← Google Cloud + OpenAI (ChatGPT) integrations

Create Completion (Send Prompt) with OpenAI (ChatGPT) API on BigQuery - New Row from Google Cloud API

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BigQuery - New Row from the Google Cloud API
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Create Completion (Send Prompt) with the OpenAI (ChatGPT) API
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Getting Started

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

  1. Select this integration
  2. Configure the BigQuery - New Row trigger
    1. Connect your Google Cloud account
    2. Configure Polling interval
    3. Configure Event Size
    4. Select a Dataset ID
    5. Select a Table Name
    6. Select a Unique Key
  3. Configure the Create Completion (Send Prompt) action
    1. Connect your OpenAI (ChatGPT) account
    2. Configure alert
    3. Select a Model
    4. Configure Prompt
    5. Optional- Configure Suffix
    6. Optional- Configure Max Tokens
    7. Optional- Configure Temperature
    8. Optional- Configure Top P
    9. Optional- Configure N
    10. Optional- Configure Stop
    11. Optional- Configure Presence Penalty
    12. Optional- Configure Frequency Penalty
    13. Optional- Configure User
    14. Optional- Configure Best Of
  4. Deploy the workflow
  5. Send a test event to validate your setup
  6. 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

Description:Emit new events when a new row is added to a table
Version:0.1.6
Key:google_cloud-bigquery-new-row

Google Cloud Overview

The Google Cloud API opens a world of possibilities for enhancing cloud operations and automating tasks. It empowers you to manage, scale, and fine-tune various services within the Google Cloud Platform (GCP) programmatically. With Pipedream, you can harness this power to create intricate workflows, trigger cloud functions based on events from other apps, manage resources, and analyze data, all in a serverless environment. The ability to interconnect GCP services with numerous other apps enriches automation, making it easier to synchronize data, streamline development workflows, and deploy applications efficiently.

Trigger Code

import crypto from "crypto";
import { isString } from "lodash-es";
import googleCloud from "../../google_cloud.app.mjs";
import common from "../common/bigquery.mjs";

export default {
  ...common,
  key: "google_cloud-bigquery-new-row",
  // eslint-disable-next-line pipedream/source-name
  name: "BigQuery - New Row",
  description: "Emit new events when a new row is added to a table",
  version: "0.1.6",
  dedupe: "unique",
  type: "source",
  props: {
    ...common.props,
    tableId: {
      propDefinition: [
        googleCloud,
        "tableId",
        ({ datasetId }) => ({
          datasetId,
        }),
      ],
    },
    uniqueKey: {
      type: "string",
      label: "Unique Key",
      description: "The name of a column in the table to use for deduplication. See [the docs](https://github.com/PipedreamHQ/pipedream/tree/master/components/google_cloud/sources/bigquery-new-row#technical-details) for more info.",
      async options(context) {
        const { page } = context;
        if (page !== 0) {
          return [];
        }

        const columnNames = await this._getColumnNames();
        return columnNames.sort();
      },
    },
  },
  hooks: {
    ...common.hooks,
    async deploy() {
      await this._validateColumn(this.uniqueKey);
      const lastResultId = await this._getIdOfLastRow(this.getInitialEventCount());
      this._setLastResultId(lastResultId);
    },
    async activate() {
      if (this._getLastResultId()) {
        // ID of the last result has already been initialised during deploy(),
        // so we skip the rest of the activation.
        return;
      }

      await this._validateColumn(this.uniqueKey);
      const lastResultId = await this._getIdOfLastRow();
      this._setLastResultId(lastResultId);
    },
    deactivate() {
      this._setLastResultId(null);
    },
  },
  methods: {
    ...common.methods,
    _getLastResultId() {
      return this.db.get("lastResultId");
    },
    _setLastResultId(lastResultId) {
      this.db.set("lastResultId", lastResultId);
      console.log(`
        Next scan of table '${this.tableId}' will start at ${this.uniqueKey}=${lastResultId}
      `);
    },
    /**
     * Utility method to make sure that a certain column exists in the target
     * table. Useful for SQL query sanitizing.
     *
     * @param {string} columnNameToValidate The name of the column to validate
     * for existence
     */
    async _validateColumn(columnNameToValidate) {
      if (!isString(columnNameToValidate)) {
        throw new Error("columnNameToValidate must be a string");
      }

      const columnNames = await this._getColumnNames();
      if (!columnNames.includes(columnNameToValidate)) {
        throw new Error(`Nonexistent column: ${columnNameToValidate}`);
      }
    },
    async _getColumnNames() {
      const table = this.googleCloud
        .getBigQueryClient()
        .dataset(this.datasetId)
        .table(this.tableId);
      const [
        metadata,
      ] = await table.getMetadata();
      const { fields } = metadata.schema;
      return fields.map(({ name }) => name);
    },
    async _getIdOfLastRow(offset = 0) {
      const limit = offset + 1;
      const query = `
        SELECT *
        FROM \`${this.tableId}\`
        ORDER BY \`${this.uniqueKey}\` DESC
        LIMIT @limit
      `;
      const queryOpts = {
        query,
        params: {
          limit,
        },
      };
      const rows = await this.getRowsForQuery(queryOpts, this.datasetId);
      if (rows.length === 0) {
        console.log(`
          No records found in the target table, will start scanning from the beginning
        `);
        return;
      }

      const startingRow = rows.pop();
      return startingRow[this.uniqueKey];
    },
    getQueryOpts() {
      const lastResultId = this._getLastResultId();
      const query = `
        SELECT *
        FROM \`${this.tableId}\`
        WHERE \`${this.uniqueKey}\` >= @lastResultId
        ORDER BY \`${this.uniqueKey}\` ASC
      `;
      const params = {
        lastResultId,
      };
      return {
        query,
        params,
      };
    },
    generateMeta(row, ts) {
      const id = row[this.uniqueKey];
      const summary = `New row: ${id}`;
      return {
        id,
        summary,
        ts,
      };
    },
    generateMetaForCollection(rows, ts) {
      const hash = crypto.createHash("sha1");
      rows
        .map((i) => i[this.uniqueKey])
        .map((i) => i.toString())
        .forEach((i) => hash.update(i));
      const id = hash.digest("base64");

      const rowCount = rows.length;
      const entity = rowCount === 1
        ? "row"
        : "rows";
      const summary = `${rowCount} new ${entity}`;

      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.
LabelPropTypeDescription
Google CloudgoogleCloudappThis component uses the Google Cloud app.
N/Adb$.service.dbThis component uses $.service.db to maintain state between executions.
Polling intervaltimer$.interface.timer

How often to run your query

Event SizeeventSizeinteger

The number of rows to include in a single event (by default, emits 1 event per row)

Dataset IDdatasetIdstringSelect a value from the drop down menu.
Table NametableIdstringSelect a value from the drop down menu.
Unique KeyuniqueKeystringSelect a value from the drop down menu.

Trigger Authentication

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

  1. Create a service account in GCP and set the permissions you need for Pipedream workflows.
  2. Generate a service account key
  3. Download the key details in JSON format
  4. Upload the key below.

About Google Cloud

The Google Cloud Platform, including BigQuery

Action

Description:OpenAI recommends using the **Chat** action for the latest `gpt-3.5-turbo` API, since it's faster and 10x cheaper. This action creates a completion for the provided prompt and parameters using the older `/completions` API. [See the documentation](https://beta.openai.com/docs/api-reference/completions/create)
Version:0.1.14
Key:openai-send-prompt

OpenAI (ChatGPT) Overview

OpenAI provides a suite of powerful AI models through its API, enabling developers to integrate advanced natural language processing and generative capabilities into their applications. Here’s an overview of the services offered by OpenAI's API:

Use Python or Node.js code to make fully authenticated API requests with your OpenAI account:

Action Code

import openai from "../../openai.app.mjs";
import common from "../common/common.mjs";

export default {
  ...common,
  name: "Create Completion (Send Prompt)",
  version: "0.1.14",
  key: "openai-send-prompt",
  description: "OpenAI recommends using the **Chat** action for the latest `gpt-3.5-turbo` API, since it's faster and 10x cheaper. This action creates a completion for the provided prompt and parameters using the older `/completions` API. [See the documentation](https://beta.openai.com/docs/api-reference/completions/create)",
  type: "action",
  props: {
    openai,
    alert: {
      type: "alert",
      alertType: "warning",
      content: "We recommend using the Pipedream **Chat** action instead of this one. It supports the latest `gpt-3.5-turbo` API, which is faster and 10x cheaper. This action, **Create Completion (Send Prompt)**, creates a completion for the provided prompt and parameters using the older `/completions` API.",
    },
    modelId: {
      propDefinition: [
        openai,
        "completionModelId",
      ],
    },
    prompt: {
      label: "Prompt",
      description: "The prompt to generate completions for",
      type: "string",
    },
    suffix: {
      label: "Suffix",
      description: "The suffix that comes after a completion of inserted text",
      type: "string",
      optional: true,
    },
    ...common.props,
    bestOf: {
      label: "Best Of",
      description: "Generates best_of completions server-side and returns the \"best\" (the one with the highest log probability per token). If set, results cannot be streamed.",
      type: "integer",
      optional: true,
    },
  },
  async run({ $ }) {
    const response = await this.openai.createCompletion({
      $,
      data: this._getCommonArgs(),
    });

    if (response) {
      $.export("$summary", `Successfully sent prompt with id ${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.

LabelPropTypeDescription
OpenAI (ChatGPT)openaiappThis component uses the OpenAI (ChatGPT) app.
ModelmodelIdstringSelect a value from the drop down menu.
Promptpromptstring

The prompt to generate completions for

Suffixsuffixstring

The suffix that comes after a completion of inserted text

Max TokensmaxTokensinteger

The maximum number of tokens to generate in the completion.

Temperaturetemperaturestring

Optional. What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.

Top PtopPstring

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. We generally recommend altering this or temperature but not both.

Nninteger

How many completions to generate for each prompt

Stopstopstring[]

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

Presence PenaltypresencePenaltystring

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.

Frequency PenaltyfrequencyPenaltystring

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.

Useruserstring

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more here.

Best OfbestOfinteger

Generates best_of completions server-side and returns the "best" (the one with the highest log probability per token). If set, results cannot be streamed.

Action Authentication

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

About OpenAI (ChatGPT)

OpenAI is an AI research and deployment company with the mission to ensure that artificial general intelligence benefits all of humanity. They are the makers of popular models like ChatGPT, DALL-E, and Whisper.

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