Ada Chat Python API Docs | dltHub

Ada Chat is a conversational AI platform that provides REST APIs for managing chats, conversations, and messages. The REST API base URL is https://{subdomain}.ada.cx/api and All requests require a rotatable API key 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 Ada Chat data in under 10 minutes.

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What data can I load from Ada Chat?

Here are some of the endpoints you can load from Ada Chat:

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How do I authenticate with the Ada Chat API?

Include the API key in the Authorization header as a Bearer token on every request.

1. Get your credentials

1. Log in to the Ada dashboard.
2. In the top‑right corner, click your account avatar and select Settings.
3. Navigate to the API Keys or Integrations tab.
4. Click Create New Key (or copy an existing rotatable key).
5. Copy the generated key and store it securely; it will be used as the Bearer token in API requests.

2. Add them to .dlt/secrets.toml

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[sources.ada_chat_source]
api_key = "your_rotatable_api_key_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 Ada Chat 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 ada_chat_pipeline.py

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

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Pipeline ada_chat_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ada_chat_data
The duckdb destination used duckdb:/ada_chat.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 ada_chat_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 conversations and messages from the Ada Chat 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 ada_chat_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.ada.cx/api",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "v1/conversations", "data_selector": "conversations"}},
            {"name": "messages", "endpoint": {"path": "v1/messages", "data_selector": "messages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ada_chat_pipeline",
        destination="duckdb",
        dataset_name="ada_chat_data",
    )
    load_info = pipeline.run(ada_chat_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("ada_chat_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ada_chat_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ada_chat_pipeline").dataset()
data.conversations.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 Ada Chat 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 a 401 Unauthorized response, verify that the rotatable API key is correct and included as a Bearer token in the Authorization header. Keys can be rotated from the Ada dashboard; make sure the latest key is used.

Rate limits

Ada enforces request rate limits per subdomain. When a 429 Too Many Requests response is returned, pause calls for the duration indicated in the Retry-After header before retrying.

Pagination quirks

Responses use cursor‑based pagination. The response includes a next_cursor field; to retrieve subsequent pages, include cursor={next_cursor} as a query parameter. Missing or incorrect cursor values will result in empty result sets.

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