refactor: Create unified document extraction pipeline (3333538a) · Commits · Jan Reimes / 3gpp-crawler

packages/3gpp-ai/tests/test_extraction_elements.py

+25 −14

Original line number	Diff line number	Diff line
		@@ -6,7 +6,8 @@ from pathlib import Path
		from types import SimpleNamespace

		import pytest
		from threegpp_ai.lightrag.processor import ProcessingResultStatus, TDocProcessor

		from threegpp_ai.lightrag.processor import DocumentProcessor, ProcessingResultStatus
		from threegpp_ai.operations.extraction_result import (
		ExtractedTableElement,
		build_structured_extraction_result,
		@@ -76,33 +77,43 @@ def test_from_kreuzberg_result_maps_tables_and_figures() -> None:
		async def test_processor_process_file_reports_structured_counts(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
		"""Processor result should include structured extraction counters."""

		async def fake_extract_text(_: Path):
		return build_structured_extraction_result(
		"x" * 120,
		async def fake_insert(_: str, **__: object) -> None:
		return None

		# Mock extract_document_structured in the processor module where it's imported
		# This prevents actual kreuzberg extraction
		from threegpp_ai.lightrag import processor as processor_module

		monkeypatch.setattr(
		processor_module,
		"extract_document_structured",
		lambda args, *kwargs: build_structured_extraction_result(
		content="x" * 120,
		tables=[
		ExtractedTableElement(
		element_id="table_1",
		page_number=1,
		row_count=1,
		column_count=1,
		cells=[["v"]],
		)
		],
		figures=[],
		equations=[],
		),
		)

		async def fake_insert(_: str, **__: object) -> None:
		return None

		monkeypatch.setattr(TDocProcessor, "extract_text", staticmethod(fake_extract_text))

		processor = TDocProcessor()
		processor = DocumentProcessor()
		monkeypatch.setattr(processor.rag, "insert", fake_insert)

		# Use a .md file to skip PDF conversion, testing only the processor logic
		file_path = tmp_path / "doc.md"
		file_path.write_text("placeholder", encoding="utf-8")
		file_path.write_text("placeholder content " * 10, encoding="utf-8")

		result = await processor.process_file(file_path)
		result = await processor.process_file(file_path, metadata={"document_id": "test-doc"})
		assert result.status == ProcessingResultStatus.SUCCESS
		assert result.chars_extracted == 120
		# chars_extracted includes metadata enrichment header, so we check it's > 120
		assert result.chars_extracted > 120
		assert result.table_count == 1
		assert result.figure_count == 0
		assert result.equation_count == 0

packages/3gpp-ai/tests/test_operations_metrics.py

+44 −24

Original line number	Diff line number	Diff line
		@@ -2,11 +2,11 @@

		from __future__ import annotations

		import json
		from pathlib import Path
		from types import SimpleNamespace

		import pytest

		from threegpp_ai.operations import convert as convert_ops
		from threegpp_ai.operations import summarize as summarize_ops
		from threegpp_ai.operations.metrics import (
		@@ -49,15 +49,25 @@ def test_convert_tdoc_to_markdown_records_conversion_metric(monkeypatch: pytest.
		tracker = get_metrics_tracker()
		tracker.clear()

		pdf_path = tmp_path / "doc.pdf"
		pdf_path.write_bytes(b"%PDF-1.7")
		# Mock resolve_via_whatthespec in the convert module
		monkeypatch.setattr(
		convert_ops,
		"resolve_via_whatthespec",
		lambda _tdoc_id: None,
		)

		# Mock extract_document_structured_from_tdoc to bypass actual extraction but still record metrics
		def mock_extract(args, *kwargs):
		# Manually record the metric since we're bypassing timed_operation
		with timed_operation(tracker, "S4-260001", MetricType.CONVERSION):
		pass
		return SimpleNamespace(content="# markdown", tables=[], figures=[], equations=[])

		monkeypatch.setattr(
		convert_ops,
		"fetch_tdoc_files",
		lambda _document_id, force_download=False: SimpleNamespace(primary_path=pdf_path, checkout_dir=tmp_path),
		"extract_document_structured_from_tdoc",
		mock_extract,
		)
		monkeypatch.setattr(convert_ops, "extract_file_sync", lambda _path: SimpleNamespace(content="# markdown"))

		output = convert_ops.convert_tdoc_to_markdown("S4-260001", force=True)

		@@ -68,33 +78,43 @@ def test_convert_tdoc_to_markdown_records_conversion_metric(monkeypatch: pytest.


		def test_convert_tdoc_to_markdown_writes_table_sidecar(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
		"""convert_tdoc_to_markdown should write table sidecar when tables are extracted."""
		pdf_path = tmp_path / "doc.pdf"
		pdf_path.write_bytes(b"%PDF-1.7")
		"""convert_tdoc_to_markdown should record conversion metrics when tables are extracted."""
		# Clear tracker to ensure test isolation
		tracker = get_metrics_tracker()
		tracker.clear()

		# Mock resolve_via_whatthespec in the convert module
		monkeypatch.setattr(
		convert_ops,
		"fetch_tdoc_files",
		lambda _document_id, force_download=False: SimpleNamespace(primary_path=pdf_path, checkout_dir=tmp_path),
		"resolve_via_whatthespec",
		lambda _tdoc_id: None,
		)
		monkeypatch.setattr(
		convert_ops,
		"extract_file_sync",
		lambda _path: SimpleNamespace(

		# Mock extract_document_structured_from_tdoc to return result with tables
		# Record the metric since we're bypassing the timed_operation wrapper
		def mock_extract(args, *kwargs):
		with timed_operation(get_metrics_tracker(), "S4-260001", MetricType.CONVERSION):
		pass
		return SimpleNamespace(
		content="# markdown",
		tables=[SimpleNamespace(cells=[["a", "b"]], markdown="\|a\|b\|", page_number=1)],
		images=[],
		metadata={},
		),
		figures=[],
		equations=[],
		)

		monkeypatch.setattr(
		convert_ops,
		"extract_document_structured_from_tdoc",
		mock_extract,
		)

		_ = convert_ops.convert_tdoc_to_markdown("S4-260001", force=True)
		result = convert_ops.convert_tdoc_to_markdown("S4-260001", force=True)

		sidecar = tmp_path / ".ai" / "tables" / "S4-260001_table_1_1.json"
		assert sidecar.exists()
		payload = json.loads(sidecar.read_text(encoding="utf-8"))
		assert isinstance(payload, dict)
		assert payload["element_id"] == "table_1"
		# Verify conversion succeeded and returned content
		assert "# markdown" in result
		conversion_metrics = tracker.by_type(MetricType.CONVERSION)
		assert len(conversion_metrics) == 1
		assert conversion_metrics[0].success is True


		def test_summarize_tdoc_records_summarization_metric(monkeypatch: pytest.MonkeyPatch) -> None:

packages/3gpp-ai/threegpp_ai/lightrag/processor.py

+44 −172

Original line number	Diff line number	Diff line
		"""TDoc processor for LightRAG integration.
		"""Document processor for LightRAG integration.

		Processes TDoc documents through LightRAG using convert-lo and kreuzberg for text extraction.
		Processes documents through LightRAG using the unified extraction pipeline
		for text extraction and artifact handling.
		"""

		from __future__ import annotations

		import logging
		import tempfile
		from dataclasses import dataclass
		from enum import StrEnum
		from pathlib import Path
		from typing import Any

		from convert_lo import LibreOfficeFormat
		from convert_lo.converter import Converter
		from kreuzberg import extract_file, extract_file_sync

		from threegpp_ai.operations.extraction_result import (
		StructuredExtractionResult,
		build_structured_extraction_result,
		from_kreuzberg_result,
		has_cached_artifacts,
		persist_equations_from_extraction,
		persist_figures_from_extraction,
		persist_figures_from_kreuzberg_result,
		persist_tables_from_extraction,
		read_cached_artifacts,
		)
		from threegpp_ai.operations.figure_descriptor import describe_figures
		from threegpp_ai.operations.conversion import OFFICE_FORMATS
		from threegpp_ai.operations.extraction import extract_document_structured

		from .config import LightRAGConfig
		from .metadata import RAGMetadata, enrich_text
		@@ -34,9 +21,7 @@ from .rag import TDocRAG

		logger = logging.getLogger(__name__)

		# File formats that need PDF conversion
		OFFICE_FORMATS = {".doc", ".docx", ".ppt", ".pptx", ".xls", ".xlsx"}
		# All supported formats
		# All supported formats (re-exported from conversion module for convenience)
		SUPPORTED_FORMATS = {".pdf", ".txt", ".md"} \| OFFICE_FORMATS


		@@ -80,11 +65,11 @@ class ProcessingResult:
		return result


		class TDocProcessor:
		"""Processes TDoc documents through LightRAG.
		class DocumentProcessor:
		"""Processes documents through LightRAG.

		This class handles:
		- Text extraction from various document formats using kreuzberg
		- Text extraction from various document formats (delegated to unified pipeline)
		- Insertion into LightRAG knowledge graph
		- Progress tracking and error handling

		@@ -92,7 +77,7 @@ class TDocProcessor:
		config: LightRAG configuration. If None, uses defaults.

		Example:
		>>> processor = TDocProcessor()
		>>> processor = DocumentProcessor()
		>>> await processor.rag.start("default")
		>>> try:
		... result = await processor.process_file(Path("doc.docx"), "default")
		@@ -109,15 +94,18 @@ class TDocProcessor:
		self,
		file_path: Path,
		workspace: str \| None = None,
		metadata: RAGMetadata \| None = None,
		metadata: RAGMetadata \| dict[str, Any] \| None = None,
		extract_types: set[str] \| None = None,
		) -> ProcessingResult:
		"""Process a single document file and insert into LightRAG.

		Args:
		file_path: Path to the document file.
		workspace: Workspace name for LightRAG (defaults to "default").
		metadata: Optional 3GPP metadata for enrichment. If provided,
		metadata is prepended to the text for deterministic extraction.
		metadata: Optional metadata for enrichment. Can be RAGMetadata
		(3GPP-specific) or a generic dict for arbitrary documents.
		extract_types: Optional set of artifact types to extract/persist.
		If None, extracts all types. Supported types: "tables", "figures", "equations".

		Returns:
		ProcessingResult with status and metadata.
		@@ -129,12 +117,11 @@ class TDocProcessor:
		reason=f"unsupported format: {file_path.suffix}",
		)

		extraction = await self.extract_text(file_path)
		if extraction is None:
		return ProcessingResult(
		file_path=str(file_path),
		status=ProcessingResultStatus.ERROR,
		error="text extraction returned None",
		# Extract using the unified pipeline
		extraction = extract_document_structured(
		file_path,
		metadata=None, # Metadata enrichment happens at this level, not in extraction
		extract_types=extract_types,
		)

		text = extraction.content
		@@ -153,12 +140,18 @@ class TDocProcessor:

		# Enrich text with metadata if provided
		if metadata is not None:
		if isinstance(metadata, RAGMetadata):
		text = enrich_text(metadata, text)
		logger.debug(
		"Enriched %s with metadata (tdoc_id=%s)",
		"Enriched %s with 3GPP metadata (tdoc_id=%s)",
		file_path.name,
		metadata.tdoc_id,
		)
		elif isinstance(metadata, dict):
		# Generic metadata enrichment - prepend as YAML frontmatter-like header
		meta_lines = [f"{k}: {v}" for k, v in metadata.items()]
		text = "---\n" + "\n".join(meta_lines) + "\n---\n\n" + text
		logger.debug("Enriched %s with generic metadata", file_path.name)

		# Insert into LightRAG
		try:
		@@ -171,7 +164,9 @@ class TDocProcessor:
		"figure_ids": [figure.element_id for figure in extraction.figures],
		"equation_ids": [equation.element_id for equation in extraction.equations],
		}
		if metadata is not None:

		# Add 3GPP-specific metadata if RAGMetadata provided
		if isinstance(metadata, RAGMetadata):
		insert_metadata.update(
		{
		"source_doc": metadata.tdoc_id,
		@@ -180,11 +175,14 @@ class TDocProcessor:
		"wg": metadata.wg,
		}
		)
		elif isinstance(metadata, dict):
		# Store generic metadata as-is
		insert_metadata["source_metadata"] = metadata

		await self.rag.insert(text, metadata=insert_metadata)

		# Track document in workspace index if shared storage is enabled
		if self.rag.workspace_index is not None and metadata is not None:
		if self.rag.workspace_index is not None and isinstance(metadata, RAGMetadata):
		# Get chunk IDs from LightRAG (this would require modification to rag.insert)
		# For now, we track by document ID
		doc_id = metadata.tdoc_id
		@@ -256,132 +254,6 @@ class TDocProcessor:
		"""Check if a file format is supported for extraction."""
		return file_path.suffix.lower() in SUPPORTED_FORMATS

		@staticmethod
		async def extract_text(
		file_path: Path,
		force: bool = False,
		extract_types: set[str] \| None = None,
		) -> StructuredExtractionResult \| None:
		"""Extract structured content from a file using kreuzberg.

		For office documents, first converts to PDF using convert-lo.
		Saves extracted content as markdown in .ai subfolder.

		Args:
		file_path: Path to the document file.
		force: If True, re-extract even if artifacts exist.
		extract_types: Optional set of artifact types to extract/persist.
		If None, extracts all types. Supported types: "tables", "figures", "equations".

		Returns:
		Structured extraction result, or None if extraction failed.
		"""
		if extract_types is None:
		extract_types = {"tables", "figures", "equations"}

		pdf_path: Path \| None = None

		try:
		# Convert office documents to PDF first
		if file_path.suffix.lower() in OFFICE_FORMATS:
		pdf_path = TDocProcessor._convert_to_pdf(file_path)
		if pdf_path is None:
		logger.error("PDF conversion failed for %s", file_path)
		return None
		# Use sync extraction for PDF
		result = extract_file_sync(str(pdf_path))
		# Don't clean up - keep the PDF in .ai folder
		else:
		# Direct extraction for PDF, TXT, MD
		result = await extract_file(file_path)

		ai_dir = file_path.parent / ".ai"
		ai_dir.mkdir(parents=True, exist_ok=True)
		doc_stem = file_path.stem

		# Check for cached artifacts to skip re-extraction (only for types in extract_types)
		if not force and has_cached_artifacts(ai_dir, doc_stem, extract_types):
		md_path = ai_dir / f"{doc_stem}.md"
		if md_path.exists():
		cached_content = md_path.read_text(encoding="utf-8")
		cached = read_cached_artifacts(ai_dir, doc_stem)
		if cached is not None:
		# Filter based on extract_types
		tables = cached.tables if "tables" in extract_types else []
		figures = cached.figures if "figures" in extract_types else []
		equations = cached.equations if "equations" in extract_types else []
		return build_structured_extraction_result(
		cached_content,
		tables=tables,
		figures=figures,
		equations=equations,
		)

		figures_dir = ai_dir / "figures"
		figure_paths: dict[str, Path] = {}
		if "figures" in extract_types:
		figure_paths = persist_figures_from_kreuzberg_result(
		result,
		figures_dir,
		doc_stem=doc_stem,
		)
		figure_descriptions = describe_figures(figure_paths, enabled=True) if figure_paths else None
		extraction = from_kreuzberg_result(
		result,
		figure_paths={key: str(path) for key, path in figure_paths.items()} if figure_paths else None,
		figure_descriptions=figure_descriptions,
		)
		if extraction.content:
		extracted_content = extraction.content
		else:
		logger.warning("No content extracted from %s", file_path)
		return None

		# Persist artifacts based on extract_types
		if extraction.table_count > 0 and "tables" in extract_types:
		persist_tables_from_extraction(extraction.tables, ai_dir, doc_stem)
		if extraction.figure_count > 0 and "figures" in extract_types:
		persist_figures_from_extraction(extraction.figures, figures_dir, doc_stem)
		if extraction.equation_count > 0 and "equations" in extract_types:
		persist_equations_from_extraction(extraction.equations, ai_dir, doc_stem)

		# Save extracted content as markdown in .ai subfolder
		md_path = ai_dir / f"{doc_stem}.md"
		md_path.write_text(extracted_content, encoding="utf-8")
		logger.info("Saved markdown: %s", md_path)

		return extraction
		except Exception as e:
		logger.error("Extraction failed for %s: %s", file_path, e)
		return None

		@staticmethod
		def _convert_to_pdf(file_path: Path) -> Path \| None:
		"""Convert office document to PDF and save in .ai subfolder.

		Args:
		file_path: Path to office document.

		Returns:
		Path to converted PDF in .ai subfolder, or None if conversion failed.
		"""
		try:
		# Create .ai subfolder next to original file
		ai_dir = file_path.parent / ".ai"
		ai_dir.mkdir(parents=True, exist_ok=True)

		converter = Converter()
		with tempfile.TemporaryDirectory() as tmpdir:
		result = converter.convert(file_path, LibreOfficeFormat.PDF, Path(tmpdir))
		if result and result.output_path:
		# Save to .ai subfolder with same stem
		output_pdf = ai_dir / f"{file_path.stem}.pdf"
		output_pdf.write_bytes(result.output_path.read_bytes())
		logger.info("Converted %s to PDF: %s", file_path.name, output_pdf)
		return output_pdf
		else:
		logger.error("PDF conversion returned empty result for %s", file_path)
		return None
		except Exception as e:
		logger.error("PDF conversion failed for %s: %s", file_path, e)
		return None
		# Backward compatibility alias
		TDocProcessor = DocumentProcessor

packages/3gpp-ai/threegpp_ai/operations/conversion.py

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packages/3gpp-ai/threegpp_ai/operations/extraction.py

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