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Load PostgresAI data in Python using dltHub

Build a PostgresAI-to-database pipeline in Python using dlt with AI Workbench support for Claude Code, Cursor, and Codex.

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In this guide, we'll set up a complete Postgres.ai Platform data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def postgres_ai_dblab_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://console.postgres.ai/",
            "auth": {
                "type": "apikey",
                "token": POSTGRES_AI_API_KEY,
            },
        },
        "resources": [
            "wal", "app", "dle"
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='postgres_ai_dblab_pipeline',
        destination='duckdb',
        dataset_name='postgres_ai_dblab_data', 
    )
    # Load the data
    load_info = pipeline.run(postgres_ai_dblab_source())
    print(load_info)

Why use dlt to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to schema evolution with type inference, resilience and self-documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from PostgresAI's API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • wal: Used for write-ahead logging operations.
  • app: Manages application-related settings and configurations.
  • dle: Involves operations related to the DBLab Engine.
  • exec: Executes commands or scripts within the environment.
  • help: Provides documentation or assistance regarding API usage.
  • hypo: Related to hypothetical or simulated database operations.
  • plan: Involves planning operations for databases.
  • alert: Manages alerts and notifications within the platform.
  • clone: Handles cloning of databases.
  • reset: Resets configurations or database states.
  • branch: Manages branching for database versions or states.
  • runner: Executes database jobs or tasks.
  • source: Manages source database connections.
  • explain: Provides explanations for database operations.
  • configs: Handles configuration settings for the platform.
  • snapshot: Manages database snapshots.
  • platform: Refers to platform-wide settings or information.
  • db_stats: Provides statistics and metrics for database performance.
  • webhooks: Manages webhooks for external notifications.
  • settings: Handles overall settings for the platform.

You will then debug the Postgres.ai Platform pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, set up a virtual environment (instructions) and install the dlt workspace:


uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

Now you're ready to get started!

  1. Install the dlt AI Workbench

    Configure the workbench for your coding assistant:

    dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

    This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent.

    Learn more about the dltHub AI Workbench and setup details for each assistant →

  2. Install the rest-api-pipeline toolkit

    The AI Workbench provides different toolkits for each phase of the data engineering lifecycle. To start you need to install the rest-api-pipeline toolkit:

    dlt ai toolkit rest-api-pipeline install

    This loads different skills and contexts about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. Importantly, it does not need to ask you for credentials directly. In dlt, API credentials are provided via a secrets.toml file (learn more about secrets management →), and the agent should use the MCP tools to see their shape and detect misconfigurations. It never needs to access the file directly.

    Learn more about the rest-api-pipeline toolkit →

  3. Start LLM-assisted coding

    Here's a prompt to get you started:

    Prompt
    Use /find-source to load data from the PostgresAI API into DuckDB.

    The AI Workbench rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and then follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

  4. View the result

    After the rest-api-pipeline workflow has finished, you will end up with a working REST API source with validated endpoints and a pipeline that writes data into a local dataset you have inspected and verified.

    > python postgres_ai_dblab_pipeline.py
Pipeline postgres_ai_dblab load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset postgres_ai_dblab_data
The duckdb destination used duckdb:/postgres_ai_dblab.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

    By launching the Pipeline Dashboard, you can see various information about the pipeline and the loaded data

    • Pipeline overview: State, load metrics
    • Data's schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline postgres_ai_dblab_pipeline show

Running into errors?

When using the Postgres.ai Platform, it's important to consider that initial data provisioning can take time based on source database size, and certain operations such as cloning do not affect billing. Additionally, users should ensure the DBLab Engine is running on the correct host and port, and be aware of potential throttling if API call limits are exceeded. Always check logs for errors related to provisioning and connectivity.

Next steps

You can go to the next phases of your data engineering journey by handing over to other toolkits of the dltHub AI Workbench:

  • data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
  • dlthub-runtime — Deploy, schedule, and monitor your pipeline in production
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

Or explore the following resources for more information:

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