Topcon Aptix Insights Python API Docs | dltHub

Topcon Aptix Integrations Platform is a data integration and analytics service for construction project performance monitoring and workflow automation. The REST API base URL is The API base URL is platform-specific and should be retrieved from the official Aptix Integration Platform documentation provided to licensed users. and all requests require a JWT token in the header.

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 Topcon Aptix Insights data in under 10 minutes.

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What data can I load from Topcon Aptix Insights?

Here are some of the endpoints you can load from Topcon Aptix Insights:

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How do I authenticate with the Topcon Aptix Insights API?

The API uses JWT-based authentication where developers must obtain a token. The token is typically passed in a header parameter to authorize requests.

1. Get your credentials

Topcon services use the Topcon Customer Identity and Access Management (CIAM) framework, which relies on OAuth 2.0 client-credentials. To obtain these credentials: 1) Sign in to the My Topcon self-service dashboard using your Topcon domain credentials. 2) Navigate to the relevant developer portal or project management section to create a new OAuth 2.0 client application. 3) Ensure an 'App Policy' is established for your application, which defines the authorized OAuth 2.0 flows, scopes, and resource servers permitted for your integration. 4) If not using the dashboard, you may alternatively authenticate against the PAP (Policy Administration Point) REST API to programmatically manage credentials. Note that you must work with the Topcon CIAM team to register necessary OAuth 2.0 scopes.

2. Add them to .dlt/secrets.toml

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[sources.topcon_aptix_insights_source]
jwt_token = "REPLACE_ME"

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 harness:

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dlthub 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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dlthub 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 Topcon Aptix Insights 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 topcon_aptix_insights_pipeline.py

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

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Pipeline topcon_aptix_insights_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset topcon_aptix_insights_data
The duckdb destination used duckdb:/topcon_aptix_insights.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 topcon_aptix_insights_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 https://token.us.auth.topcon.com/as/token.oauth2 and https://token.us.auth.topcon.com/as/authorization.oauth2 (for authentication/token exchange), or platform-specific OData/REST endpoints as defined in your specific Aptix integration documentation. from the Topcon Aptix Insights 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 topcon_aptix_insights_source(jwt_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The API base URL is platform-specific and should be retrieved from the official Aptix Integration Platform documentation provided to licensed users.",
            "auth": {"type": "bearer", "token": jwt_token},
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "v1/projects"}},
            {"name": "insights", "endpoint": {"path": "v1/insights"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="topcon_aptix_insights_pipeline",
        destination="duckdb",
        dataset_name="topcon_aptix_insights_data",
    )
    load_info = pipeline.run(topcon_aptix_insights_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("topcon_aptix_insights_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM topcon_aptix_insights_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("topcon_aptix_insights_pipeline").dataset()
data.projects.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 Topcon Aptix Insights 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

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

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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