum number of generated response messages to return. This value must be between [1, 8], inclusive. If unset, this will default to 1. Note: Only unique candidates are returned. Higher temperatures are more likely to produce unique candidates. Setting temperature=0.0 will always return 1 candidate regardless of the candidate_count.",
      optional: true,
      default: 1,
      min: 1,
      max: 8,
    },
    topK: {
      type: "string",
      label: "Top K",
      description: "The API uses combined nucleus and top-k sampling. top_k sets the maximum number of tokens to sample from on each step.",
      optional: true,
    },
    topP: {
      type: "string",
      label: "Top P",
      description: "  The API uses combined nucleus and top-k sampling. top_p configures the nucleus sampling. It sets the maximum cumulative probability of tokens to sample from. For example, if the sorted probabilities are [0.5, 0.2, 0.1, 0.1, 0.05, 0.05] a top_p of 0.8 will sample as [0.625, 0.25, 0.125, 0, 0, 0]. Typical values are in the [0.9, 1.0] range.",
      optional: true,
    },
    maxOutputTokens: {
      type: "integer",
      label: "Max Output Tokens",
      description: "Maximum number of tokens to include in a candidate. Must be greater than zero. If unset, will default to 64.",
      optional: true,
    },
    stopSequences: {
      type: "string",
      label: "Stop Sequences",
      description: "A set of up to 5 character sequences that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.",
      optional: true,
    },
    harmCategories: {
      type: "string[]",
      label: "Harm Categories",
      description: "To set safety settings, select the harm categories to set a threshold for",
      optional: true,
      options() {
        return constants.HARM_CATEGORIES.map(({
          value, label,
        }) => ({
          value,
          label,
        }));
      },
      reloadProps: true,
    },
  },
  async additionalProps() {
    const props = {};
    if (!this.harmCategories?.length) {
      return props;
    }
    for (const category of this.harmCategories) {
      props[`${category}_threshold`] = {
        type: "string",
        label: `${category} - Harm Block Threshold`,
        description: `Select the harm block threshold to set for the category ${category}`,
        options: this.getThresholdOptions(),
      };
    }
    return props;
  },
  methods: {
    getThresholdOptions() {
      return constants.HARM_BLOCK_THRESHOLD.map(({
        value, label,
      }) => ({
        value,
        label,
      }));
    },
    async chat({
      promptText,
      previousMessages,
      temperature,
      context,
      candidateCount,
      topK,
      topP,
      maxOutputTokens,
      stopSequences,
      safetySettings,
    }) {
      return this.app.chat({
        temperature,
        prompt: {
          context,
          messages: [
            ...previousMessages.map((message) => ({
              content: message,
            })),
            {
              content: promptText,
            },
          ],
        },
        candidate_count: candidateCount,
        top_k: topK
          ? +topK
          : undefined,
        top_p: topP
          ? +topP
          : undefined,
        max_output_tokens: maxOutputTokens,
        stop_sequences: stopSequences,
        safety_settings: safetySettings,
      });
    },
  },
  async run({ $ }) {
    const safetySettings = [];
    if (this.harmCategories?.length) {
      for (const category of this.harmCategories) {
        const threshold = constants.HARM_BLOCK_THRESHOLD.find(({ value }) => value === this[`${category}_threshold`]);
        safetySettings.push({
          category: (constants.HARM_CATEGORIES.find(({ value }) => value === category)).numValue,
          threshold: threshold?.numValue,
        });
      }
    }

    const response = await this.chat({
      promptText: this.promptText,
      previousMessages: this.previousMessages || [],
      temperature: parseFloat(this.temperature || "0.5"),
      context: this.context,
      candidteaCount: this.candidateCount,
      topK: this.topK,
      topP: this.topP,
      maxOutputTokens: this.maxOutputTokens,
      stopSequences: this.stopSequences,
      safetySettings,
    });
    $.export("$summary", "Successfully received response from Google PaLM");
    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
Google PaLM	`app`	`app`	This component uses the Google PaLM app.
Prompt Text	`promptText`	`string`	The text to be used as a prompt for the chat
Previous Messages	`previousMessages`	`string[]`	The previous messages in the chat. If provided, will override the chat history
Temperature	`temperature`	`string`	The temperature to use for the chat. Values can range from [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in more straightforward responses from the model. Defaults to `0.5`
Context	`context`	`string`	Text that should be provided to the model first, to ground the response
Candidate Count	`candidateCount`	`integer`	The maximum number of generated response messages to return. This value must be between [1, 8], inclusive. If unset, this will default to 1. Note: Only unique candidates are returned. Higher temperatures are more likely to produce unique candidates. Setting temperature=0.0 will always return 1 candidate regardless of the candidate_count.
Top K	`topK`	`string`	The API uses combined nucleus and top-k sampling. top_k sets the maximum number of tokens to sample from on each step.
Top P	`topP`	`string`	The API uses combined nucleus and top-k sampling. top_p configures the nucleus sampling. It sets the maximum cumulative probability of tokens to sample from. For example, if the sorted probabilities are [0.5, 0.2, 0.1, 0.1, 0.05, 0.05] a top_p of 0.8 will sample as [0.625, 0.25, 0.125, 0, 0, 0]. Typical values are in the [0.9, 1.0] range.
Max Output Tokens	`maxOutputTokens`	`integer`	Maximum number of tokens to include in a candidate. Must be greater than zero. If unset, will default to 64.
Stop Sequences	`stopSequences`	`string`	A set of up to 5 character sequences that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
Harm Categories	`harmCategories`	`string[]`	Select a value from the drop down menu.