Files
mcp-nextcloud/nextcloud_mcp_server/document_processors/pymupdf.py
T
Chris CoutinhoandClaude Opus 4.8 5d205fcaab feat(observability): astrolabe_* metrics + traces for the document pipeline
Make per-tier bottlenecks in the document-processing pipeline
(scan -> fetch -> parse -> chunk -> embed -> Qdrant upsert) visible via
metrics, traces, and structured logs. Today the document_processors layer
emits only a logger.info line: no metric, no span, and page counts live only
inside a log string. The single processing-duration histogram is unlabeled and
whole-document, so it cannot isolate parse vs embed vs upsert.

New astrolabe_* metric family (distinct from the mcp_* protocol metrics):
- astrolabe_document_parse_{duration_seconds,total} + pages/chars/bytes counters
  recorded at the ProcessorRegistry.process() boundary (covers all current and
  future processors uniformly)
- astrolabe_document_escalation_total (dormant; tiered-pipeline readiness)
- astrolabe_embedding_{duration_seconds,requests_total,chunks_total,chars_total}
- astrolabe_document_chunks_total, astrolabe_documents_indexed_total{source,status}

Tracing: new document_processor.parse child span + enriched embed/chunk span
attributes (provider/model/batch_size/chunk_count). Structured logs gain a
consistent field vocabulary (doc_id, doc_type, processor, tier, pages, chars,
byte_size, chunks, duration_ms, status) so Loki can aggregate without regex.

Tier-readiness: processor/tier are labels from day one and a tier property is
added to DocumentProcessor, so adding docling/OCR/LLM tiers later is additive
(new label values, never new metrics). Tenant comes from the kube namespace
label; mime_type/model are span attributes only (cardinality). Existing
mcp_vector_sync_*/mcp_qdrant_* are left untouched.

Refs Deck #175 (superset of #173 Phase 2). Dashboard/recording-rules follow-up
tracked on #175 for homelab-argocd.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-02 17:46:20 +02:00

266 lines
9.2 KiB
Python

"""Document processor using PyMuPDF (fitz) library."""
import logging
import pathlib
import tempfile
from collections.abc import Awaitable, Callable
from typing import Any, Optional
import anyio
# NOTE: Do NOT call pymupdf.layout.activate() here!
# It changes the behavior of pymupdf4llm.to_markdown() when page_chunks=True,
# causing it to return a string instead of a list[dict].
# See: https://github.com/pymupdf/pymupdf4llm/issues/323
import pymupdf
import pymupdf4llm
from .base import DocumentProcessor, ProcessingResult, ProcessorError
logger = logging.getLogger(__name__)
class PyMuPDFProcessor(DocumentProcessor):
"""Document processor using PyMuPDF library for PDF processing.
PyMuPDF (fitz) is a fast, local PDF processing library that extracts text,
metadata, and images without requiring external API calls.
Features:
- Fast text extraction with layout preservation
- PDF metadata extraction (title, author, creation date, page count)
- Image extraction for future multimodal support
- Page number tracking for precise citations
"""
SUPPORTED_TYPES = {
"application/pdf",
}
def __init__(
self,
extract_images: bool = True,
image_dir: Optional[str | pathlib.Path] = None,
):
"""Initialize PyMuPDF processor.
Args:
extract_images: Whether to extract embedded images from PDFs
image_dir: Directory to store extracted images (defaults to temp directory)
"""
self.extract_images = extract_images
if image_dir is None:
self.image_dir = pathlib.Path(tempfile.gettempdir()) / "pdf-images"
else:
self.image_dir = pathlib.Path(image_dir)
# Create image directory if it doesn't exist
if self.extract_images:
self.image_dir.mkdir(exist_ok=True, parents=True)
logger.info(
"Initialized PyMuPDFProcessor with image extraction to %s",
self.image_dir,
)
else:
logger.info("Initialized PyMuPDFProcessor without image extraction")
@property
def name(self) -> str:
return "pymupdf"
@property
def supported_mime_types(self) -> set[str]:
return self.SUPPORTED_TYPES
async def process(
self,
content: bytes,
content_type: str,
filename: Optional[str] = None,
options: Optional[dict[str, Any]] = None,
progress_callback: Optional[
Callable[[float, Optional[float], Optional[str]], Awaitable[None]]
] = None,
) -> ProcessingResult:
"""Process a PDF document and extract text, metadata, and images.
Args:
content: PDF document bytes
content_type: MIME type (should be application/pdf)
filename: Optional filename for better error messages
options: Processing options (currently unused)
progress_callback: Optional callback for progress updates
Returns:
ProcessingResult with extracted text and metadata
Raises:
ProcessorError: If PDF processing fails
"""
try:
if progress_callback:
await progress_callback(0, 100, "Opening PDF document")
# Open document and extract metadata in thread
doc = await anyio.to_thread.run_sync( # type: ignore[attr-defined]
lambda: pymupdf.open("pdf", content)
)
metadata = self._extract_metadata(doc, filename)
metadata["file_size"] = len(content)
page_count = doc.page_count
if progress_callback:
await progress_callback(10, 100, f"Extracting {page_count} pages")
# Prepare image directory if needed
pdf_image_dir = None
if self.extract_images:
pdf_id = filename.replace("/", "_") if filename else "unknown"
pdf_image_dir = self.image_dir / pdf_id
pdf_image_dir.mkdir(exist_ok=True, parents=True)
# Extract all pages in a single call with page_chunks=True
def do_extract() -> list[dict[str, Any]]:
# When page_chunks=True, to_markdown returns list[dict] not str
return pymupdf4llm.to_markdown( # type: ignore[return-value]
doc,
write_images=self.extract_images,
image_path=pdf_image_dir if self.extract_images else None,
page_chunks=True,
)
page_chunks: list[dict[str, Any]] = await anyio.to_thread.run_sync( # type: ignore[attr-defined]
do_extract
)
if progress_callback:
await progress_callback(90, 100, "Building result")
# Extract page texts and build boundaries from chunks
page_texts: list[str] = []
page_boundaries: list[dict[str, Any]] = []
current_offset = 0
for chunk in page_chunks:
text = chunk.get("text", "")
page_num = chunk.get("metadata", {}).get("page", len(page_texts) + 1)
page_texts.append(text)
page_boundaries.append(
{
"page": page_num,
"start_offset": current_offset,
"end_offset": current_offset + len(text),
}
)
current_offset += len(text)
# Collect image paths
image_paths = []
if pdf_image_dir and pdf_image_dir.exists():
image_paths = [str(p) for p in pdf_image_dir.glob("*")]
# Build final text and metadata
md_text = "".join(page_texts)
metadata["has_images"] = len(image_paths) > 0
if image_paths:
metadata["image_count"] = len(image_paths)
metadata["image_paths"] = image_paths
metadata["page_boundaries"] = page_boundaries
# Close document
doc.close()
if progress_callback:
await progress_callback(100, 100, "Processing complete")
logger.info(
"Successfully processed PDF %s: %s pages, %s chars, %s images",
filename or "<bytes>",
metadata["page_count"],
len(md_text),
metadata.get("image_count", 0),
extra={
"processor": self.name,
"tier": self.tier,
"pages": metadata["page_count"],
"chars": len(md_text),
"images": metadata.get("image_count", 0),
"byte_size": len(content),
},
)
return ProcessingResult(
text=md_text,
metadata=metadata,
processor=self.name,
success=True,
)
except Exception as e:
error_msg = f"Failed to process PDF {filename or '<bytes>'}: {e}"
logger.error(error_msg, exc_info=True)
raise ProcessorError(error_msg) from e
def _extract_metadata(
self, doc: pymupdf.Document, filename: Optional[str]
) -> dict[str, Any]:
"""Extract metadata from PDF document.
Args:
doc: Opened PyMuPDF document
filename: Optional filename
Returns:
Dictionary with PDF metadata
"""
metadata: dict[str, Any] = {}
# Basic document info
metadata["page_count"] = doc.page_count
metadata["format"] = "PDF 1." + str(
doc.pdf_version() if hasattr(doc, "pdf_version") else "?" # type: ignore[call-non-callable]
)
if filename:
metadata["filename"] = filename
# Extract PDF metadata dictionary
pdf_metadata = doc.metadata
if pdf_metadata:
# Standard PDF metadata fields
if pdf_metadata.get("title"):
metadata["title"] = pdf_metadata["title"]
if pdf_metadata.get("author"):
metadata["author"] = pdf_metadata["author"]
if pdf_metadata.get("subject"):
metadata["subject"] = pdf_metadata["subject"]
if pdf_metadata.get("keywords"):
metadata["keywords"] = pdf_metadata["keywords"]
if pdf_metadata.get("creator"):
metadata["creator"] = pdf_metadata["creator"]
if pdf_metadata.get("producer"):
metadata["producer"] = pdf_metadata["producer"]
if pdf_metadata.get("creationDate"):
metadata["creation_date"] = pdf_metadata["creationDate"]
if pdf_metadata.get("modDate"):
metadata["modification_date"] = pdf_metadata["modDate"]
return metadata
async def health_check(self) -> bool:
"""Check if PyMuPDF is available and working.
Returns:
True if processor is ready to use
"""
try:
# Try to create a simple PDF in memory
test_doc = pymupdf.open()
test_doc.close()
return True
except Exception as e:
logger.error("PyMuPDF health check failed: %s", e)
return False