python-tind-client 0.2.1

Python library for interacting with the TIND DA API

python-tind-client

Python library for interacting with the TIND DA API.

Requirements

• Python 3.12+

Installation

pip install python-tind-client

Install with optional development dependencies:

pip install "python-tind-client[dev]"    # test + lint + debug
pip install "python-tind-client[test]"   # pytest + requests-mock
pip install "python-tind-client[lint]"   # mypy + pydoclint + pylint
pip install "python-tind-client[debug]"  # debugpy

Configuration

Create a TINDClient with optional configuration values:

• api_key (optional): Your TIND API token. Falls back to the TIND_API_KEY environment variable.
• api_url (optional): Base URL of the TIND instance (e.g. https://tind.example.edu). Falls back to the TIND_API_URL environment variable.
• default_storage_dir (optional): Default output directory for downloaded files. Defaults to ./tmp.

Usage

TIND Credentials / Optional ENV config

You will need to either pass your TIND API credentials as arguments when instantiating a TINDClient or set them as environment variables with the following names:

• TIND_API_KEY
• TIND_API_URL

instantiate a client

from tind_client import TINDClient

client = TINDClient(
	api_key="your-token",
	api_url="https://tind.example.edu",
	default_storage_dir="/tmp",
)

Fetch pyMARC metadata for a record

record = client.fetch_metadata("116262")
print(record["245"]["a"])  # title

Fetch file metadata for a record

metadata = client.fetch_file_metadata("116262")
print(metadata[0])         # first file metadata dict
print(metadata[0]["url"])  # file download URL

Download a file

# use metadata from previous example
path_to_download = client.fetch_file(metadata[0]["url"])

Search for records

# return a list of record IDs matching a query
ids = client.fetch_ids_search("collection:'Disabled Students Program Photos'")

# return PyMARC records matching a query
records = client.fetch_search_metadata("collection:'Disabled Students Program Photos'")

# return raw XML or PyMARC records from a paginated search
# NOTE: for large result sets, use the write_search_results_to_file() method and then parse that file
xml_results = client.search("collection:'Disabled Students Program Photos'", result_format="xml")
pymarc_results = client.search("collection:'Disabled Students Program Photos'", result_format="pymarc")

# search Tind with a query and write results to an XML file in the default storage directory
records_written = client.write_search_results_to_file("Old Emperor Norton", "full_norton_results.xml")

Running tests

pytest

License

MIT — © 2026 The Regents of the University of California