Create Fine Tuning Job with OpenAI (ChatGPT) API on New Row from PostgreSQL API

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New Row from the PostgreSQL API

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Create Fine Tuning Job with the OpenAI (ChatGPT) API

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Getting Started#

This integration creates a workflow with a PostgreSQL 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 New Row trigger
1. Connect your PostgreSQL account
2. Configure Polling Interval
3. Select a Schema
4. Select a Table
5. Optional- Select a Column
Configure the Create Fine Tuning Job action
1. Connect your OpenAI (ChatGPT) account
2. Select a Fine Tuning Model
3. Select a Training File
4. Optional- Configure Hyperparameters
5. Optional- Configure Suffix
6. Optional- Select a Validation File
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 Row on PostgreSQL

Description:Emit new event when a new row is added to a table. [See the documentation](https://node-postgres.com/features/queries)

Version:3.0.8

Key:postgresql-new-row

View on GitHub

PostgreSQL Overview#

On Pipedream, you can leverage the PostgreSQL app to create workflows that automate database operations, synchronize data across platforms, and react to database events in real-time. Think handling new row entries, updating records from webhooks, or even compiling reports on a set schedule. Pipedream's serverless platform provides a powerful way to connect PostgreSQL with a variety of apps, enabling you to create tailored automation that fits your specific needs.

Trigger Code#

import common from "../common.mjs"; export default { ...common, name: "New Row", key: "postgresql-new-row", description: "Emit new event when a new row is added to a table. [See the documentation](https://node-postgres.com/features/queries)", version: "3.0.8", type: "source", dedupe: "unique", props: { ...common.props, schema: { propDefinition: [ common.props.postgresql, "schema", ], }, table: { propDefinition: [ common.props.postgresql, "table", (c) => ({ schema: c.schema, }), ], }, column: { propDefinition: [ common.props.postgresql, "column", (c) => ({ schema: c.schema, table: c.table, }), ], description: "An ID or timestamp column where new rows will always contain larger values than the previous row. Defaults to the table's primary key.", optional: true, }, }, hooks: { /** If column prop is left blank, get the table's primary key to use * for ordering and deduping. */ async deploy() { const column = this.column ? this.column : await this.postgresql.getPrimaryKey(this.table, this.schema); const isColumnUnique = await this.isColumnUnique(this.schema, this.table, column); if (!isColumnUnique) { throw new Error("The column selected contains duplicate values. Column must be unique"); } this._setColumn(column); await this.initialRows(this.schema, this.table, column); }, }, methods: { ...common.methods, _getColumn() { return this.db.get("column"); }, _setColumn(column) { this.db.set("column", column); }, generateMeta(row, column) { return { id: row[column], summary: "New Row Added", ts: Date.now(), }; }, }, async run() { const column = this._getColumn(); await this.newRows(this.schema, this.table, column); }, };

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
PostgreSQL	`postgresql`	`app`	This component uses the PostgreSQL app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling Interval	`timer`	`$.interface.timer`	Pipedream will poll the API on this schedule
Schema	`schema`	`string`	Select a value from the drop down menu.
Table	`table`	`string`	Select a value from the drop down menu.
Column	`column`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

Connecting to Restricted Databases

Either enable the shared static IP for this account below, or configure a VPC to deploy any workflow in your workspace to a private network with a dedicated static IP. Learn more in our docs

SSL Setup

Configure SSL on your PostgreSQL database by providing the CA (Certificate Authority), and choosing between Full Verification, Verify Certificate Authority (CA), or Skip Verification. Skipping verification is not recommended as this has serious security implications

About PostgreSQL#

PostgreSQL is a free and open-source relational database management system emphasizing extensibility and SQL compliance.

Action#

Create Fine Tuning Job on OpenAI (ChatGPT)

Description:Creates a job that fine-tunes a specified model from a given dataset. [See the documentation](https://platform.openai.com/docs/api-reference/fine-tuning/create)

Version:0.0.16

Key:openai-create-fine-tuning-job

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"; export default { key: "openai-create-fine-tuning-job", name: "Create Fine Tuning Job", description: "Creates a job that fine-tunes a specified model from a given dataset. [See the documentation](https://platform.openai.com/docs/api-reference/fine-tuning/create)", version: "0.0.16", annotations: { destructiveHint: false, openWorldHint: true, readOnlyHint: false, }, type: "action", props: { openai, model: { propDefinition: [ openai, "fineTuningModel", ], }, trainingFile: { propDefinition: [ openai, "fileId", () => ({ purpose: "fine-tune", }), ], label: "Training File", description: "The ID of an uploaded file that contains training data. You can use the **Upload File** action and reference the returned ID here.", }, hyperParameters: { type: "object", label: "Hyperparameters", description: "The hyperparameters used for the fine-tuning job. [See details in the documentation](https://platform.openai.com/docs/api-reference/fine-tuning/create#fine-tuning-create-hyperparameters).", optional: true, }, suffix: { type: "string", label: "Suffix", description: "A string of up to 18 characters that will be added to your fine-tuned model name.", optional: true, }, validationFile: { propDefinition: [ openai, "fileId", () => ({ purpose: "fine-tune", }), ], label: "Validation File", description: "The ID of an uploaded file that contains validation data. [See details in the documentation](https://platform.openai.com/docs/api-reference/fine-tuning/create#fine-tuning-create-validation_file).", optional: true, }, }, async run({ $ }) { const response = await this.openai.createFineTuningJob({ $, data: { model: this.model, training_file: this.trainingFile, hyperparameters: this.hyperParameters, suffix: this.suffix, validation_file: this.validationFile, }, }); $.export("$summary", `Successfully created fine-tuning job with 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
OpenAI (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
Fine Tuning Model	`model`	`string`	Select a value from the drop down menu:`{ "label": "gpt-3.5-turbo-1106 (recommended)", "value": "gpt-3.5-turbo-1106" }{ "label": "gpt-3.5-turbo-0613", "value": "gpt-3.5-turbo-0613" }{ "label": "babbage-002", "value": "babbage-002" }{ "label": "davinci-002", "value": "davinci-002" }{ "label": "gpt-4-0613 (experimental — eligible users will be presented with an option to request access in the fine-tuning UI)", "value": "gpt-4-0613" }{ "label": "gpt-4.1-mini-2025-04-14", "value": "gpt-4.1-mini-2025-04-14" }{ "label": "gpt-4.1-2025-04-14", "value": "gpt-4.1-2025-04-14" }`
Training File	`trainingFile`	`string`	Select a value from the drop down menu.
Hyperparameters	`hyperParameters`	`object`	The hyperparameters used for the fine-tuning job. See details in the documentation
Suffix	`suffix`	`string`	A string of up to 18 characters that will be added to your fine-tuned model name.
Validation File	`validationFile`	`string`	Select a value from the drop down menu.