Simon DiesenreiterandClaude Opus 5.5
9da5a1dc48
Initial import of Politrack, a self-hosted service that tracks Austrian politicians from federal to municipal level - roles, votes, money flows, statements, meetings, social media and legal proceedings - with every claim linked to its sources. - backend: FastAPI + Postgres/pgvector; adapter framework (fetch -> parse -> load -> enrich) with robots.txt/TDM-aware fetching, a raw-response archive for reparsing, and a claim/evidence model with corroboration - sources: Parliament, Wikidata, EU Parliament/Commission, Council of Ministers, Rechnungshof party reports and donations, procurement, RTR media transparency, political ad libraries, WKStA, news feeds, Wikipedia edits, Bluesky, Mastodon, public Telegram channels, YouTube and X (official API) - web: Next.js frontend in German and English with typed API client - mcp: MCP server for AI agents and RAG - docs: architecture, data model, sources, legal notes, Unraid deployment and contributor guides; Docker Compose setup for Unraid Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
"""Keyword-based topic tagging for claims that did not get topics from their source.
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Parliament items carry official topics; news mentions, donations and posts
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are tagged here with the keyword lists of the `topic` table. An optional LLM
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classifier can replace this later (see docs/roadmap.md).
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"""
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import re
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from politrack.core import Enricher, RunContext, register
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from politrack.core.spec import DERIVED
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def keyword_pattern(keywords: list[str]) -> re.Pattern:
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"""Keywords match as word prefixes ("Klima" -> "Klimaschutz"); short ones (<= 3 chars) only as whole words."""
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parts = [re.escape(k) + (r"\b" if len(k) <= 3 else "") for k in keywords]
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return re.compile(r"\b(" + "|".join(parts) + ")", re.IGNORECASE)
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@register
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class TopicTagger(Enricher):
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spec = DERIVED
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name = "enrich.topics"
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description = "Keyword topics for claims without official topics"
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schedule = "8-59/10 * * * *"
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def run(self, ctx: RunContext) -> None:
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while (n := tag_claims(ctx.conn)):
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ctx.stats["claims_tagged"] += n
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return None
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def tag_claims(conn, batch: int = 2000) -> int:
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topics = [
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(row["id"], keyword_pattern(row["keywords"]))
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for row in conn.execute("SELECT id, keywords FROM topic WHERE cardinality(keywords) > 0")
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]
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claims = conn.execute(
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"""
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SELECT c.id, c.summary_de || ' ' || COALESCE(string_agg(d.title, ' '), '') AS text
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FROM claim c
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LEFT JOIN claim_evidence e ON e.claim_id = c.id
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LEFT JOIN document d ON d.id = e.document_id
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WHERE c.claim_type IN ('media_mention', 'statement', 'donation', 'side_job', 'appearance', 'meeting', 'travel',
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'money_flow', 'government_decision', 'wiki_edit', 'career')
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AND NOT (c.data ? 'topics_tagged')
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GROUP BY c.id ORDER BY c.id LIMIT %s
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""",
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(batch,),
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).fetchall()
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for claim in claims:
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for topic_id, pattern in topics:
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if pattern.search(claim["text"]):
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conn.execute(
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"INSERT INTO topic_assignment (topic_id, claim_id, confidence, method) "
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"VALUES (%s, %s, 0.5, 'keyword') ON CONFLICT DO NOTHING",
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(topic_id, claim["id"]),
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)
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conn.execute("UPDATE claim SET data = data || '{\"topics_tagged\": true}' WHERE id = %s", (claim["id"],))
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conn.commit()
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return len(claims)
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