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# Semantica Framework Development Plan
## Overview
Build a production-ready Python framework for transforming unstructured data into semantic layers, knowledge graphs, and embeddings. Follow SDK best practices with clean architecture, comprehensive testing, and extensive documentation.
## Detailed Project Structure
```
semantica-3/
├── libs/ # Core framework implementation
│ ├── semantica/ # Main package
│ │ ├── __init__.py
│ │ │
│ │ ├── core/ # Core orchestration
│ │ │ ├── __init__.py
│ │ │ ├── orchestrator.py
│ │ │ ├── config_manager.py
│ │ │ ├── plugin_registry.py
│ │ │ └── lifecycle.py
│ │ │
│ │ ├── ingest/ # Data ingestion modules
│ │ │ ├── __init__.py
│ │ │ ├── file_ingestor.py
│ │ │ ├── web_ingestor.py
│ │ │ ├── feed_ingestor.py
│ │ │ ├── stream_ingestor.py
│ │ │ ├── repo_ingestor.py
│ │ │ ├── email_ingestor.py
│ │ │ └── db_ingestor.py
│ │ │
│ │ ├── parse/ # Format parsers
│ │ │ ├── __init__.py
│ │ │ ├── pdf_parser.py
│ │ │ ├── docx_parser.py
│ │ │ ├── pptx_parser.py
│ │ │ ├── excel_parser.py
│ │ │ ├── html_parser.py
│ │ │ ├── csv_parser.py
│ │ │ ├── json_parser.py
│ │ │ ├── xml_parser.py
│ │ │ ├── image_parser.py
│ │ │ ├── latex_parser.py
│ │ │ ├── epub_parser.py
│ │ │ └── archive_parser.py
│ │ │
│ │ ├── normalize/ # Data normalization
│ │ │ ├── __init__.py
│ │ │ ├── text_cleaner.py
│ │ │ ├── language_detector.py
│ │ │ ├── encoding_handler.py
│ │ │ ├── entity_normalizer.py
│ │ │ ├── date_normalizer.py
│ │ │ └── number_normalizer.py
│ │ │
│ │ ├── split/ # Chunking strategies
│ │ │ ├── __init__.py
│ │ │ ├── sliding_window_chunker.py
│ │ │ ├── semantic_chunker.py
│ │ │ ├── structural_chunker.py
│ │ │ ├── table_chunker.py
│ │ │ ├── provenance_tracker.py
│ │ │ └── chunk_validator.py
│ │ │
│ │ ├── semantic_extract/ # NER, relations, triples
│ │ │ ├── __init__.py
│ │ │ ├── ner_extractor.py
│ │ │ ├── relation_extractor.py
│ │ │ ├── event_detector.py
│ │ │ ├── coref_resolver.py
│ │ │ ├── triple_extractor.py
│ │ │ ├── llm_enhancer.py
│ │ │ └── extraction_validator.py
│ │ │
│ │ ├── ontology/ # Ontology generation
│ │ │ ├── __init__.py
│ │ │ ├── class_inferrer.py
│ │ │ ├── property_generator.py
│ │ │ ├── owl_generator.py
│ │ │ ├── schema_mapper.py
│ │ │ ├── version_manager.py
│ │ │ ├── ontology_validator.py
│ │ │ └── domain_ontologies.py
│ │ │
│ │ ├── triple_store/ # Triple store adapters
│ │ │ ├── __init__.py
│ │ │ ├── blazegraph_adapter.py
│ │ │ ├── jena_adapter.py
│ │ │ ├── rdf4j_adapter.py
│ │ │ ├── virtuoso_adapter.py
│ │ │ ├── graphdb_adapter.py
│ │ │ ├── triple_manager.py
│ │ │ ├── query_engine.py
│ │ │ └── bulk_loader.py
│ │ │
│ │ ├── kg/ # Knowledge graph
│ │ │ ├── __init__.py
│ │ │ ├── graph_builder.py
│ │ │ ├── entity_resolver.py
│ │ │ ├── deduplicator.py
│ │ │ ├── seed_manager.py
│ │ │ ├── provenance_tracker.py
│ │ │ ├── conflict_detector.py
│ │ │ ├── conflict_resolver.py
│ │ │ ├── graph_validator.py
│ │ │ ├── graph_analyzer.py
│ │ │ ├── graph_metrics.py
│ │ │ ├── centrality_calculator.py
│ │ │ ├── community_detector.py
│ │ │ ├── path_finder.py
│ │ │ ├── graph_embedder.py
│ │ │ ├── subgraph_extractor.py
│ │ │ ├── anomaly_detector.py
│ │ │ ├── temporal_graph.py
│ │ │ └── graph_visualizer.py
│ │ │
│ │ ├── embeddings/ # Embedding generation
│ │ │ ├── __init__.py
│ │ │ ├── text_embedder.py
│ │ │ ├── image_embedder.py
│ │ │ ├── audio_embedder.py
│ │ │ ├── multimodal_embedder.py
│ │ │ ├── context_manager.py
│ │ │ ├── pooling_strategies.py
│ │ │ ├── provider_adapters.py
│ │ │ └── embedding_optimizer.py
│ │ │
│ │ ├── vector_store/ # Vector DB adapters
│ │ │ ├── __init__.py
│ │ │ ├── pinecone_adapter.py
│ │ │ ├── faiss_adapter.py
│ │ │ ├── milvus_adapter.py
│ │ │ ├── weaviate_adapter.py
│ │ │ ├── qdrant_adapter.py
│ │ │ ├── chroma_adapter.py
│ │ │ ├── namespace_manager.py
│ │ │ ├── metadata_store.py
│ │ │ ├── hybrid_search.py
│ │ │ └── index_optimizer.py
│ │ │
│ │ ├── reasoning/ # Inference engine
│ │ │ ├── __init__.py
│ │ │ ├── inference_engine.py
│ │ │ ├── sparql_reasoner.py
│ │ │ ├── rete_engine.py
│ │ │ ├── abductive_reasoner.py
│ │ │ ├── deductive_reasoner.py
│ │ │ ├── rule_manager.py
│ │ │ ├── reasoning_validator.py
│ │ │ └── explanation_generator.py
│ │ │
│ │ ├── pipeline/ # Pipeline orchestration
│ │ │ ├── __init__.py
│ │ │ ├── pipeline_builder.py
│ │ │ ├── execution_engine.py
│ │ │ ├── failure_handler.py
│ │ │ ├── parallelism_manager.py
│ │ │ ├── resource_scheduler.py
│ │ │ ├── pipeline_validator.py
│ │ │ ├── monitoring_hooks.py
│ │ │ └── pipeline_templates.py
│ │ │
│ │ ├── streaming/ # Real-time processing
│ │ │ ├── __init__.py
│ │ │ ├── kafka_adapter.py
│ │ │ ├── pulsar_adapter.py
│ │ │ ├── rabbitmq_adapter.py
│ │ │ ├── kinesis_adapter.py
│ │ │ ├── stream_processor.py
│ │ │ ├── checkpoint_manager.py
│ │ │ ├── exactly_once.py
│ │ │ ├── backpressure_handler.py
│ │ │ └── stream_monitor.py
│ │ │
│ │ ├── monitoring/ # Analytics & QA
│ │ │ ├── __init__.py
│ │ │ ├── analytics_dashboard.py
│ │ │ ├── quality_assurance.py
│ │ │ ├── performance_monitor.py
│ │ │ ├── alert_manager.py
│ │ │ ├── metrics_collector.py
│ │ │ └── health_checker.py
│ │ │
│ │ ├── export/ # Export utilities
│ │ │ ├── __init__.py
│ │ │ ├── rdf_exporter.py
│ │ │ ├── json_exporter.py
│ │ │ ├── csv_exporter.py
│ │ │ ├── graph_exporter.py
│ │ │ └── report_generator.py
│ │ │
│ │ ├── security/ # Security & compliance
│ │ │ ├── __init__.py
│ │ │ ├── access_control.py
│ │ │ ├── data_masking.py
│ │ │ ├── pii_redactor.py
│ │ │ ├── audit_logger.py
│ │ │ ├── encryption_manager.py
│ │ │ ├── security_validator.py
│ │ │ ├── compliance_manager.py
│ │ │ ├── threat_monitor.py
│ │ │ └── vulnerability_scanner.py
│ │ │
│ │ ├── quality/ # Quality assurance
│ │ │ ├── __init__.py
│ │ │ ├── qa_engine.py
│ │ │ ├── validation_engine.py
│ │ │ ├── schema_validator.py
│ │ │ ├── triple_validator.py
│ │ │ ├── confidence_calculator.py
│ │ │ ├── test_generator.py
│ │ │ ├── quality_reporter.py
│ │ │ ├── data_profiler.py
│ │ │ └── compliance_checker.py
│ │ │
│ │ └── utils/ # Shared utilities
│ │ ├── __init__.py
│ │ ├── logging.py
│ │ ├── exceptions.py
│ │ ├── validators.py
│ │ ├── helpers.py
│ │ ├── constants.py
│ │ └── types.py
│ │
│ ├── pyproject.toml # Package metadata (Poetry/PDM)
│ ├── setup.py # Fallback setup
│ ├── requirements.txt # Dependencies
│ ├── requirements-dev.txt # Dev dependencies
│ ├── README.md # Package README
│ ├── LICENSE # MIT License
│ └── tests/ # Test suite
│ ├── __init__.py
│ ├── conftest.py
│ ├── unit/
│ │ ├── __init__.py
│ │ ├── test_core.py
│ │ ├── test_ingest.py
│ │ ├── test_parse.py
│ │ ├── test_normalize.py
│ │ ├── test_split.py
│ │ ├── test_semantic_extract.py
│ │ ├── test_ontology.py
│ │ ├── test_triple_store.py
│ │ ├── test_kg.py
│ │ ├── test_embeddings.py
│ │ ├── test_vector_store.py
│ │ ├── test_reasoning.py
│ │ ├── test_pipeline.py
│ │ ├── test_streaming.py
│ │ ├── test_monitoring.py
│ │ ├── test_export.py
│ │ └── test_utils.py
│ ├── integration/
│ │ ├── __init__.py
│ │ ├── test_document_pipeline.py
│ │ ├── test_kg_construction.py
│ │ ├── test_semantic_search.py
│ │ ├── test_streaming_pipeline.py
│ │ └── test_end_to_end.py
│ ├── fixtures/
│ │ ├── __init__.py
│ │ ├── sample_documents/
│ │ │ ├── sample.pdf
│ │ │ ├── sample.docx
│ │ │ ├── sample.xlsx
│ │ │ ├── sample.html
│ │ │ └── sample.json
│ │ ├── sample_configs/
│ │ │ ├── pipeline_config.yaml
│ │ │ └── ontology_config.yaml
│ │ └── expected_outputs/
│ │ ├── sample_triples.ttl
│ │ └── sample_graph.json
│ └── performance/
│ ├── __init__.py
│ ├── benchmark_parsing.py
│ ├── benchmark_extraction.py
│ └── benchmark_graph_ops.py
├── cookbook/ # Examples and use cases
│ ├── README.md
│ │
│ ├── examples/ # Basic examples
│ │ ├── 01_quick_start/
│ │ │ ├── README.md
│ │ │ ├── simple_processing.py
│ │ │ ├── knowledge_graph_101.py
│ │ │ └── semantic_search.py
│ │ │
│ │ ├── 02_document_processing/
│ │ │ ├── README.md
│ │ │ ├── pdf_extraction.py
│ │ │ ├── office_documents.py
│ │ │ ├── multi_format.py
│ │ │ ├── web_scraping.py
│ │ │ └── email_processing.py
│ │ │
│ │ ├── 03_knowledge_graphs/
│ │ │ ├── README.md
│ │ │ ├── building_kg.py
│ │ │ ├── entity_resolution.py
│ │ │ ├── conflict_detection.py
│ │ │ ├── graph_analytics.py
│ │ │ └── sparql_queries.py
│ │ │
│ │ ├── 04_semantic_search/
│ │ │ ├── README.md
│ │ │ ├── vector_search.py
│ │ │ ├── hybrid_search.py
│ │ │ ├── rag_pipeline.py
│ │ │ └── semantic_ranking.py
│ │ │
│ │ └── 05_real_time_streams/
│ │ ├── README.md
│ │ ├── kafka_processing.py
│ │ ├── feed_monitoring.py
│ │ ├── web_monitoring.py
│ │ └── event_driven_kg.py
│ │
│ ├── use_cases/ # Domain-specific examples
│ │ ├── healthcare/
│ │ │ ├── README.md
│ │ │ ├── medical_literature.py
│ │ │ ├── clinical_reports.py
│ │ │ ├── drug_interactions.py
│ │ │ └── patient_knowledge_graph.py
│ │ │
│ │ ├── finance/
│ │ │ ├── README.md
│ │ │ ├── financial_reports.py
│ │ │ ├── market_intelligence.py
│ │ │ ├── regulatory_compliance.py
│ │ │ └── risk_analysis.py
│ │ │
│ │ ├── cybersecurity/
│ │ │ ├── README.md
│ │ │ ├── threat_intelligence.py
│ │ │ ├── vulnerability_tracking.py
│ │ │ ├── incident_analysis.py
│ │ │ └── ioc_extraction.py
│ │ │
│ │ ├── research/
│ │ │ ├── README.md
│ │ │ ├── citation_network.py
│ │ │ ├── research_trends.py
│ │ │ ├── collaboration_graph.py
│ │ │ └── paper_recommendation.py
│ │ │
│ │ └── legal/
│ │ ├── README.md
│ │ ├── case_law.py
│ │ ├── contract_analysis.py
│ │ ├── regulatory_mapping.py
│ │ └── legal_entity_extraction.py
│ │
│ ├── notebooks/ # Jupyter notebooks
│ │ ├── tutorials/
│ │ │ ├── tutorial_01_getting_started.ipynb
│ │ │ ├── tutorial_02_data_ingestion.ipynb
│ │ │ ├── tutorial_03_semantic_extraction.ipynb
│ │ │ ├── tutorial_04_knowledge_graphs.ipynb
│ │ │ ├── tutorial_05_embeddings.ipynb
│ │ │ └── tutorial_06_advanced_pipelines.ipynb
│ │ │
│ │ └── experiments/
│ │ ├── experiment_custom_parsers.ipynb
│ │ ├── experiment_reasoning.ipynb
│ │ ├── experiment_optimization.ipynb
│ │ └── experiment_multimodal.ipynb
│ │
│ └── data/ # Sample datasets
│ ├── documents/
│ │ ├── sample_papers.pdf
│ │ ├── sample_reports.docx
│ │ └── sample_data.csv
│ ├── graphs/
│ │ ├── sample_ontology.owl
│ │ └── sample_triples.ttl
│ └── configs/
│ ├── pipeline_examples.yaml
│ └── extraction_rules.json
├── docs/ # Documentation
│ ├── index.md
│ ├── getting-started.md
│ ├── installation.md
│ ├── architecture.md
│ ├── api-reference/
│ │ ├── index.md
│ │ ├── core.md
│ │ ├── ingest.md
│ │ ├── parse.md
│ │ ├── semantic_extract.md
│ │ ├── ontology.md
│ │ ├── kg.md
│ │ ├── embeddings.md
│ │ ├── vector_store.md
│ │ ├── reasoning.md
│ │ ├── pipeline.md
│ │ └── streaming.md
│ ├── tutorials/
│ │ ├── quickstart.md
│ │ ├── building-first-kg.md
│ │ ├── semantic-search.md
│ │ └── production-deployment.md
│ ├── best-practices.md
│ ├── performance-tuning.md
│ ├── deployment.md
│ └── contributing.md
├── .github/ # GitHub Actions CI/CD
│ ├── workflows/
│ │ ├── ci.yml
│ │ ├── publish.yml
│ │ └── docs.yml
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug_report.md
│ │ └── feature_request.md
│ └── PULL_REQUEST_TEMPLATE.md
├── docker/ # Docker configurations
│ ├── Dockerfile
│ ├── docker-compose.yml
│ ├── Dockerfile.dev
│ └── .dockerignore
├── scripts/ # Utility scripts
│ ├── setup_dev.sh
│ ├── run_tests.sh
│ ├── build_docs.sh
│ ├── format_code.sh
│ └── release.sh
├── README.md
├── CONTRIBUTING.md
├── LICENSE
├── .gitignore
├── .pre-commit-config.yaml
└── mkdocs.yml
```
## Implementation Phases
### Phase 1: Foundation & Core Architecture (Week 1-2)
**1.1 Project Setup**
- Create folder structure (libs/ and cookbook/)
- Configure pyproject.toml with Poetry/PDM for dependency management
- Setup development environment with pre-commit hooks
- Configure pytest, black, flake8, mypy for code quality
- Setup GitHub Actions CI/CD pipeline
- Create basic README and CONTRIBUTING guidelines
**1.2 Core Module (libs/semantica/core/)**
- `orchestrator.py`: Main Semantica class, pipeline coordination
- `config_manager.py`: YAML/JSON configuration handling
- `plugin_registry.py`: Dynamic plugin loading system
- `lifecycle.py`: Initialization, health checks, shutdown
- Base classes and interfaces for all modules
- Error handling and logging infrastructure
**1.3 Utilities Module (libs/semantica/utils/)**
- `logging.py`: Structured logging with levels and handlers
- `exceptions.py`: Custom exception hierarchy
- `validators.py`: Common validation functions
- `helpers.py`: Shared utility functions
- `constants.py`: Framework-wide constants
- `types.py`: Type definitions and protocols
### Phase 2: Data Ingestion & Parsing (Week 3-4)
**2.1 Ingest Module (libs/semantica/ingest/)**
- `file_ingestor.py`: Local file system, cloud storage (S3, GCS)
- `web_ingestor.py`: HTTP scraping, sitemap crawling
- `feed_ingestor.py`: RSS/Atom feed parsing
- `stream_ingestor.py`: Real-time stream connections
- `repo_ingestor.py`: Git repository processing
- `email_ingestor.py`: Email protocol handlers
- `db_ingestor.py`: Database export handling
- `base_ingestor.py`: Abstract base class with common functionality
**2.2 Parse Module (libs/semantica/parse/)**
- `pdf_parser.py`: PDF text/table/image extraction (PyPDF2, pdfplumber)
- `docx_parser.py`: Word document parsing (python-docx)
- `pptx_parser.py`: PowerPoint parsing (python-pptx)
- `excel_parser.py`: Excel parsing (openpyxl, pandas)
- `html_parser.py`: HTML parsing (BeautifulSoup, lxml)
- `csv_parser.py`: CSV/TSV parsing (pandas, csv)
- `json_parser.py`: JSON/JSONL parsing
- `xml_parser.py`: XML parsing (lxml, xmltodict)
- `image_parser.py`: OCR and image analysis (Tesseract, PIL)
- `base_parser.py`: Parser interface and registry
### Phase 3: Normalization & Chunking (Week 5)
**3.1 Normalize Module (libs/semantica/normalize/)**
- `text_cleaner.py`: HTML removal, whitespace normalization
- `language_detector.py`: Multi-language detection (langdetect)
- `encoding_handler.py`: UTF-8 conversion, BOM handling
- `entity_normalizer.py`: Entity standardization
- `date_normalizer.py`: Date format standardization
- `number_normalizer.py`: Number and unit normalization
**3.2 Split Module (libs/semantica/split/)**
- `sliding_window_chunker.py`: Fixed-size chunking with overlap
- `semantic_chunker.py`: Meaning-based splitting (spaCy)
- `structural_chunker.py`: Document-aware splitting
- `table_chunker.py`: Table-aware chunking
- `provenance_tracker.py`: Source tracking for chunks
- `chunk_validator.py`: Chunk quality validation
### Phase 4: Semantic Extraction (Week 6-7)
**4.1 Semantic Extract Module (libs/semantica/semantic_extract/)**
- `ner_extractor.py`: Named entity recognition (spaCy, transformers)
- `relation_extractor.py`: Relationship detection
- `event_detector.py`: Event identification
- `coref_resolver.py`: Co-reference resolution
- `triple_extractor.py`: RDF triple generation
- `llm_enhancer.py`: LLM-based extraction (OpenAI, Anthropic)
- `extraction_validator.py`: Quality validation
**4.2 Ontology Module (libs/semantica/ontology/)**
- `class_inferrer.py`: Automatic class discovery
- `property_generator.py`: Property inference
- `owl_generator.py`: OWL/RDF generation (rdflib)
- `base_mapper.py`: Schema.org, FOAF, Dublin Core mapping
- `version_manager.py`: Ontology versioning
- `ontology_validator.py`: Schema validation
- `domain_ontologies.py`: Pre-built domain ontologies
### Phase 5: Knowledge Graph & Storage (Week 8-9)
**5.1 Triple Store Module (libs/semantica/triple_store/)**
- `base_adapter.py`: Abstract triple store interface
- `blazegraph_adapter.py`: Blazegraph integration
- `jena_adapter.py`: Apache Jena integration
- `rdf4j_adapter.py`: RDF4J integration
- `virtuoso_adapter.py`: Virtuoso integration
- `triple_manager.py`: CRUD operations
- `query_engine.py`: SPARQL query execution
- `bulk_loader.py`: High-volume loading
**5.2 KG Module (libs/semantica/kg/)**
- `graph_builder.py`: Knowledge graph construction
- `entity_resolver.py`: Entity disambiguation
- `deduplicator.py`: Duplicate detection and merging
- `seed_manager.py`: Initial data loading
- `provenance_tracker.py`: Source tracking
- `conflict_detector.py`: Conflict identification
- `graph_validator.py`: Consistency validation
- `graph_analyzer.py`: Analytics and metrics
### Phase 6: Embeddings & Vector Storage (Week 10)
**6.1 Embeddings Module (libs/semantica/embeddings/)**
- `text_embedder.py`: Text embeddings (sentence-transformers)
- `image_embedder.py`: Image embeddings (CLIP)
- `audio_embedder.py`: Audio embeddings
- `multimodal_embedder.py`: Cross-modal embeddings
- `context_manager.py`: Context window management
- `pooling_strategies.py`: Various pooling methods
- `provider_adapters.py`: OpenAI, BGE, Llama adapters
- `embedding_optimizer.py`: Optimization utilities
**6.2 Vector Store Module (libs/semantica/vector_store/)**
- `base_adapter.py`: Abstract vector store interface
- `pinecone_adapter.py`: Pinecone integration
- `faiss_adapter.py`: FAISS integration
- `milvus_adapter.py`: Milvus integration
- `weaviate_adapter.py`: Weaviate integration
- `qdrant_adapter.py`: Qdrant integration
- `namespace_manager.py`: Namespace isolation
- `metadata_store.py`: Metadata indexing
- `hybrid_search.py`: Vector + metadata search
### Phase 7: Reasoning & Pipeline (Week 11)
**7.1 Reasoning Module (libs/semantica/reasoning/)**
- `inference_engine.py`: Rule-based inference
- `sparql_reasoner.py`: SPARQL-based reasoning
- `rete_engine.py`: Rete algorithm implementation
- `abductive_reasoner.py`: Abductive reasoning
- `deductive_reasoner.py`: Deductive reasoning
- `rule_manager.py`: Rule management
- `explanation_generator.py`: Explanation generation
**7.2 Pipeline Module (libs/semantica/pipeline/)**
- `pipeline_builder.py`: Pipeline construction DSL
- `execution_engine.py`: Pipeline execution
- `failure_handler.py`: Error handling and retry
- `parallelism_manager.py`: Parallel execution
- `resource_scheduler.py`: Resource allocation
- `pipeline_validator.py`: Pipeline validation
- `pipeline_templates.py`: Pre-built templates
### Phase 8: Streaming & Real-time (Week 12)
**8.1 Streaming Module (libs/semantica/streaming/)**
- `kafka_adapter.py`: Apache Kafka integration
- `pulsar_adapter.py`: Apache Pulsar integration
- `rabbitmq_adapter.py`: RabbitMQ integration
- `kinesis_adapter.py`: AWS Kinesis integration
- `stream_processor.py`: Stream processing logic
- `checkpoint_manager.py`: Checkpoint management
- `exactly_once.py`: Exactly-once semantics
- `backpressure_handler.py`: Flow control
### Phase 9: Monitoring & Export (Week 13)
**9.1 Monitoring Module (libs/semantica/monitoring/)**
- `analytics_dashboard.py`: Real-time analytics
- `quality_assurance.py`: Quality validation
- `performance_monitor.py`: Performance tracking
- `alert_manager.py`: Alert configuration
- `metrics_collector.py`: Metrics collection
- `health_checker.py`: Health monitoring
**9.2 Export Module (libs/semantica/export/)**
- `rdf_exporter.py`: RDF format exports
- `json_exporter.py`: JSON/JSON-LD exports
- `csv_exporter.py`: CSV exports
- `graph_exporter.py`: Graph format exports
- `report_generator.py`: Report generation
### Phase 10: Examples & Documentation (Week 14-15)
**10.1 Cookbook Examples (cookbook/examples/)**
- **01_quick_start/**
- `simple_processing.py`: Basic document processing
- `knowledge_graph_101.py`: Simple KG construction
- `semantic_search.py`: Basic semantic search
- **02_document_processing/**
- `pdf_extraction.py`: PDF processing pipeline
- `office_documents.py`: DOCX/XLSX/PPTX processing
- `multi_format.py`: Processing multiple formats
- **03_knowledge_graphs/**
- `building_kg.py`: Complete KG construction
- `entity_resolution.py`: Entity disambiguation
- `conflict_detection.py`: Handling conflicts
- **04_semantic_search/**
- `vector_search.py`: Vector similarity search
- `hybrid_search.py`: Combined vector + metadata
- `rag_pipeline.py`: RAG implementation
- **05_real_time_streams/**
- `kafka_processing.py`: Kafka stream processing
- `feed_monitoring.py`: RSS feed monitoring
- `web_monitoring.py`: Website change detection
**10.2 Domain Use Cases (cookbook/use_cases/)**
- **healthcare/**
- `medical_literature.py`: PubMed processing
- `clinical_reports.py`: Clinical report analysis
- `drug_interactions.py`: Drug interaction detection
- **finance/**
- `financial_reports.py`: Financial document analysis
- `market_intelligence.py`: News and market data
- `regulatory_compliance.py`: Compliance monitoring
- **cybersecurity/**
- `threat_intelligence.py`: Threat report processing
- `vulnerability_tracking.py`: CVE database integration
- `incident_analysis.py`: Security incident analysis
- **research/**
- `citation_network.py`: Citation network building
- `research_trends.py`: Trend analysis
- `collaboration_graph.py`: Researcher networks
- **legal/**
- `case_law.py`: Legal case processing
- `contract_analysis.py`: Contract intelligence
- `regulatory_mapping.py`: Regulation tracking
**10.3 Jupyter Notebooks (cookbook/notebooks/)**
- `tutorial_01_getting_started.ipynb`: Introduction
- `tutorial_02_data_ingestion.ipynb`: Data ingestion
- `tutorial_03_semantic_extraction.ipynb`: Semantic extraction
- `tutorial_04_knowledge_graphs.ipynb`: KG construction
- `tutorial_05_embeddings.ipynb`: Embeddings and search
- `tutorial_06_advanced_pipelines.ipynb`: Advanced pipelines
- `experiment_custom_parsers.ipynb`: Custom parser development
- `experiment_reasoning.ipynb`: Reasoning experiments
**10.4 Documentation (docs/)**
- `getting-started.md`: Quick start guide
- `installation.md`: Installation instructions
- `architecture.md`: System architecture
- `api-reference/`: Complete API documentation
- `tutorials/`: Step-by-step tutorials
- `best-practices.md`: SDK best practices
- `performance-tuning.md`: Optimization guide
- `deployment.md`: Production deployment
### Phase 11: Testing & Quality (Week 16)
**11.1 Test Suite (libs/tests/)**
- **unit/**: Unit tests for all modules (80%+ coverage)
- **integration/**: Integration tests for workflows
- **fixtures/**: Test data and mocks
- **performance/**: Performance benchmarks
- **e2e/**: End-to-end scenarios
**11.2 Quality Assurance**
- Comprehensive test coverage (target: 85%+)
- Type hints throughout (mypy strict mode)
- Code formatting (black, isort)
- Linting (flake8, pylint)
- Security scanning (bandit)
- Documentation coverage (docstrings everywhere)
### Phase 12: Packaging & Distribution (Week 17)
**12.1 Package Configuration**
- Configure pyproject.toml with proper metadata
- Setup entry points for CLI tools
- Configure optional dependencies ([all], [pdf], [web], etc.)
- Build wheels and source distributions
- Test installation in clean environments
**12.2 Distribution**
- PyPI package publishing
- Docker image creation and DockerHub
- Conda package (conda-forge)
- GitHub releases with binaries
- Documentation hosting (ReadTheDocs)
### Phase 13: Final Polish (Week 18)
**13.1 Documentation Review**
- Complete API documentation
- Tutorial walkthroughs
- Example verification
- FAQ and troubleshooting
- Contributing guidelines
**13.2 Community Setup**
- GitHub repository setup
- Issue templates
- Pull request templates
- Code of conduct
- Community guidelines
- Discord/Slack community
## SDK Best Practices Applied
1. **API-First Design**: Clear interfaces and protocols
2. **Modular Architecture**: Pluggable components
3. **Type Safety**: Full type hints throughout
4. **Error Handling**: Specific exceptions with context
5. **Documentation**: Comprehensive docstrings and guides
6. **Testing**: High coverage with unit and integration tests
7. **Versioning**: Semantic versioning (SemVer)
8. **Backward Compatibility**: Deprecation warnings
9. **Configuration**: Flexible, validated configuration
10. **Observability**: Built-in logging and metrics
11. **Performance**: Optimized with benchmarks
12. **Security**: Input validation and secure defaults
## Technology Stack
**Core:**
- Python 3.8+ (type hints, dataclasses, async/await)
- Poetry/PDM for dependency management
- pydantic for data validation
- click for CLI
**Data Processing:**
- pandas, numpy for data manipulation
- PyPDF2, pdfplumber for PDF
- python-docx, python-pptx, openpyxl for Office
- BeautifulSoup, lxml for HTML/XML
- Pillow, pytesseract for images
**NLP & Semantic:**
- spaCy for NLP
- transformers (HuggingFace) for models
- sentence-transformers for embeddings
- rdflib for RDF/OWL
- nltk for text processing
**Storage:**
- Neo4j driver for graph DB
- pinecone-client, faiss-cpu for vectors
- requests for HTTP
- SQLAlchemy for SQL databases
**Streaming:**
- kafka-python for Kafka
- pika for RabbitMQ
- redis for caching
**Testing & Quality:**
- pytest for testing
- pytest-cov for coverage
- black, isort for formatting
- flake8, mypy for linting
- pre-commit for hooks
**Documentation:**
- Sphinx for API docs
- mkdocs-material for user docs
- Jupyter for notebooks
## Success Criteria
1. ✅ Complete modular framework with 15+ modules
2. ✅ Support for 20+ file formats
3. ✅ 80%+ test coverage
4. ✅ Comprehensive documentation
5. ✅ 20+ working examples in cookbook
6. ✅ 5+ domain-specific use cases
7. ✅ PyPI package published
8. ✅ Docker images available
9. ✅ CI/CD pipeline functional
10. ✅ Community resources setup
## Milestones
- **Week 2**: Core foundation and project structure
- **Week 4**: Data ingestion and parsing complete
- **Week 7**: Semantic extraction and ontology working
- **Week 9**: Knowledge graph and storage functional
- **Week 11**: Reasoning and pipelines operational
- **Week 13**: Streaming and monitoring integrated
- **Week 15**: Complete documentation and examples
- **Week 17**: Package ready for distribution
- **Week 18**: Launch ready with community support