eb045d0ed3
Zentrale `app/parteien.py` als Single Source of Truth für die Partei- Auflösung: - `PARTEIEN`-Tabelle mit kanonischem Key, langem Display-Namen, allen bekannten Aliasen, optionalem `bundesland_scope` und Government- Marker. 14 Einträge (CDU, CSU, SPD, GRÜNE, FDP, LINKE, AfD, BSW, SSW, BiW + die Freie-Wähler-Familie BVB-FW, FW-BAYERN, FW-SL und der generische FREIE WÄHLER-Eintrag). - `normalize_partei(raw, *, bundesland=None)` für Single-String-Lookups mit Government-Vorrang und FW-Familien-Disambiguierung - `extract_fraktionen(text, *, bundesland=None)` als Funnel für die vier alten Adapter-Helper. Kommagetrennte Listen, MdL-mit-Klammer- partei, HTML-Reste — alles fließt durch eine Stelle, mit BL-Scope- Filter (SSW nur in SH, BVB-FW nur in BB, etc.). - `display_name(canonical, *, long=False)` für UI/PDF — kurze Form bleibt der kanonische Key, lange Form ist "BÜNDNIS 90/DIE GRÜNEN" statt "GRÜNE" etc. Adapter-Migration in `app/parlamente.py`: - Vier nahezu identische `_normalize_fraktion()`-Methoden in PortalaAdapter, ParLDokAdapter, StarFinderCGIAdapter, PARLISAdapter durch einen einzeiligen Shim ersetzt, der `extract_fraktionen` mit `self.bundesland` aufruft. ~120 Zeilen Duplikation entfernt. - `@staticmethod` aufgehoben, weil wir jetzt `self.bundesland` brauchen für die FW-Disambiguierung — alle Aufrufer waren bereits `self._...`, also keine Call-Site-Änderung nötig. `app/embeddings.py:496` Workaround-Hack entfernt: - `partei.upper() if partei != "GRÜNE" else "GRÜNE"` durch zentralen `normalize_partei()`-Aufruf ersetzt — der Hack war ein Kommentarzeichen dafür, dass die Partei-Schreibweise irgendwo zwischen Adapter und Embedding-Lookup driften konnte. Mit dem Mapper ist die Schreibweise überall garantiert kanonisch. Tests: - Neue `tests/test_parteien.py` mit 52 Cases — Single-Lookup, FW- Disambiguierung (BVB/Bayern/Saarland/RP), Volltext-Extraktion, Government-Marker, Tabellen-Konsistenz - `tests/test_parlamente.py` Test-Klasse umgeschrieben: statt der 6 statischen `PortalaAdapter._normalize_fraktion(...)`-Tests jetzt 4 Roundtrip-Tests über echte Adapter-Instanzen, inkl. expliziter BB→BVB-FW vs. RP→FREIE WÄHLER-Verifikation 157 Unit-Tests grün (105 alt + 52 neu). Backwards-kompatibel — die kanonischen Keys sind exakt die in der DB stehenden Strings, kein Migrations-Schritt nötig. Refs: #55, #59 (Phase B) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
636 lines
23 KiB
Python
636 lines
23 KiB
Python
"""Semantic search for Wahlprogramme and Parteiprogramme using Qwen embeddings."""
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import json
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import sqlite3
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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)
|
|
if chunk.strip():
|
|
chunks.append(chunk)
|
|
i += chunk_size - overlap
|
|
|
|
return chunks
|
|
|
|
|
|
def extract_text_with_pages(pdf_path: Path) -> list[tuple[int, str]]:
|
|
"""Extract text from PDF with page numbers."""
|
|
doc = fitz.open(pdf_path)
|
|
pages = []
|
|
|
|
for page_num in range(len(doc)):
|
|
page = doc[page_num]
|
|
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_upper] = {
|
|
"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 format_quotes_for_prompt(quotes: dict) -> str:
|
|
"""Format quotes for inclusion in LLM prompt.
|
|
|
|
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(
|
|
"Verwende **ausschließlich** die hier gelisteten Quellenangaben "
|
|
"(Programm-Name + Seite) wörtlich in deinen Zitaten — erfinde "
|
|
"keine Quellen aus dem Gedächtnis.\n"
|
|
)
|
|
|
|
for partei, data in quotes.items():
|
|
lines.append(f"\n### {partei}\n")
|
|
|
|
if data.get("wahlprogramm"):
|
|
lines.append("**Wahlprogramm:**")
|
|
for chunk in data["wahlprogramm"]:
|
|
text = chunk["text"][:500] + "..." if len(chunk["text"]) > 500 else chunk["text"]
|
|
lines.append(f'- {_chunk_source_label(chunk)}: "{text}"')
|
|
|
|
if data.get("parteiprogramm"):
|
|
lines.append("\n**Grundsatzprogramm:**")
|
|
for chunk in data["parteiprogramm"]:
|
|
text = chunk["text"][:500] + "..." if len(chunk["text"]) > 500 else chunk["text"]
|
|
lines.append(f'- {_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,
|
|
}
|