cfc5bb5c35
Set up a full E2E test suite (39 scenarios) using Karate against the real API stack. Hybrid architecture: domain-based features + cross-domain workflows, with data-driven testing and callable helpers. Structure: - e2e/pom.xml: Maven + karate-core 1.5 - 3 helpers (upload, analyze+poll, cleanup) - 3 JSON schemas (health, document, analysis) - 12 feature files across health, documents, analyses, workflows - Tags: @smoke (2), @regression (35), @e2e (2) - generate-test-data.py: fpdf2-based PDF generation (no binaries) Also adds: - RATE_LIMIT_RPM env var to make rate limiter configurable (0=disabled) - CI job e2e with needs: [backend, frontend] - e2e/ in .dockerignore Closes #119
78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
"""Generate deterministic test PDFs for E2E tests.
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Uses fpdf2 to create valid PDFs with real text content so Docling
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can extract and chunk them. No binary files committed to the repo.
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Usage:
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python e2e/generate-test-data.py
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Dependencies:
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pip install fpdf2
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"""
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from __future__ import annotations
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import os
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from fpdf import FPDF
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OUTPUT_DIR = os.path.join(
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os.path.dirname(__file__), "src", "test", "resources", "common", "data", "generated"
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)
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_PARAGRAPHS = [
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"Document processing is a critical step in building retrieval-augmented generation systems.",
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"Docling Studio provides tools for analyzing PDF documents and extracting structured content.",
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"The conversion pipeline supports OCR, table detection, and formula enrichment features.",
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"Chunking splits document content into semantically meaningful segments for vector indexing.",
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"Each chunk preserves metadata such as page number, bounding boxes, and heading hierarchy.",
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"The hybrid chunker combines hierarchical document structure with token-based splitting.",
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"Vector stores like OpenSearch enable fast similarity search over embedded chunk vectors.",
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"Quality control requires visual inspection of chunk boundaries and extracted text accuracy.",
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"Batch processing of large documents uses page ranges to prevent memory exhaustion.",
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"Progress reporting allows users to monitor long-running document conversion tasks.",
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]
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def _make_pdf(page_count: int, path: str) -> None:
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"""Create a valid PDF with N pages containing text paragraphs."""
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pdf = FPDF()
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pdf.set_auto_page_break(auto=True, margin=25)
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for page_num in range(page_count):
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pdf.add_page()
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pdf.set_font("Helvetica", "B", 16)
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pdf.cell(0, 10, f"Page {page_num + 1} of {page_count}", new_x="LMARGIN", new_y="NEXT")
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pdf.ln(5)
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pdf.set_font("Helvetica", "", 11)
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for i in range(5):
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para = _PARAGRAPHS[(page_num * 5 + i) % len(_PARAGRAPHS)]
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pdf.multi_cell(0, 6, para)
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pdf.ln(3)
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pdf.output(path)
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size_kb = os.path.getsize(path) / 1024
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print(f" {os.path.basename(path)}: {page_count} pages, {size_kb:.1f} KB")
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def _make_non_pdf(path: str) -> None:
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"""Create a non-PDF file for negative testing."""
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with open(path, "wb") as f:
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f.write(b"This is not a PDF file.\n")
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print(f" {os.path.basename(path)}: not-a-pdf")
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def main() -> None:
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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print(f"Generating test data in {OUTPUT_DIR}")
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_make_pdf(1, os.path.join(OUTPUT_DIR, "small.pdf"))
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_make_pdf(5, os.path.join(OUTPUT_DIR, "medium.pdf"))
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_make_pdf(25, os.path.join(OUTPUT_DIR, "large.pdf"))
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_make_non_pdf(os.path.join(OUTPUT_DIR, "not-a-pdf.txt"))
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print("Done.")
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if __name__ == "__main__":
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main()
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