Chat with Google PaLM API on New Deleted User from Snowflake API

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New Deleted User from the Snowflake API

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Chat with the Google PaLM API

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Getting Started#

This integration creates a workflow with a Snowflake 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 New Deleted User trigger
1. Connect your Snowflake account
2. Configure timer
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#

New Deleted User on Snowflake

Description:Emit new event when a user is deleted

Version:0.1.2

Key:snowflake-deleted-user

View on GitHub

Snowflake Overview#

Snowflake offers a cloud database and related tools to help developers create robust, secure, and scalable data warehouses. See Snowflake's Key Concepts & Architecture.

Getting Started

1. Create a user, role and warehouse in Snowflake

Snowflake recommends you create a new user, role, and warehouse when you integrate a third-party tool like Pipedream. This way, you can control permissions via the user / role, and separate Pipedream compute and costs with the warehouse. You can do this directly in the Snowflake UI.

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

2. Enter those details in Pipedream

Visit https://pipedream.com/accounts. Click the button to Connect an App. Enter the required Snowflake account data.

You'll only need to connect your account once in Pipedream. You can connect this account to multiple workflows to run queries against Snowflake, insert data, and more.

3. Build your first workflow

Visit https://pipedream.com/new to build your first workflow. Pipedream workflows let you connect Snowflake with 1,000+ other apps. You can trigger workflows on Snowflake queries, sending results to Slack, Google Sheets, or any app that exposes an API. Or you can accept data from another app, transform it with Python, Node.js, Go or Bash code, and insert it into Snowflake.

Learn more at Pipedream University.

Trigger Code#

import common from "../common-delete.mjs";

export default {
  ...common,
  type: "source",
  key: "snowflake-deleted-user",
  name: "New Deleted User",
  description: "Emit new event when a user is deleted",
  version: "0.1.2",
  methods: {
    ...common.methods,
    getSqlText() {
      return "show users";
    },
  },
};

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
Snowflake	`snowflake`	`app`	This component uses the Snowflake app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`	Monitor for changes on this schedule.

Trigger Authentication#

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

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

About Snowflake#

A data warehouse built for the cloud

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.