ladon-mimir 0.1.0

Async Wikipedia adapter for Ladon — mathematical finance corpus for LLM fine-tuning.

ladon-mimir

Async Wikipedia adapter for the Ladon crawler framework.

Crawls a Wikipedia category tree via async BFS, fetches full article text through the MediaWiki API, and persists everything to a DuckDB database — ready to export as Parquet for LLM fine-tuning pipelines or downstream analysis.

Built as a first-party reference adapter for Category:Mathematical_finance, but works with any Wikipedia category.

Quick start

pip install ladon-mimir
ladon-mimir --category "Mathematical finance" --out mimir.db

No authentication. No external server. Wikipedia's API is public.

Re-running against the same --out file resumes automatically — already-stored article page IDs are skipped.

What you get

Each run writes two tables to mimir.db:

mimir_articles — one row per article (upserted on page_id):

column	type	description
run_id	TEXT	UUID of the crawl run that last wrote this row
page_id	INTEGER	Wikipedia page ID (primary key)
title	TEXT	Article title
summary	TEXT	First paragraph of the article
full_text	TEXT	Full article text (extract)
categories	TEXT	JSON array of category names
last_modified	TIMESTAMPTZ	Last edit timestamp (UTC)
word_count	INTEGER	Word count of full text
url	TEXT	Canonical Wikipedia URL

ladon_runs — one row per crawl run:

column	type	description
run_id	TEXT	UUID for this crawl run
category	TEXT	Root category name
started_at	TIMESTAMPTZ	When the run started (UTC)
finished_at	TIMESTAMPTZ	When the run finished; NULL while running
status	TEXT	running, done, or failed
articles_fetched	INTEGER	Articles successfully saved
articles_failed	INTEGER	Articles that failed to fetch or parse

Sample DuckDB query

-- Longest articles in the corpus
SELECT title, word_count, url
FROM mimir_articles
ORDER BY word_count DESC
LIMIT 10;

Export to Parquet

ladon-mimir --category "Mathematical finance" --out mimir.db --sync

Or from Python:

from ladon_mimir import export_parquet

count = export_parquet("mimir.db", "mimir.parquet")
print(f"Exported {count} articles")

Note: The categories column is exported as a JSON-encoded VARCHAR. Parse it with json.loads or DuckDB's json_extract / json_array_elements.

CLI reference

ladon-mimir --category NAME [options]

flag	default	description
--category NAME	required	Wikipedia category name without the Category: prefix
--out PATH	mimir.db	Output DuckDB database path
--concurrency N	10	Maximum concurrent article fetches
--depth N	2	BFS depth for sub-category traversal
--limit N	0 (unlimited)	Maximum articles to fetch
--exclude-category NAME	—	Sub-category to prune from BFS (repeatable)
--sync	off	Export to <out>.parquet after crawl
--dry-run	off	Print what would be done without crawling
--verbose, -v	off	Show DEBUG-level framework messages

Examples

# Crawl with depth 3, 5 concurrent fetches, cap at 500 articles
ladon-mimir --category "Mathematical finance" --depth 3 --concurrency 5 --limit 500

# Crawl and immediately export to Parquet
ladon-mimir --category "Mathematical finance" --out mimir.db --sync

# Exclude noisy sub-categories
ladon-mimir --category "Mathematical finance" \
    --exclude-category "Stubs" \
    --exclude-category "Mathematical finance stubs"

# Preview what would run without touching the network
ladon-mimir --category "Mathematical finance" --dry-run

Use as a library

import asyncio
from ladon import async_run_crawl
from ladon.networking import AsyncHttpClient
from ladon.networking.config import HttpClientConfig
from ladon.runner import RunConfig

from ladon_mimir import MimirPlugin, export_parquet
from ladon_mimir.models import ArticleRecord, CategoryRecord
from ladon_mimir.repository import MimirRepository

async def crawl(category: str, db_path: str) -> None:
    config = RunConfig(async_concurrency=10)
    client_config = HttpClientConfig(
        user_agent="my-bot/1.0",
        min_request_interval_seconds=0.2,
    )

    with MimirRepository(db_path) as repo:
        existing_ids = repo.get_existing_page_ids()
        repo.start_run(category)

        plugin = MimirPlugin(
            category=category,
            max_depth=2,
            skip_page_ids=existing_ids,  # resume: skip already-stored articles
        )

        async def on_leaf(record: object, parent: object) -> None:
            if isinstance(record, ArticleRecord) and isinstance(parent, CategoryRecord):
                repo.save_article(record, parent)

        async with AsyncHttpClient(client_config) as client:
            root_refs = await plugin.source.discover(client)
            result = await async_run_crawl(
                root_refs[0], plugin, client, config, on_leaf=on_leaf
            )
            repo.finish_run("done")

        print(f"Saved {result.leaves_persisted} articles, {result.leaves_failed} failed")

asyncio.run(crawl("Mathematical finance", "mimir.db"))
export_parquet("mimir.db", "mimir.parquet")

How it works

ladon-mimir implements the Ladon SES (Source / Expander / Sink) protocol against the MediaWiki Action API.

Pipeline

flowchart TB
    subgraph plugin ["Async SES Plugin"]
        direction LR
        SRC["WikiCategorySource\ndiscover()"] -- "CategoryRef × 1" --> EXP["WikiCategoryExpander\nexpand() BFS"] -- "CategoryRecord\nArticleRef × N" --> SNK["WikiArticleSink\nconsume()"]
    end
    subgraph persistence ["Persistence"]
        direction LR
        REPO["MimirRepository"] --> DB[("mimir.db")] -- "export_parquet()" --> PQ[("mimir.parquet")]
    end
    SNK -- "ArticleRecord" --> REPO

SES class map

Layer	Class	MediaWiki API call
Source	WikiCategorySource	— (returns the root CategoryRef directly)
Expander	WikiCategoryExpander	action=query&list=categorymembers (BFS, paginated)
Sink	WikiArticleSink	action=query&prop=extracts|categories|info

The expander performs async BFS with asyncio.gather at each depth level — all sub-categories at a given depth are fetched concurrently. Article refs are deduplicated by page_id across the entire traversal; already-stored IDs are skipped for resume.

Development

git clone https://github.com/MoonyFringers/ladon-mimir
cd ladon-mimir
pip install -e ".[dev]"
pre-commit install
pytest tests/ -v

License

Apache-2.0 — see LICENSE.

The Ladon core framework is AGPL-3.0-only. ladon-mimir is Apache-2.0 but has a runtime dependency on Ladon core; review the AGPL terms if you plan to distribute or run this as a network service.