eds4jinja2 1.0.2

Embed the data source specifications in your JINJA templates directly, and enjoy the dynamic data contexts.

eds4jinja2

Embed the data-source specification directly in your Jinja2 templates. Declare how to fetch the data inside the template, and eds4jinja2 resolves it at render time — so you build dynamic, data-driven reports (HTML, LaTeX/PDF, or any text format) from SPARQL endpoints, in-memory RDF graphs, and tabular/JSON files without writing data-loading glue code.

Why eds4jinja2?

Generating a report usually tangles two jobs: fetching the data (Python code that queries endpoints, reads files, shapes DataFrames and assembles a context dict) and presenting it (a template). Every change to what the report shows forces a round-trip through the Python code.

eds4jinja2 collapses that coupling: the template itself declares its data sources. Your Python code merely points the engine at a template and renders it — it stays agnostic of what data the report displays.

This pays off when you:

• iterate quickly on report content and queries — change the template, not the code;
• want queries and their visualisation to live together and evolve together;
• build many report variants over the same data;
• need the same report to run against a live SPARQL endpoint or an in-process graph with no code change.

Problems it solves

• ❌ bespoke context-building / data-plumbing code for every report → ✅ none.
• ❌ queries in Python, layout in templates, drifting apart → ✅ co-located and easy to change.
• ❌ one source per report → ✅ one template, many heterogeneous sources (endpoint + file + graph).
• ❌ "needs a running SPARQL server" → ✅ query an in-memory rdflib/oxigraph graph, server-less.
• ❌ slow, query-bound reports → ✅ opt-in parallel pre-fetch.

How it works

eds4jinja2 adds data-source builders as Jinja globals. In a template you call one, optionally set a query, and fetch. Every fetch returns a fail-safe (content, error) pair — a failure is surfaced into the template rather than crashing the render:

{% set rows, error = from_endpoint(endpoint).with_query("SELECT * WHERE { ?s ?p ?o } LIMIT 10").fetch_tabular() %}
{% set tree, error = from_file("config.json").fetch_tree() %}

fetch_tabular() returns a pandas DataFrame; fetch_tree() returns a nested dict (JSON / SPARQL-JSON). The data context is generated during rendering, so template.render() needs no pre-built context.

Installation

pip install eds4jinja2
# optional fast in-memory SPARQL engine (oxigraph):
pip install eds4jinja2[oxigraph]

Requires Python 3.11+.

Quick start

A. Render a template through the eds4jinja2 environment

from jinja2 import DictLoader
from eds4jinja2 import build_eds_environment

env = build_eds_environment(loader=DictLoader({
    "report.txt": "{% set rows, err = from_file('data.csv').fetch_tabular() %}"
                  "Rows: {{ rows | length }}\n{{ rows.to_string() }}",
}))
print(env.get_template("report.txt").render())   # data is fetched while rendering

B. Drive a whole report folder with the mkreport CLI

mkreport --target ./report --output ./out

The target folder follows a small convention:

report/
├── config.json     # { "template": "main.html", "conf": { "default_endpoint": "...", ... } }
├── templates/      # main.html and any includes
└── static/         # css/js/images, copied into the output (tree preserved)

config.json names the entry template and carries a conf object of variables available to all templates. Set "template_flavour_syntax": "latex" for LaTeX templates.

Data sources

builder	source	fetch_tabular	fetch_tree
from_file(path)	CSV/TSV/Excel and JSON/YAML/TOML files	✅	✅
from_endpoint(url)	a remote SPARQL endpoint	✅	✅
from_graph(graph) (alias from_memory)	an in-process rdflib.Graph, pyoxigraph store, or query(sparql) callable	✅	✅
from_rdf(sources, engine="rdflib")	RDF file(s)/URL(s) loaded into an in-memory graph (rdflib or oxigraph)	✅	✅
from_rdf_file(path)	a single RDF file (legacy; superseded by from_rdf)	✅	—

In-memory graph data sources

Render reports against an in-process RDF graph — no SPARQL server required:

• from_graph(graph) (alias from_memory) — query an rdflib.Graph, a pyoxigraph store, or any query(sparql) callable you already hold.
• from_rdf(sources, engine="rdflib") — load one or more RDF files/URLs into an in-memory graph once (engine "rdflib" default, or "oxigraph") and query it; tabular and tree results.

To render an existing report against an in-memory graph with the templates unchanged, inject a builder that overrides from_endpoint (which the templates already call):

import rdflib
from eds4jinja2 import InMemorySPARQLDataSource
from eds4jinja2.services.report_builder import ReportBuilder

graph = rdflib.Graph().parse("dataset.ttl")  # the consumer owns loading / manipulation
ReportBuilder(
    "report/",
    external_data_source_builders={"from_endpoint": lambda _endpoint: InMemorySPARQLDataSource(graph)},
).make_document()

Parallel report execution

For large reports whose runtime is dominated by SPARQL query latency, set parallelism in the report config.json to pre-fetch all data concurrently before rendering:

{ "template": "report.html", "conf": {}, "parallelism": 16 }

Execution is threads-only and all-or-nothing (any fetch failure aborts the report, no partial output); results are staged in a temp folder that is cleaned up afterwards. With parallelism unset or 1 the behaviour is exactly the previous sequential render. Threaded speed-up is real for remote endpoints and oxigraph in-memory graphs (both release the GIL); rdflib in-memory queries are GIL-bound (correct, limited speed-up).

Documentation

Full documentation (concepts, the ReportBuilder API, extending with new data sources): eds4jinja2.readthedocs.io

Contributing

You are more than welcome to help expand and mature this project. We adhere to the Apache code of conduct; please follow it in all your interactions on the project. When contributing, please first discuss the change you wish to make via an issue, email, or another method with the maintainers before making a change.

Licence

This project is licensed under Apache License 2.0. Powered by Meaningfy.