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"""Core processing logic for the MarkItDown Testing Platform."""
from __future__ import annotations
import logging
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, Dict, Optional
from pydantic import JsonValue
from core.modules import (
HFConversionEngine,
ProcessingConfig,
ProcessingResult,
ResourceManager,
StreamlineFileHandler,
)
from llm.gemini_connector import (
AnalysisRequest,
AnalysisType,
GeminiConfig,
GeminiConnectionManager,
GeminiModel,
)
from visualization.analytics_engine import QualityMetricsCalculator
logger = logging.getLogger(__name__)
JSONDict = Dict[str, JsonValue]
@dataclass(frozen=True)
class ProcessingRequest:
"""Immutable request container describing a processing job."""
file_content: bytes
file_metadata: JSONDict
gemini_api_key: Optional[str] = None
analysis_type: str = AnalysisType.QUALITY_ANALYSIS.value
model_preference: str = GeminiModel.PRO.value
use_llm: bool = False
enable_plugins: bool = False
azure_endpoint: Optional[str] = None
session_context: JSONDict = field(default_factory=dict)
@dataclass(frozen=True)
class ProcessingResponse:
"""Standardized response describing the outcome of processing."""
success: bool
conversion_result: Optional[ProcessingResult]
analysis_result: Optional[Any]
quality_metrics: JSONDict
error_details: Optional[str]
processing_metadata: JSONDict
@classmethod
def success_response(
cls,
conversion_result: ProcessingResult,
analysis_result: Optional[Any] = None,
quality_metrics: Optional[JSONDict] = None,
) -> "ProcessingResponse":
return cls(
success=True,
conversion_result=conversion_result,
analysis_result=analysis_result,
quality_metrics=quality_metrics or {},
error_details=None,
processing_metadata={"completed_at": datetime.now().isoformat()},
)
@classmethod
def error_response(
cls,
error_message: str,
error_context: Optional[JSONDict] = None,
) -> "ProcessingResponse":
return cls(
success=False,
conversion_result=None,
analysis_result=None,
quality_metrics={},
error_details=error_message,
processing_metadata=error_context or {"failed_at": datetime.now().isoformat()},
)
class DocumentProcessingOrchestrator:
"""Coordinates the document conversion and optional AI analysis pipeline."""
def __init__(
self,
file_handler: StreamlineFileHandler,
conversion_engine: HFConversionEngine,
gemini_manager: GeminiConnectionManager,
quality_calculator: QualityMetricsCalculator,
) -> None:
self.file_handler = file_handler
self.conversion_engine = conversion_engine
self.gemini_manager = gemini_manager
self.quality_calculator = quality_calculator
self.processing_count = 0
self.error_count = 0
self.total_processing_time = 0.0
async def process_document(self, request: ProcessingRequest) -> ProcessingResponse:
"""Process a document and optionally run Gemini analysis."""
processing_start = datetime.now()
self.processing_count += 1
try:
logger.info(
"Starting document processing - Session: %s | LLM Enabled: %s",
request.session_context.get("session_id", "unknown"),
request.use_llm,
)
conversion_result = await self._execute_conversion_pipeline(request)
if not conversion_result.success:
return ProcessingResponse.error_response(
f"Conversion failed: {conversion_result.error_message}",
{"phase": "conversion", "request_metadata": request.file_metadata},
)
analysis_result = None
if request.gemini_api_key:
analysis_result = await self._execute_analysis_pipeline(request, conversion_result)
quality_metrics = self.quality_calculator.calculate_conversion_quality_metrics(
conversion_result, analysis_result
)
processing_duration = (datetime.now() - processing_start).total_seconds()
self.total_processing_time += processing_duration
logger.info("Processing completed successfully in %.2fs", processing_duration)
return ProcessingResponse.success_response(
conversion_result=conversion_result,
analysis_result=analysis_result,
quality_metrics=quality_metrics,
)
except Exception as exc: # pragma: no cover - defensive logging
self.error_count += 1
error_duration = (datetime.now() - processing_start).total_seconds()
logger.error("Processing failed after %.2fs: %s", error_duration, exc)
return ProcessingResponse.error_response(
error_message=f"System processing error: {exc}",
error_context={
"processing_duration": error_duration,
"error_type": type(exc).__name__,
"processing_phase": "unknown",
},
)
async def _execute_conversion_pipeline(self, request: ProcessingRequest) -> ProcessingResult:
"""Handle file ingestion, validation, and conversion to Markdown."""
class ProcessingFile:
def __init__(self, content: bytes, metadata: JSONDict) -> None:
self.content = content
self.name = metadata.get("filename", "uploaded_file")
def read(self) -> bytes:
return self.content
@property
def size(self) -> int:
return len(self.content)
processing_file = ProcessingFile(request.file_content, request.file_metadata)
file_result = await self.file_handler.process_upload(
processing_file,
metadata_override=request.file_metadata,
)
if not file_result.success:
return file_result
conversion_result = await self.conversion_engine.convert_stream(
request.file_content,
request.file_metadata,
)
return conversion_result
async def _execute_analysis_pipeline(
self,
request: ProcessingRequest,
conversion_result: ProcessingResult,
) -> Optional[Any]:
"""Run Gemini analysis with retry and error handling."""
try:
gemini_config = GeminiConfig(api_key=request.gemini_api_key)
try:
engine_id = await self.gemini_manager.create_engine(
request.gemini_api_key,
gemini_config,
)
except Exception as exc:
raise
engine = self.gemini_manager.get_engine(engine_id)
if not engine:
logger.warning("Gemini engine creation failed - skipping analysis")
return None
analysis_request = AnalysisRequest(
content=conversion_result.content,
analysis_type=AnalysisType(request.analysis_type),
model=GeminiModel.from_str(request.model_preference),
)
logging.info(
"Executing Gemini analysis | Type: %s | Model: %s",
analysis_request.analysis_type,
analysis_request.model,
)
analysis_result = await engine.analyze_content(analysis_request)
if analysis_result.success:
logger.info("Gemini analysis completed - Type: %s", request.analysis_type)
return analysis_result
logger.warning("Gemini analysis failed: %s", analysis_result.error_message)
return None
except Exception as exc: # pragma: no cover - defensive logging
logger.warning("Gemini analysis pipeline error: %s", exc)
return None
def get_processing_status(self) -> JSONDict:
"""Expose operational metrics for status dashboards."""
success_rate = (
((self.processing_count - self.error_count) / self.processing_count * 100)
if self.processing_count
else 0
)
average_processing_time = (
self.total_processing_time / self.processing_count if self.processing_count else 0
)
return {
"total_documents_processed": self.processing_count,
"success_rate_percent": success_rate,
"error_count": self.error_count,
"average_processing_time_seconds": average_processing_time,
"total_processing_time_seconds": self.total_processing_time,
"status": "healthy"
if success_rate > 90
else "degraded"
if success_rate > 70
else "unhealthy",
}
__all__ = [
"JSONDict",
"ProcessingRequest",
"ProcessingResponse",
"DocumentProcessingOrchestrator",
"ProcessingConfig",
"ResourceManager",
"StreamlineFileHandler",
"HFConversionEngine",
"GeminiConnectionManager",
"QualityMetricsCalculator",
]
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