Fakturo Python API Docs | dltHub

Fakturo is an integration gateway to the Faktura.uz electronic document exchange system. The REST API base URL is https://api.faktura.uz and all requests require an OAuth2 bearer access token (token endpoint at account.faktura.uz).

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 Fakturo data in under 10 minutes.

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What data can I load from Fakturo?

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

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How do I authenticate with the Fakturo API?

Obtain an access token using the token endpoint (POST https://account.faktura.uz/token) with grant_type=password plus username, password, client_id and client_secret. Include the returned access_token in requests as the Authorization: Bearer <access_token> header.

1. Get your credentials

1. Log in to your Faktura.uz account and open Client Settings in account.faktura.uz. 2) Copy ClientId and ClientSecret shown in Client Settings and note your account username and password. 3) Request a token: POST to https://account.faktura.uz/token with form data grant_type=password&username=&password=&client_id=<client_id>&client_secret=<client_secret>. 4) Use the access_token from the JSON response in Authorization: Bearer <access_token> for API requests.

2. Add them to .dlt/secrets.toml

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[sources.fakturo_source]
access_token = "your_access_token_here"

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 Fakturo 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 fakturo_pipeline.py

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

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Pipeline fakturo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fakturo_data
The duckdb destination used duckdb:/fakturo.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 fakturo_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 document_types and download_archive from the Fakturo 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 fakturo_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.faktura.uz",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "document_types", "endpoint": {"path": "Api/Document/GetDocumentTypes"}},
            {"name": "download_archive", "endpoint": {"path": "Api/DownloadArchive/:uniqueId"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fakturo_pipeline",
        destination="duckdb",
        dataset_name="fakturo_data",
    )
    load_info = pipeline.run(fakturo_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("fakturo_pipeline").dataset()
sessions_df = data.document_types.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fakturo_data.document_types LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fakturo_pipeline").dataset()
data.document_types.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 Fakturo 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 token request to https://account.faktura.uz/token fails, verify username, password, client_id and client_secret. The token endpoint returns JSON with access_token on success; expired or invalid tokens produce 401 responses. Ensure Authorization: Bearer <access_token> header is sent on API calls.

Download interruptions and large archives

When downloading large ZIP archives via Api/DownloadArchive/:uniqueId the docs warn connections may close; use HTTP streaming/resume range headers or split requests per server boundary conditions as recommended.

Response wrapper and POST endpoints

Some endpoints (notably GetDocumentStatus) return a wrapper JSON: {"Success": true/false, "Errors": ..., "Data": {...}}. The list of statuses is at Data.DocumentStatuses. Handle Errors and Success fields accordingly.

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