Process of extracting, transforming, and storing document content for AI and search systems
Core Idea: Document ingestion converts unstructured documents into structured, machine-readable data through parsing, OCR, chunking, and metadata extraction for use in search systems and AI applications.
Key Elements
Ingestion Workflow
- Document Acquisition: Obtaining files from various sources
- File systems, cloud storage, web scraping, email attachments
- Supporting multiple formats (PDF, DOCX, HTML, images, etc.)
- Preprocessing: Preparing documents for content extraction
- Format detection and validation
- Decryption and access validation
- Deduplication and versioning
- Content Extraction: Converting documents to plain text
- Text parsing from structured documents
- OCR for image-based text
- Table and chart data extraction
- Document Structuring: Organizing extracted content
- Chunking text into manageable segments
- Preserving document hierarchy (headers, sections)
- Identifying logical boundaries
Technical Components
- Parsing Tools:
- deepdoc: Advanced document understanding system
- Apache Tika: Content detection and extraction
- PyPDF, python-docx: Format-specific extractors
- OCR Systems:
- OLMOCR: AI-based OCR from Allen AI
- PaddleOCR: Open-source OCR system
- Tesseract: Traditional OCR engine
- Metadata Extraction:
- Author information, creation dates
- Version history, classification data
- Custom tags and categories
- Storage Systems:
- MinIO: Object storage for raw documents
- PostgreSQL: Metadata and relationship storage
- Redis: Processing queue management
Optimization Strategies
- Pipeline Orchestration: Coordinating multi-step processing workflows
- NVIDIA NV-Ingest: Enterprise-grade ingestion framework
- Apache Airflow: Workflow management
- Quality Assurance:
- Extraction validation and error detection
- Content integrity verification
- Missing information detection
- Scalability Approaches:
- Parallel processing for high-volume ingestion
- Incremental updates for changed documents
- Priority queuing for critical documents
Connections
- Related Concepts: Document Processing Pipeline (broader workflow), OCR Technology (extraction component), Text Chunking (content segmentation)
- Broader Context: ETL Processes (similar data flow pattern), Content Management Systems (enterprise applications)
- Applications: RAG Systems (knowledge source), Enterprise Search (indexed content)
- Components: Vector Search (retrieval mechanism), Document Storage (persistence layer)
References
- GitHub: infiniflow/ragflow/deepdoc
- GitHub: nvidia/nv-ingest
- GitHub: allenai/olmocr
#document-processing #data-ingestion #ocr #content-extraction #rag
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