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mcp-nextcloud/nextcloud_mcp_server/document_processors/base.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

140 lines
4.3 KiB
Python

"""Abstract base class for document processing plugins."""
from abc import ABC, abstractmethod
from collections.abc import Awaitable, Callable
from typing import Any, Optional
from pydantic import BaseModel
class ProcessingResult(BaseModel):
"""Standardized result from any document processor."""
text: str
"""Extracted text content"""
metadata: dict[str, Any]
"""Processor-specific metadata"""
processor: str
"""Name of processor that handled this (e.g., 'unstructured', 'tesseract')"""
success: bool = True
"""Whether processing succeeded"""
error: Optional[str] = None
"""Error message if processing failed"""
class DocumentProcessor(ABC):
"""Abstract base class for document processing plugins.
Document processors extract text from various file formats (PDF, DOCX, images, etc.).
Each processor implements this interface and can be registered with the ProcessorRegistry.
Example:
class MyProcessor(DocumentProcessor):
@property
def name(self) -> str:
return "my_processor"
@property
def supported_mime_types(self) -> set[str]:
return {"application/pdf", "image/jpeg"}
async def process(self, content: bytes, content_type: str, **kwargs) -> ProcessingResult:
# Extract text from content
return ProcessingResult(text="...", metadata={}, processor=self.name)
async def health_check(self) -> bool:
return True
"""
@property
@abstractmethod
def name(self) -> str:
"""Unique identifier for this processor (e.g., 'unstructured', 'tesseract')."""
pass
@property
def tier(self) -> str:
"""Extraction tier this processor belongs to (escalation ladder).
Used as the ``tier`` label/attribute in observability so that adding new
extraction tiers later (docling, OCR, LLM) is purely additive. Vocabulary
(cheapest first): ``fast`` -> ``structured`` -> ``ocr`` -> ``llm``.
Defaults to ``"fast"``; override in processors that belong to a higher
tier.
"""
return "fast"
@property
@abstractmethod
def supported_mime_types(self) -> set[str]:
"""Set of MIME types this processor can handle.
Examples: {"application/pdf", "image/jpeg", "image/png"}
"""
pass
@abstractmethod
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 document and extract text.
Args:
content: Document bytes
content_type: MIME type of the document
filename: Optional filename for format detection
options: Processor-specific options (e.g., OCR language, strategy)
progress_callback: Optional async callback for progress updates.
Called as: await progress_callback(progress, total, message)
- progress: Current progress value (monotonically increasing)
- total: Optional total value (None if unknown)
- message: Optional human-readable status message
Returns:
ProcessingResult with extracted text and metadata
Raises:
ProcessorError: If processing fails
"""
pass
@abstractmethod
async def health_check(self) -> bool:
"""Check if processor is available and healthy.
Returns:
True if processor is ready to use, False otherwise
"""
pass
def supports(self, content_type: str) -> bool:
"""Check if this processor supports the given MIME type.
Args:
content_type: MIME type (may include parameters like "application/pdf; charset=utf-8")
Returns:
True if this processor can handle the type
"""
# Strip parameters from content type
base_type = content_type.split(";")[0].strip().lower()
return base_type in self.supported_mime_types
class ProcessorError(Exception):
"""Raised when document processing fails."""
pass