- Add Type import to typing imports in helpers.py to fix retry_on_error decorator
- Remove unused Type import from config_manager.py
- Update capability gap notebook with comment about the fix
- Resolves ImportError when importing semantica modules
Fixes: NameError: name 'Type' is not defined in retry_on_error decorator
- Fixed duplicate setup cells and consolidated into single setup cell
- Resolved undefined variable references in corpus creation
- Moved ontology evaluation to optimal position after semantic extraction
- Enhanced ontology evaluation with extraction context integration
- Removed empty placeholder cells and improved logical flow
- Added semantica package installation requirement
- Updated pipeline sequence to follow correct data processing order
- Improved error handling and variable validation throughout notebook
- Update notebook to use corrected RelationExtractor API
- Move provider/model parameters to initialization
- Add verbose logging for debugging
- Include working relation extraction examples
- Add API key handling in extract_entities_llm(), extract_relations_llm(), and extract_triplets_llm()
- Add explicit api_key handling in NERExtractor and RelationExtractor
- Add llm_model parameter support in extract_triplets_llm() for consistency
- Fix relation extraction bug with type checking for subject_text/object_text
- Add environment variable fallback for API keys
- Update notebook with standard API key pattern
Fixes#147
- Add API key handling in extract_entities_llm(), extract_relations_llm(), and extract_triplets_llm()
- Add llm_model parameter support in extract_triplets_llm() for consistency
- Fix relation extraction bug with type checking for subject_text/object_text
- Add environment variable fallback for API keys
- Include providers.py for context (GroqProvider implementation)
Fixes#145
- Fix import logic in __init__.py to properly export DoclingParser
- Rewrite docling_parser.py to use docling's native API (direct attribute access)
- Remove unsupported features (table_extraction_mode, invalid format_options)
- Use doc.tables, doc.pictures, doc.pages directly instead of dict parsing
- Update notebook with improved code and documentation
- Add proper error handling for when docling is not available
Fixes#138
- Add 8-stage progress tracking (0-100%) with ETA to DoclingParser
- Update earnings call analysis notebook with MDA Space Q3 2025 example
- Simplify notebook code structure
- Add real-time progress visibility for PDF parsing
Closes#133
- Remove top-level torch import from providers.py
- Add lazy imports in HuggingFaceLLMProvider and HuggingFaceModelLoader
- Remove hardcoded API key from notebook
- PyTorch now only loads when HuggingFace providers are instantiated
Fixes#129
- Added DoclingParser class in semantica/parse/ module
- Created earnings call analysis notebook with Docling integration
- Added docling to pyproject.toml as optional dependency
- Maintained backward compatibility with existing parsers
Closes#124
- Deleted cookbook/use_cases/healthcare/02_Drug_Interactions_Analysis.ipynb
- Removed Healthcare section from README.md
- Removed Healthcare section from docs/cookbook.md
- Removed Drug Interactions references from STRATEGIES_SUMMARY.md
- Updated cookbook count from 18 to 17 in all documentation
- Updated docs/index.md to reflect 17 cookbooks
- Add real CSV and JSON data sources for transactions and accounts
- Fix ConflictDetector, TemporalGraphQuery, and Reasoner errors
- Simplify code to use Semantica modules properly
- Enhance GraphRAG section with Context Graph and Groq LLM
- Add temporal interactive visualization using TemporalVisualizer
- Fix CSV export to use CSVExporter instead of GraphExporter
- Update README.md to mention Context Graph and Context Retriever
- Switch entity and relation extraction to ML-based methods (spaCy)
- Fix conflict detection to use detect_temporal_conflicts directly
- Fix graph building to use correct Relation attributes (subject/object/predicate)
- Improve GraphRAG with LLM-based multi-hop reasoning
- Enhance graph analytics output to show all entity types
- Update markdown descriptions with concise bullet points
- Enhanced progress tracker with automatic Jupyter/Colab detection
- Added detailed progress tracking to all deduplication modules
- Added detailed progress tracking to all semantic_extract modules
- Progress tracker now always enabled automatically
- Shows remaining items, percentages, ETA, and processing rates
- Works in both Jupyter notebooks and Google Colab
- Dynamic update intervals based on dataset size
- Improved display handling for Colab compatibility
- Fixed ConflictDetector to use update_progress() with counts/ETA for type, temporal, and logical conflict detection
- Fixed NERExtractor batch operations to show progress with ETA
- Fixed RelationExtractor batch operations to show progress with ETA
- All modules now display clear progress bars with percentage, counts, and estimated time remaining
- Enhanced ProgressItem with ETA fields (progress_percentage, total_items, processed_items, estimated_remaining)
- Added update_progress() and _calculate_eta() methods to ProgressTracker
- Updated ConsoleProgressDisplay and JupyterProgressDisplay to show progress with ETA
- Added progress tracking to deduplication modules (DuplicateDetector, EntityMerger, SimilarityCalculator, ClusterBuilder)
- Added progress tracking to conflicts modules (ConflictDetector, ConflictResolver)
- Added progress tracking to ingest, parse, kg, core, embeddings, and triplet_store modules
- All modules now display progress percentage, item counts, ETA, and processing rate
- Add fast path for dictionary entities/relationships to bypass _process_item overhead
- Improve entity recognition to handle 'text' and 'type' fields directly
- Significantly improve processing speed from ~0.8/s to thousands/s
- Fixes performance bottleneck in knowledge graph building
- Add semantica.llms module with Groq, OpenAI, HuggingFace, and LiteLLM providers
- Add query_with_reasoning() method for multi-hop reasoning with LLM-generated responses
- Update ContextRetriever and AgentContext with reasoning capabilities
- Add comprehensive documentation for LLM providers and GraphRAG reasoning
- Update README and docs with new features
- Update notebook examples to use new query_with_reasoning() method
- Fix TemporalGraphQuery: Change detect_temporal_patterns to query_temporal_pattern
- Fix GraphAnalyzer: Replace find_paths with direct relationship queries and BFS implementation
- Fix KGVisualizer: Change visualize() to visualize_network() with interactive visualization
- Fix GraphExporter: Remove unsupported CSV format, use export_csv for CSV export
- Add proper imports and improve error handling
- Enhance visualization with force-directed layout and better interactivity
- Added support for detecting and merging list of dict sources with entities/relationships
- Progress tracking now shows ETA and remaining items when sources is a list
- Fixes issue where progress wasn't displayed when passing list of dicts to build()
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