Chat with OpenAI (ChatGPT) API on BigQuery - New Row from Google Cloud API

Pipedream makes it easy to connect APIs for OpenAI (ChatGPT), Google Cloud and 2,400+ other apps remarkably fast.

Trigger workflow on

BigQuery - New Row from the Google Cloud API

Next, do this

Chat with the OpenAI (ChatGPT) API

No credit card required

▶

Watch us build a workflow

8 min

Watch now ➜

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

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.

Select this integration
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
Configure the Chat action
1. Connect your OpenAI (ChatGPT) account
2. Select a Model
3. Configure User Message
4. Optional- Configure Max Tokens
5. Optional- Configure Temperature
6. Optional- Configure Top P
7. Optional- Configure N
8. Optional- Configure Stop
9. Optional- Configure Presence Penalty
10. Optional- Configure Frequency Penalty
11. Optional- Configure User
12. Optional- Configure System Instructions
13. Optional- Configure Prior Message History
14. Optional- Configure Images
15. Optional- Configure Audio
16. Optional- Select a Response Format
17. Optional- Select one or more Tool Types
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#

BigQuery - New Row on Google Cloud

Description:Emit new events when a new row is added to a table

Version:0.1.6

Key:google_cloud-bigquery-new-row

View on GitHub

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.

Label	Prop	Type	Description
Google Cloud	`googleCloud`	`app`	This component uses the Google Cloud app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling interval	`timer`	`$.interface.timer`	How often to run your query
Event Size	`eventSize`	`integer`	The number of rows to include in a single event (by default, emits 1 event per row)
Dataset ID	`datasetId`	`string`	Select a value from the drop down menu.
Table Name	`tableId`	`string`	Select a value from the drop down menu.
Unique Key	`uniqueKey`	`string`	Select 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.

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

About Google Cloud#

The Google Cloud Platform, including BigQuery

Action#

Chat on OpenAI (ChatGPT)

Description:The Chat API, using the `gpt-3.5-turbo` or `gpt-4` model. [See the documentation](https://platform.openai.com/docs/api-reference/chat)

Version:0.2.3

Key:openai-chat

View on GitHub

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";
import constants from "../../common/constants.mjs";
import { ConfigurationError } from "@pipedream/platform";

export default {
  ...common,
  name: "Chat",
  version: "0.2.3",
  key: "openai-chat",
  description: "The Chat API, using the `gpt-3.5-turbo` or `gpt-4` model. [See the documentation](https://platform.openai.com/docs/api-reference/chat)",
  type: "action",
  props: {
    openai,
    modelId: {
      propDefinition: [
        openai,
        "chatCompletionModelId",
      ],
    },
    userMessage: {
      label: "User Message",
      type: "string",
      description: "The user messages provide instructions to the assistant. They can be generated by the end users of an application, or set by a developer as an instruction.",
    },
    ...common.props,
    systemInstructions: {
      label: "System Instructions",
      type: "string",
      description: "The system message helps set the behavior of the assistant. For example: \"You are a helpful assistant.\" [See these docs](https://platform.openai.com/docs/guides/chat/instructing-chat-models) for tips on writing good instructions.",
      optional: true,
    },
    messages: {
      label: "Prior Message History",
      type: "string[]",
      description: "_Advanced_. Because [the models have no memory of past chat requests](https://platform.openai.com/docs/guides/chat/introduction), all relevant information must be supplied via the conversation. You can provide [an array of messages](https://platform.openai.com/docs/guides/chat/introduction) from prior conversations here. If this param is set, the action ignores the values passed to **System Instructions** and **Assistant Response**, appends the new **User Message** to the end of this array, and sends it to the API.",
      optional: true,
    },
    images: {
      label: "Images",
      type: "string[]",
      description: "Provide one or more images to [OpenAI's vision model](https://platform.openai.com/docs/guides/vision). Accepts URLs or base64 encoded strings. Compatible with the `gpt4-vision-preview` model",
      optional: true,
    },
    audio: {
      type: "string",
      label: "Audio",
      description: "Provide the file path to an audio file in the `/tmp` directory. For use with the `gpt-4o-audio-preview` model. Currently supports `wav` and `mp3` files.",
      optional: true,
    },
    responseFormat: {
      type: "string",
      label: "Response Format",
      description: "Specify the format that the model must output. \n- **Text** (default): Returns unstructured text output.\n- **JSON Object**: Ensures the model's output is a valid JSON object.\n- **JSON Schema** (GPT-4o and later): Enables you to define a specific structure for the model's output using a JSON schema. Supported with models `gpt-4o-2024-08-06` and later, and `gpt-4o-mini-2024-07-18` and later.",
      options: Object.values(constants.CHAT_RESPONSE_FORMAT),
      default: constants.CHAT_RESPONSE_FORMAT.TEXT.value,
      optional: true,
      reloadProps: true,
    },
    toolTypes: {
      type: "string[]",
      label: "Tool Types",
      description: "The types of tools to enable on the assistant",
      options: constants.TOOL_TYPES.filter((toolType) => toolType === "function"),
      optional: true,
      reloadProps: true,
    },
  },
  additionalProps() {
    const {
      responseFormat,
      toolTypes,
      numberOfFunctions,
    } = this;
    const props = {};

    if (responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) {
      props.jsonSchema = {
        type: "string",
        label: "JSON Schema",
        description: "Define the schema that the model's output must adhere to. [See the documentation here](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas).",
      };
    }

    if (toolTypes?.includes("function")) {
      props.numberOfFunctions = {
        type: "integer",
        label: "Number of Functions",
        description: "The number of functions to define",
        optional: true,
        reloadProps: true,
        default: 1,
      };

      for (let i = 0; i < (numberOfFunctions || 1); i++) {
        props[`functionName_${i}`] = {
          type: "string",
          label: `Function Name ${i + 1}`,
          description: "The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.",
        };
        props[`functionDescription_${i}`] = {
          type: "string",
          label: `Function Description ${i + 1}`,
          description: "A description of what the function does, used by the model to choose when and how to call the function.",
          optional: true,
        };
        props[`functionParameters_${i}`] = {
          type: "object",
          label: `Function Parameters ${i + 1}`,
          description: "The parameters the functions accepts, described as a JSON Schema object. See the [guide](https://platform.openai.com/docs/guides/text-generation/function-calling) for examples, and the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for documentation about the format.",
          optional: true,
        };
      }
    }

    return props;
  },
  methods: {
    ...common.methods,
    _buildTools() {
      const tools = this.toolTypes?.filter((toolType) => toolType !== "function")?.map((toolType) => ({
        type: toolType,
      })) || [];
      if (this.toolTypes?.includes("function")) {
        const numberOfFunctions = this.numberOfFunctions || 1;
        for (let i = 0; i < numberOfFunctions; i++) {
          tools.push({
            type: "function",
            function: {
              name: this[`functionName_${i}`],
              description: this[`functionDescription_${i}`],
              parameters: this[`functionParameters_${i}`],
            },
          });
        }
      }
      return tools.length
        ? tools
        : undefined;
    },
  },
  async run({ $ }) {
    if (this.audio && !this.modelId.includes("gpt-4o-audio-preview")) {
      throw new ConfigurationError("Use of audio files requires using the `gpt-4o-audio-preview` model.");
    }

    const args = this._getChatArgs();

    const response = await this.openai.createChatCompletion({
      $,
      data: {
        ...args,
        tools: this._buildTools(),
      },
    });

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

    const { messages } = args;
    return {
      original_messages: messages,
      original_messages_with_assistant_response: messages.concat(response.choices[0]?.message),
      ...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
OpenAI (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
Model	`modelId`	`string`	Select a value from the drop down menu.
User Message	`userMessage`	`string`	The user messages provide instructions to the assistant. They can be generated by the end users of an application, or set by a developer as an instruction.
Max Tokens	`maxTokens`	`integer`	The maximum number of tokens to generate in the completion.
Temperature	`temperature`	`string`	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 P	`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. We generally recommend altering this or `temperature` but not both.
N	`n`	`integer`	How many completions to generate for each prompt
Stop	`stop`	`string[]`	Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
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.
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.
User	`user`	`string`	A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more here.
System Instructions	`systemInstructions`	`string`	The system message helps set the behavior of the assistant. For example: "You are a helpful assistant." See these docs for tips on writing good instructions.
Prior Message History	`messages`	`string[]`	Advanced. Because the models have no memory of past chat requests, all relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here. If this param is set, the action ignores the values passed to System Instructions and Assistant Response, appends the new User Message to the end of this array, and sends it to the API.
Images	`images`	`string[]`	Provide one or more images to OpenAI's vision model. Accepts URLs or base64 encoded strings. Compatible with the `gpt4-vision-preview` model
Audio	`audio`	`string`	Provide the file path to an audio file in the `/tmp` directory. For use with the `gpt-4o-audio-preview` model. Currently supports `wav` and `mp3` files.
Response Format	`responseFormat`	`string`	Select a value from the drop down menu:`{ "label": "Text", "value": "text" }{ "label": "JSON Object", "value": "json_object" }{ "label": "JSON Schema", "value": "json_schema" }`
Tool Types	`toolTypes`	`string[]`	Select a value from the drop down menu:`function`

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.

More Ways to Connect OpenAI (ChatGPT) + Google Cloud#

Other Popular Integrations#

Create Image with OpenAI (ChatGPT) API on New Pub/Sub Messages from Google Cloud API

Google Cloud + OpenAI (ChatGPT)

Chat with OpenAI (ChatGPT) API on BigQuery - New Row from Google Cloud API

Pipedream makes it easy to connect APIs for OpenAI (ChatGPT), Google Cloud and 2,400+ other apps remarkably fast.

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

1-24of2,400+apps by most popular

1
-
24
of
2,400+
apps by most popular