Evisort Python API Docs | dltHub

Evisort is an AI‑powered contract lifecycle management and contract‑analysis platform exposing REST APIs to upload, search, download, and manage contract documents and workspace resources. The REST API base URL is https://api.evisort.com/v1 and API key (EVISORT-API-KEY) exchanged for a JWT; all subsequent requests use a Bearer token in 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 Evisort data in under 10 minutes.

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What data can I load from Evisort?

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

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How do I authenticate with the Evisort API?

Clients first POST to /auth/token with the EVISORT-API-KEY header to obtain a JWT (response contains a "token"). Include Authorization: Bearer {token} and Content-Type: application/json on subsequent requests.

1. Get your credentials

1. Log into the Evisort web UI. 2) Open your user/profile settings (API Keys or Integrations). 3) Create a new API Key and copy it (labeled EVISORT-API-KEY). 4) Use that API key in your client to call POST https://api.evisort.com/v1/auth/token to obtain a JWT.

2. Add them to .dlt/secrets.toml

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[sources.evisort_source]
evisort_api_key = "your_evisort_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 Evisort 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 evisort_pipeline.py

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

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Pipeline evisort_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset evisort_data
The duckdb destination used duckdb:/evisort.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 evisort_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 documents and search from the Evisort 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 evisort_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.evisort.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "documents", "data_selector": "documents"}},
            {"name": "search", "endpoint": {"path": "search", "data_selector": "documents"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="evisort_pipeline",
        destination="duckdb",
        dataset_name="evisort_data",
    )
    load_info = pipeline.run(evisort_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("evisort_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM evisort_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("evisort_pipeline").dataset()
data.documents.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 Evisort 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 POST /auth/token returns 401/403: verify you are sending header EVISORT-API-KEY: <your_api_key>. If token request succeeds but subsequent calls return 401, ensure Authorization header is "Bearer {token}" and that the token has not expired; re‑run /auth/token to get a fresh JWT.

Pagination and large result sets

Search and listing endpoints support page and pageSize query parameters (examples use page=1&pageSize=100). For very large exports use the auditlogs/records endpoint which returns a records array and supports bulk retrieval.

Binary/document downloads

Document content endpoints (/documents/{id}/content) return binary streams (PDF/DOCX). Do not attempt to JSON‑decode responses; use streaming download. Ensure correct query parameters (type=pdf|docx and ocr=true|false) as needed.

Rate limits and 429s

The docs do not publish a public rate‑limit value; handle 429 responses by backing off and retrying with exponential backoff. If rate limits are encountered frequently, contact Evisort support or your account manager.

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