FocusVision Decipher Python API Docs | dltHub

Decipher is the REST API for automating workflows with the FocusVision/Forsta Decipher survey platform. The REST API base URL is https://v2.decipherinc.com and All requests require an API key passed in the x-apikey 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 FocusVision Decipher data in under 10 minutes.

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What data can I load from FocusVision Decipher?

Here are some of the endpoints you can load from FocusVision Decipher:

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How do I authenticate with the FocusVision Decipher API?

Authentication uses a 64‑character API key (public + private parts) that must be sent in the HTTP header x-apikey: <API_KEY>.

1. Get your credentials

1. Log into your Decipher tenant (e.g., https://v2.decipherinc.com).
2. Open the API or Research Hub API page from the user menu.
3. Click the button to generate a new API key.
4. Copy the displayed 64‑character key and store it securely.
5. (Optional) Use the tenant’s admin console to rename, revoke, or regenerate keys as needed.

2. Add them to .dlt/secrets.toml

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[sources.focusvision_decipher_source]
api_key = "your_64_char_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 FocusVision Decipher 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 focusvision_decipher_pipeline.py

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

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Pipeline focusvision_decipher_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset focusvision_decipher_data
The duckdb destination used duckdb:/focusvision_decipher.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 focusvision_decipher_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 rh_users and rh_apikeys from the FocusVision Decipher 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 focusvision_decipher_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://v2.decipherinc.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "rh_users", "endpoint": {"path": "rh/users"}},
            {"name": "rh_apikeys", "endpoint": {"path": "rh/apikeys"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="focusvision_decipher_pipeline",
        destination="duckdb",
        dataset_name="focusvision_decipher_data",
    )
    load_info = pipeline.run(focusvision_decipher_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("focusvision_decipher_pipeline").dataset()
sessions_df = data.rh_users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM focusvision_decipher_data.rh_users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("focusvision_decipher_pipeline").dataset()
data.rh_users.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 FocusVision Decipher 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

• 401 INVALID AUTHENTICATION – The API key is missing, malformed, or expired. Verify that the x-apikey header contains the full 64‑character key.
• 403 INVALID AUTHORIZATION – The key is valid but lacks permission for the requested resource. Check the key’s scopes in the Decipher dashboard.

Rate Limiting & Concurrency

• 429 TOO MANY CONCURRENT REQUESTS – The tenant has exceeded the allowed concurrent call limit (typically three per survey). Reduce parallelism or add retry logic with backoff.
• 428 REACTIVATION REQUIRED – The target survey is hibernated and must be re‑activated before API access.

Pagination & Limits

• Many list endpoints support limit, offset, select, and sort query parameters (as shown in beacon examples). Use these to page through large result sets.
• If a response returns a top‑level array, treat the entire payload as the record set; otherwise, locate the array using the documented data selector.

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