Process Content Macro with Thoughtful GPT API on New Row Custom Query from PostgreSQL API

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New Row Custom Query from the PostgreSQL API

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Process Content Macro with the Thoughtful GPT API

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Getting Started#

This integration creates a workflow with a PostgreSQL trigger and Thoughtful GPT 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 Custom Query trigger
1. Connect your PostgreSQL account
2. Configure Polling Interval
3. Select a Schema
4. Select a Table
5. Select a Column
6. Configure SQL Query
7. Optional- Configure Values
Configure the Process Content Macro action
1. Connect your Thoughtful GPT account
2. Configure Content
3. Configure Macro
4. Optional- Configure LLM Model
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 Custom Query on PostgreSQL

Description:Emit new event when new rows are returned from a custom query that you provide. [See the documentation](https://node-postgres.com/features/queries)

Version:2.0.6

Key:postgresql-new-row-custom-query

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 Custom Query",
  key: "postgresql-new-row-custom-query",
  description: "Emit new event when new rows are returned from a custom query that you provide. [See the documentation](https://node-postgres.com/features/queries)",
  version: "2.0.6",
  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) => ({
          table: c.table,
          schema: c.schema,
        }),
      ],
    },
    query: {
      propDefinition: [
        common.props.postgresql,
        "query",
      ],
    },
    values: {
      propDefinition: [
        common.props.postgresql,
        "values",
      ],
    },
  },
  methods: {
    ...common.methods,
    generateMeta(row, column) {
      return {
        id: row[column],
        summary: "New Row",
        ts: Date.now(),
      };
    },
  },
  async run() {
    const {
      schema,
      table,
      column,
      query,
      values = [],
    } = this;

    if (!Array.isArray(values)) {
      throw new Error("No valid values provided. The values property must be an array.");
    }

    const numberOfValues = query?.match(/\$/g)?.length || 0;
    if (values.length !== numberOfValues) {
      throw new Error("The number of values provided does not match the number of values in the query.");
    }

    const isColumnUnique = await this.isColumnUnique(schema, table, column);
    if (!isColumnUnique) {
      throw new Error("The column selected contains duplicate values. Column must be unique");
    }

    const rows = await this.postgresql.executeQuery({
      text: query,
      values,
    });
    for (const row of rows) {
      const meta = this.generateMeta(row, column);
      this.$emit(row, meta);
    }
  },
};

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.
SQL Query	`query`	`string`	Your custom SQL Query using `$1`, `$2`... to represent values (eg. `INSERT INTO users(name, email) VALUES($1, $2)`
Values	`values`	`string[]`	List of values represented in your SQL Query above

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#

Process Content Macro on Thoughtful GPT

Description:Processes given content using a specified macro. [See the documentation](https://docs.thoughtfulgpt.com/thoughtfulgpt-documentation/product-guide/api-reference)

Version:0.0.1

Key:thoughtful_gpt-process-content-macro

View on GitHub

Thoughtful GPT Overview#

The Thoughtful GPT API lets you integrate advanced natural language processing into your workflows on Pipedream. Tap into the power of generative AI to create content, analyze text, or automate responses. With Pipedream, harness this API in serverless workflows that trigger from numerous events and connect to other apps for expanded functionality.

Action Code#

import thoughtfulgpt from "../../thoughtful_gpt.app.mjs";

export default {
  key: "thoughtful_gpt-process-content-macro",
  name: "Process Content Macro",
  description: "Processes given content using a specified macro. [See the documentation](https://docs.thoughtfulgpt.com/thoughtfulgpt-documentation/product-guide/api-reference)",
  version: "0.0.1",
  type: "action",
  props: {
    thoughtfulgpt,
    content: {
      type: "string",
      label: "Content",
      description: "Transcript content to be processed",
    },
    macro: {
      type: "string",
      label: "Macro",
      description: "The macro to apply to the content",
    },
    llmModel: {
      type: "string",
      label: "LLM Model",
      description: "The LLM Model to use for processing",
      optional: true,
      default: "gpt-3.5",
    },
  },
  async run({ $ }) {
    const response = await this.thoughtfulgpt.processContent({
      $,
      data: {
        content: this.content,
        macro: this.macro,
        llm_model: this.llmModel,
      },
    });
    $.export("$summary", `Successfully processed content using macro: ${this.macro}`);
    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
Thoughtful GPT	`thoughtfulgpt`	`app`	This component uses the Thoughtful GPT app.
Content	`content`	`string`	Transcript content to be processed
Macro	`macro`	`string`	The macro to apply to the content
LLM Model	`llmModel`	`string`	The LLM Model to use for processing