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gpt-trainer

Create a personalized AI chatbot using your own data

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PostgreSQL

Integrate the gpt-trainer API with the PostgreSQL API

Setup the gpt-trainer API trigger to run a workflow which integrates with the PostgreSQL API. Pipedream's integration platform allows you to integrate gpt-trainer and PostgreSQL remarkably fast. Free for developers.

Popular Ways to Connect gpt-trainer with PostgreSQL

Create Chat Session with gpt-trainer API on New Column from PostgreSQL API
PostgreSQL + gpt-trainer
 
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Create Chat Session with gpt-trainer API on New or Updated Row from PostgreSQL API
PostgreSQL + gpt-trainer
 
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Create Chat Session with gpt-trainer API on New Row Custom Query from PostgreSQL API
PostgreSQL + gpt-trainer
 
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Create Chat Session with gpt-trainer API on New Row from PostgreSQL API
PostgreSQL + gpt-trainer
 
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Create Chat Session with gpt-trainer API on New Table from PostgreSQL API
PostgreSQL + gpt-trainer
 
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Popular gpt-trainer and PostgreSQL Triggers

New Column from the PostgreSQL API

Emit new event when a new column is added to a table. See the documentation

 
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New or Updated Row from the PostgreSQL API

Emit new event when a row is added or modified. See the documentation

 
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New Row from the PostgreSQL API

Emit new event when a new row is added to a table. See the documentation

 
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New Row Custom Query from the PostgreSQL API

Emit new event when new rows are returned from a custom query that you provide. See the documentation

 
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New Table from the PostgreSQL API

Emit new event when a new table is added to the database. See the documentation

 
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Popular gpt-trainer and PostgreSQL Actions

Create Chat Session with the gpt-trainer API

Create a chat session for a chatbot specified by chatbot UUID. See the documentation

 
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Delete Row(s) with the PostgreSQL API

Deletes a row or rows from a table. See the documentation

 
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Create Chatbot with the gpt-trainer API

Creates a new chatbot that belongs to the authenticated user. See the documentation

 
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Execute Custom Query with the PostgreSQL API

Executes a custom query you provide. See the documentation

 
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Create Message with the gpt-trainer API

Create a session message for a chatbot session specified by session UUID. See the documentation

 
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Overview of gpt-trainer

The gpt-trainer API is a tool designed to train, run, and manage custom GPT-2 and GPT-3 models. It provides endpoints for submitting training data, starting the training process, and generating text from the trained model. With Pipedream's serverless integration platform, you can automate workflows that interact with the gpt-trainer API. You can trigger workflows using webhooks, schedule them, or even run them in response to events from other apps. Integrate the gpt-trainer API with other services on Pipedream to create powerful applications such as automated content creation, personalized messaging, or AI-driven data analysis.

Connect gpt-trainer

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    gpt_trainer: {
      type: "app",
      app: "gpt_trainer",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://app.gpt-trainer.com/api/v1/chatbots`,
      headers: {
        Authorization: `Bearer ${this.gpt_trainer.$auth.api_key}`,
      },
    })
  },
})

Overview of PostgreSQL

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.

Connect PostgreSQL

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import postgresql from "@pipedream/postgresql"

export default defineComponent({
  props: {
    postgresql,
  },
  async run({ steps, $ }) {
    // Component source code:
    // https://github.com/PipedreamHQ/pipedream/tree/master/components/postgresql

    const queryObj = {
      text: "SELECT NOW()",
      values: [], // Ignored since query does not contain placeholders
    };
    const { rows } = await this.postgresql.executeQuery(queryObj);
    return rows;
  },
})
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