Faya Python API Docs | dltHub

Faye is a publish‑subscribe messaging system that implements the Bayeux protocol for real‑time communication. The REST API base URL is http://localhost:8000/faye and All messages must include a token in the ext token field 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 Faya data in under 10 minutes.

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What data can I load from Faya?

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

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How do I authenticate with the Faya API?

Clients add a token value to the ext object of each outgoing Bayeux message; the server validates this token via a server‑side extension before allowing the operation.

1. Get your credentials

1. Decide on a secret token string to use for authentication (e.g., a UUID or random string).
2. Configure the server-side Faye extension to accept this token (see the authentication documentation).
3. Distribute the token to client applications that will set it in the ext.token field.
4. No public dashboard exists; credentials are managed manually by the service operator.

2. Add them to .dlt/secrets.toml

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[sources.faya_source]
token = "your_faye_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 Faya 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 faya_pipeline.py

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

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Pipeline faya_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset faya_data
The duckdb destination used duckdb:/faya.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 faya_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 messages and email_new from the Faya 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 faya_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:8000/faye",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "messages"}},
            {"name": "email_new", "endpoint": {"path": "email/new"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="faya_pipeline",
        destination="duckdb",
        dataset_name="faya_data",
    )
    load_info = pipeline.run(faya_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("faya_pipeline").dataset()
sessions_df = data.messages.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM faya_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("faya_pipeline").dataset()
data.messages.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 Faya 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 errors

• Missing or invalid token – If ext.token is absent or does not match the server‑side secret, the server will reject the handshake or subscription with a 403 error.
• Incorrect userId – Supplying a mismatched ext.userId can also cause authentication failures.

Connection issues

• Handshake timeout – The server may close the connection if the client does not complete the Bayeux handshake within the configured timeout (default 25 seconds).
• Unexpected disconnects – Network interruptions can cause the client to lose its session ID; reconnect using a fresh client_id.

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