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
505 lines
22 KiB
Python
505 lines
22 KiB
Python
"""Tests for embeddings.py prompt formatting.
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Reproduces the LLM-Halluzinations-Bug from the 2026-04-08 session
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(commits 1b5fd96 + bc7f4a6): the original ``format_quotes_for_prompt``
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rendered each chunk as ``- S. X: "text"`` without any reference to the
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programme name. As a result the LLM hallucinated familiar source labels
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("FDP NRW Wahlprogramm 2022") for chunks that actually came from MV/BE,
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because that was the strongest training-set prior for budget-policy
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citations.
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Fix: prepend the fully-qualified PROGRAMME[programm_id]["name"] to each
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quote.
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"""
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import sys
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import types
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import pytest
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# Stub openai before importing embeddings, since the test environment may
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# not have it installed and we don't actually need to make API calls.
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if "openai" not in sys.modules:
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openai_stub = types.ModuleType("openai")
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openai_stub.OpenAI = lambda **kw: None
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sys.modules["openai"] = openai_stub
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# On dev machines an older third-party "fitz" package may shadow PyMuPDF's
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# legacy import alias — verify the loaded module actually has ``open`` and
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# fall back to ``pymupdf`` (the canonical name in PyMuPDF ≥ 1.24) when the
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# wrong "fitz" is in front of pymupdf on sys.path.
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try:
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import fitz as _fitz
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if not hasattr(_fitz, "open"):
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import pymupdf as _pymupdf
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sys.modules["fitz"] = _pymupdf
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except ImportError:
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try:
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import pymupdf as _pymupdf
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sys.modules["fitz"] = _pymupdf
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except ImportError:
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pass # render tests will skip via fixture below
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from app import embeddings as embeddings_mod
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from app.embeddings import (
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_chunk_pdf_url,
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_chunk_source_label,
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find_chunk_for_text,
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format_quotes_for_prompt,
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get_relevant_quotes_for_antrag,
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reconstruct_zitate,
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render_highlighted_page,
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PROGRAMME,
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)
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# ─────────────────────────────────────────────────────────────────────────────
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# _chunk_source_label — fully-qualified programme name + page
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# ─────────────────────────────────────────────────────────────────────────────
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class TestChunkSourceLabel:
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def test_known_programme_id(self):
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chunk = {"programm_id": "fdp-mv-2021", "seite": 73, "text": "..."}
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label = _chunk_source_label(chunk)
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assert "FDP Mecklenburg-Vorpommern" in label
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assert "S. 73" in label
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def test_known_programme_id_for_be(self):
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chunk = {"programm_id": "spd-be-2023", "seite": 24, "text": "..."}
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label = _chunk_source_label(chunk)
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assert "SPD Berlin" in label
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assert "2021" in label # the BE-2023.pdf files contain 2021er programmes
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assert "S. 24" in label
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def test_unknown_programme_id_falls_back_to_id(self):
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chunk = {"programm_id": "fake-xx-9999", "seite": 1, "text": "..."}
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label = _chunk_source_label(chunk)
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# Should not crash, should at least include the id and the page
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assert "fake-xx-9999" in label
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assert "S. 1" in label
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def test_missing_seite_uses_questionmark(self):
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chunk = {"programm_id": "cdu-mv-2021", "text": "..."}
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label = _chunk_source_label(chunk)
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assert "?" in label
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# ─────────────────────────────────────────────────────────────────────────────
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# format_quotes_for_prompt — every chunk must carry programme identification
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# ─────────────────────────────────────────────────────────────────────────────
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EXAMPLE_QUOTES = {
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"FDP": {
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"wahlprogramm": [
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{
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"programm_id": "fdp-mv-2021",
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"partei": "FDP",
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"typ": "wahlprogramm",
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"seite": 73,
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"text": "Die Grundsätze von Wirtschaftlichkeit und Sparsamkeit",
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"similarity": 0.63,
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},
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],
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"parteiprogramm": [
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{
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"programm_id": "fdp-grundsatz",
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"partei": "FDP",
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"typ": "parteiprogramm",
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"seite": 93,
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"text": "Liberale Marktwirtschaft erfordert solide Haushalte",
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"similarity": 0.60,
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},
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],
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},
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"SPD": {
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"wahlprogramm": [
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{
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"programm_id": "spd-mv-2021",
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"partei": "SPD",
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"typ": "wahlprogramm",
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"seite": 22,
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"text": "Verkehrswende weg vom motorisierten Individualverkehr",
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"similarity": 0.58,
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},
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],
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},
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}
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class TestFormatQuotesForPrompt:
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def test_empty_input_returns_empty_string(self):
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assert format_quotes_for_prompt({}) == ""
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def test_renders_party_headings(self):
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out = format_quotes_for_prompt(EXAMPLE_QUOTES)
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assert "### FDP" in out
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assert "### SPD" in out
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def test_every_chunk_has_programme_name(self):
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"""Regression: pre-fix this used "S. X:" only, no programme name —
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the LLM then hallucinated NRW-2022 sources from training data."""
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out = format_quotes_for_prompt(EXAMPLE_QUOTES)
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# Each of the three chunks must reference its source programme
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assert "FDP Mecklenburg-Vorpommern" in out
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assert "FDP Grundsatzprogramm" in out
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assert "SPD Mecklenburg-Vorpommern" in out
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def test_contains_strict_citation_instruction(self):
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"""The prompt header must explicitly forbid hallucinated sources."""
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out = format_quotes_for_prompt(EXAMPLE_QUOTES)
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assert "wörtlich" in out.lower()
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def test_chunks_get_enum_ids(self):
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"""Issue #60 fix: each chunk must be tagged with a stable [Qn] id
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so the LLM can be forced to anchor every citation in a specific
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retrieved chunk instead of inventing snippets from training data.
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"""
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out = format_quotes_for_prompt(EXAMPLE_QUOTES)
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# 2 wahlprogramm chunks + 1 grundsatz chunk = 3 IDs total
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assert "[Q1]" in out
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assert "[Q2]" in out
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assert "[Q3]" in out
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assert "[Q4]" not in out # only 3 chunks in EXAMPLE_QUOTES
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def test_zitateregel_mentions_enum_anchor(self):
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out = format_quotes_for_prompt(EXAMPLE_QUOTES)
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# The prompt header must mention the ENUM anchor mechanism so
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# the LLM understands what [Qn] means.
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assert "[Q" in out
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assert "ZITATEREGEL" in out
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def test_no_nrw_2022_appears_unless_chunks_are_actually_nrw(self):
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"""Sanity: a pure MV+SPD chunk set must not mention NRW anywhere."""
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out = format_quotes_for_prompt(EXAMPLE_QUOTES)
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assert "NRW" not in out
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assert "Nordrhein-Westfalen" not in out
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def test_renders_separate_blocks_for_wahl_and_parteiprogramm(self):
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out = format_quotes_for_prompt(EXAMPLE_QUOTES)
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assert "**Wahlprogramm:**" in out
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assert "**Grundsatzprogramm:**" in out
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def test_get_relevant_quotes_for_antrag_populates_results(self, monkeypatch):
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"""Regression for the partei_upper NameError (Phase B / #55 / eb045d0):
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The dict-write line still referenced ``partei_upper`` after the
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rest of the function had been renamed to ``partei_lookup``. The
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result was that ``get_relevant_quotes_for_antrag`` raised
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``NameError`` on every call, was silently swallowed by the
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``except Exception`` in ``analyzer.run_analysis``, and silently
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downgraded *every* assessment to keyword search — which then
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caused the LLM hallucinations tracked in #60.
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Test strategy: monkeypatch ``find_relevant_chunks`` so we don't
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need real embeddings, then call the wrapper and assert it
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actually returns a populated dict instead of crashing.
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"""
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def fake_find_relevant_chunks(query, parteien=None, typ=None,
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bundesland=None, top_k=3,
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min_similarity=0.5):
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return [{
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"programm_id": "gruene-nrw-2022",
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"partei": parteien[0] if parteien else "GRÜNE",
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"typ": typ or "wahlprogramm",
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"seite": 58,
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"text": "Wahlalter ab 16",
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"similarity": 0.7,
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}]
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monkeypatch.setattr(embeddings_mod, "find_relevant_chunks",
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fake_find_relevant_chunks)
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result = get_relevant_quotes_for_antrag(
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antrag_text="Wahlalter ab 16",
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fraktionen=["GRÜNE"],
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bundesland="NRW",
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top_k_per_partei=2,
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)
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assert result, "Expected a non-empty result dict, got empty"
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# The keys are canonical party names; either GRÜNE itself or
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# whatever the canonical mapper returns for it.
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assert any("GR" in k.upper() for k in result.keys())
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# And the structure must be the {wahlprogramm, parteiprogramm} dict
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first = next(iter(result.values()))
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assert "wahlprogramm" in first
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assert "parteiprogramm" in first
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# ─────────────────────────────────────────────────────────────────────────────
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# reconstruct_zitate — Issue #60 Option B (server-side citation rewrite)
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# ─────────────────────────────────────────────────────────────────────────────
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class TestReconstructZitate:
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"""Verify the post-processing pass that overwrites LLM-emitted quelle/url
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with the canonical source label of whichever retrieved chunk actually
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contains the cited text. Drops zitate that don't match any chunk.
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Background: BB 8/673 (Sub-D run after the A+C deploy) showed the LLM
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copying real text from one chunk but writing the page number from a
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different chunk into ``quelle``. The ENUM-anchor in the prompt is only
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a soft hint; this post-processing step is the structural binding.
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"""
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def _make_chunk(self, programm_id: str, seite: int, text: str) -> dict:
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return {
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"programm_id": programm_id,
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"partei": programm_id.split("-")[0].upper(),
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"typ": "wahlprogramm",
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"seite": seite,
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"text": text,
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"similarity": 0.7,
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}
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def test_overwrites_wrong_seite_with_real_chunk_seite(self):
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"""The BB 8/673 case: LLM cites text from S.27 chunk but writes
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S.4 in quelle. After reconstruct_zitate the quelle must point to
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the real S.27 chunk."""
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real_chunk = self._make_chunk(
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"bsw-bb-2024", 27,
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"wertschätzung für lehrerinnen und lehrer abbau von arbeitsüberlastung",
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)
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wrong_chunk = self._make_chunk(
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"bsw-bb-2024", 4,
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"in brandenburg weniger als 14 euro in der stunde verdient",
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)
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semantic_quotes = {
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"BSW": {"wahlprogramm": [wrong_chunk, real_chunk], "parteiprogramm": []},
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}
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data = {
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"wahlprogrammScores": [{
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"fraktion": "BSW",
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"wahlprogramm": {
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"score": 7,
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"begründung": "...",
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"zitate": [{
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"text": "Wertschätzung für Lehrerinnen und Lehrer Abbau von Arbeitsüberlastung",
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"quelle": "BSW Brandenburg Wahlprogramm 2024, S. 4", # WRONG
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"url": "/static/referenzen/bsw-bb-2024.pdf#page=4",
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}],
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},
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"parteiprogramm": {"score": 0, "begründung": "...", "zitate": []},
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}],
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}
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out = reconstruct_zitate(data, semantic_quotes)
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z = out["wahlprogrammScores"][0]["wahlprogramm"]["zitate"][0]
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assert z["quelle"] == "BSW Brandenburg Wahlprogramm 2024, S. 27"
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# Post-#47: URL ist der Highlight-Cite-Endpoint mit pid+seite+q.
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# Static-Fallback nur noch wenn der Chunk kein text-Feld hat.
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assert z["url"].startswith("/api/wahlprogramm-cite?")
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assert "pid=bsw-bb-2024" in z["url"]
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assert "seite=27" in z["url"]
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def test_drops_zitat_not_found_in_any_chunk(self):
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"""If a snippet was hallucinated entirely (no matching chunk),
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the zitat must be removed rather than persisted."""
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chunk = self._make_chunk(
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"spd-lsa-2021", 41,
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"die stärkung einer geschlechtersensiblen berufsorientierung",
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)
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semantic_quotes = {
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"SPD": {"wahlprogramm": [chunk], "parteiprogramm": []},
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}
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data = {
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"wahlprogrammScores": [{
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"fraktion": "SPD",
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"wahlprogramm": {
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"score": 7,
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"begründung": "...",
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"zitate": [
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{"text": "Wir Sozialdemokratinnen ächten Rechtsextremismus seit 1863",
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"quelle": "SPD Sachsen-Anhalt 2021, S. 37"},
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{"text": "die Stärkung einer geschlechtersensiblen Berufsorientierung",
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"quelle": "SPD Sachsen-Anhalt 2021, S. 41"},
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],
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},
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"parteiprogramm": {"score": 0, "begründung": "...", "zitate": []},
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}],
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}
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out = reconstruct_zitate(data, semantic_quotes)
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zitate = out["wahlprogrammScores"][0]["wahlprogramm"]["zitate"]
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assert len(zitate) == 1
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assert "geschlechtersensiblen" in zitate[0]["text"]
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def test_empty_semantic_quotes_is_noop(self):
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data = {"wahlprogrammScores": [{
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"fraktion": "X",
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"wahlprogramm": {"score": 5, "begründung": "x",
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"zitate": [{"text": "abc def ghi jkl mno pqr", "quelle": "X"}]},
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"parteiprogramm": {"score": 0, "begründung": "x", "zitate": []},
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}]}
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out = reconstruct_zitate(data, {})
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# No chunks → no postprocessing applied; data passes through unchanged
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assert out["wahlprogrammScores"][0]["wahlprogramm"]["zitate"][0]["quelle"] == "X"
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def test_anchor_match_when_full_substring_misses(self):
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"""LLM may slightly truncate a snippet — 5-word-anchor still binds."""
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chunk = self._make_chunk(
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"cdu-nrw-2022", 24,
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"wir wollen interprofessionelle netzwerkstrukturen für kinderschutz fördern dazu werden wir stellen schaffen",
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)
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semantic_quotes = {"CDU": {"wahlprogramm": [chunk], "parteiprogramm": []}}
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data = {"wahlprogrammScores": [{
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"fraktion": "CDU",
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"wahlprogramm": {
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"score": 8, "begründung": "...",
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"zitate": [{
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"text": "Wir wollen interprofessionelle Netzwerkstrukturen für Kinderschutz fördern",
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"quelle": "CDU NRW Wahlprogramm 2022, S. 999", # wrong page
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}],
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},
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"parteiprogramm": {"score": 0, "begründung": "...", "zitate": []},
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}]}
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out = reconstruct_zitate(data, semantic_quotes)
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z = out["wahlprogrammScores"][0]["wahlprogramm"]["zitate"][0]
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assert z["quelle"] == "CDU NRW Wahlprogramm 2022, S. 24"
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def test_find_chunk_for_text_short_needle_returns_none(self):
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chunk = self._make_chunk("x", 1, "egal was hier steht")
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assert find_chunk_for_text("ja", [chunk]) is None
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def test_find_chunk_for_text_handles_soft_hyphen(self):
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chunk = self._make_chunk(
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"bsw-bb-2024", 27,
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"handys und tablets wertschätzung für lehrerinnen und lehrer",
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)
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# LLM-emitted text with the soft hyphen \xad mid-word, as PyMuPDF
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# would extract from a PDF line break.
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text = "Handys und Tablets. Wertschätzung für Lehrerinnen und Lehrer"
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assert find_chunk_for_text(text, [chunk]) is chunk
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# ─────────────────────────────────────────────────────────────────────────────
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# _chunk_pdf_url + render_highlighted_page — Issue #47 PDF-Highlighting
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# ─────────────────────────────────────────────────────────────────────────────
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class TestChunkPdfUrl:
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"""Verify the URL builder switches between the cite-endpoint (when
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chunk text is present) and the static fallback (Pre-#47 chunks).
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"""
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def test_cite_url_when_text_present(self):
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chunk = {
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"programm_id": "gruene-grundsatz",
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"seite": 36,
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"text": "Plattformen müssen umfassend reguliert werden",
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}
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url = _chunk_pdf_url(chunk)
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assert url is not None
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assert url.startswith("/api/wahlprogramm-cite?")
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assert "pid=gruene-grundsatz" in url
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assert "seite=36" in url
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# URL-encoded query (urlencode/quote_plus uses + for space)
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assert "Plattformen" in url
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def test_static_fallback_when_no_text(self):
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chunk = {"programm_id": "fdp-mv-2021", "seite": 73}
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url = _chunk_pdf_url(chunk)
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assert url == "/static/referenzen/fdp-mv-2021.pdf#page=73"
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def test_unknown_programme_returns_none(self):
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chunk = {"programm_id": "fake-xx-9999", "seite": 1, "text": "x" * 50}
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assert _chunk_pdf_url(chunk) is None
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|
|
|
def test_url_truncates_long_text_to_200_chars(self):
|
|
chunk = {
|
|
"programm_id": "gruene-grundsatz",
|
|
"seite": 36,
|
|
"text": "A" * 1000,
|
|
}
|
|
url = _chunk_pdf_url(chunk)
|
|
assert url is not None
|
|
# Eingebettete Text-Länge ist auf 200 Zeichen begrenzt — sonst
|
|
# blasen 500-Zeichen-Snippets das Assessment-JSON auf.
|
|
# Der `q=`-Parameter darf nicht 1000 'A' enthalten.
|
|
assert "A" * 1000 not in url
|
|
assert "A" * 200 in url
|
|
|
|
|
|
class TestRenderHighlightedPage:
|
|
"""Smoke-Test gegen ein reales Wahlprogramm-PDF aus dem
|
|
referenzen-Verzeichnis. Bestätigt dass PyMuPDF einen 1-Seiten-PDF
|
|
mit Highlight-Annotation produziert. Skipped wenn das Test-PDF
|
|
nicht im Repo vorhanden ist.
|
|
"""
|
|
|
|
@pytest.fixture
|
|
def sample_pid(self):
|
|
# Wir nehmen einen kleinen, sicher vorhandenen Eintrag aus PROGRAMME.
|
|
# spd-grundsatz ist seit Tag 1 indexiert und im Repo committed.
|
|
from pathlib import Path
|
|
from app.embeddings import PROGRAMME
|
|
pid = "spd-grundsatz"
|
|
info = PROGRAMME.get(pid)
|
|
if not info:
|
|
pytest.skip("PROGRAMME registry missing spd-grundsatz")
|
|
path = Path(__file__).parent.parent / "app" / "static" / "referenzen" / info["pdf"]
|
|
if not path.exists():
|
|
pytest.skip(f"Test-PDF {path} nicht im Repo")
|
|
return pid
|
|
|
|
def test_unknown_pid_returns_none(self):
|
|
assert render_highlighted_page("fake-xx-9999", 1, "x") is None
|
|
|
|
def test_invalid_seite_returns_none(self, sample_pid):
|
|
assert render_highlighted_page(sample_pid, 99999, "x") is None
|
|
assert render_highlighted_page(sample_pid, 0, "x") is None
|
|
|
|
def test_renders_single_page_pdf(self, sample_pid):
|
|
out = render_highlighted_page(sample_pid, 1, "Soziale Gerechtigkeit")
|
|
assert out is not None
|
|
assert isinstance(out, bytes)
|
|
# PDF magic header
|
|
assert out[:5] == b"%PDF-"
|
|
# PyMuPDF behält bei insert_pdf gemeinsame Resources (Fonts, Images)
|
|
# mit, deshalb ist ein 1-Seiten-Sub-PDF nicht zwangsläufig winzig.
|
|
# Wir prüfen nur dass es überhaupt deutlich kleiner als das Original
|
|
# ist (< 50% der Programm-Größe).
|
|
from pathlib import Path
|
|
info = PROGRAMME[sample_pid]
|
|
original_size = (
|
|
Path(__file__).parent.parent / "app" / "static" / "referenzen" / info["pdf"]
|
|
).stat().st_size
|
|
assert len(out) < original_size, (
|
|
f"sub-PDF {len(out)} not smaller than original {original_size}"
|
|
)
|
|
|
|
def test_returns_pdf_even_when_query_empty(self, sample_pid):
|
|
# Empty query → render the page without any annotations
|
|
out = render_highlighted_page(sample_pid, 1, "")
|
|
assert out is not None
|
|
assert out[:5] == b"%PDF-"
|
|
|
|
def test_returns_pdf_even_when_query_not_found(self, sample_pid):
|
|
# No match → still render the page (no highlights)
|
|
out = render_highlighted_page(
|
|
sample_pid, 1, "this exact phrase definitely does not exist anywhere",
|
|
)
|
|
assert out is not None
|
|
assert out[:5] == b"%PDF-"
|
|
|
|
|
|
def test_format_quotes_truncates_long_chunks_at_500_chars():
|
|
"""Truncation-Test for format_quotes_for_prompt — sat lange als
|
|
Methode in TestRenderHighlightedPage (falsche Class-Zuordnung
|
|
durch Edit-Reihenfolge), jetzt module-level."""
|
|
long_chunk = {
|
|
"FDP": {
|
|
"wahlprogramm": [
|
|
{
|
|
"programm_id": "fdp-mv-2021",
|
|
"seite": 1,
|
|
"text": "A" * 1000, # 1000 chars → should be truncated
|
|
"similarity": 0.7,
|
|
}
|
|
],
|
|
}
|
|
}
|
|
out = format_quotes_for_prompt(long_chunk)
|
|
# Truncation marker
|
|
assert "..." in out
|
|
# Original chunk text 1000 chars not present in full
|
|
assert "A" * 1000 not in out
|