Load RChilli Resume Parser data to DuckDB

RChilli Resume Parser is an API service that extracts structured information from resume files in various formats. Everything needed to build a working RChilli Resume Parser → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your RChilli Resume Parser to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from RChilli Resume Parser to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the RChilli Resume Parser API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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RChilli Resume Parser API at a glance

These values come from the RChilli Resume Parser API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the RChilli Resume Parser API?

Authentication is performed by passing a 'userkey' directly within the JSON request body, which also requires a 'Content-Type: application/json' header.

1. Get your credentials

1. Navigate to the RChilli My Account portal (typically accessed via the RChilli website). 2. Log in using your registered Email and Password, or via Office 365/Google single sign-on. 3. Once logged in, select the Dashboard from the navigation panel. 4. Locate the User Key field; click the icon/symbol next to it to display and copy your API User Key. If you do not have an account or require support for plans and access, contact support@rchilli.com.

2. Add them to .dlt/secrets.toml

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[sources.rchilli_resume_parser_source]
user_key = "your_actual_api_user_key_here"
sub_user_id = "your_company_name"
version = "8.0.0"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What RChilli Resume Parser data can I load into DuckDB?

These are the RChilli Resume Parser endpoints dlt can load into DuckDB:

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How do I load only new RChilli Resume Parser records?

RChilli Resume Parser exposes pageStart on RchilliParser/Rchilli/search, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "search_boolean", "endpoint": {
    "path": "RchilliParser/Rchilli/search",
    "data_selector": "records",
    "incremental": {"cursor_path": "pageStart", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated RChilli Resume Parser pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading parseResumeBinary and parseResumePublicUrl from the RChilli Resume Parser API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def rchilli_resume_parser_source(userkey=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rest.rchilli.com/RChilliParser/Rchilli",
            "auth": {"type": "api_key", "api_key": userkey, "name": "userkey"},
        },
        "resources": [
            {"name": "search_boolean", "endpoint": {"path": "RchilliParser/Rchilli/search", "data_selector": "records"}},
            {"name": "search_freetext", "endpoint": {"path": "RchilliParser/Rchilli/search", "data_selector": "records"}}
        ],
    }
    yield from rest_api_resources(config)


def load_rchilli_resume_parser_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rchilli_resume_parser_pipeline",
        destination="duckdb",
        dataset_name="rchilli_resume_parser_data",
    )
    load_info = pipeline.run(rchilli_resume_parser_source())
    print(load_info)


if __name__ == "__main__":
    load_rchilli_resume_parser_to_duckdb()

Run it with python rchilli_resume_parser_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query RChilli Resume Parser data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("rchilli_resume_parser_pipeline").dataset()
df = data.search_boolean.df()
print(df.head())

SQL:

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SELECT * FROM rchilli_resume_parser_data.search_boolean LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the RChilli Resume Parser to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw RChilli Resume Parser loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load RChilli Resume Parser data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack