1b5fd96e16
format_quotes_for_prompt previously rendered each retrieved chunk as just "S. X: text", giving the LLM no way to know which Bundesland or Wahlprogramm the passage came from. Result: even when the embedding search correctly returned MV-only chunks, the LLM hallucinated familiar source labels from its training set (typically "FDP NRW Wahlprogramm 2022, S. 75") because that was its strongest prior for budget/transparency policy citations. Fix: prepend the fully-qualified PROGRAMME[programm_id]["name"] to each quote and explicitly instruct the model to use these labels verbatim. Discovered while smoke-testing MV after indexing the new MV+BE programmes — embedding retrieval was clean (sim ~0.6 chunks all from fdp-mv-2021), only the prompt serialisation was lossy. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
591 lines
18 KiB
Python
591 lines
18 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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# 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()
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if text.strip():
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pages.append((page_num + 1, text))
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doc.close()
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return pages
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def index_programm(programm_id: str, pdf_dir: Path) -> int:
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"""Index a single program PDF into embeddings database."""
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if programm_id not in PROGRAMME:
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raise ValueError(f"Unknown program: {programm_id}")
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info = PROGRAMME[programm_id]
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pdf_path = pdf_dir / info["pdf"]
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if not pdf_path.exists():
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print(f"PDF not found: {pdf_path}")
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return 0
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conn = sqlite3.connect(EMBEDDINGS_DB)
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# Remove existing chunks for this program
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conn.execute("DELETE FROM chunks WHERE programm_id = ?", (programm_id,))
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# Extract and chunk
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pages = extract_text_with_pages(pdf_path)
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total_chunks = 0
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for page_num, page_text in pages:
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chunks = chunk_text(page_text, chunk_size=400, overlap=50)
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for chunk_text_content in chunks:
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if len(chunk_text_content.split()) < 20: # Skip tiny chunks
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continue
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try:
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embedding = create_embedding(chunk_text_content)
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embedding_blob = json.dumps(embedding).encode()
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conn.execute("""
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INSERT INTO chunks (programm_id, partei, typ, seite, text, embedding, bundesland)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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""", (
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programm_id,
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info["partei"],
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info["typ"],
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page_num,
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chunk_text_content,
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embedding_blob,
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info.get("bundesland"), # NULL für Grundsatzprogramme
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))
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total_chunks += 1
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except Exception as e:
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print(f"Error embedding chunk: {e}")
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continue
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conn.commit()
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conn.close()
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print(f"Indexed {total_chunks} chunks from {programm_id}")
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return total_chunks
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def cosine_similarity(a: list[float], b: list[float]) -> float:
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"""Calculate cosine similarity between two vectors."""
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dot = sum(x * y for x, y in zip(a, b))
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norm_a = sum(x * x for x in a) ** 0.5
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norm_b = sum(x * x for x in b) ** 0.5
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if norm_a == 0 or norm_b == 0:
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return 0.0
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return dot / (norm_a * norm_b)
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def find_relevant_chunks(
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query: str,
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parteien: list[str] = None,
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typ: str = None,
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bundesland: str = None,
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top_k: int = 3,
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min_similarity: float = 0.5,
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) -> list[dict]:
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"""Find most relevant chunks for a query.
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Args:
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bundesland: Wenn gesetzt, werden nur Chunks dieses Bundeslands ODER
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globale Chunks (bundesland IS NULL, z.B. Grundsatzprogramme)
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berücksichtigt. Wenn None, kein Filter.
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"""
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query_embedding = create_embedding(query)
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conn = sqlite3.connect(EMBEDDINGS_DB)
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conn.row_factory = sqlite3.Row
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# Build query
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sql = "SELECT * FROM chunks WHERE 1=1"
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params = []
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if parteien:
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placeholders = ",".join("?" * len(parteien))
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sql += f" AND partei IN ({placeholders})"
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params.extend(parteien)
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if typ:
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sql += " AND typ = ?"
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params.append(typ)
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if bundesland:
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# Bundesland-spezifische ODER globale Chunks (Grundsatzprogramme).
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sql += " AND (bundesland = ? OR bundesland IS NULL)"
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params.append(bundesland)
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rows = conn.execute(sql, params).fetchall()
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conn.close()
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# Calculate similarities
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results = []
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for row in rows:
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chunk_embedding = json.loads(row["embedding"])
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similarity = cosine_similarity(query_embedding, chunk_embedding)
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if similarity >= min_similarity:
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results.append({
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"programm_id": row["programm_id"],
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"partei": row["partei"],
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"typ": row["typ"],
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"seite": row["seite"],
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"text": row["text"],
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"similarity": similarity,
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})
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# Sort by similarity and return top_k
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results.sort(key=lambda x: x["similarity"], reverse=True)
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return results[:top_k]
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def get_relevant_quotes_for_antrag(
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antrag_text: str,
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fraktionen: list[str],
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bundesland: str,
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top_k_per_partei: int = 2,
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) -> dict[str, list[dict]]:
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"""Get relevant quotes from Wahl- and Parteiprogramme for an Antrag.
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Args:
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bundesland: Pflicht. Bestimmt, welche Wahlprogramme durchsucht werden
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und welche Regierungsfraktionen zusätzlich zu den Antragstellern
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einbezogen werden.
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"""
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# Lokaler Import vermeidet Zirkularität: bundeslaender.py importiert nichts
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# aus diesem Modul, aber der saubere Trennstrich bleibt erhalten.
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from .bundeslaender import BUNDESLAENDER
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if bundesland not in BUNDESLAENDER:
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raise ValueError(f"Unbekanntes Bundesland: {bundesland}")
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regierungsfraktionen = BUNDESLAENDER[bundesland].regierungsfraktionen
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parteien_to_search = list(dict.fromkeys(fraktionen + regierungsfraktionen)) # dedupe, Reihenfolge stabil
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results = {}
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for partei in parteien_to_search:
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partei_upper = partei.upper() if partei != "GRÜNE" else "GRÜNE"
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# Wahlprogramm — bundesland-gefiltert
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wahl_chunks = find_relevant_chunks(
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antrag_text,
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parteien=[partei_upper],
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typ="wahlprogramm",
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bundesland=bundesland,
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top_k=top_k_per_partei,
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min_similarity=0.45,
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)
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# Parteiprogramm (Grundsatz, federal — bundesland=NULL matched implizit)
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partei_chunks = find_relevant_chunks(
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antrag_text,
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parteien=[partei_upper],
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typ="parteiprogramm",
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bundesland=bundesland,
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top_k=top_k_per_partei,
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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,
|
|
}
|