'Complete Blueprint: Building a Local LLM Document Processing Pipeline with [post] deterministic


Building a Local Document Processing Pipeline with LLMs: The Ultimate Architecture

> *"The ability to process, understand, and transform documents is not merely a technical challenge—it is the foundation of knowledge work in the digital age."*

This comprehensive guide presents a production-grade, locally-hosted document processing pipeline that combines elegance with power. By the end, you'll have a system that extracts meaning from documents, structures information intelligently, and enables limitless transformations of your content—all without sending sensitive data to external APIs.

📋 Architecture Overview

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │ │ │ │ │ │ │ │ Document │─→ │ Extraction │─→ │ Semantic │─→ │ Storage & │ │ Ingestion │ │ Engine │ │ Processing │ │ Retrieval │ │ │ │ │ │ │ │ │ └─────────────────┘ └─────────────────┘ └─────────────────┘ └─────────────────┘ ↑ ↓ ┌─────────────────────────┴───────────────────────┐ │ │ │ Transformation Layer │ │ │ └─────────────────────────────────────────────────┘

1. High-Fidelity Document Extraction System

The foundation of our pipeline is a robust extraction engine that preserves document structure while efficiently handling multiple formats.

```python # document_extractor.py from typing import Dict, Union, List, Optional import pdfplumber from docx import Document import fitz # PyMuPDF import logging import concurrent.futures from dataclasses import dataclass

@dataclass class DocumentMetadata: """Structured metadata for any document.""" filename: str file_type: str page_count: int author: Optional[str] = None creation_date: Optional[str] = None last_modified: Optional[str] = None

@dataclass class DocumentElement: """Represents a structural element of a document.""" element_type: str # 'paragraph', 'heading', 'list_item', 'table', etc. content: str metadata: Dict = None position: Dict = None # For spatial positioning in the document

@dataclass class DocumentContent: """Full representation of a document's content and structure.""" metadata: DocumentMetadata elements: List[DocumentElement] raw_text: str = None

class DocumentExtractor: """Universal document extraction class with advanced capabilities.""" def __init__(self, max_workers: int = 4): self.logger = logging.getLogger(__name__) self.max_workers = max_workers def extract(self, file_path: str) -> DocumentContent: """Extract content from document with appropriate extractor.""" lower_path = file_path.lower() if lower_path.endswith('.pdf'): return self._extract_pdf(file_path) elif lower_path.endswith('.docx'): return self._extract_docx(file_path) else: raise ValueError(f"Unsupported file format: {file_path}") def _extract_pdf(self, file_path: str) -> DocumentContent: """Extract content from PDF with advanced structure recognition.""" try: # Using PyMuPDF for metadata and pdfplumber for content pdf_doc = fitz.open(file_path) metadata = DocumentMetadata( filename=file_path.split('/')[-1], file_type="pdf", page_count=len(pdf_doc), author=pdf_doc.metadata.get('author'), creation_date=pdf_doc.metadata.get('creationDate'), last_modified=pdf_doc.metadata.get('modDate') ) elements = [] raw_text = "" # Process pages in parallel for large documents def process_page(page_num): with pdfplumber.open(file_path) as pdf: page = pdf.pages[page_num] page_text = page.extract_text() or "" # Extract tables separately to maintain structure tables = page.extract_tables() # Identify text blocks with their positions blocks = page.extract_words( keep_blank_chars=True, x_tolerance=3, y_tolerance=3, extra_attrs=['fontname', 'size'] ) page_elements = [] # Process text blocks to identify paragraphs and headings current_block = "" current_metadata = {} for word in blocks: # Simplified logic - in production would have more sophisticated # heading/paragraph detection based on font, size, etc. if not current_metadata: current_metadata = { 'font': word.get('fontname'), 'size': word.get('size'), 'page': page_num + 1 } if word.get('size') != current_metadata.get('size'): # Font size changed, likely a new element if current_block: element_type = 'heading' if current_metadata.get('size', 0) > 11 else 'paragraph' page_elements.append(DocumentElement( element_type=element_type, content=current_block.strip(), metadata=current_metadata.copy(), position={'page': page_num + 1} )) current_block = "" current_metadata = { 'font': word.get('fontname'), 'size': word.get('size'), 'page': page_num + 1 } current_block += word.get('text', '') + " " # Add the last block if current_block: element_type = 'heading' if current_metadata.get('size', 0) > 11 else 'paragraph' page_elements.append(DocumentElement( element_type=element_type, content=current_block.strip(), metadata=current_metadata, position={'page': page_num + 1} )) # Add tables as structured elements for i, table in enumerate(tables): table_text = "\n".join([" | ".join([cell or "" for cell in row]) for row in table]) page_elements.append(DocumentElement( element_type='table', content=table_text, metadata={'table_index': i}, position={'page': page_num + 1} )) return page_text, page_