Chaport Python API Docs | dltHub

Chaport is a live chat platform and API that lets you manage chats, visitors, operators, messages, and webhooks via REST endpoints. The REST API base URL is https://app.chaport.com/api/v1 and all requests require a Bearer token for authentication.

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 Chaport data in under 10 minutes.

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What data can I load from Chaport?

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

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How do I authenticate with the Chaport API?

The API uses HTTP Bearer tokens. Include an Authorization header: "Authorization: Bearer <your_token>" on all requests and send Accept: application/json.

1. Get your credentials

1. Log in to your Chaport account. 2) Open Settings → API (app.chaport.com/#/settings/api). 3) Generate or copy the access token shown. 4) Use that token in the Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.chaport_source]
token = "your_chaport_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 Chaport 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 chaport_pipeline.py

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

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Pipeline chaport_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chaport_data
The duckdb destination used duckdb:/chaport.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 chaport_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 operators and visitors from the Chaport 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 chaport_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.chaport.com/api/v1",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "operators", "endpoint": {"path": "operators", "data_selector": "result"}},
            {"name": "visitors", "endpoint": {"path": "visitors", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="chaport_pipeline",
        destination="duckdb",
        dataset_name="chaport_data",
    )
    load_info = pipeline.run(chaport_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("chaport_pipeline").dataset()
sessions_df = data.visitors.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM chaport_data.visitors LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chaport_pipeline").dataset()
data.visitors.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 Chaport 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 get 401/403, verify Authorization header is present and formatted as "Authorization: Bearer ". Ensure token copied from Settings → API is valid and that your account plan includes REST API access (REST API is available on paid plans).

Pagination and "links.more"

Some endpoints (chats, visitor chats) return a links.more array of API URLs for older pages. Follow the provided URLs rather than constructing page tokens yourself; responses commonly wrap record arrays under the "result" key.

Common HTTP errors

• 400 Bad Request: invalid or missing parameters; response body contains details.
• 404 Not Found: resource not found (e.g., invalid IDs).
• 500 Internal Server Error: server-side error. Handle by retry/backoff.

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