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DSpace Python Client

A comprehensive Python client for the DSpace REST API with version-aware compatibility checking and automatic documentation management.

Table of Contents

This README is organised in two parts. If you just want to run the bundled examples against a DSpace server, start with Running the Examples; many users never need to go further. If you want to write your own scripts with the library, continue to Building with the Library.

Key Features

  • Version-first initialization with automatic documentation fetching
  • Pre-execution validation for all API operations
  • Multi-version compatibility support (DSpace 7.6, 8.x, 9.x; 7.6 REST contract in docs/dspace-rest-api/7.6/)
  • Git-based documentation management with auto-updates
  • Rich console output for beautiful user experience
  • Batch operations with adaptive concurrency control
  • Comprehensive error handling with actionable messages

Important Safety Notice

Please read this section before running anything (examples or your own scripts) against a real DSpace instance.

Warning

Always run against a test or staging server first. This client can create, modify, and delete communities, collections, items, bitstreams, EPeople, and groups, often in bulk. Batch operations and cleanup flags are irreversible at scale. Verify behaviour on a non-production instance (e.g. demo.dspace.org or your own staging environment) before pointing any script at a live repository.

Warning

If you use AI to generate or modify scripts, you must understand every line before running it. Large language models readily produce plausible-looking code that deletes the wrong things, silently skips validation, or hits the API in ways that look fine in isolation but misbehave against real data. You are responsible for the effects of any code you run against a DSpace repository. Do not run AI-assisted code (even a small edit to an existing example) unless you can explain what each operation does, have read the DSpace REST contract for the endpoints it touches, and have tested it on a throwaway instance first.

These two rules apply equally to the bundled examples, your own scripts, and anything copied out of the Recipes section.

Installation

Warning

This client is not yet published to PyPI. The name dspace-client on PyPI currently belongs to an unrelated project, so pip install dspace-client will install the wrong package. Install from source instead.

Prerequisites

  • Python 3.11 or higher (check with python3 --version)
  • Git - also required at runtime, because the client automatically fetches the DSpace REST API docs from GitHub using git
  • pip 21.3 or newer (bundled with recent Python releases). pip install -e . uses PEP 660 editable installs, which need at least pip 21.3. If you hit errors during the install step below, run pip install --upgrade pip inside the activated venv and retry.

Getting the code

Pick whichever is more convenient:

Option A: Clone with git

git clone https://git.atmire.com/scripts/dspace-python-client.git
cd dspace-python-client

Option B: Unpack a zip archive

If you received the project as a zip file (for example, downloaded from GitLab's download menu or shared with you directly), unzip it and open a terminal inside the unpacked folder:

cd path/to/dspace-python-client

Installing

From inside the project folder, create a virtual environment, upgrade pip (to satisfy the 21.3+ requirement), and install the client:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install --upgrade pip
pip install -e .

For optional dependencies (seed scenarios, dev tools) and more detail, see Contributing → Installation from Source.

Running the Examples

The fastest way to get a feel for what the client can do is to run the scripts in the examples/ directory against a test DSpace instance. Each example is self-contained and prints what it creates/reads/deletes.

Install optional deps for seed scenarios (PyYAML for examples/seed/):

pip install -e ".[examples]"

All examples follow the same pattern: they prompt for a base URL, username, and password at runtime, and declare which DSpace versions they support. They will refuse to run against a server that does not match.

Run any example by invoking it with Python from the project root, for example:

python examples/advanced_auth.py

Each example's .py file starts with a docstring describing its purpose, required DSpace version, and usage. Scripts grouped under a folder (examples/seed/, examples/full-text-finder/) have their own README.md with full usage details.

Warning

Re-read the Important Safety Notice before pointing any example at a live repository. The seed scenarios in particular create and optionally delete substantial amounts of content.

General Tutorials

  • basic_usage.py - Short generic CRUD: community, collection, item, bitstream
  • bulk_import.py - Batch item creation with adaptive concurrency (BatchItemCreator)
  • advanced_auth.py - Session management and error handling

Reporting and Data Extraction

  • extract_items_by_year.py - Export all items from a given publication year to CSV (DSpace 7, 8, 9)
  • recent_items_with_submitters.py - Recent items plus submitter email, as CSV. DSpace 9+ only (uses a submitter endpoint introduced in 9.0)
  • count_items_with_pdf_bitstream.py - ⚠️ Anti-pattern; see the anti-pattern notice below
  • count_items_with_pdf_bitstream_oai.py - ⚠️ Anti-pattern; see the anti-pattern notice below

Data Modification

  • delete_item.py - Delete a single item with a retype-to-confirm safeguard (re-type the item's dc.title, or DELETE if the title is empty). DSpace 7.x
  • link_author_authorities.py - Interactive linking of free-text author metadata to records already in this repository's local SOLR authority core. Item / Repository / ORCID / Name modes; exact or fuzzy matching; timestamped log files. Does not query the public ORCID registry.

Larger Workflows (folder-based)

  • examples/full-text-finder/ - Find open-access PDFs (Unpaywall → OpenAlex → OpenAIRE → CORE) for items with a DOI but no PDF in the ORIGINAL bundle, optionally upload. See examples/full-text-finder/README.md for setup, modes, and prompts.
  • examples/seed/ - dspace-seed-style scenarios (MiniSpace, MegaSpace) for filling a repository with communities, collections, items, bitstreams, EPeople, groups, and stats. See examples/seed/README.md. MiniSpace declares DSpace 9.0 and runs verify_server_version by default. MegaSpace requires --collections 2 or more and supports the on_metrics_sample callback on create_items_batch for time-series metrics. The large file examples/seed/seedpacks/default.yml is copied from dspace-seed; sync it manually if the upstream pack changes.

Anti-pattern: counting items with PDF bitstreams

The two count_items_with_pdf_bitstream* scripts are preserved in this repo as a reference for library patterns (paging, caching, slow-request logging, OAI-PMH harvesting), not as the recommended way to answer the question "how many items in this repository have a PDF?".

For any non-trivial repository:

  • A direct SQL query against the DSpace database is dramatically faster (seconds vs. hours on large repos) and more accurate.
  • Walking the REST API or OAI-PMH feed puts avoidable load on the server and still produces an approximation.

Use the scripts only if you do not have DB access and are prepared to accept the runtime cost. The same caveat applies to the REST and OAI code snippets in the Recipes section below.

Building with the Library

This section is for developers writing their own scripts against the library. If you only want to run the bundled examples, you can skip it.

Getting Started

For a minimal, runnable walkthrough (declaring target versions, authenticating, and creating your first community/collection/item), see QUICKSTART.md. The rest of this section covers the architecture, API surface, and library conventions that QUICKSTART references.

Version-First Architecture

The client requires you to specify target DSpace version(s) at initialization:

# Single version
client = DSpaceClient(..., target_versions="8.0")

# Multiple versions (strictest validation)
client = DSpaceClient(..., target_versions=["7.6", "8.0", "9.0"])

# Latest development (default)
client = DSpaceClient(..., target_versions="bleeding-edge")

What target_versions Means

Important: The target_versions parameter restricts which DSpace servers you can connect to based on version compatibility rules.

Key points:

  1. Version restrictions - When you specify target_versions, you declare which DSpace versions your code is compatible with. The client will validate the server version and:

    • Allow connections to servers with exact version matches (e.g., target 9.0 → server 9.0)
    • Warn but allow connections to servers with minor version differences (e.g., target 9.0 → server 9.1, same major version)
    • Reject connections to servers with major version mismatches (e.g., target 8.0 → server 7.6, different major version)

  2. Multiple versions - When you specify multiple versions (e.g., ["8.0", "9.0"]), the server must match at least one of the target versions. This allows your code to work with multiple DSpace installations.

  3. Pre-execution validation - Before each API call, the client also validates that the operation is supported in your target version(s). If not, it raises a VersionIncompatibilityError before making the request.

Developer Workflow:

As a developer, you declare which DSpace versions your script supports when you write it:

# DEVELOPER DECLARES: This script is compatible with DSpace 8.0 and 9.0
TARGET_VERSIONS = ["8.0", "9.0"]

# ... later in your script, when user provides URL ...

from dspace_client import create_validated_client, ServerVersionMismatchError

try:
    # Authenticates, creates client, and validates server version automatically
    auth, client = await create_validated_client(
        base_url=base_url,  # User provides this at runtime
        username=username,
        password=password,
        target_versions=TARGET_VERSIONS  # Developer-declared versions
    )
    # Version validation happens automatically - server version is checked against TARGET_VERSIONS
    # If major mismatch, ServerVersionMismatchError is raised
    await client.create_community("My Community")
except ServerVersionMismatchError as e:
    print(f"Cannot connect: Server version doesn't match declared compatibility")
    print(f"This script only works with DSpace versions: {', '.join(TARGET_VERSIONS)}")

Manual Version Validation

If you create the client manually, call verify_server_version() after initialization:

# DEVELOPER DECLARES: This script is compatible with DSpace 8.0 and 9.0
TARGET_VERSIONS = ["8.0", "9.0"]

from dspace_client import DSpaceAuthClient, DSpaceClient, ServerVersionMismatchError

auth = DSpaceAuthClient("https://demo.dspace.org")
jwt, status = await auth.authenticate("user", "pass")

client = DSpaceClient(
    base_url="https://demo.dspace.org",
    jwt_token=jwt,
    csrf_token=auth.csrf_token,
    http_client=auth.client,
    target_versions=TARGET_VERSIONS  # Developer-declared versions
)

# Validate server version (raises ServerVersionMismatchError on major mismatch)
try:
    await client.verify_server_version(raise_on_mismatch=True)
except ServerVersionMismatchError as e:
    print(f"Version mismatch: {e}")
    print(f"This script only works with DSpace versions: {', '.join(TARGET_VERSIONS)}")
    # Handle error...

This ensures:

  • Server version validation prevents connecting to incompatible servers
  • Operation compatibility validation before every API call
  • Automatic documentation fetching for target versions
  • Clear error messages for version incompatibilities

Documentation Management

The client automatically manages DSpace REST API documentation:

  • Git-based storage in docs/dspace-rest-api/{version}/
  • Automatic updates if docs are older than 24 hours
  • Version-specific branches from DSpace/RestContract repository
  • CLI tools for manual management

Note: The dspace-docs command is available after installing the package. If using a virtual environment, activate it first (source venv/bin/activate). Run these from the project root so documentation is stored under docs/dspace-rest-api/{version}/ (paths are relative to the current working directory).

# First-time (or missing cache): clone the RestContract docs for a given API version
dspace-docs fetch 9.0
# Other examples: 7.6, 8.0, bleeding-edge (see `dspace-docs list` for supported labels)

# List supported versions and whether each is already present locally
dspace-docs list

# Refresh every version that is already cached (does not fetch versions you never downloaded)
dspace-docs update

# Per-version git details and last update time
dspace-docs status

If a version was fetched recently, dspace-docs fetch <version> may reuse the cache until it is older than 24 hours (same logic as the client’s automatic fetch).

API Reference

Core Classes

  • DSpaceAuthClient - Authentication and session management
  • DSpaceClient - Main API client with version validation
  • BatchItemCreator - High-performance bulk operations; create_items_batch accepts an optional on_metrics_sample callback (invoked with PerformanceMetrics when progress is logged, every 50 items and at completion) for benchmarks and degradation analysis
  • ConcurrencyController - Adaptive concurrency control

Key Methods

# Communities
await client.create_community(name, metadata=None, parent_uuid=None)
await client.delete_community(uuid)

# Collections
await client.create_collection(name, parent_community_uuid, metadata=None)
await client.delete_collection(uuid)

# Items
await client.create_item(name, owning_collection_uuid, metadata=None)
await client.delete_item(uuid)

# Bitstreams
await client.upload_bitstream(bundle_uuid, filename, content, metadata=None)
await client.delete_bitstream(uuid)
await client.get_item_bundles(item_uuid)
await client.get_bundle_bitstreams(bundle_uuid, embed_format=True)
await client.get_bitstream_format(bitstream_uuid)
await client.get_bitstream_formats(page=0, size=100)

# Reporting: count items with at least one PDF bitstream
result = await client.count_items_with_pdf_bitstream(
    pdf_format_id=3,  # optional; resolved from registry if omitted
    page_size=100,
    delay_between_pages=1.0,
)
# result["count"], result["total_items_processed"], result["pdf_format_id"]

# EPeople
await client.create_eperson(email, first_name, last_name)
await client.add_eperson_to_group(group_uuid, eperson_uuid)

# Groups
await client.create_group(name, description=None)
await client.add_subgroup_to_group(parent_group_uuid, subgroup_uuid)

Recipes

Warning

Both recipes below count items with a PDF bitstream via the DSpace REST/OAI interfaces. For any non-trivial repository this is an anti-pattern: a direct SQL query against the DSpace database is dramatically faster and more accurate. These snippets are kept here as references for paging, caching, and slow-request logging. See Anti-pattern: counting items with PDF bitstreams for context.

Counting items with PDF bitstreams (REST, includes non-public items)

To report how many items have at least one bitstream in PDF format (equivalent to a DB count over items with PDF bitstreams), use the REST API with authentication so that all items (including non-public) are considered. The client pages through discovery (item UUIDs only), then for each item fetches only bundles and bitstreams (with format).

Caching and resuming: Use RestPDFCountCache so that already-known items are skipped on subsequent runs (items are assumed immutable). Use force_rerun=True to re-check everything.

Slow-request logging: To identify which endpoints are slow, set slow_request_threshold_seconds and slow_request_callback on the client; requests exceeding the threshold are also logged at WARNING and can be collected for analysis.

from dspace_client import create_validated_client, RestPDFCountCache

auth, client = await create_validated_client(base_url=..., username=..., password=...)

cache = RestPDFCountCache(base_url=base_url)  # default dir: ~/.cache/dspace-rest-pdf
cache.load()

result = await client.count_items_with_pdf_bitstream(
    page_size=100,
    delay_between_pages=1.0,
    cache=cache,
    force_rerun=False,  # use cache; set True to re-check all
)
cache.save()
print(f"Items with ≥1 PDF: {result['count']} (of {result['total_items_processed']} processed)")

To log and inspect slow requests, pass slow_request_threshold_seconds and slow_request_callback into create_validated_client(..., **client_kwargs); the example script does this and prints a table of slow requests at the end.

Example script: examples/count_items_with_pdf_bitstream.py. Set DSPACE_REST_PDF_CACHE_DIR to override the cache directory.

Counting items with PDF via OAI-PMH (no auth, cacheable)

For large or slow repositories, you can count items with PDF using the OAI-PMH endpoint at {base_url}/server/oai/request. No authentication is required. The client harvests ListRecords with metadataPrefix=oai_dc and infers PDF from <dc:format>application/pdf</dc:format>. Results can be stored in a persistent CSV cache so resumed runs skip already-seen items; incremental harvest (from last_until) is supported.

from dspace_client.oai import OAIClient, OAIPDFCountCache, iterate_oai_dc_records

base_url = "https://your-dspace.edu"
cache = OAIPDFCountCache(base_url=base_url)  # default: ~/.cache/dspace-oai-pdf
cache.load()

async with OAIClient(base_url=base_url) as client:
    async for parsed in iterate_oai_dc_records(client, from_=cache.last_until):
        cache.update(parsed["identifier"], parsed["datestamp"], parsed["has_pdf"])
cache.save(last_until=max_datestamp)
total, with_pdf = cache.totals()

Example script: examples/count_items_with_pdf_bitstream_oai.py. Set DSPACE_OAI_CACHE_DIR to override the cache directory.

Configuration

Concurrency Control

from dspace_client import ConcurrencyConfig

config = ConcurrencyConfig(
    initial=8,           # Starting concurrency
    max_concurrency=32,  # Maximum concurrent operations
    min_concurrency=2,   # Minimum concurrent operations
)

batch_creator = BatchItemCreator(client, config)

Version Compatibility

# Check compatibility report
report = client.validator.get_compatibility_report()
print(f"create_community supported in: {report['create_community']}")

# Check incompatible operations
incompatible = client.validator.get_incompatible_operations()
if incompatible:
    print(f"Incompatible operations: {incompatible}")

Error Handling

The client provides comprehensive error handling:

from dspace_client import (
    DSpaceClientError,
    AuthenticationError,
    DSpaceAPIError,
    VersionIncompatibilityError,
    ServerVersionMismatchError
)

try:
    auth, client = await create_validated_client(
        base_url="https://demo.dspace.org",
        username="user",
        password="pass",
        target_versions=["8.0", "9.0"]
    )
    await client.create_community("My Community")
except ServerVersionMismatchError as e:
    print(f"Server version mismatch: {e}")
    print(f"Server version: {e.server_version}")
    print(f"Target versions: {e.target_versions}")
except VersionIncompatibilityError as e:
    print(f"Operation not supported: {e}")
    print(f"Supported versions: {e.supported_versions}")
except DSpaceAPIError as e:
    print(f"API error: {e}")
except AuthenticationError as e:
    print(f"Authentication failed: {e}")

Contributing

Installation from Source

git clone https://git.atmire.com/scripts/dspace-python-client.git
cd dspace-python-client

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Upgrade pip (pip install -e . requires pip >= 21.3)
pip install --upgrade pip

# Install dependencies
pip install -r requirements.txt
pip install -e .

Running Examples from Source

When running examples from a source checkout, make sure to use the venv's Python:

# Option 1: Activate venv first
source venv/bin/activate
python examples/recent_items_with_submitters.py

# Option 2: Use venv Python directly
./venv/bin/python examples/recent_items_with_submitters.py

Running Tests

pytest tests/

Code Quality

ruff check .
mypy dspace_client/

Atmire Promotional Messages (Optional)

The client can show a single non-blocking Rich panel when you await auth.close() on a session that had an open HTTP client: a thank-you line, a rotating Did you know fact, and https://www.atmire.com/ (where the terminal supports Rich links). There is no session-start promotional UI and no browser prompt.

create_validated_client does not print Atmire messaging at connect time. The examples/seed/seed_client.connect_seed_client helper still calls show_atmire_promo_start for API compatibility; that call is a no-op.

To disable all promotional output, set DSPACE_CLIENT_DISABLE_ATMIRE_PROMO=1 (or true / yes).

You can call show_atmire_promo_end from dspace_client manually if you use a custom auth flow without integrated close() messaging.

License

Copyright © 2024–2026 Atmire. Licensed under GPL-3.0-or-later — see LICENSE, NOTICE, and THIRD_PARTY_LICENSES.md.

Author

Bram Luyten - bram@atmire.com

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A comprehensive Python client for the DSpace REST API with version-aware compatibility checking and automatic documentation management.

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