4ec6190416
Klick auf eine Zitat-Quelle im Report öffnet jetzt eine 1-Seiten-PDF- Variante des Wahlprogramms mit gelb markiertem Snippet, statt nur zum Page-Anchor zu springen und den Leser selbst suchen zu lassen. Implementation: embeddings.render_highlighted_page(programm_id, seite, query) - Validiert programm_id gegen PROGRAMME (Path-Traversal-Schutz) - Lädt das volle Wahlprogramm-PDF, extrahiert via insert_pdf nur die angeforderte Seite in einen neuen Document → kleinere Response - search_for(query[:200]) → Bounding-Boxes aller Treffer - Fallback: 5-Wort-Anker wenn Volltext-Match leer (LLM-Truncation, identisch zu find_chunk_for_text/Sub-D-Logik) - add_highlight_annot mit gelber stroke-Color (1.0, 0.93, 0.0) - Returns serialisierte PDF-Bytes oder None embeddings._chunk_pdf_url - Wenn chunk["text"] vorhanden: emittiert /api/wahlprogramm-cite-URL mit pid=, seite=, q=urlencoded(text[:200]) - Sonst: alter statischer /static/referenzen/X.pdf#page=N (Pre-#47 rückwärts-kompatibel) - text wird auf 200 Zeichen abgeschnitten, sonst blasen 500-Zeichen-Snippets jedes Assessment-JSON auf main.py /api/wahlprogramm-cite Endpoint - Validiert pid gegen PROGRAMME registry - seite: 1 ≤ n ≤ 2000 - Response: application/pdf, Cache-Control max-age=86400 - 404 bei unknown pid oder fehlendem PDF, 400 bei seite out of range Reconstruct-Pipeline (Issue #60 Option B) zieht das automatisch durch: reconstruct_zitate ruft _chunk_pdf_url(matched_chunk) auf, der jetzt bevorzugt die Cite-URL emittiert. Keine Änderung an reconstruct_zitate selbst nötig. Tests: 194/194 grün (185 + 9 neue): - TestChunkPdfUrl: 4 Cases (cite vs static, unknown prog, 200-char-truncate) - TestRenderHighlightedPage: 5 Cases (unknown pid, invalid seite, valid render, empty query, query-not-found-falls-back-zu-leerem-Highlight) - Plus Bridge im Test-Stub: pymupdf-as-fitz Shim falls eine third-party "fitz" das Pkg shadowt (kommt auf älteren Dev-Setups vor) Refs: #47
878 lines
33 KiB
Python
878 lines
33 KiB
Python
"""Semantic search for Wahlprogramme and Parteiprogramme using Qwen embeddings."""
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import json
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import re
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import sqlite3
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import urllib.parse
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from pathlib import Path
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from typing import Optional
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import fitz # PyMuPDF
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from openai import OpenAI
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from .config import settings
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# Embedding model
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EMBEDDING_MODEL = "text-embedding-v3"
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EMBEDDING_DIMENSIONS = 1024
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# Database path
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EMBEDDINGS_DB = settings.data_dir / "embeddings.db"
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# Programme definitions
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PROGRAMME = {
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# Wahlprogramme NRW 2022
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"spd-nrw-2022": {
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"name": "SPD NRW Wahlprogramm 2022",
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"typ": "wahlprogramm",
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"partei": "SPD",
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"bundesland": "NRW",
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"pdf": "spd-nrw-2022.pdf",
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},
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"cdu-nrw-2022": {
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"name": "CDU NRW Wahlprogramm 2022",
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"typ": "wahlprogramm",
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"partei": "CDU",
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"bundesland": "NRW",
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"pdf": "cdu-nrw-2022.pdf",
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},
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"gruene-nrw-2022": {
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"name": "Grüne NRW Wahlprogramm 2022",
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"typ": "wahlprogramm",
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"partei": "GRÜNE",
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"bundesland": "NRW",
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"pdf": "gruene-nrw-2022.pdf",
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},
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"fdp-nrw-2022": {
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"name": "FDP NRW Wahlprogramm 2022",
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"typ": "wahlprogramm",
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"partei": "FDP",
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"bundesland": "NRW",
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"pdf": "fdp-nrw-2022.pdf",
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},
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"afd-nrw-2022": {
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"name": "AfD NRW Wahlprogramm 2022",
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"typ": "wahlprogramm",
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"partei": "AfD",
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"bundesland": "NRW",
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"pdf": "afd-nrw-2022.pdf",
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},
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# Sachsen-Anhalt (LTW 2021)
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"cdu-lsa-2021": {
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"name": "CDU Sachsen-Anhalt Regierungsprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "CDU",
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"bundesland": "LSA",
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"pdf": "cdu-lsa-2021.pdf",
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},
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"spd-lsa-2021": {
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"name": "SPD Sachsen-Anhalt Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "SPD",
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"bundesland": "LSA",
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"pdf": "spd-lsa-2021.pdf",
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},
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"gruene-lsa-2021": {
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"name": "Grüne Sachsen-Anhalt Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "GRÜNE",
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"bundesland": "LSA",
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"pdf": "gruene-lsa-2021.pdf",
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},
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"fdp-lsa-2021": {
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"name": "FDP Sachsen-Anhalt Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "FDP",
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"bundesland": "LSA",
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"pdf": "fdp-lsa-2021.pdf",
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},
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"afd-lsa-2021": {
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"name": "AfD Sachsen-Anhalt Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "AfD",
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"bundesland": "LSA",
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"pdf": "afd-lsa-2021.pdf",
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},
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"linke-lsa-2021": {
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"name": "DIE LINKE Sachsen-Anhalt Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "LINKE",
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"bundesland": "LSA",
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"pdf": "linke-lsa-2021.pdf",
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},
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# Mecklenburg-Vorpommern (LTW 26.09.2021, WP 8) — Issue #4
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"cdu-mv-2021": {
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"name": "CDU Mecklenburg-Vorpommern Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "CDU",
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"bundesland": "MV",
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"pdf": "cdu-mv-2021.pdf",
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},
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"spd-mv-2021": {
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"name": "SPD Mecklenburg-Vorpommern Regierungsprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "SPD",
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"bundesland": "MV",
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"pdf": "spd-mv-2021.pdf",
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},
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"gruene-mv-2021": {
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"name": "Grüne Mecklenburg-Vorpommern Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "GRÜNE",
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"bundesland": "MV",
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"pdf": "gruene-mv-2021.pdf",
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},
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"fdp-mv-2021": {
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"name": "FDP Mecklenburg-Vorpommern Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "FDP",
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"bundesland": "MV",
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"pdf": "fdp-mv-2021.pdf",
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},
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"afd-mv-2021": {
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"name": "AfD Mecklenburg-Vorpommern Landeswahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "AfD",
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"bundesland": "MV",
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"pdf": "afd-mv-2021.pdf",
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},
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"linke-mv-2021": {
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"name": "DIE LINKE Mecklenburg-Vorpommern Zukunftsprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "LINKE",
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"bundesland": "MV",
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"pdf": "linke-mv-2021.pdf",
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},
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# Berlin (AGH-Wahl 26.09.2021, Wiederholung 12.02.2023, WP 19) —
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# Issue #10. Programme stammen aus dem Wahlkampf 2021 — die
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# Wiederholungswahl 2023 nutzte dieselben Programme.
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"cdu-be-2023": {
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"name": "CDU Berlin Berlin-Plan 2021",
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"typ": "wahlprogramm",
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"partei": "CDU",
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"bundesland": "BE",
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"pdf": "cdu-be-2023.pdf",
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},
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"spd-be-2023": {
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"name": "SPD Berlin Wahlprogramm AGH 2021",
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"typ": "wahlprogramm",
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"partei": "SPD",
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"bundesland": "BE",
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"pdf": "spd-be-2023.pdf",
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},
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"gruene-be-2023": {
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"name": "Grüne Berlin Landeswahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "GRÜNE",
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"bundesland": "BE",
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"pdf": "gruene-be-2023.pdf",
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},
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"linke-be-2023": {
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"name": "DIE LINKE Berlin Wahlprogramm 2021",
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"typ": "wahlprogramm",
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"partei": "LINKE",
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"bundesland": "BE",
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"pdf": "linke-be-2023.pdf",
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},
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"afd-be-2023": {
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"name": "AfD Berlin Wahlprogramm AGH 2021",
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"typ": "wahlprogramm",
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"partei": "AfD",
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"bundesland": "BE",
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"pdf": "afd-be-2023.pdf",
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},
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# Thüringen — LTW 01.09.2024, WP 8 (Issue #37)
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"cdu-th-2024": {"name": "CDU Thüringen Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "CDU", "bundesland": "TH", "pdf": "cdu-th-2024.pdf"},
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"afd-th-2024": {"name": "AfD Thüringen Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "AfD", "bundesland": "TH", "pdf": "afd-th-2024.pdf"},
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"linke-th-2024": {"name": "DIE LINKE Thüringen Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "LINKE", "bundesland": "TH", "pdf": "linke-th-2024.pdf"},
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"bsw-th-2024": {"name": "BSW Thüringen Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "BSW", "bundesland": "TH", "pdf": "bsw-th-2024.pdf"},
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"spd-th-2024": {"name": "SPD Thüringen Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "SPD", "bundesland": "TH", "pdf": "spd-th-2024.pdf"},
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# Brandenburg — LTW 22.09.2024, WP 8 (Issue #39)
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"spd-bb-2024": {"name": "SPD Brandenburg Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "SPD", "bundesland": "BB", "pdf": "spd-bb-2024.pdf"},
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"afd-bb-2024": {"name": "AfD Brandenburg Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "AfD", "bundesland": "BB", "pdf": "afd-bb-2024.pdf"},
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"cdu-bb-2024": {"name": "CDU Brandenburg Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "CDU", "bundesland": "BB", "pdf": "cdu-bb-2024.pdf"},
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"bsw-bb-2024": {"name": "BSW Brandenburg Wahlprogramm 2024", "typ": "wahlprogramm", "partei": "BSW", "bundesland": "BB", "pdf": "bsw-bb-2024.pdf"},
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# Hamburg — Bürgerschaftswahl 02.03.2025, WP 23 (Issue #40)
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"spd-hh-2025": {"name": "SPD Hamburg Wahlprogramm 2025", "typ": "wahlprogramm", "partei": "SPD", "bundesland": "HH", "pdf": "spd-hh-2025.pdf"},
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"cdu-hh-2025": {"name": "CDU Hamburg Wahlprogramm 2025", "typ": "wahlprogramm", "partei": "CDU", "bundesland": "HH", "pdf": "cdu-hh-2025.pdf"},
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"gruene-hh-2025": {"name": "Grüne Hamburg Regierungsprogramm 2025", "typ": "wahlprogramm", "partei": "GRÜNE", "bundesland": "HH", "pdf": "gruene-hh-2025.pdf"},
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"linke-hh-2025": {"name": "DIE LINKE Hamburg Wahlprogramm 2025", "typ": "wahlprogramm", "partei": "LINKE", "bundesland": "HH", "pdf": "linke-hh-2025.pdf"},
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"afd-hh-2025": {"name": "AfD Hamburg Wahlprogramm 2025", "typ": "wahlprogramm", "partei": "AfD", "bundesland": "HH", "pdf": "afd-hh-2025.pdf"},
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# Schleswig-Holstein — LTW 08.05.2022, WP 20 (Issue #32)
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"cdu-sh-2022": {"name": "CDU Schleswig-Holstein Wahlprogramm 2022", "typ": "wahlprogramm", "partei": "CDU", "bundesland": "SH", "pdf": "cdu-sh-2022.pdf"},
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"spd-sh-2022": {"name": "SPD Schleswig-Holstein Wahlprogramm 2022", "typ": "wahlprogramm", "partei": "SPD", "bundesland": "SH", "pdf": "spd-sh-2022.pdf"},
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"gruene-sh-2022": {"name": "Grüne Schleswig-Holstein Wahlprogramm 2022", "typ": "wahlprogramm", "partei": "GRÜNE", "bundesland": "SH", "pdf": "gruene-sh-2022.pdf"},
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"fdp-sh-2022": {"name": "FDP Schleswig-Holstein Wahlprogramm 2022", "typ": "wahlprogramm", "partei": "FDP", "bundesland": "SH", "pdf": "fdp-sh-2022.pdf"},
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"ssw-sh-2022": {"name": "SSW Schleswig-Holstein Wahlprogramm 2022", "typ": "wahlprogramm", "partei": "SSW", "bundesland": "SH", "pdf": "ssw-sh-2022.pdf"},
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# Baden-Württemberg — LTW 14.03.2021, WP 17 (Issue #41)
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"gruene-bw-2021": {"name": "Grüne Baden-Württemberg Wahlprogramm 2021", "typ": "wahlprogramm", "partei": "GRÜNE", "bundesland": "BW", "pdf": "gruene-bw-2021.pdf"},
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"cdu-bw-2021": {"name": "CDU Baden-Württemberg Regierungsprogramm 2021", "typ": "wahlprogramm", "partei": "CDU", "bundesland": "BW", "pdf": "cdu-bw-2021.pdf"},
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"afd-bw-2021": {"name": "AfD Baden-Württemberg Wahlprogramm 2021", "typ": "wahlprogramm", "partei": "AfD", "bundesland": "BW", "pdf": "afd-bw-2021.pdf"},
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"spd-bw-2021": {"name": "SPD Baden-Württemberg Wahlprogramm 2021", "typ": "wahlprogramm", "partei": "SPD", "bundesland": "BW", "pdf": "spd-bw-2021.pdf"},
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"fdp-bw-2021": {"name": "FDP Baden-Württemberg Wahlprogramm 2021", "typ": "wahlprogramm", "partei": "FDP", "bundesland": "BW", "pdf": "fdp-bw-2021.pdf"},
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# Rheinland-Pfalz — LTW 14.03.2021, WP 18 (Issue #42)
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"spd-rp-2021": {"name": "SPD Rheinland-Pfalz Regierungsprogramm 2021", "typ": "wahlprogramm", "partei": "SPD", "bundesland": "RP", "pdf": "spd-rp-2021.pdf"},
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"cdu-rp-2021": {"name": "CDU Rheinland-Pfalz Regierungsprogramm 2021", "typ": "wahlprogramm", "partei": "CDU", "bundesland": "RP", "pdf": "cdu-rp-2021.pdf"},
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"afd-rp-2021": {"name": "AfD Rheinland-Pfalz Wahlprogramm 2021", "typ": "wahlprogramm", "partei": "AfD", "bundesland": "RP", "pdf": "afd-rp-2021.pdf"},
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"gruene-rp-2021": {"name": "Grüne Rheinland-Pfalz Wahlprogramm 2021", "typ": "wahlprogramm", "partei": "GRÜNE", "bundesland": "RP", "pdf": "gruene-rp-2021.pdf"},
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"fw-rp-2021": {"name": "FREIE WÄHLER Rheinland-Pfalz Wahlprogramm 2021", "typ": "wahlprogramm", "partei": "FREIE WÄHLER", "bundesland": "RP", "pdf": "fw-rp-2021.pdf"},
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"fdp-rp-2021": {"name": "FDP Rheinland-Pfalz Wahlprogramm 2021", "typ": "wahlprogramm", "partei": "FDP", "bundesland": "RP", "pdf": "fdp-rp-2021.pdf"},
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# Grundsatzprogramme (Bund)
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"spd-grundsatz": {
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"name": "SPD Grundsatzprogramm 2007",
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"typ": "parteiprogramm",
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"partei": "SPD",
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"pdf": "spd-grundsatzprogramm.pdf",
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},
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"cdu-grundsatz": {
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"name": "CDU Grundsatzprogramm 2007",
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"typ": "parteiprogramm",
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"partei": "CDU",
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"pdf": "cdu-grundsatzprogramm.pdf",
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},
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"gruene-grundsatz": {
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"name": "Grüne Grundsatzprogramm 2020",
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"typ": "parteiprogramm",
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"partei": "GRÜNE",
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"pdf": "gruene-grundsatzprogramm.pdf",
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},
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"fdp-grundsatz": {
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"name": "FDP Grundsatzprogramm 2012",
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"typ": "parteiprogramm",
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"partei": "FDP",
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"pdf": "fdp-grundsatzprogramm.pdf",
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},
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}
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def init_embeddings_db():
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"""Initialize the embeddings database.
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Includes a forward-only migration step (Issue #5): adds the
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``bundesland`` column if missing and backfills existing rows from the
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``PROGRAMME`` registry. Grundsatzprogramme (federal level) keep
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``bundesland = NULL``; the ``find_relevant_chunks`` query treats NULL
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as "matches any state".
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"""
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conn = sqlite3.connect(EMBEDDINGS_DB)
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conn.execute("""
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CREATE TABLE IF NOT EXISTS chunks (
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id INTEGER PRIMARY KEY,
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programm_id TEXT NOT NULL,
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partei TEXT NOT NULL,
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typ TEXT NOT NULL,
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seite INTEGER,
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text TEXT NOT NULL,
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embedding BLOB NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)
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""")
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conn.execute("CREATE INDEX IF NOT EXISTS idx_chunks_partei ON chunks(partei)")
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conn.execute("CREATE INDEX IF NOT EXISTS idx_chunks_typ ON chunks(typ)")
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# Migration: bundesland-Spalte ergänzen, falls Tabelle aus Pre-#5-Zeit
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cols = {row[1] for row in conn.execute("PRAGMA table_info(chunks)").fetchall()}
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if "bundesland" not in cols:
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conn.execute("ALTER TABLE chunks ADD COLUMN bundesland TEXT")
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conn.execute("CREATE INDEX IF NOT EXISTS idx_chunks_bundesland ON chunks(bundesland)")
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# Backfill: Bundesland aus PROGRAMME-Registry für bestehende Zeilen
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# nachtragen. Grundsatzprogramme bleiben NULL.
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for prog_id, info in PROGRAMME.items():
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bl = info.get("bundesland")
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if bl is not None:
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conn.execute(
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"UPDATE chunks SET bundesland = ? WHERE programm_id = ? AND bundesland IS NULL",
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(bl, prog_id),
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)
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conn.commit()
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conn.close()
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def get_client() -> OpenAI:
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"""Get DashScope client."""
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return OpenAI(
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api_key=settings.dashscope_api_key,
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base_url=settings.dashscope_base_url,
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)
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def create_embedding(text: str) -> list[float]:
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"""Create embedding for text using Qwen."""
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client = get_client()
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response = client.embeddings.create(
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model=EMBEDDING_MODEL,
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input=text,
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dimensions=EMBEDDING_DIMENSIONS,
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)
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return response.data[0].embedding
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def chunk_text(text: str, chunk_size: int = 500, overlap: int = 50) -> list[str]:
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"""Split text into overlapping chunks by words."""
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words = text.split()
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chunks = []
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i = 0
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while i < len(words):
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chunk_words = words[i:i + chunk_size]
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chunk = " ".join(chunk_words)
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if chunk.strip():
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chunks.append(chunk)
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i += chunk_size - overlap
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return chunks
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def extract_text_with_pages(pdf_path: Path) -> list[tuple[int, str]]:
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"""Extract text from PDF with page numbers."""
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doc = fitz.open(pdf_path)
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pages = []
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for page_num in range(len(doc)):
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page = doc[page_num]
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text = page.get_text()
|
|
if text.strip():
|
|
pages.append((page_num + 1, text))
|
|
|
|
doc.close()
|
|
return pages
|
|
|
|
|
|
def index_programm(programm_id: str, pdf_dir: Path) -> int:
|
|
"""Index a single program PDF into embeddings database."""
|
|
if programm_id not in PROGRAMME:
|
|
raise ValueError(f"Unknown program: {programm_id}")
|
|
|
|
info = PROGRAMME[programm_id]
|
|
pdf_path = pdf_dir / info["pdf"]
|
|
|
|
if not pdf_path.exists():
|
|
print(f"PDF not found: {pdf_path}")
|
|
return 0
|
|
|
|
conn = sqlite3.connect(EMBEDDINGS_DB)
|
|
|
|
# Remove existing chunks for this program
|
|
conn.execute("DELETE FROM chunks WHERE programm_id = ?", (programm_id,))
|
|
|
|
# Extract and chunk
|
|
pages = extract_text_with_pages(pdf_path)
|
|
total_chunks = 0
|
|
|
|
for page_num, page_text in pages:
|
|
chunks = chunk_text(page_text, chunk_size=400, overlap=50)
|
|
|
|
for chunk_text_content in chunks:
|
|
if len(chunk_text_content.split()) < 20: # Skip tiny chunks
|
|
continue
|
|
|
|
try:
|
|
embedding = create_embedding(chunk_text_content)
|
|
embedding_blob = json.dumps(embedding).encode()
|
|
|
|
conn.execute("""
|
|
INSERT INTO chunks (programm_id, partei, typ, seite, text, embedding, bundesland)
|
|
VALUES (?, ?, ?, ?, ?, ?, ?)
|
|
""", (
|
|
programm_id,
|
|
info["partei"],
|
|
info["typ"],
|
|
page_num,
|
|
chunk_text_content,
|
|
embedding_blob,
|
|
info.get("bundesland"), # NULL für Grundsatzprogramme
|
|
))
|
|
total_chunks += 1
|
|
except Exception as e:
|
|
print(f"Error embedding chunk: {e}")
|
|
continue
|
|
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
print(f"Indexed {total_chunks} chunks from {programm_id}")
|
|
return total_chunks
|
|
|
|
|
|
def cosine_similarity(a: list[float], b: list[float]) -> float:
|
|
"""Calculate cosine similarity between two vectors."""
|
|
dot = sum(x * y for x, y in zip(a, b))
|
|
norm_a = sum(x * x for x in a) ** 0.5
|
|
norm_b = sum(x * x for x in b) ** 0.5
|
|
if norm_a == 0 or norm_b == 0:
|
|
return 0.0
|
|
return dot / (norm_a * norm_b)
|
|
|
|
|
|
def find_relevant_chunks(
|
|
query: str,
|
|
parteien: list[str] = None,
|
|
typ: str = None,
|
|
bundesland: str = None,
|
|
top_k: int = 3,
|
|
min_similarity: float = 0.5,
|
|
) -> list[dict]:
|
|
"""Find most relevant chunks for a query.
|
|
|
|
Args:
|
|
bundesland: Wenn gesetzt, werden nur Chunks dieses Bundeslands ODER
|
|
globale Chunks (bundesland IS NULL, z.B. Grundsatzprogramme)
|
|
berücksichtigt. Wenn None, kein Filter.
|
|
"""
|
|
|
|
query_embedding = create_embedding(query)
|
|
|
|
conn = sqlite3.connect(EMBEDDINGS_DB)
|
|
conn.row_factory = sqlite3.Row
|
|
|
|
# Build query
|
|
sql = "SELECT * FROM chunks WHERE 1=1"
|
|
params = []
|
|
|
|
if parteien:
|
|
placeholders = ",".join("?" * len(parteien))
|
|
sql += f" AND partei IN ({placeholders})"
|
|
params.extend(parteien)
|
|
|
|
if typ:
|
|
sql += " AND typ = ?"
|
|
params.append(typ)
|
|
|
|
if bundesland:
|
|
# Bundesland-spezifische ODER globale Chunks (Grundsatzprogramme).
|
|
sql += " AND (bundesland = ? OR bundesland IS NULL)"
|
|
params.append(bundesland)
|
|
|
|
rows = conn.execute(sql, params).fetchall()
|
|
conn.close()
|
|
|
|
# Calculate similarities
|
|
results = []
|
|
for row in rows:
|
|
chunk_embedding = json.loads(row["embedding"])
|
|
similarity = cosine_similarity(query_embedding, chunk_embedding)
|
|
|
|
if similarity >= min_similarity:
|
|
results.append({
|
|
"programm_id": row["programm_id"],
|
|
"partei": row["partei"],
|
|
"typ": row["typ"],
|
|
"seite": row["seite"],
|
|
"text": row["text"],
|
|
"similarity": similarity,
|
|
})
|
|
|
|
# Sort by similarity and return top_k
|
|
results.sort(key=lambda x: x["similarity"], reverse=True)
|
|
return results[:top_k]
|
|
|
|
|
|
def get_relevant_quotes_for_antrag(
|
|
antrag_text: str,
|
|
fraktionen: list[str],
|
|
bundesland: str,
|
|
top_k_per_partei: int = 2,
|
|
) -> dict[str, list[dict]]:
|
|
"""Get relevant quotes from Wahl- and Parteiprogramme for an Antrag.
|
|
|
|
Args:
|
|
bundesland: Pflicht. Bestimmt, welche Wahlprogramme durchsucht werden
|
|
und welche Regierungsfraktionen zusätzlich zu den Antragstellern
|
|
einbezogen werden.
|
|
"""
|
|
# Lokaler Import vermeidet Zirkularität: bundeslaender.py importiert nichts
|
|
# aus diesem Modul, aber der saubere Trennstrich bleibt erhalten.
|
|
from .bundeslaender import BUNDESLAENDER
|
|
|
|
if bundesland not in BUNDESLAENDER:
|
|
raise ValueError(f"Unbekanntes Bundesland: {bundesland}")
|
|
|
|
regierungsfraktionen = BUNDESLAENDER[bundesland].regierungsfraktionen
|
|
parteien_to_search = list(dict.fromkeys(fraktionen + regierungsfraktionen)) # dedupe, Reihenfolge stabil
|
|
|
|
results = {}
|
|
|
|
from .parteien import normalize_partei
|
|
|
|
for partei in parteien_to_search:
|
|
# Kanonischer Lookup-Key über den zentralen Mapper (#55). Ersetzt
|
|
# den alten Hack ``partei.upper() if partei != "GRÜNE" else "GRÜNE"``,
|
|
# der nur die Schreibweisen-Drift in einer einzigen Partei
|
|
# abgefangen hat. Wenn der Mapper nichts findet, fallen wir auf
|
|
# den Originalstring zurück — die DB-Lookup-Schicht macht ohnehin
|
|
# eigene Case-insensitive-Vergleiche.
|
|
canonical = normalize_partei(partei, bundesland=bundesland)
|
|
partei_lookup = canonical or partei
|
|
|
|
# Wahlprogramm — bundesland-gefiltert
|
|
wahl_chunks = find_relevant_chunks(
|
|
antrag_text,
|
|
parteien=[partei_lookup],
|
|
typ="wahlprogramm",
|
|
bundesland=bundesland,
|
|
top_k=top_k_per_partei,
|
|
min_similarity=0.45,
|
|
)
|
|
|
|
# Parteiprogramm (Grundsatz, federal — bundesland=NULL matched implizit)
|
|
partei_chunks = find_relevant_chunks(
|
|
antrag_text,
|
|
parteien=[partei_lookup],
|
|
typ="parteiprogramm",
|
|
bundesland=bundesland,
|
|
top_k=top_k_per_partei,
|
|
min_similarity=0.45,
|
|
)
|
|
|
|
if wahl_chunks or partei_chunks:
|
|
results[partei_lookup] = {
|
|
"wahlprogramm": wahl_chunks,
|
|
"parteiprogramm": partei_chunks,
|
|
}
|
|
|
|
return results
|
|
|
|
|
|
def _chunk_source_label(chunk: dict) -> str:
|
|
"""Build a fully-qualified source label like 'FDP MV Wahlprogramm 2021, S. 73'.
|
|
|
|
Without the programme name + Bundesland in the prompt, the LLM
|
|
halluzinates familiar sources from its training (typically NRW 2022)
|
|
even when the retrieved chunks all come from a different state.
|
|
"""
|
|
prog_id = chunk.get("programm_id", "")
|
|
info = PROGRAMME.get(prog_id, {})
|
|
name = info.get("name") or prog_id
|
|
seite = chunk.get("seite", "?")
|
|
return f"{name}, S. {seite}"
|
|
|
|
|
|
def _chunk_pdf_url(chunk: dict) -> Optional[str]:
|
|
"""Build the canonical PDF URL with page anchor for a chunk.
|
|
|
|
Wenn der Chunk einen ``text`` enthält, wird stattdessen die
|
|
Highlight-Endpoint-URL ``/api/wahlprogramm-cite?pid=…&seite=…&q=…``
|
|
emittiert (Issue #47). Der Endpoint rendert die Wahlprogramm-Seite
|
|
mit gelb markiertem Zitat und liefert ein 1-Seiten-PDF. Klick im
|
|
Report öffnet die Quelle direkt mit visuell hervorgehobener Stelle.
|
|
|
|
Fallback: ohne text → statische ``/static/referenzen/<pdf>#page=<n>``
|
|
URL (rückwärts-kompatibel für Pre-#47 Assessments).
|
|
"""
|
|
prog_id = chunk.get("programm_id", "")
|
|
info = PROGRAMME.get(prog_id)
|
|
if not info:
|
|
return None
|
|
pdf = info.get("pdf")
|
|
if not pdf:
|
|
return None
|
|
seite = chunk.get("seite")
|
|
text = (chunk.get("text") or "").strip()
|
|
|
|
if text and seite:
|
|
# Highlight-Endpoint mit URL-encoded query. Den Text auf 200 Zeichen
|
|
# abschneiden — search_for matched ohnehin nur Substring-Anker, und
|
|
# die URL bleibt bounded (sonst würden 500-Zeichen-Snippets in jeder
|
|
# Zitat-URL stehen und das HTML-Report-JSON aufblähen).
|
|
q = urllib.parse.quote_plus(text[:200])
|
|
return f"/api/wahlprogramm-cite?pid={prog_id}&seite={seite}&q={q}"
|
|
|
|
if seite:
|
|
return f"/static/referenzen/{pdf}#page={seite}"
|
|
return f"/static/referenzen/{pdf}"
|
|
|
|
|
|
def render_highlighted_page(programm_id: str, seite: int, query: str) -> Optional[bytes]:
|
|
"""Render a single Wahlprogramm-page with yellow highlights for a query.
|
|
|
|
Used by the ``/api/wahlprogramm-cite`` endpoint to serve a one-page
|
|
PDF where the cited snippet is visually highlighted via PyMuPDF
|
|
``add_highlight_annot``. Returns the serialized PDF bytes, or None
|
|
if the programme/page can't be resolved.
|
|
|
|
Args:
|
|
programm_id: Key into PROGRAMME registry — validated by caller.
|
|
seite: 1-indexed page number within the programme PDF.
|
|
query: Snippet text to search and highlight on the page. Long
|
|
queries are truncated to the first 200 characters before the
|
|
search; PyMuPDF's ``search_for`` falls over on huge needles
|
|
anyway and a short anchor is what we want for the visual hit.
|
|
"""
|
|
info = PROGRAMME.get(programm_id)
|
|
if not info:
|
|
return None
|
|
pdf_filename = info.get("pdf")
|
|
if not pdf_filename:
|
|
return None
|
|
|
|
referenzen = Path(__file__).parent / "static" / "referenzen"
|
|
pdf_path = referenzen / pdf_filename
|
|
if not pdf_path.exists():
|
|
return None
|
|
|
|
needle = (query or "").strip()[:200]
|
|
|
|
src = fitz.open(str(pdf_path))
|
|
try:
|
|
if seite < 1 or seite > len(src):
|
|
return None
|
|
|
|
# Single-page Sub-PDF erzeugen — hält den Response klein und
|
|
# schließt versehentliche Cross-Page-Highlights aus.
|
|
new = fitz.open()
|
|
try:
|
|
new.insert_pdf(src, from_page=seite - 1, to_page=seite - 1)
|
|
page = new[0]
|
|
|
|
if needle:
|
|
# PyMuPDF ist tolerant gegen Whitespace, aber Soft-Hyphen
|
|
# bricht den Match — analog zu _normalize_for_match
|
|
# entfernen wir \xad vor dem search_for.
|
|
clean = needle.replace("\u00ad", "")
|
|
rects = page.search_for(clean)
|
|
if not rects:
|
|
# Fallback: nur die ersten 5 Wörter als Anker — analog
|
|
# zu find_chunk_for_text. Wenn der LLM den Snippet
|
|
# mid-sentence gekürzt hat, bricht der Volltext-Match,
|
|
# aber 5-Wort-Sequenz findet die Stelle trotzdem.
|
|
words = clean.split()
|
|
if len(words) >= 5:
|
|
anchor = " ".join(words[:5])
|
|
rects = page.search_for(anchor)
|
|
for rect in rects:
|
|
annot = page.add_highlight_annot(rect)
|
|
if annot is not None:
|
|
annot.set_colors(stroke=(1.0, 0.93, 0.0)) # gelb
|
|
annot.update()
|
|
|
|
return new.tobytes()
|
|
finally:
|
|
new.close()
|
|
finally:
|
|
src.close()
|
|
|
|
|
|
# ─────────────────────────────────────────────────────────────────────────────
|
|
# Citation post-processing — Issue #60 Option B
|
|
#
|
|
# Pre-#60 the LLM was free to fabricate `quelle`/`url` strings even when the
|
|
# `text` was a real snippet from a retrieved chunk. The A+C fix made the
|
|
# prompt more strict, but BB 8/673 (post-deploy) showed the LLM still
|
|
# cross-mixed: it copied text from chunk Qn but wrote the page from chunk Qm
|
|
# in the `quelle` field.
|
|
#
|
|
# The structural fix is to take quelle/url generation away from the LLM
|
|
# entirely. After the LLM responds, we walk over every Zitat and try to
|
|
# locate its `text` (substring or 5-word anchor) in any of the chunks the
|
|
# LLM was actually shown. If we find a match, we *overwrite* quelle and url
|
|
# with the canonical values from that chunk. If we don't find a match, the
|
|
# Zitat is dropped — it cannot be backed by retrieved evidence.
|
|
# ─────────────────────────────────────────────────────────────────────────────
|
|
|
|
|
|
_RE_WHITESPACE = re.compile(r"\s+")
|
|
_RE_HYPHEN_BREAK = re.compile(r"(\w)-\s+(\w)")
|
|
_RE_TRUNCATION = re.compile(r"^\s*\.{2,}|\.{2,}\s*$")
|
|
|
|
|
|
def _normalize_for_match(text: str) -> str:
|
|
"""Lowercase, collapse whitespace, bridge soft-hyphen line breaks.
|
|
|
|
Mirrors the matcher used in tests/integration/test_citations_substring.py
|
|
so that the analyzer's post-processing and Sub-D's verification stay in
|
|
lockstep.
|
|
"""
|
|
s = (text or "").lower()
|
|
s = _RE_TRUNCATION.sub("", s)
|
|
s = s.replace("\u00ad", "") # soft hyphen
|
|
s = _RE_WHITESPACE.sub(" ", s).strip()
|
|
prev = None
|
|
while prev != s:
|
|
prev = s
|
|
s = _RE_HYPHEN_BREAK.sub(r"\1\2", s)
|
|
return s
|
|
|
|
|
|
def find_chunk_for_text(text: str, chunks: list[dict]) -> Optional[dict]:
|
|
"""Locate the retrieved chunk that a Zitat snippet was copied from.
|
|
|
|
Two-stage match identical to Sub-D:
|
|
1. **Strict substring** — full needle as substring of any chunk.
|
|
2. **5-word anchor** — any 5 consecutive words of the needle as
|
|
substring of any chunk.
|
|
|
|
Snippets shorter than 20 characters are rejected (too weak to bind).
|
|
Returns the matching chunk dict, or None.
|
|
"""
|
|
needle = _normalize_for_match(text)
|
|
if len(needle) < 20:
|
|
return None
|
|
chunks_norm = [(c, _normalize_for_match(c.get("text", ""))) for c in chunks]
|
|
for c, norm in chunks_norm:
|
|
if needle in norm:
|
|
return c
|
|
words = needle.split()
|
|
if len(words) < 5:
|
|
return None
|
|
for i in range(len(words) - 4):
|
|
anchor = " ".join(words[i:i + 5])
|
|
for c, norm in chunks_norm:
|
|
if anchor in norm:
|
|
return c
|
|
return None
|
|
|
|
|
|
def reconstruct_zitate(data: dict, semantic_quotes: dict) -> dict:
|
|
"""Replace LLM-emitted quelle/url with canonical chunk values; drop unbacked.
|
|
|
|
Walks over ``data['wahlprogrammScores'][i][kind]['zitate']`` (the raw
|
|
LLM-output dict, not the Pydantic model). For each Zitat:
|
|
|
|
* Locate the chunk whose text contains the snippet (or a 5-word anchor
|
|
from it). Search across **all** retrieved chunks regardless of party,
|
|
so cross-mixes between Q-IDs become invisible to the persisted output.
|
|
* If found: overwrite ``quelle`` and ``url`` with values derived from
|
|
the matching chunk's ``programm_id`` + ``seite``. The LLM is no longer
|
|
trusted for these fields.
|
|
* If not found: drop the Zitat entirely.
|
|
|
|
Returns the same ``data`` dict (mutated in place) for chaining.
|
|
"""
|
|
if not semantic_quotes:
|
|
return data
|
|
|
|
all_chunks: list[dict] = []
|
|
for d in semantic_quotes.values():
|
|
all_chunks.extend(d.get("wahlprogramm", []))
|
|
all_chunks.extend(d.get("parteiprogramm", []))
|
|
if not all_chunks:
|
|
return data
|
|
|
|
for fs in data.get("wahlprogrammScores", []) or []:
|
|
for kind in ("wahlprogramm", "parteiprogramm"):
|
|
blk = fs.get(kind) or {}
|
|
zitate = blk.get("zitate") or []
|
|
cleaned = []
|
|
for z in zitate:
|
|
text = z.get("text", "")
|
|
matched = find_chunk_for_text(text, all_chunks)
|
|
if matched is None:
|
|
continue
|
|
z["quelle"] = _chunk_source_label(matched)
|
|
url = _chunk_pdf_url(matched)
|
|
if url:
|
|
z["url"] = url
|
|
cleaned.append(z)
|
|
blk["zitate"] = cleaned
|
|
return data
|
|
|
|
|
|
def format_quotes_for_prompt(quotes: dict) -> str:
|
|
"""Format quotes for inclusion in LLM prompt.
|
|
|
|
Each chunk gets a stable ENUM-ID ([Q1], [Q2], …) and the prompt
|
|
instructs the LLM to anchor every citation in one of those IDs and
|
|
to copy the snippet **verbatim** from the cited chunk. This is the
|
|
structural fix for Issue #60: pre-#60 the LLM was free to invent
|
|
snippets under real source labels because nothing in the prompt
|
|
bound a citation to a specific retrieved chunk.
|
|
|
|
Each quote is annotated with the fully-qualified source (programme
|
|
name + page) so the LLM cannot fall back on training-set defaults
|
|
when constructing its citations.
|
|
"""
|
|
if not quotes:
|
|
return ""
|
|
|
|
lines = ["\n## Relevante Passagen aus Wahl- und Parteiprogrammen\n"]
|
|
lines.append(
|
|
"**ZITATEREGEL** — verbindlich für alle Zitate in `wahlprogramm`/"
|
|
"`parteiprogramm`-Blöcken:\n"
|
|
"1. Jedes Zitat MUSS auf genau einen der unten aufgelisteten "
|
|
"Chunks verweisen (Format `[Q1]`, `[Q2]`, …).\n"
|
|
"2. Der `text`-String MUSS eine **wörtliche, zusammenhängende** "
|
|
"Passage von mindestens 5 Wörtern aus genau diesem Chunk sein — "
|
|
"keine Paraphrasen, keine Zusammenfassungen, keine "
|
|
"Cross-References aus dem Gedächtnis.\n"
|
|
"3. Der `quelle`-String MUSS exakt das Source-Label des "
|
|
"gewählten Chunks sein (Programm-Name + Seitenzahl, wie unten "
|
|
"ausgeschrieben).\n"
|
|
"4. Wenn kein Chunk wirklich passt: lass das Zitat-Array leer. "
|
|
"Lieber 0 Zitate als ein erfundenes Zitat.\n"
|
|
)
|
|
|
|
counter = 0
|
|
for partei, data in quotes.items():
|
|
lines.append(f"\n### {partei}\n")
|
|
|
|
if data.get("wahlprogramm"):
|
|
lines.append("**Wahlprogramm:**")
|
|
for chunk in data["wahlprogramm"]:
|
|
counter += 1
|
|
text = chunk["text"][:500] + "..." if len(chunk["text"]) > 500 else chunk["text"]
|
|
lines.append(f'- [Q{counter}] {_chunk_source_label(chunk)}: "{text}"')
|
|
|
|
if data.get("parteiprogramm"):
|
|
lines.append("\n**Grundsatzprogramm:**")
|
|
for chunk in data["parteiprogramm"]:
|
|
counter += 1
|
|
text = chunk["text"][:500] + "..." if len(chunk["text"]) > 500 else chunk["text"]
|
|
lines.append(f'- [Q{counter}] {_chunk_source_label(chunk)}: "{text}"')
|
|
|
|
return "\n".join(lines)
|
|
|
|
|
|
def get_programme_info() -> list[dict]:
|
|
"""Get list of all indexed programmes with metadata."""
|
|
info_list = []
|
|
|
|
for prog_id, info in PROGRAMME.items():
|
|
info_list.append({
|
|
"id": prog_id,
|
|
"name": info["name"],
|
|
"typ": info["typ"],
|
|
"partei": info["partei"],
|
|
"bundesland": info.get("bundesland"),
|
|
"pdf": info["pdf"],
|
|
"pdf_url": f"/static/referenzen/{info['pdf']}",
|
|
})
|
|
|
|
return info_list
|
|
|
|
|
|
def get_indexing_status() -> dict:
|
|
"""Get status of indexed programmes."""
|
|
if not EMBEDDINGS_DB.exists():
|
|
return {"indexed": 0, "programmes": []}
|
|
|
|
conn = sqlite3.connect(EMBEDDINGS_DB)
|
|
|
|
# Count chunks per program
|
|
rows = conn.execute("""
|
|
SELECT programm_id, COUNT(*) as chunks
|
|
FROM chunks
|
|
GROUP BY programm_id
|
|
""").fetchall()
|
|
|
|
conn.close()
|
|
|
|
indexed = {row[0]: row[1] for row in rows}
|
|
|
|
programmes = []
|
|
for prog_id, info in PROGRAMME.items():
|
|
programmes.append({
|
|
"id": prog_id,
|
|
"name": info["name"],
|
|
"partei": info["partei"],
|
|
"chunks": indexed.get(prog_id, 0),
|
|
"indexed": prog_id in indexed,
|
|
})
|
|
|
|
return {
|
|
"indexed": len(indexed),
|
|
"total": len(PROGRAMME),
|
|
"programmes": programmes,
|
|
}
|