ProfessorNovaandClaude Opus 5
784b76baf7
Self-hosted knowledge management for SMEs: a split-screen Markdown editor whose sections an LLM refines while you write, and RAG question answering over the documents that result. FastAPI + Postgres/pgvector on the back, SvelteKit on the front, everything OpenAI-compatible and self-hostable. Squashed into a single commit; the development history stays local. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CA43ZJda8Rbp2hKXNy8f6b
75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
import pytest
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from sqlalchemy import select, text
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from sqlalchemy.exc import IntegrityError
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models import (
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EMBEDDING_DIM,
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Chunk,
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Document,
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DocumentStatus,
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)
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async def _make_document(db: AsyncSession, content: str) -> Document:
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document = Document(
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title="Server maintenance",
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status=DocumentStatus.published,
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content_md=content,
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)
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db.add(document)
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await db.flush()
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return document
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async def test_chunk_tsv_is_generated_with_german_config(db: AsyncSession) -> None:
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document = await _make_document(db, "# Maintenance")
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chunk = Chunk(
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document_id=document.id,
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chunk_index=0,
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content="The servers are maintained and checked regularly.",
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embedding=[0.1] * EMBEDDING_DIM,
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)
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db.add(chunk)
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await db.commit()
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# Same word in content and query stems identically under any config;
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# real German retrieval assertions come with the M4 fixture corpus.
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matches = (
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await db.execute(
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select(Chunk.id).where(
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text("tsv @@ websearch_to_tsquery('german', 'maintained')")
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)
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)
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).all()
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assert len(matches) == 1
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async def test_chunk_index_unique_per_document(db: AsyncSession) -> None:
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document = await _make_document(db, "# Duplicate")
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for _ in range(2):
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db.add(
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Chunk(
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document_id=document.id,
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chunk_index=0,
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content="same index",
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embedding=[0.0] * EMBEDDING_DIM,
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)
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)
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with pytest.raises(IntegrityError):
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await db.commit()
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async def test_embedding_dimension_enforced(db: AsyncSession) -> None:
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document = await _make_document(db, "# Dimension")
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db.add(
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Chunk(
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document_id=document.id,
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chunk_index=0,
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content="wrong dimension",
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embedding=[0.0] * (EMBEDDING_DIM - 1),
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)
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)
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with pytest.raises(Exception, match="expected 1024 dimensions"):
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await db.commit()
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