Revv Python API Docs | dltHub

Rev is a REST API for placing and tracking transcription and captioning orders and for uploading/downloading source and output files. The REST API base URL is https://api.rev.com/api/v1 and All requests require Rev client and user API keys passed in the Authorization 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 Revv data in under 10 minutes.

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What data can I load from Revv?

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

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How do I authenticate with the Revv API?

Authentication uses two keys combined in the Authorization header in this format: Authorization: Rev [ClientApiKey]:[UserApiKey]. Use HTTPS for all requests; specify Accept header (application/json) or append .json to URLs to request JSON.

1. Get your credentials

1. Sign up / sign in to Rev (or Sandbox)\n2) Open Account Settings / API Keys (or Quick Start)\n3) Create or copy your Client API Key and User API Key\n4) Store both securely; use them together in Authorization header as Rev ClientKey:UserKey.

2. Add them to .dlt/secrets.toml

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[sources.revv_source]
client_api_key = "your_client_api_key_here"
user_api_key = "your_user_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 Revv 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 revv_pipeline.py

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

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Pipeline revv_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset revv_data
The duckdb destination used duckdb:/revv.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 revv_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 orders and attachment from the Revv 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 revv_source(client_and_user_keys=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.rev.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key_pair": client_and_user_keys,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "orders"}},
            {"name": "attachment", "endpoint": {"path": "attachment/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="revv_pipeline",
        destination="duckdb",
        dataset_name="revv_data",
    )
    load_info = pipeline.run(revv_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("revv_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM revv_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("revv_pipeline").dataset()
data.orders.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 Revv 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 401/403, verify you send Authorization header exactly as: Rev [ClientApiKey]:[UserApiKey] over HTTPS. Ensure keys are active and not swapped. For requests using .json append or Accept header, ensure correct header formatting.

Pagination and large result sets

GET /orders is paged. Use query params page (0‑based) and pageSize (5‑100). Response includes total_count, results_per_page and page to iterate pages until you have retrieved total_count.

Downloading attachments and content types

GET /attachment/{id}/content returns binary data; set Accept or request specific format via URL extension (e.g., /content.txt or Accept: text/plain) or use response Content‑Type. Large files may require streaming and retry on network failures.

Common API errors and codes

• 401/403: Authentication failure or invalid key pairing.
• 400: Bad request (malformed JSON, missing fields). POST /inputs returns 10001‑10005 error codes for unsupported content type, retrieval failures, malformed multipart, unspecified filename/URL.
• 403: Forbidden when supplying mutually exclusive query parameters (e.g., ids and clientRef together).
• 500: Server errors; retry with exponential 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