- [ ] 13. Create knowledge base integration and management
- [ ] 13.1 Implement document processing pipeline
- Write OCR and NLP processing for PDF documents
- Create automated document ingestion and indexing
- Implement version control and change detection
- Write multilingual document processing support
- Requirements: 3.6, 3.7, 3.8, 3.10
✅ Task 13.1: Document Processing Pipeline
Intelligent Document Ingestion & Knowledge Extraction for Semiconductor Manufacturing
A fully implemented, production-ready document processing system that enables automated ingestion, OCR, NLP analysis, and semantic search of technical documents across the semiconductor AI ecosystem.
Designed specifically for high-precision manufacturing environments, this pipeline transforms unstructured documents into structured, searchable, and actionable knowledge — powering RAG systems, AI assistants, and compliance workflows.
📄 Multi-format ingestion | 🔍 Advanced OCR & NLP | 🏭 Semiconductor-domain intelligence
🔗 Vector search integration | 🧠 Knowledge extraction | 📚 Standards-aware processing
🔧 Core Components Implemented
| Component |
File Path |
Description |
| Main Service |
services/knowledge-base/document-processing/src/document_processor.py |
FastAPI-based service with endpoints for upload, processing, search, and retrieval |
| OCR Engine |
services/knowledge-base/document-processing/src/ocr_engine.py |
Multi-backend OCR engine using Tesseract, EasyOCR, and PaddleOCR with layout analysis and table extraction |
| NLP Processor |
services/knowledge-base/document-processing/src/nlp_processor.py |
Domain-specialized NLP engine for entity recognition, relationship extraction, and topic modeling |
| Documentation |
services/knowledge-base/document-processing/README.md |
Complete system overview, architecture, deployment guide, and API reference |
🚀 Key Features Implemented
📥 Document Ingestion & Processing
-
Multi-format Support: PDF, Word, Excel, PowerPoint, images (PNG, JPG), plain text
-
Batch & Real-time Processing:
- Batch: Scheduled processing of document repositories
- Real-time: API-triggered processing for new uploads
-
Version Control: Tracks document versions and detects changes
-
Metadata Extraction: Automatically extracts:
- Title, author, creation date
- Document type (SOP, spec, report)
- Equipment ID, process step, fab location
🖼️ Advanced OCR Capabilities
| Feature |
Implementation |
| Multiple OCR Backends |
Tesseract (custom models), EasyOCR (deep learning), PaddleOCR (high accuracy) |
| Best-Result Selection |
Ensemble approach: runs all backends and selects highest-confidence output |
| Layout Analysis |
Preserves document structure (headings, paragraphs, sections) |
| Table Extraction |
Uses camelot and pandas to extract structured data from tables |
| Image & Diagram Processing |
Analyzes technical diagrams, flowcharts, and schematics |
| Multi-language Support |
Handles English, Japanese, Korean, Chinese, German |
🧠 Semiconductor-Specific NLP
| Capability |
Details |
| Domain Entity Recognition |
Detects: |
- Equipment names (e.g.,
ETCH-004, ASML NXT:2000i)
- Process parameters (e.g.,
RF Power, Chamber Pressure)
- Measurements with units (e.g.,
50W, 2.3mTorr, 150°C) |
| Technical Terminology | Recognizes SEMI, JEDEC, ISO, and internal fab standards |
| Relationship Extraction | Identifies dependencies (e.g., “RF Power affects etch rate”) |
| Topic Modeling | Automatically categorizes documents (e.g., “Etch”, “Lithography”, “Yield”) |
| Semantic Analysis | Understands context (e.g., “high particle count” → defect root cause) |
📚 Knowledge Extraction
| Knowledge Type |
Extraction Target |
| Standards Processing |
SEMI E10, E30, JEDEC J-STD-012, etc. |
| Process Parameters |
Specifications, tolerances, control limits |
| Equipment Information |
Tool specs, maintenance procedures, calibration steps |
| Quality Requirements |
Test procedures, inspection criteria, defect classifications |
| Failure Analysis |
Root causes, corrective actions, recurrence prevention |
🔍 Search & Integration
| Feature |
Technology |
| Vector Database Integration |
ChromaDB, Pinecone, Weaviate for semantic search |
| Full-Text Search |
Elasticsearch for keyword, phrase, and fuzzy search |
| API Integration |
RESTful APIs for MES, AI assistants, dashboards |
| Real-time Indexing |
Automatic updates to search indices after processing |
🏭 Semiconductor Manufacturing Specialization
📄 Document Types Supported
| Document Type |
Use Case |
| Equipment Manuals & SOPs |
Maintenance, calibration, operation |
| Process Specifications |
Parameter control, recipe management |
| Test Procedures |
Quality assurance, inspection protocols |
| Failure Analysis Reports |
Root cause tracking, yield improvement |
| Yield Reports |
Performance analysis, trend identification |
| SEMI/JEDEC Standards |
Compliance, audit readiness |
🧩 Domain-Specific Processing
| Feature |
Function |
| Technical Term Recognition |
Extracts semiconductor-specific vocabulary |
| Unit Extraction & Normalization |
Converts units (e.g., kV → 1000V) and validates ranges |
| Parameter Relationships |
Maps cause-effect relationships (e.g., “Pressure ↑ → Uniformity ↓”) |
| Equipment Hierarchies |
Recognizes tool families, chambers, and subsystems |
🔍 Advanced Processing Capabilities
🖼️ OCR & Text Extraction
- High-accuracy text extraction from scanned PDFs and images
-
Layout preservation with section detection (header, body, footer)
-
Table detection and conversion to structured CSV/JSON
-
Image and diagram analysis with caption extraction
-
Multi-language OCR with language detection
🧠 NLP & Semantic Analysis
-
Named Entity Recognition (NER): Custom models for semiconductor entities
-
Relationship Extraction: Identifies “Equipment → Parameter → Effect” links
-
Topic Modeling: LDA and BERT-based categorization
-
Sentiment & Readability: Assesses clarity and urgency
-
Key Phrase Extraction & Summarization: Generates document summaries
✅ Quality & Accuracy
-
Confidence Scoring: Each extracted entity has a confidence score (0–1.0)
-
Ensemble Processing: Multiple methods used; best result selected
-
Error Handling: Graceful fallbacks (e.g., OCR failure → retry with alternate backend)
-
Processing Quality Metrics: Logs accuracy, speed, and error rates
📊 Integration & APIs
RESTful API Endpoints
| Endpoint |
Method |
Function |
POST /documents/upload |
Upload a document for processing |
|
GET /documents/{id} |
Retrieve processed document and metadata |
|
POST /documents/search |
Search documents with filters (type, equipment, fab) |
|
GET /processing/status/{job_id} |
Check processing status and progress |
|
Storage & Database Integration
| System |
Purpose |
| PostgreSQL |
Stores document metadata, processing logs, version history |
| MinIO |
S3-compatible object storage for raw and processed documents |
| Elasticsearch |
Full-text search index |
| Redis |
Caching, job queue (Celery), and rate limiting |
External Integrations
| System |
Integration |
| Vector Databases |
ChromaDB, Pinecone, Weaviate – for semantic search and RAG |
| Knowledge Graphs |
Neo4j, Amazon Neptune – for relationship visualization |
| Message Queues |
Apache Kafka, Celery – for async processing |
| Monitoring |
Prometheus, Grafana – for performance and error tracking |
🛠 Technology Stack
Core Technologies
| Technology |
Purpose |
| Python |
Primary language with rich NLP and ML ecosystem |
| FastAPI |
High-performance REST API framework |
| Apache Tika |
Universal document parsing (PDF, DOCX, PPTX, XLSX) |
| Tesseract OCR |
Industry-standard OCR with custom trained models |
| spaCy |
Industrial-strength NLP pipeline |
| Transformers (Hugging Face) |
BERT-based models for semantic understanding |
Specialized Libraries
| Library |
Use Case |
| OpenCV / PIL |
Image preprocessing and diagram analysis |
| scikit-learn / PyTorch |
ML models for classification and anomaly detection |
| NLTK / Gensim |
Advanced text processing, topic modeling |
| pdf2image |
Convert PDF pages to images for OCR |
| camelot |
Table extraction from PDFs |
| EasyOCR / PaddleOCR |
Deep learning-based OCR alternatives |
✅ Conclusion
The Document Processing Pipeline is now fully implemented, tested, and production-ready, delivering:
📄 Intelligent ingestion of complex technical documents
🔍 High-accuracy OCR and NLP with semiconductor specialization
🧠 Structured knowledge extraction for AI and search systems
🔗 Seamless integration with vector databases, knowledge graphs, and monitoring
🏭 Deep domain understanding of process, equipment, and quality data
This system forms the foundation of the semiconductor knowledge base, enabling:
- AI-powered assistants
- RAG-enhanced LLMs
- Automated compliance reporting
- Rapid root cause analysis
- Enterprise-wide knowledge retrieval
✅ Status: Complete, Verified, and Deployment-Ready
📁 Fully documented, containerized, and aligned with enterprise MLOps and data governance standards
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