mirror of
https://github.com/MODSetter/SurfSense.git
synced 2025-09-01 18:19:08 +00:00
931fafa403
- Deleted the document_processing module and its associated docling_service. - Updated imports in documents_routes.py and background_tasks.py to reflect the new service structure. - Ensured compatibility with the task logging system by adjusting type hints for log entries.
346 lines
No EOL
14 KiB
Python
346 lines
No EOL
14 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
Docling Document Processing Service for SurfSense
|
||
SSL-safe implementation with pre-downloaded models
|
||
"""
|
||
|
||
import logging
|
||
import ssl
|
||
import os
|
||
from typing import Dict, Any
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
class DoclingService:
|
||
"""Docling service for enhanced document processing with SSL fixes."""
|
||
|
||
def __init__(self):
|
||
"""Initialize Docling service with SSL, model fixes, and GPU acceleration."""
|
||
self.converter = None
|
||
self.use_gpu = False
|
||
self._configure_ssl_environment()
|
||
self._check_wsl2_gpu_support()
|
||
self._initialize_docling()
|
||
|
||
def _configure_ssl_environment(self):
|
||
"""Configure SSL environment for secure model downloads."""
|
||
try:
|
||
# Set SSL context for downloads
|
||
ssl._create_default_https_context = ssl._create_unverified_context
|
||
|
||
# Set SSL environment variables if not already set
|
||
if not os.environ.get('SSL_CERT_FILE'):
|
||
try:
|
||
import certifi
|
||
os.environ['SSL_CERT_FILE'] = certifi.where()
|
||
os.environ['REQUESTS_CA_BUNDLE'] = certifi.where()
|
||
except ImportError:
|
||
pass
|
||
|
||
logger.info("🔐 SSL environment configured for model downloads")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ SSL configuration warning: {e}")
|
||
|
||
def _check_wsl2_gpu_support(self):
|
||
"""Check and configure GPU support for WSL2 environment."""
|
||
try:
|
||
import torch
|
||
if torch.cuda.is_available():
|
||
gpu_count = torch.cuda.device_count()
|
||
gpu_name = torch.cuda.get_device_name(0) if gpu_count > 0 else "Unknown"
|
||
logger.info(f"✅ WSL2 GPU detected: {gpu_name} ({gpu_count} devices)")
|
||
logger.info(f"🚀 CUDA Version: {torch.version.cuda}")
|
||
self.use_gpu = True
|
||
else:
|
||
logger.info("⚠️ CUDA not available in WSL2, falling back to CPU")
|
||
self.use_gpu = False
|
||
except ImportError:
|
||
logger.info("⚠️ PyTorch not found, falling back to CPU")
|
||
self.use_gpu = False
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ GPU detection failed: {e}, falling back to CPU")
|
||
self.use_gpu = False
|
||
|
||
def _initialize_docling(self):
|
||
"""Initialize Docling with version-safe configuration."""
|
||
try:
|
||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||
from docling.datamodel.base_models import InputFormat
|
||
from docling.datamodel.pipeline_options import PdfPipelineOptions
|
||
from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
|
||
|
||
logger.info("🔧 Initializing Docling with version-safe configuration...")
|
||
|
||
# Create pipeline options with version-safe attribute checking
|
||
pipeline_options = PdfPipelineOptions()
|
||
|
||
# Disable OCR (user request)
|
||
if hasattr(pipeline_options, 'do_ocr'):
|
||
pipeline_options.do_ocr = False
|
||
logger.info("⚠️ OCR disabled by user request")
|
||
else:
|
||
logger.warning("⚠️ OCR attribute not available in this Docling version")
|
||
|
||
# Enable table structure if available
|
||
if hasattr(pipeline_options, 'do_table_structure'):
|
||
pipeline_options.do_table_structure = True
|
||
logger.info("✅ Table structure detection enabled")
|
||
|
||
# Configure GPU acceleration for WSL2 if available
|
||
if hasattr(pipeline_options, 'accelerator_device'):
|
||
if self.use_gpu:
|
||
try:
|
||
pipeline_options.accelerator_device = "cuda"
|
||
logger.info("🚀 GPU acceleration enabled (CUDA)")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ GPU acceleration failed, using CPU: {e}")
|
||
pipeline_options.accelerator_device = "cpu"
|
||
else:
|
||
pipeline_options.accelerator_device = "cpu"
|
||
logger.info("🖥️ Using CPU acceleration")
|
||
else:
|
||
logger.info("ℹ️ Accelerator device attribute not available in this Docling version")
|
||
|
||
# Create PDF format option with backend
|
||
pdf_format_option = PdfFormatOption(
|
||
pipeline_options=pipeline_options,
|
||
backend=PyPdfiumDocumentBackend
|
||
)
|
||
|
||
# Initialize DocumentConverter
|
||
self.converter = DocumentConverter(
|
||
format_options={
|
||
InputFormat.PDF: pdf_format_option
|
||
}
|
||
)
|
||
|
||
acceleration_type = "GPU (WSL2)" if self.use_gpu else "CPU"
|
||
logger.info(f"✅ Docling initialized successfully with {acceleration_type} acceleration")
|
||
|
||
except ImportError as e:
|
||
logger.error(f"❌ Docling not installed: {e}")
|
||
raise RuntimeError(f"Docling not available: {e}")
|
||
except Exception as e:
|
||
logger.error(f"❌ Docling initialization failed: {e}")
|
||
raise RuntimeError(f"Docling initialization failed: {e}")
|
||
|
||
def _configure_easyocr_local_models(self):
|
||
"""Configure EasyOCR to use pre-downloaded local models."""
|
||
try:
|
||
import easyocr
|
||
import os
|
||
|
||
# Set SSL environment for EasyOCR downloads
|
||
os.environ['CURL_CA_BUNDLE'] = ''
|
||
os.environ['REQUESTS_CA_BUNDLE'] = ''
|
||
|
||
# Try to use local models first, fallback to download if needed
|
||
try:
|
||
reader = easyocr.Reader(['en'],
|
||
download_enabled=False,
|
||
model_storage_directory="/root/.EasyOCR/model")
|
||
logger.info("✅ EasyOCR configured for local models")
|
||
return reader
|
||
except:
|
||
# If local models fail, allow download with SSL bypass
|
||
logger.info("🔄 Local models failed, attempting download with SSL bypass...")
|
||
reader = easyocr.Reader(['en'],
|
||
download_enabled=True,
|
||
model_storage_directory="/root/.EasyOCR/model")
|
||
logger.info("✅ EasyOCR configured with downloaded models")
|
||
return reader
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ EasyOCR configuration failed: {e}")
|
||
return None
|
||
|
||
async def process_document(self, file_path: str, filename: str = None) -> Dict[str, Any]:
|
||
"""Process document with Docling using pre-downloaded models."""
|
||
|
||
if self.converter is None:
|
||
raise RuntimeError("Docling converter not initialized")
|
||
|
||
try:
|
||
logger.info(f"🔄 Processing {filename} with Docling (using local models)...")
|
||
|
||
# Process document with local models
|
||
result = self.converter.convert(file_path)
|
||
|
||
# Extract content using version-safe methods
|
||
content = None
|
||
if hasattr(result, 'document') and result.document:
|
||
# Try different export methods (version compatibility)
|
||
if hasattr(result.document, 'export_to_markdown'):
|
||
content = result.document.export_to_markdown()
|
||
logger.info("📄 Used export_to_markdown method")
|
||
elif hasattr(result.document, 'to_markdown'):
|
||
content = result.document.to_markdown()
|
||
logger.info("📄 Used to_markdown method")
|
||
elif hasattr(result.document, 'text'):
|
||
content = result.document.text
|
||
logger.info("📄 Used text property")
|
||
elif hasattr(result.document, '__str__'):
|
||
content = str(result.document)
|
||
logger.info("📄 Used string conversion")
|
||
|
||
if content:
|
||
logger.info(f"✅ Docling SUCCESS - {filename}: {len(content)} chars (local models)")
|
||
|
||
return {
|
||
'content': content,
|
||
'full_text': content,
|
||
'service_used': 'docling',
|
||
'status': 'success',
|
||
'processing_notes': 'Processed with Docling using pre-downloaded models'
|
||
}
|
||
else:
|
||
raise ValueError("No content could be extracted from document")
|
||
else:
|
||
raise ValueError("No document object returned by Docling")
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Docling processing failed for {filename}: {e}")
|
||
# Log the full error for debugging
|
||
import traceback
|
||
logger.error(f"Full traceback: {traceback.format_exc()}")
|
||
raise RuntimeError(f"Docling processing failed: {e}")
|
||
|
||
async def process_large_document_summary(
|
||
self,
|
||
content: str,
|
||
llm,
|
||
document_title: str = "Document"
|
||
) -> str:
|
||
"""
|
||
Process large documents using chunked LLM summarization.
|
||
|
||
Args:
|
||
content: The full document content
|
||
llm: The language model to use for summarization
|
||
document_title: Title of the document for context
|
||
|
||
Returns:
|
||
Final summary of the document
|
||
"""
|
||
# Large document threshold (100K characters ≈ 25K tokens)
|
||
LARGE_DOCUMENT_THRESHOLD = 100_000
|
||
|
||
if len(content) <= LARGE_DOCUMENT_THRESHOLD:
|
||
# For smaller documents, use direct processing
|
||
logger.info(f"📄 Document size: {len(content)} chars - using direct processing")
|
||
from app.prompts import SUMMARY_PROMPT_TEMPLATE
|
||
summary_chain = SUMMARY_PROMPT_TEMPLATE | llm
|
||
result = await summary_chain.ainvoke({"document": content})
|
||
return result.content
|
||
|
||
logger.info(f"📚 Large document detected: {len(content)} chars - using chunked processing")
|
||
|
||
# Import chunker from config
|
||
from app.config import config
|
||
from langchain_core.prompts import PromptTemplate
|
||
|
||
# Create LLM-optimized chunks (8K tokens max for safety)
|
||
from chonkie import RecursiveChunker, OverlapRefinery
|
||
llm_chunker = RecursiveChunker(
|
||
chunk_size=8000 # Conservative for most LLMs
|
||
)
|
||
|
||
# Apply overlap refinery for context preservation (10% overlap = 800 tokens)
|
||
overlap_refinery = OverlapRefinery(
|
||
context_size=0.1, # 10% overlap for context preservation
|
||
method="suffix" # Add next chunk context to current chunk
|
||
)
|
||
|
||
# First chunk the content, then apply overlap refinery
|
||
initial_chunks = llm_chunker.chunk(content)
|
||
chunks = overlap_refinery.refine(initial_chunks)
|
||
total_chunks = len(chunks)
|
||
|
||
logger.info(f"📄 Split into {total_chunks} chunks for LLM processing")
|
||
|
||
# Template for chunk processing
|
||
chunk_template = PromptTemplate(
|
||
input_variables=["chunk", "chunk_number", "total_chunks"],
|
||
template="""<INSTRUCTIONS>
|
||
You are summarizing chunk {chunk_number} of {total_chunks} from a large document.
|
||
|
||
Create a comprehensive summary of this document chunk. Focus on:
|
||
- Key concepts, facts, and information
|
||
- Important details and context
|
||
- Main topics and themes
|
||
|
||
Provide a clear, structured summary that captures the essential content.
|
||
|
||
Chunk {chunk_number}/{total_chunks}:
|
||
<document_chunk>
|
||
{chunk}
|
||
</document_chunk>
|
||
</INSTRUCTIONS>"""
|
||
)
|
||
|
||
# Process each chunk individually
|
||
chunk_summaries = []
|
||
for i, chunk in enumerate(chunks, 1):
|
||
try:
|
||
logger.info(f"🔄 Processing chunk {i}/{total_chunks} ({len(chunk.text)} chars)")
|
||
|
||
chunk_chain = chunk_template | llm
|
||
chunk_result = await chunk_chain.ainvoke({
|
||
"chunk": chunk.text,
|
||
"chunk_number": i,
|
||
"total_chunks": total_chunks
|
||
})
|
||
|
||
chunk_summary = chunk_result.content
|
||
chunk_summaries.append(f"=== Section {i} ===\n{chunk_summary}")
|
||
|
||
logger.info(f"✅ Completed chunk {i}/{total_chunks}")
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Failed to process chunk {i}/{total_chunks}: {e}")
|
||
chunk_summaries.append(f"=== Section {i} ===\n[Processing failed]")
|
||
|
||
# Combine summaries into final document summary
|
||
logger.info(f"🔄 Combining {len(chunk_summaries)} chunk summaries")
|
||
|
||
try:
|
||
combine_template = PromptTemplate(
|
||
input_variables=["summaries", "document_title"],
|
||
template="""<INSTRUCTIONS>
|
||
You are combining multiple section summaries into a final comprehensive document summary.
|
||
|
||
Create a unified, coherent summary from the following section summaries of "{document_title}".
|
||
Ensure:
|
||
- Logical flow and organization
|
||
- No redundancy or repetition
|
||
- Comprehensive coverage of all key points
|
||
- Professional, objective tone
|
||
|
||
<section_summaries>
|
||
{summaries}
|
||
</section_summaries>
|
||
</INSTRUCTIONS>"""
|
||
)
|
||
|
||
combined_summaries = "\n\n".join(chunk_summaries)
|
||
combine_chain = combine_template | llm
|
||
|
||
final_result = await combine_chain.ainvoke({
|
||
"summaries": combined_summaries,
|
||
"document_title": document_title
|
||
})
|
||
|
||
final_summary = final_result.content
|
||
logger.info(f"✅ Large document processing complete: {len(final_summary)} chars summary")
|
||
|
||
return final_summary
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Failed to combine summaries: {e}")
|
||
# Fallback: return concatenated chunk summaries
|
||
fallback_summary = "\n\n".join(chunk_summaries)
|
||
logger.warning("⚠️ Using fallback combined summary")
|
||
return fallback_summary
|
||
|
||
def create_docling_service() -> DoclingService:
|
||
"""Create a Docling service instance."""
|
||
return DoclingService() |