PostgreSQL Python API Docs | dltHub

PostgREST is a standalone web server that turns your PostgreSQL database directly into a RESTful API. The REST API base URL is The base URL for PostgREST is server-specific (e.g., http://localhost:3000/<table> for default PostgREST), while for Supabase REST API, it is https://<project_ref>.supabase.co/rest/v1/. and Requests use JWT bearer authentication for PostgREST, while Supabase REST API requires both an API key and a Bearer token..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading PostgreSQL data in under 10 minutes.

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What data can I load from PostgreSQL?

Here are some of the endpoints you can load from PostgreSQL:

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How do I authenticate with the PostgreSQL API?

PostgREST commonly validates JWTs (HS256) provided in the Authorization: Bearer header. Supabase REST requires both an apikey header and Authorization: Bearer <access_token> for authenticated requests.

1. Get your credentials

1. For PostgREST self-hosted: generate a JWT signing secret (HS256) and configure postgrest with JWT_SECRET; create roles and RLS policies in Postgres. 2) For Supabase: in Dashboard -> Settings -> API copy Project URL (base) and anon/public or service_role keys; use anon key for client reads and service_role for server operations.

2. Add them to .dlt/secrets.toml

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[sources.postgres_data_source]
api_key = "YOUR_API_KEY_OR_JWT"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

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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 →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

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

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

4. Run the pipeline:

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python postgres_data_pipeline.py

If everything is configured correctly, you'll see output like this:

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

Inspect your pipeline and data:

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dlt pipeline postgres_data_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads table_name and rpc_function from the PostgreSQL API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def postgres_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL for PostgREST is server-specific (e.g., http://localhost:3000/<table> for default PostgREST), while for Supabase REST API, it is https://<project_ref>.supabase.co/rest/v1/.",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "table_name", "endpoint": {"path": "rest/v1/<table_name>"}},
            {"name": "rpc_function", "endpoint": {"path": "rpc/<function_name>"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="postgres_data_pipeline",
        destination="duckdb",
        dataset_name="postgres_data_data",
    )
    load_info = pipeline.run(postgres_data_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("postgres_data_pipeline").dataset()
sessions_df = data.table_name.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM postgres_data_data.table_name LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("postgres_data_pipeline").dataset()
data.table_name.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load PostgreSQL data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401/403, verify Authorization: Bearer header, JWT signing secret on server matches issuer, and PostgreSQL role/RLS policies permit access. For Supabase ensure both apikey and Authorization headers are provided.

Pagination and large result sets

PostgREST uses Range and Content-Range headers for paging/counts; very large responses may require limiting with ?limit and using Range to request subsets.

OpenAPI / metadata not available

If OpenAPI JSON is missing, confirm PostgREST was started with the correct db schema exposed and that OpenAPI generation is enabled in configuration.

Common API errors: 401 Unauthorized, 403 Forbidden (RLS/permissions), 404 Not Found (missing table/endpoint), 406/415 (content negotiation), 416/206 when using Range headers; use 400 for invalid queries.

Notes: Exact base URL and available table endpoints depend on your PostgREST host and DB schema. Table GET responses are returned as a top-level JSON array (no wrapper key). Supabase REST URL pattern: https://<project_ref>.supabase.co/rest/v1/ and requires apikey + Authorization headers.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the 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.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack