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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
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