Release 0.2.2

Improve `semantic_extract` performance and add Groq LLM smoke tests

.gitattributes

@@ -0,0 +1,20 @@
+# Linguist documentation and generated files
+# This ensures GitHub language statistics reflect the core Python code
+
+# Mark the entire docs directory as documentation
+docs/* linguist-documentation
+
+# Mark the cookbook directory as documentation/examples
+cookbook/* linguist-documentation
+
+# Specifically ignore large generated HTML/JSON files in cookbook
+cookbook/**/*.html linguist-documentation
+cookbook/**/*.json linguist-documentation
+cookbook/**/*.graphml linguist-documentation
+cookbook/**/*.ttl linguist-documentation
+
+# Ensure .ipynb files are treated as documentation/examples
+cookbook/**/*.ipynb linguist-documentation
+
+# Mark data directories as documentation or vendored
+**/data/* linguist-vendored

.github/workflows/docs.yml

@@ -8,7 +8,11 @@ on:
     branches: [main]
     paths:
       - 'docs/**'
+      - 'semantica/**'
       - 'mkdocs.yml'
+      - 'requirements-docs.txt'
+      - 'CHANGELOG.md'
+      - 'RELEASE.md'
   workflow_dispatch:
 
 # Permissions needed to deploy to GitHub Pages

.gitignore

@@ -61,6 +61,7 @@ wheels/
 .installed.cfg
 *.egg
 MANIFEST
+.python-version
 
 # IDE
 .vscode/

CHANGELOG.md

@@ -7,6 +7,177 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+
+## [0.2.2] - 2026-01-15
+
+### Added
+- **Parallel Extraction Engine**:
+    - Implemented high-throughput parallel batch processing across all core extractors (`NERExtractor`, `RelationExtractor`, `TripletExtractor`, `EventDetector`, `SemanticNetworkExtractor`) using `concurrent.futures.ThreadPoolExecutor`.
+    - Added `max_workers` configuration parameter (default: 1) to all extractor `extract()` methods, allowing users to tune concurrency based on available CPU cores or API rate limits.
+    - **Parallel Chunking**: Implemented parallel processing for large document chunking in `_extract_entities_chunked` and `_extract_relations_chunked`, significantly reducing latency for long-form text analysis.
+    - **Thread-Safe Progress Tracking**: Enhanced `ProgressTracker` to handle concurrent updates from multiple threads without race conditions during batch processing.
+- **Semantic Extract Performance & Regression**:
+    - Added edge-case regression suite covering max worker defaults, LLM prompt entity filtering, and extractor reuse.
+    - Added a runnable real-use-case benchmark script for batch latency across `NERExtractor`, `RelationExtractor`, `TripletExtractor`, `EventDetector`, `SemanticAnalyzer`, and `SemanticNetworkExtractor`.
+    - Added Groq LLM smoke tests that exercise LLM-based entities/relations/triplets when `GROQ_API_KEY` is available via environment configuration.
+
+### Security
+- **Credential Sanitization**:
+    - Removed hardcoded API keys from 8 cookbook notebooks to prevent secret leakage.
+    - Enforced environment variable usage for `GROQ_API_KEY` across all examples.
+- **Secure Caching**:
+    - Updated `ExtractionCache` to exclude sensitive parameters (e.g., `api_key`, `token`, `password`) from cache key generation, preventing secret leakage and enabling safe cache sharing.
+    - Upgraded cache key hashing algorithm from MD5 to **SHA-256** for enhanced collision resistance and security.
+
+### Changed
+- **Gemini SDK Migration**:
+    - Migrated `GeminiProvider` to use the new `google-genai` SDK (v0.1.0+) to address deprecation warnings.
+    - Implemented graceful fallback to `google.generativeai` for backward compatibility.
+- **Dependency Resolution**:
+    - Pinned `opentelemetry-api` and `opentelemetry-sdk` to `1.37.0` to resolve pip conflicts.
+    - Updated `protobuf` and `grpcio` constraints for better stability.
+- **Entity Filtering Scope**:
+    - Removed entity filtering from non-LLM extraction flows to avoid accuracy regressions.
+    - Applied entity downselection only to LLM relation prompt construction, while matching returned entities against the full original entity list.
+- **Batch Concurrency Defaults**:
+    - Standardized `max_workers` defaulting across `semantic_extract` and tuned for low-latency: ML-backed methods default to single-worker, while pattern/regex/rules/LLM/huggingface methods use a higher parallelism default capped by CPU.
+    - Raised the global `optimization.max_workers` default to 8 for better throughput on batch workloads.
+
+### Performance
+- **Bottleneck Optimization (GitHub Issue #186)**:
+    - **Resolved Bottleneck #1 (Sequential Processing)**: Replaced sequential `for` loops with parallel execution for both document-level batches and intra-document chunks.
+    - **Performance Gains**: Achieved **~1.89x speedup** in real-world extraction scenarios (tested with Groq `llama-3.3-70b-versatile` on standard datasets).
+    - **Initialization Optimization**: Refactored test suite to use class-level `setUpClass` for LLM provider initialization, eliminating redundant API client creation overhead.
+- **Low-Latency Entity Matching**:
+    - Avoided heavyweight embedding stack imports on common matches by improving fast matching heuristics and short-circuiting before embedding similarity.
+    - Optimized entity matching to prioritize exact/substring/word-boundary matches and only fall back to embedding similarity when needed, reducing CPU overhead in LLM relation/triplet mapping.
+
+
+## [0.2.1] - 2026-01-12
+
+### Fixed
+- **LLM Output Stability (Bug #176)**:
+    - Fixed incomplete JSON output issues by correctly propagating `max_tokens` parameter in `extract_relations_llm`.
+    - Implemented automatic error handling that halves chunk sizes and retries when LLM context or output limits are exceeded.
+    - Fixed `AttributeError` in provider integration by ensuring consistent parameter passing via `**kwargs`.
+- **Constraint Relaxations**:
+    - Removed hardcoded `max_length` constraints from `Entity`, `Relation`, and `Triplet` classes to support long-form semantic extraction (e.g., long descriptions or names).
+- Fixed orchestrator lazy property initialization and configuration normalization logic in `Orchestrator`.
+- Resolved `AssertionError` in orchestrator tests by aligning test mocks with production component usage.
+- Fixed dependency compatibility issues by pinning `protobuf>=5.29.1,<7.0` and `grpcio>=1.71.2`.
+- Added missing dependencies `GitPython` and `chardet` to `pyproject.toml`.
+- Verified and aligned `FileObject.text` property usage in GraphRAG notebooks for consistent content decoding.
+
+### Changed
+- **Chunking Defaults**:
+    - Increased default `max_text_length` for auto-chunking to **64,000 characters** (from 32k/16k) for OpenAI, Anthropic, Gemini, Groq, and DeepSeek providers.
+    - Unified chunking logic across `extract_entities_llm`, `extract_relations_llm`, and `extract_triplets_llm`.
+- **Groq Support**:
+    - Standardized Groq provider defaults to use `llama-3.3-70b-versatile` with a 64k context window.
+    - Added native support for `max_tokens` and `max_completion_tokens` to prevent output truncation.
+
+### Added
+- **Testing**:
+    - Added `tests/reproduce_issue_176.py` to validate `max_tokens` propagation and chunking behavior across all extractors.
+
+
+## [0.2.0] - 2026-01-10
+
+### Added
+- **Amazon Neptune Support**:
+    - Added `AmazonNeptuneStore` providing Amazon Neptune graph database integration via Bolt protocol and OpenCypher.
+    - Implemented `NeptuneAuthTokenManager` extending Neo4j AuthManager for AWS IAM SigV4 signing with automatic token refresh.
+    - Added robust connection handling: retry logic with backoff for transient errors (signature expired, connection closed) and driver recreation.
+    - Added `graph-amazon-neptune` optional dependency group (boto3, neo4j).
+    - Comprehensive test suite covering all GraphStore interface methods.
+- **Docling Integration**:
+    - Added `DoclingParser` in `semantica.parse` for high-fidelity document parsing using the Docling library.
+    - Supports multi-format parsing (PDF, DOCX, PPTX, XLSX, HTML, images) with superior table extraction and structure understanding.
+    - Implemented as a standalone parser supporting local execution, OCR, and multiple export formats (Markdown, HTML, JSON).
+- **Robust Extraction Fallbacks**:
+    - Implemented comprehensive fallback chains ("ML/LLM" -> "Pattern" -> "Last Resort") across `NERExtractor`, `RelationExtractor`, and `TripletExtractor` to prevent empty result lists.
+    - Added "Last Resort" pattern matching in `NERExtractor` to identify capitalized words as generic entities when all other methods fail.
+    - Added "Last Resort" adjacency-based relation extraction in `RelationExtractor` to create weak connections between adjacent entities if no relations are found.
+    - Added fallback logic in `TripletExtractor` to convert relations to triplets or use rule-based extraction if standard methods fail.
+- **Provenance & Tracking**:
+    - Added count tracking to batch processing logs in `NERExtractor`, `RelationExtractor`, and `TripletExtractor`.
+    - Added `batch_index` and `document_id` to the metadata of all extracted entities, relations, triplets, semantic roles, and clusters for better traceability.
+- **Semantic Extract Improvements**:
+    - Introduced `auto-chunking` for long text processing in LLM extraction methods (`extract_entities_llm`, `extract_relations_llm`, `extract_triplets_llm`).
+    - Added `silent_fail` parameter to LLM extraction methods for configurable error handling.
+    - Implemented robust JSON parsing and automatic retry logic (3 attempts with exponential backoff) in `BaseProvider` for all LLM providers.
+    - Enhanced `GroqProvider` with better diagnostics and connectivity testing.
+    - Added comprehensive entity, relation, and triplet deduplication for chunked extraction.
+    - Added `semantica/semantic_extract/schemas.py` with canonical Pydantic models for consistent structured output.
+- **Testing**:
+    - Added comprehensive robustness test suite `tests/semantic_extract/test_robustness_fallback.py` for validating extraction fallbacks and metadata propagation.
+    - Added comprehensive unit test suite `tests/embeddings/test_model_switching.py` for verifying dynamic model transitions and dimension updates.
+    - Added end-to-end integration test suite for Knowledge Graph pipeline validation (GraphBuilder -> EntityResolver -> GraphAnalyzer).
+- **Other**:
+    - Added missing dependencies `GitPython` and `chardet` to `pyproject.toml`.
+    - Robustified ID extraction across `CentralityCalculator`, `CommunityDetector`, and `ConnectivityAnalyzer` to handle various entity formats.
+    - Improved `Entity` class hashability and equality logic in `utils/types.py`.
+
+### Changed
+- **Deduplication & Conflict Logic**:
+    - Removed internal deduplication logic from `NERExtractor`, `RelationExtractor`, and `TripletExtractor`.
+    - Removed consistency/conflict checking from `ExtractionValidator` to defer to dedicated `semantica/conflicts` module.
+    - Removed `_deduplicate_*` methods from `semantica/semantic_extract/methods.py`.
+- **Batch Processing & Consistency**:
+    - Standardized batch processing across all extractors (`NERExtractor`, `RelationExtractor`, `TripletExtractor`, `SemanticNetworkExtractor`, `EventDetector`, `SemanticAnalyzer`, `CoreferenceResolver`) using a unified `extract`/`analyze`/`resolve` method pattern with progress tracking.
+    - Added provenance metadata (`batch_index`, `document_id`) to `SemanticNetwork` nodes/edges, `Event` objects, `SemanticRole` results, `CoreferenceChain` mentions, and `SemanticCluster` (tracking source `document_ids`).
+    - Updated `SemanticClusterer.cluster` and `SemanticAnalyzer.cluster_semantically` to accept list of dictionaries (with `content` and `id` keys) for better document tracking during clustering.
+    - Removed legacy `check_triplet_consistency` from `TripletExtractor`.
+    - Removed `validate_consistency` and `_check_consistency` from `ExtractionValidator`.
+- **Weighted Scoring**:
+    - Clarified weighted confidence scoring (50% Method Confidence + 50% Type Similarity) in comments.
+    - Explicitly labeled "Type Similarity" as "user-provided" in code comments to remove ambiguity.
+- **Refactoring**:
+    - Fixed orchestrator lazy property initialization and configuration normalization logic in `Orchestrator`.
+    - Verified and aligned `FileObject.text` property usage in GraphRAG notebooks for consistent content decoding.
+
+### Fixed
+- **Critical Fixes**:
+    - Resolved `NameError` in `extraction_validator.py` by adding missing `Union` import.
+    - Resolved issues where extractors would return empty lists for valid input text when primary extraction methods failed.
+    - Fixed metadata initialization issue in batch processing where `batch_index` and `document_id` were occasionally missing from extracted items.
+    - Ensured `LLMExtraction` methods (`enhance_entities`, `enhance_relations`) return original input instead of failing or returning empty results when LLM providers are unavailable.
+- **Component Fixes**:
+    - Fixed model switching bug in `TextEmbedder` where internal state was not cleared, preventing dynamic updates between `fastembed` and `sentence_transformers` (#160).
+    - Implemented model-intrinsic embedding dimension detection in `TextEmbedder` to ensure consistency between models and vector databases.
+    - Updated `set_model` to properly refresh configuration and dimensions during model switches.
+    - Fixed `TypeError: unhashable type: 'Entity'` in `GraphAnalyzer` when processing graphs with raw `Entity` objects or dictionaries in relationships (#159).
+    - Resolved `AssertionError` in orchestrator tests by aligning test mocks with production component usage.
+    - Fixed dependency compatibility issues by pinning `protobuf==4.25.3` and `grpcio==1.67.1`.
+    - Fixed a bug in `TripletExtractor` where the `validate_triplets` method was shadowed by an internal attribute.
+    - Fixed incorrect `TextSplitter` import path in the `semantic_extract.methods` module.
+
+## [0.1.1] - 2026-01-05
+
+### Added
+- Exported `DoclingParser` and `DoclingMetadata` from `semantica.parse` for easier access.
+- Added comprehensive `DoclingParser` usage examples to README and documentation.
+- Added Windows-specific troubleshooting note for PyTorch DLL issues.
+
+### Fixed
+- Fixed `DoclingParser` import/export issues across platforms (Windows, Linux, Google Colab).
+- Improved error messaging when optional `docling` dependency is missing.
+- Fixed versioning inconsistencies across the framework.
+
+## [0.1.0] - 2025-12-31
+
+### Added
+- New command-line interface (`semantica` CLI) with support for knowledge base building and info commands.
+- Integrated FastAPI-based REST API server for remote access to framework functionality.
+- Dedicated background worker component for scalable task processing and pipeline execution.
+- Framework-level versioning configuration for PyPI distribution.
+- Automated release workflow with Trusted Publishing support.
+
+### Changed
+- Updated versioning across the framework to 0.1.0.
+- Refined entry point configurations in `pyproject.toml`.
+- Improved lazy module loading for core framework components.
+
 ## [0.0.5] - 2025-11-26
 
 ### Changed
@@ -49,7 +220,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 - Core framework architecture
-- Universal data ingestion (50+ file formats)
+- Universal data ingestion (multiple file formats)
 - Semantic intelligence engine (NER, relation extraction, event detection)
 - Knowledge graph construction with entity resolution
 - 6-stage ontology generation pipeline

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2025 Hawksight AI
+Copyright (c) 2026 Hawksight AI
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

RELEASE.md

@@ -0,0 +1,56 @@
+# Release Process for Semantica
+
+This document outlines the steps to release a new version of the Semantica framework.
+
+## 1. Versioning Policy
+
+Semantica follows [Semantic Versioning (SemVer)](https://semver.org/).
+- **MAJOR** version for incompatible API changes.
+- **MINOR** version for functionality added in a backwards compatible manner.
+- **PATCH** version for backwards compatible bug fixes.
+
+## 2. Pre-release Checklist
+
+Before releasing, ensure:
+- [ ] All tests pass: `pytest`
+- [ ] Documentation is up to date in `docs/` and `MkDocs` config.
+- [ ] `CHANGELOG.md` is updated with the latest changes.
+- [ ] Version is updated in:
+  - `semantica/__init__.py`
+  - `pyproject.toml`
+  - `docs/citation.md` (BibTeX entry)
+
+## 3. Release Steps
+
+### Automated Release (Recommended)
+
+The project uses GitHub Actions for automated releases to PyPI.
+
+1. **Tag the commit**: Create a new git tag for the version (e.g., `v0.2.2`).
+   ```bash
+   git tag -a v0.2.2 -m "Release v0.2.2"
+   git push origin v0.2.2
+   ```
+2. **GitHub Action**: The `Release` workflow will automatically trigger, build the package, create a GitHub Release, and publish to PyPI using Trusted Publishing.
+
+### Manual Release
+
+If you need to release manually:
+
+1. **Build the package**:
+   ```bash
+   python -m build
+   ```
+2. **Verify the build**:
+   ```bash
+   twine check dist/*
+   ```
+3. **Upload to PyPI**:
+   ```bash
+   twine upload dist/*
+   ```
+
+## 4. Post-release
+
+- Verify the new version is available on [PyPI](https://pypi.org/project/semantica/).
+- Check the [GitHub Releases](https://github.com/your-org/semantica/releases) page for the new release notes.

SECURITY.md

@@ -6,8 +6,12 @@ We actively support the following versions of Semantica with security updates:
 
 | Version | Supported          |
 | ------- | ------------------ |
-| 0.0.1   | :white_check_mark: |
-| < 0.0.1 | :x:                |
+| 0.2.2   | :white_check_mark: |
+| 0.2.1   | :white_check_mark: |
+| 0.2.0   | :white_check_mark: |
+| 0.1.1   | :white_check_mark: |
+| 0.1.0   | :white_check_mark: |
+| < 0.1.0 | :x:                |
 
 ## Reporting a Vulnerability
 

STRATEGIES_SUMMARY.md

@@ -0,0 +1,105 @@
+# Deduplication & Conflict Resolution Strategies Summary
+
+## Quick Reference by Use Case
+
+| Use Case | Deduplication Method | Merge Strategy | Conflict Detection | Conflict Resolution |
+|----------|---------------------|----------------|-------------------|---------------------|
+| **Finance** |
+| `01_Financial_Data_Integration_MCP` | `DuplicateDetector` (incremental) | `keep_highest_confidence` | `temporal` | `most_recent` |
+| `02_Fraud_Detection` | `ClusterBuilder` (graph_based) | `merge_all` | `logical` | `expert_review` |
+| **Biomedical** |
+| `01_Drug_Discovery_Pipeline` | `EntityResolver` (semantic) | - | `relationship` | `voting` |
+| `02_Genomic_Variant_Analysis` | `DuplicateDetector` (group) | `keep_most_complete` | `value` | `credibility_weighted` |
+| **Cybersecurity** |
+| `01_Real_Time_Anomaly_Detection` | `DuplicateDetector` (pairwise) | `keep_first` | `entity` | `first_seen` |
+| `02_Threat_Intelligence_Hybrid_RAG` | `EntityResolver` (exact) | - | `type` | `highest_confidence` |
+| **Blockchain** |
+| `01_DeFi_Protocol_Intelligence` | `DuplicateDetector` (group) | `keep_last` | `relationship` | `voting` |
+| `02_Transaction_Network_Analysis` | `ClusterBuilder` (hierarchical) | `keep_most_complete` | `temporal` | `most_recent` |
+| **Intelligence** |
+| `01_Criminal_Network_Analysis` | `EntityResolver` (fuzzy) | - | `value` | `credibility_weighted` |
+| `02_Intelligence_Analysis_Orchestrator_Worker` | `DuplicateDetector` (batch) | `merge_all` | `entity` | `voting` |
+| **Renewable Energy** |
+| `01_Energy_Market_Analysis` | `DuplicateDetector` (pairwise) | `keep_highest_confidence` | `temporal` | `most_recent` |
+| **Supply Chain** |
+| `01_Supply_Chain_Data_Integration` | `DuplicateDetector` (incremental) | `keep_most_complete` | `value` | `credibility_weighted` |
+
+---
+
+## Strategy Rationale by Domain
+
+### Finance
+- **Financial Data Integration**: Incremental for streaming data; most_recent for time-sensitive financial data
+- **Fraud Detection**: Graph-based clustering for fraud groups; expert_review for fraud assessment
+
+### Biomedical
+- **Drug Discovery**: Semantic matching for drug compounds; voting for research source aggregation
+- **Genomic Variants**: Group method for related variants; credibility weighting for research sources
+
+### Cybersecurity
+- **Real-Time Anomaly**: Pairwise for real-time streams; keep_first for first detection priority
+- **Threat Intelligence**: Exact matching for IOCs; highest_confidence for threat classification
+
+### Blockchain
+- **DeFi Protocols**: Group method for related protocols; keep_last for latest protocol info
+- **Transaction Networks**: Hierarchical clustering for nested groups; temporal for time-sensitive data
+
+### Intelligence
+- **Criminal Networks**: Fuzzy matching for intelligence data; credibility weighting for intelligence sources
+- **Intelligence Analysis**: Batch for multi-source integration; merge_all to combine all intelligence sources
+
+### Renewable Energy
+- **Energy Markets**: Pairwise for real-time market data; most_recent for time-sensitive energy data
+
+### Supply Chain
+- **Supply Chain Integration**: Incremental for continuous updates; credibility weighting for supply chain sources
+
+---
+
+## Method Distribution
+
+### Deduplication Methods (9 total)
+- `pairwise`: 2 notebooks (real-time processing)
+- `batch`: 3 notebooks (large datasets)
+- `incremental`: 2 notebooks (streaming/continuous)
+- `group`: 2 notebooks (related entities)
+- `graph_based` (ClusterBuilder): 2 notebooks (interconnected entities)
+- `hierarchical` (ClusterBuilder): 1 notebook (nested groups)
+- `exact` (EntityResolver): 1 notebook (exact matching)
+- `semantic` (EntityResolver): 2 notebooks (semantic similarity)
+- `fuzzy` (EntityResolver): 1 notebook (fuzzy matching)
+
+### Merge Strategies (5 total)
+- `keep_first`: 1 notebook (first detection priority)
+- `keep_last`: 1 notebook (latest information)
+- `keep_most_complete`: 5 notebooks (preserve all details)
+- `keep_highest_confidence`: 2 notebooks (most reliable data)
+- `merge_all`: 3 notebooks (combine all information)
+
+### Conflict Detection Methods (6 total)
+- `value`: 4 notebooks (property value conflicts)
+- `type`: 2 notebooks (type/classification conflicts)
+- `entity`: 2 notebooks (entity-wide conflicts)
+- `relationship`: 3 notebooks (relationship conflicts)
+- `temporal`: 3 notebooks (time-sensitive conflicts)
+- `logical`: 2 notebooks (logical inconsistencies)
+
+### Conflict Resolution Strategies (6 total)
+- `voting`: 5 notebooks (majority vote)
+- `credibility_weighted`: 4 notebooks (source credibility)
+- `most_recent`: 3 notebooks (latest data)
+- `first_seen`: 1 notebook (first detection)
+- `highest_confidence`: 2 notebooks (most confident)
+- `expert_review`: 1 notebook (manual review)
+
+---
+
+## Key Patterns
+
+1. **Real-Time Systems**: Use `pairwise` + `keep_first` + `first_seen`
+2. **Time-Sensitive Data**: Use `temporal` + `most_recent`
+3. **Multi-Source Integration**: Use `batch` + `merge_all` + `voting`
+4. **Medical/Research**: Use `credibility_weighted` for authoritative sources
+5. **Fraud/Security**: Use `graph_based` + `logical` + `expert_review`
+6. **Exact Matching Required**: Use `exact` strategy (IOCs, identifiers)
+

SUPPORT.md

@@ -27,7 +27,7 @@ Start with our comprehensive documentation:
 
 **Best for**: Real-time chat and quick questions
 
-- [Join Discord](https://discord.gg/semantica)
+- [Join Discord](https://discord.gg/pMHguUzG)
 
 #### GitHub Issues
 

cookbook/advanced/01_Advanced_Extraction.ipynb

@@ -42,55 +42,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install -q semantica"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>EventDetector</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>CoreferenceResolver</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>TripletExtractor</td><td>-</td><td>1.32s</td></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>NERExtractor</td><td>-</td><td>0.51s</td></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>RelationExtractor</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>SemanticNetworkExtractor</td><td>-</td><td>1.21s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detected 1 events\n",
-      "  Event: founded - founded\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import (\n",
     "    EventDetector, CoreferenceResolver, TripletExtractor,\n",
@@ -118,17 +81,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Resolved 0 coreference chains\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "coreference_resolver = CoreferenceResolver()\n",
     "\n",
@@ -148,24 +103,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DEBUG: Entity map keys: ['apple inc.', 'steve jobs', '1976', 'tim cook']\n",
-      "DEBUG: Match found! Subject='Apple Inc.', Object='Steve Jobs'\n",
-      "DEBUG: Subject Entity found: True, Object Entity found: True\n",
-      "DEBUG: Match found! Subject='Steve Jobs', Object='1976'\n",
-      "DEBUG: Subject Entity found: True, Object Entity found: True\n",
-      "Extracted 2 triplets\n",
-      "  (Apple Inc., founded_by, Steve Jobs)\n",
-      "  (Steve Jobs, located_in, 1976)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "triplet_extractor = TripletExtractor()\n",
     "\n",
@@ -187,17 +127,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Analyzed semantic roles: 6\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "semantic_analyzer = SemanticAnalyzer()\n",
     "\n",
@@ -217,23 +149,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DEBUG: Entity map keys: ['apple inc.', 'steve jobs', '1976', 'tim cook']\n",
-      "DEBUG: Match found! Subject='Apple Inc.', Object='Steve Jobs'\n",
-      "DEBUG: Subject Entity found: True, Object Entity found: True\n",
-      "DEBUG: Match found! Subject='Steve Jobs', Object='1976'\n",
-      "DEBUG: Subject Entity found: True, Object Entity found: True\n",
-      "Extracted semantic network with 4 nodes\n",
-      "Edges: 2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "semantic_network_extractor = SemanticNetworkExtractor()\n",
     "\n",
@@ -254,21 +172,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'LLMEnhancer' object has no attribute 'enhance_extractions'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[7], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m llm_enhancer \u001b[38;5;241m=\u001b[39m LLMEnhancer()\n\u001b[1;32m----> 3\u001b[0m enhanced_extractions \u001b[38;5;241m=\u001b[39m \u001b[43mllm_enhancer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menhance_extractions\u001b[49m(events, text)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnhanced \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(enhanced_extractions)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m extractions\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'LLMEnhancer' object has no attribute 'enhance_extractions'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "llm_enhancer = LLMEnhancer()\n",
     "\n",

cookbook/advanced/02_Advanced_Graph_Analytics.ipynb

@@ -22,33 +22,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "695d435c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install -q semantica"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,17 +73,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 6 raw entities and 4 raw relationships.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Our \"Raw\" Messy Data\n",
     "raw_entities = [\n",
@@ -141,25 +117,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "bd8fb13d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Validation Check...\n",
-      "Validation Failed! Issues found:\n",
-      "   - [ERROR] Target entity ID not found: startup_999 (Code: DANGLING_EDGE)\n",
-      "     Auto-Fixing: Removing invalid relationship...\n",
-      "   - [WARNING] Found 2 orphan nodes (no relationships). (Code: ORPHAN_NODES)\n",
-      "\n",
-      "Re-validating after fixes...\n",
-      "Graph is now clean and valid!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize Validator\n",
     "validator = GraphValidator()\n",
@@ -203,180 +164,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scanning for duplicates...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>DuplicateDetector</td><td>-</td><td>0.25s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>SimilarityCalculator</td><td>-</td><td>0.02s</td></tr><tr><td>✅</td><td>Semantica is resolving</td><td>⚠️ conflicts</td><td>ConflictDetector</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is building</td><td>🧠 kg</td><td>CentralityCalculator</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is building</td><td>🧠 kg</td><td>CommunityDetector</td><td>-</td><td>0.01s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-19 16:09:21,008 - INFO - [RUNNING] | Module: deduplication | Submodule: DuplicateDetector | Message: Detecting duplicates in 6 entities\n",
-      "2025-12-19 16:09:21,008 - INFO - Detecting duplicates in 6 entities (threshold: 0.7)\n",
-      "2025-12-19 16:09:21,011 - INFO - [RUNNING] | Module: deduplication | Submodule: DuplicateDetector | Message: Calculating similarities...\n",
-      "2025-12-19 16:09:21,013 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,015 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,018 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,020 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,023 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,026 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.56\n",
-      "2025-12-19 16:09:21,029 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,031 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,034 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,036 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,038 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,040 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.86\n",
-      "2025-12-19 16:09:21,043 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,046 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,049 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,052 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,055 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,058 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.61\n",
-      "2025-12-19 16:09:21,060 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,063 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,065 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,068 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,069 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,072 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.61\n",
-      "2025-12-19 16:09:21,075 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,077 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,080 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,083 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,087 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,089 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.54\n",
-      "2025-12-19 16:09:21,092 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,093 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,098 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,100 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,103 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,106 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.57\n",
-      "2025-12-19 16:09:21,109 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,112 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,115 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,117 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,121 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,124 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.59\n",
-      "2025-12-19 16:09:21,126 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,129 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,131 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,136 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,138 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,142 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.59\n",
-      "2025-12-19 16:09:21,146 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,150 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,153 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,156 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,159 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,162 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.30\n",
-      "2025-12-19 16:09:21,166 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,168 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,170 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,173 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,175 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,177 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.56\n",
-      "2025-12-19 16:09:21,179 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,180 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,184 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,186 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,187 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,188 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.66\n",
-      "2025-12-19 16:09:21,192 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,193 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,196 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,198 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,200 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,201 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.55\n",
-      "2025-12-19 16:09:21,203 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,206 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,206 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,210 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,211 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,213 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.56\n",
-      "2025-12-19 16:09:21,215 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,217 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,221 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,225 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,228 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,231 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.50\n",
-      "2025-12-19 16:09:21,234 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating similarity between entities\n",
-      "2025-12-19 16:09:21,237 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating string similarity...\n",
-      "2025-12-19 16:09:21,240 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating property similarity...\n",
-      "2025-12-19 16:09:21,243 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Calculating relationship similarity...\n",
-      "2025-12-19 16:09:21,246 - INFO - [RUNNING] | Module: deduplication | Submodule: SimilarityCalculator | Message: Aggregating similarity scores...\n",
-      "2025-12-19 16:09:21,249 - INFO - [COMPLETED] | Module: deduplication | Submodule: SimilarityCalculator | Message: Similarity score: 0.56\n",
-      "2025-12-19 16:09:21,252 - INFO - [RUNNING] | Module: deduplication | Submodule: DuplicateDetector | Message: Creating duplicate candidates...\n",
-      "2025-12-19 16:09:21,254 - INFO - Detected 1 duplicate candidate(s) (confidence >= 0.6)\n",
-      "2025-12-19 16:09:21,258 - INFO - [COMPLETED] | Module: deduplication | Submodule: DuplicateDetector | Message: Detected 1 duplicate candidates\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found potential duplicate pair (Score: 0.86):\n",
-      "   - TechFlow AI (ID: startup_1)\n",
-      "   - TechFlow Inc. (ID: startup_1_dup)\n",
-      "   Merging entities...\n",
-      "\n",
-      "Checking for data conflicts...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-19 16:09:21,261 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Detecting all conflicts\n",
-      "2025-12-19 16:09:21,265 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Detecting value conflicts for property: founded\n",
-      "2025-12-19 16:09:21,268 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Analyzing 2 entities...\n",
-      "2025-12-19 16:09:21,271 - INFO - [COMPLETED] | Module: conflicts | Submodule: ConflictDetector | Message: Detected 0 conflicts\n",
-      "2025-12-19 16:09:21,274 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Detecting value conflicts for property: revenue\n",
-      "2025-12-19 16:09:21,277 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Analyzing 2 entities...\n",
-      "2025-12-19 16:09:21,279 - WARNING - Value conflict detected: startup_1.revenue has conflicting values: ['1000000', '1200000']\n",
-      "2025-12-19 16:09:21,280 - INFO - [COMPLETED] | Module: conflicts | Submodule: ConflictDetector | Message: Detected 1 conflicts\n",
-      "2025-12-19 16:09:21,283 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Detecting value conflicts for property: name\n",
-      "2025-12-19 16:09:21,286 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Analyzing 2 entities...\n",
-      "2025-12-19 16:09:21,288 - WARNING - Value conflict detected: startup_1.name has conflicting values: ['TechFlow Inc.', 'TechFlow AI']\n",
-      "2025-12-19 16:09:21,291 - INFO - [COMPLETED] | Module: conflicts | Submodule: ConflictDetector | Message: Detected 1 conflicts\n",
-      "2025-12-19 16:09:21,295 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Detecting type conflicts\n",
-      "2025-12-19 16:09:21,296 - INFO - [COMPLETED] | Module: conflicts | Submodule: ConflictDetector | Message: Detected 0 type conflicts\n",
-      "2025-12-19 16:09:21,299 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Detecting temporal conflicts\n",
-      "2025-12-19 16:09:21,302 - INFO - [COMPLETED] | Module: conflicts | Submodule: ConflictDetector | Message: Detected 0 temporal conflicts\n",
-      "2025-12-19 16:09:21,308 - INFO - [RUNNING] | Module: conflicts | Submodule: ConflictDetector | Message: Detecting logical conflicts\n",
-      "2025-12-19 16:09:21,313 - INFO - [COMPLETED] | Module: conflicts | Submodule: ConflictDetector | Message: Detected 0 logical conflicts\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   Conflict detected in field 'revenue':\n",
-      "      Values: [1000000, 1200000]\n",
-      "      Resolved to: 1200000.0\n",
-      "   Conflict detected in field 'name':\n",
-      "      Values: ['TechFlow AI', 'TechFlow Inc.']\n",
-      "\n",
-      "Cleaned Data: 5 entities remaining.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 1. Detect Duplicates\n",
     "print(\"Scanning for duplicates...\")\n",
@@ -435,17 +225,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Knowledge Graph Assembled Successfully!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Manual Graph Construction (since we already cleaned it)\n",
     "kg = {\n",
@@ -470,76 +252,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-19 16:09:52,395 - INFO - Centrality calculator initialized\n",
-      "2025-12-19 16:09:52,396 - INFO - Graph analyzer initialized (temporal: True)\n",
-      "2025-12-19 16:09:52,398 - INFO - Analyzing graph connectivity\n",
-      "2025-12-19 16:09:52,398 - INFO - Finding connected components\n",
-      "2025-12-19 16:09:52,399 - INFO - Calculating connectivity metrics\n",
-      "2025-12-19 16:09:52,399 - INFO - Calculating all centrality measures\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "--- Connectivity Analysis ---\n",
-      "   • Graph Connected? Yes\n",
-      "   • Connected Components: 1\n",
-      "\n",
-      "--- Centrality Analysis ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-19 16:09:52,403 - INFO - [RUNNING] | Module: kg | Submodule: CentralityCalculator | Message: Calculating degree centrality\n",
-      "2025-12-19 16:09:52,403 - INFO - Calculating degree centrality\n",
-      "2025-12-19 16:09:52,406 - INFO - [RUNNING] | Module: kg | Submodule: CentralityCalculator | Message: Processing graph structure...\n",
-      "2025-12-19 16:09:52,409 - INFO - [COMPLETED] | Module: kg | Submodule: CentralityCalculator | Message: Calculated degree centrality for 4 nodes\n",
-      "2025-12-19 16:09:52,410 - INFO - Detecting communities using louvain algorithm\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   • Top Influencers (Degree Centrality):\n",
-      "     - founder_1: 0.67\n",
-      "     - startup_1: 0.67\n",
-      "     - startup_2: 0.33\n",
-      "\n",
-      "--- Community Detection ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-19 16:09:52,413 - INFO - [RUNNING] | Module: kg | Submodule: CommunityDetector | Message: Detecting communities using Louvain algorithm\n",
-      "2025-12-19 16:09:52,414 - INFO - Detecting communities using Louvain algorithm\n",
-      "2025-12-19 16:09:52,417 - INFO - [RUNNING] | Module: kg | Submodule: CommunityDetector | Message: Detecting communities with NetworkX...\n",
-      "2025-12-19 16:09:52,422 - INFO - [COMPLETED] | Module: kg | Submodule: CommunityDetector | Message: Detected 2 communities\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   • Detected 2 communities.\n",
-      "     Community 1: investor_1, startup_1\n",
-      "     Community 2: startup_2, founder_1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize the Master Analyzer\n",
     "analyzer = GraphAnalyzer(enable_temporal=True)\n",
@@ -585,33 +300,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-19 16:09:58,419 - INFO - Querying graph at time: 2020-06-01\n",
-      "2025-12-19 16:09:58,420 - INFO - Querying graph at time: 2023-07-01\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "--- Time Travel: 2020 ---\n",
-      "   Active Relationships in 2020: 1\n",
-      "   - founder_1 --[ADVISED]--> startup_2\n",
-      "\n",
-      "--- Time Travel: 2023 ---\n",
-      "   Active Relationships in 2023: 2\n",
-      "   - founder_1 --[FOUNDED]--> startup_1\n",
-      "   - investor_1 --[INVESTED_IN]--> startup_1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "temporal_engine = TemporalGraphQuery(temporal_granularity=\"year\")\n",
     "\n",
@@ -643,23 +334,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "--- Provenance Report: TechFlow AI ---\n",
-      "   Entity: startup_1\n",
-      "   First Seen: 2025-12-19T16:13:07.782408\n",
-      "   Sources:\n",
-      "     - Crunchbase_API_v2 (at 2025-12-19T16:13:07.782408)\n",
-      "     - Manual_Entry_User_Bob (at 2025-12-19T16:13:07.782408)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "tracker = ProvenanceTracker()\n",
     "\n",
@@ -711,9 +388,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.9"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 5

cookbook/advanced/05_Multi_Format_Export.ipynb

@@ -602,14 +602,14 @@
     "    \"exports/report.txt\"\n",
     "]\n",
     "\n",
-    "print(\"\ud83d\udcca Export Summary:\")\n",
+    "print(\"📊 Export Summary:\")\n",
     "print(\"=\" * 60)\n",
     "for file in export_files:\n",
     "    if os.path.exists(file):\n",
     "        size = os.path.getsize(file)\n",
-    "        print(f\"\u2705 {file:50} ({size:>10,} bytes)\")\n",
+    "        print(f\"✅ {file:50} ({size:>10,} bytes)\")\n",
     "    else:\n",
-    "        print(f\"\u274c {file:50} (not found)\")\n",
+    "        print(f\"❌ {file:50} (not found)\")\n",
     "\n",
     "print(\"=\" * 60)\n",
     "print(f\"Total files checked: {len(export_files)}\")\n",
@@ -623,10 +623,15 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "name": "python"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

cookbook/advanced/08_Reasoning_and_Inference.ipynb

@@ -30,37 +30,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install -qU semantica\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from semantica.kg import GraphBuilder\n",
-    "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n"
+    "from semantica.reasoning import Reasoner, ExplanationGenerator\n"
    ]
   },
   {
@@ -72,22 +56,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is building</td><td>🧠 kg</td><td>GraphBuilder</td><td>-</td><td>0.31s</td></tr><tr><td>🔄</td><td>Semantica is building</td><td>🧠 kg</td><td>EntityResolver</td><td>-</td><td>2.16s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>DuplicateDetector</td><td>-</td><td>0.02s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>SimilarityCalculator</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>EntityMerger</td><td>-</td><td>0.05s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>MergeStrategyManager</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is resolving</td><td>⚠️ conflicts</td><td>ConflictDetector</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is reasoning</td><td>🤔 reasoning</td><td>RuleManager</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is reasoning</td><td>🤔 reasoning</td><td>InferenceEngine</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is reasoning</td><td>🤔 reasoning</td><td>ExplanationGenerator</td><td>-</td><td>0.01s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "builder = GraphBuilder()\n",
     "\n",
@@ -118,12 +89,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Initialize Inference Engine\n",
-    "engine = InferenceEngine()\n",
+    "# Initialize Reasoner\n",
+    "reasoner = Reasoner()\n",
     "\n",
     "# Define rules using logic syntax\n",
     "rules = [\n",
@@ -132,7 +103,7 @@
     "]\n",
     "\n",
     "for rule in rules:\n",
-    "    engine.add_rule(rule)\n"
+    "    reasoner.add_rule(rule)\n"
    ]
   },
   {
@@ -144,32 +115,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Inferred 2 new facts:\n",
-      " - grandparent_of(alice, charlie) (Rule: Rule 1)\n",
-      " - lives_in(alice, california) (Rule: Rule 2)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Load facts from relationships into the engine\n",
-    "for rel in relationships:\n",
-    "    # Format: predicate(subject, object)\n",
-    "    fact_str = f\"{rel['type']}({rel['source']}, {rel['target']})\"\n",
-    "    engine.add_fact(fact_str)\n",
-    "\n",
     "# Perform forward chaining to derive new facts\n",
-    "results = engine.forward_chain()\n",
+    "# The Reasoner can infer facts directly from the knowledge graph or a list of facts\n",
+    "inferred_facts = reasoner.infer_facts(knowledge_graph)\n",
     "\n",
-    "print(f\"Inferred {len(results)} new facts:\")\n",
-    "for result in results:\n",
-    "    print(f\" - {result.conclusion} (Rule: {result.rule_used.name})\")\n"
+    "print(f\"Inferred {len(inferred_facts)} new facts:\")\n",
+    "for fact in inferred_facts:\n",
+    "    print(f\" - {fact}\")\n"
    ]
   },
   {
@@ -181,23 +137,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Goal 'grandparent_of(alice, charlie)' proven successfully!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Define a goal to prove\n",
     "goal = \"grandparent_of(alice, charlie)\"\n",
     "\n",
     "# Perform backward chaining\n",
-    "proof = engine.backward_chain(goal)\n",
+    "proof = reasoner.backward_chain(goal)\n",
     "\n",
     "if proof:\n",
     "    print(f\"Goal '{goal}' proven successfully!\")\n",
@@ -214,34 +162,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Explanation for first inferred fact:\n",
-      "Given the premises: parent_of(alice, bob), parent_of(bob, charlie), we conclude: grandparent_of(alice, charlie) using rule 'Rule 1'.\n",
-      "\n",
-      "Explanation for backward chaining proof:\n",
-      "Given the premises: , we conclude: grandparent_of(alice, charlie).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "generator = ExplanationGenerator()\n",
     "\n",
-    "# Explain the last forward chaining inference\n",
-    "if results:\n",
-    "    explanation = generator.generate_explanation(results[0])\n",
-    "    print(\"Explanation for first inferred fact:\")\n",
-    "    print(explanation.natural_language)\n",
-    "\n",
     "# If we have a proof from backward chaining, explain it\n",
     "if proof:\n",
     "    proof_explanation = generator.generate_explanation(proof)\n",
-    "    print(\"\\nExplanation for backward chaining proof:\")\n",
+    "    print(\"Explanation for backward chaining proof:\")\n",
     "    print(proof_explanation.natural_language)\n"
    ]
   },
@@ -279,15 +209,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from semantica.kg import GraphBuilder\n",
-    "from semantica.reasoning import InferenceEngine, ExplanationGenerator\n",
+    "from semantica.reasoning import Reasoner, ExplanationGenerator\n",
     "\n",
     "builder = GraphBuilder()\n",
-    "engine = InferenceEngine()\n",
+    "reasoner = Reasoner()\n",
     "explainer = ExplanationGenerator()\n"
    ]
   },
@@ -306,18 +236,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n",
-      "4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "entities = [\n",
     "    {\"id\": \"alice\", \"type\": \"Person\", \"name\": \"Alice\"},\n",
@@ -341,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -350,30 +271,20 @@
     "    \"IF lives_in(?x, ?y) AND located_in(?y, ?z) THEN lives_in(?x, ?z)\"\n",
     "]\n",
     "for r in rules:\n",
-    "    engine.add_rule(r)\n"
+    "    reasoner.add_rule(r)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "grandparent_of(alice, charlie)\n",
-      "lives_in(alice, california)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for rel in relationships:\n",
     "    fact = f\"{rel['type']}({rel['source']}, {rel['target']})\"\n",
-    "    engine.add_fact(fact)\n",
+    "    reasoner.add_fact(fact)\n",
     "\n",
-    "derived = engine.forward_chain()\n",
+    "derived = reasoner.forward_chain()\n",
     "print(len(derived))\n",
     "for d in derived:\n",
     "    print(d.conclusion)\n"
@@ -381,52 +292,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "grandparent_of(alice, charlie)\n",
-      "True\n",
-      "lives_in(alice, california)\n",
-      "True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "goals = [\n",
     "    \"grandparent_of(alice, charlie)\",\n",
     "    \"lives_in(alice, california)\"\n",
     "]\n",
     "for g in goals:\n",
-    "    proof = engine.backward_chain(g)\n",
+    "    proof = reasoner.backward_chain(g)\n",
     "    print(g)\n",
     "    print(bool(proof))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Given the premises: parent_of(alice, bob), parent_of(bob, charlie), we conclude: grandparent_of(alice, charlie) using rule 'Rule 1'.\n",
-      "Given the premises: , we conclude: grandparent_of(alice, charlie).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if derived:\n",
     "    exp = explainer.generate_explanation(derived[0])\n",
     "    print(exp.natural_language)\n",
     "\n",
     "goal = \"grandparent_of(alice, charlie)\"\n",
-    "proof = engine.backward_chain(goal)\n",
+    "proof = reasoner.backward_chain(goal)\n",
     "if proof:\n",
     "    pexp = explainer.generate_explanation(proof)\n",
     "    print(pexp.natural_language)\n"

cookbook/advanced/09_Semantic_Layer_Construction.ipynb

@@ -25,7 +25,7 @@
     "pip install semantica[all]\n",
     "```\n",
     "\n",
-    "## Workflow: Build KG \u2192 Generate Ontology \u2192 Create Semantic Layer \u2192 Export RDF \u2192 Triplet Store\n"
+    "## Workflow: Build KG → Generate Ontology → Create Semantic Layer → Export RDF \n"
    ]
   },
   {
@@ -34,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install semantica\n"
+    "!pip install -qU semantica\n"
    ]
   },
   {
@@ -169,23 +169,6 @@
     "print(\"Exported knowledge graph to knowledge_graph.ttl\")\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5: Store in Triplet Store\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "triplet_store = TripletStore()\n",
-    "triplet_store.store(knowledge_graph, ontology)\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -196,16 +179,44 @@
     "- Knowledge Graph Built\n",
     "- Ontology Generated\n",
     "- Semantic Layer Created with Mappings\n",
-    "- RDF Export Completed\n",
-    "- Triplet Store Storage Completed\n"
+    "- RDF Export Completed\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

cookbook/advanced/10_Temporal_Knowledge_Graphs.ipynb

@@ -6,35 +6,29 @@
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/advanced/10_Temporal_Knowledge_Graphs.ipynb)\n",
     "\n",
-    "# Temporal Knowledge Graphs\n",
+    "# Deep Dive: Temporal Knowledge Graphs\n",
     "\n",
     "## Overview\n",
     "\n",
-    "This notebook demonstrates advanced temporal knowledge graph capabilities using TemporalGraphQuery, TemporalPatternDetector, TemporalVersionManager, and TemporalVisualizer.\n",
+    "This notebook provides a comprehensive deep dive into **Temporal Knowledge Graphs (TKGs)** using Semantica. Unlike static KGs, TKGs capture the evolution of facts, relationships, and entities over time. This capability is crucial for applications like:\n",
     "\n",
+    "- **Corporate History Analysis**: Tracking mergers, acquisitions, and leadership changes.\n",
+    "- **Supply Chain Monitoring**: Tracing product movement and status changes.\n",
+    "- **Financial Fraud Detection**: Analyzing sequences of transactions.\n",
+    "\n",
+    "We will build a rich scenario modeling the history of a tech ecosystem, covering 40 years of evolution.\n",
+    "\n",
+    "### Key Components Covered\n",
+    "\n",
+    "1.  **`GraphBuilder` (Temporal Mode)**: Constructing KGs with time-aware properties.\n",
+    "2.  **`TemporalGraphQuery`**: Performing point-in-time, interval, and path queries.\n",
+    "3.  **`TemporalPatternDetector`**: Identifying sequences and cyclic patterns.\n",
+    "4.  **`TemporalVersionManager`**: Managing snapshots and comparing graph states.\n",
+    "5.  **`TemporalVisualizer`**: Interactive timelines and evolution plots.\n",
     "\n",
     "**Documentation**: [API Reference](https://semantica.readthedocs.io/reference/kg/)\n",
     "\n",
-    "### Learning Objectives\n",
-    "\n",
-    "- Use TemporalGraphQuery for time-aware queries\n",
-    "- Use TemporalPatternDetector to detect temporal patterns\n",
-    "- Use TemporalVersionManager for temporal versioning and snapshots\n",
-    "- Use TemporalVisualizer to visualize temporal data\n",
-    "\n",
-    "## Installation\n",
-    "\n",
-    "Install Semantica from PyPI:\n",
-    "\n",
-    "```bash\n",
-    "pip install semantica\n",
-    "# Or with all optional dependencies:\n",
-    "pip install semantica[all]\n",
-    "```\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## Workflow: Build Temporal KG \u2192 Time-Aware Queries \u2192 Pattern Detection \u2192 Version Management \u2192 Visualization\n"
+    "## Installation\n"
    ]
   },
   {
@@ -43,7 +37,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install semantica\n"
+    "# !pip install semantica[all]"
    ]
   },
   {
@@ -52,33 +46,114 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import json\n",
+    "from datetime import datetime\n",
     "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, TemporalVersionManager\n",
     "from semantica.visualization import TemporalVisualizer\n",
-    "from datetime import datetime\n",
+    "import plotly.offline as pyo\n",
+    "pyo.init_notebook_mode(connected=True)\n",
     "\n",
-    "builder = GraphBuilder()\n",
+    "# Ensure consistent output for reproducibility\n",
+    "import random\n",
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Scenario Definition & Data Preparation\n",
     "\n",
+    "We define a dataset representing the history of \"TechCorp\" and \"InnovateInc\", including their founders, products, and eventual merger.\n",
+    "\n",
+    "**Temporal Properties**:\n",
+    "- Entities have `founded`, `born`, `released` dates.\n",
+    "- Relationships have `timestamp` (point event) or `valid_from`/`valid_to` (intervals).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Define Entities with Temporal Metadata\n",
     "entities = [\n",
-    "    {\"id\": \"e1\", \"type\": \"Organization\", \"name\": \"Apple Inc.\", \"properties\": {\"founded\": \"1976\"}},\n",
-    "    {\"id\": \"e2\", \"type\": \"Person\", \"name\": \"Steve Jobs\", \"properties\": {\"born\": \"1955\"}}\n",
+    "    # Organizations\n",
+    "    {\"id\": \"org_1\", \"type\": \"Organization\", \"name\": \"TechCorp\", \"properties\": {\"founded\": \"1980-01-01\", \"industry\": \"Hardware\"}},\n",
+    "    {\"id\": \"org_2\", \"type\": \"Organization\", \"name\": \"InnovateInc\", \"properties\": {\"founded\": \"1995-06-15\", \"industry\": \"Software\"}},\n",
+    "    {\"id\": \"org_3\", \"type\": \"Organization\", \"name\": \"FutureSystems\", \"properties\": {\"founded\": \"2010-03-10\", \"industry\": \"AI\"}},\n",
+    "    \n",
+    "    # People\n",
+    "    {\"id\": \"per_1\", \"type\": \"Person\", \"name\": \"Alice Founder\", \"properties\": {\"born\": \"1955-05-20\"}},\n",
+    "    {\"id\": \"per_2\", \"type\": \"Person\", \"name\": \"Bob Coder\", \"properties\": {\"born\": \"1970-08-12\"}},\n",
+    "    {\"id\": \"per_3\", \"type\": \"Person\", \"name\": \"Charlie CEO\", \"properties\": {\"born\": \"1980-02-28\"}},\n",
+    "    \n",
+    "    # Products\n",
+    "    {\"id\": \"prod_1\", \"type\": \"Product\", \"name\": \"HomePC\", \"properties\": {\"released\": \"1985-11-20\"}},\n",
+    "    {\"id\": \"prod_2\", \"type\": \"Product\", \"name\": \"SoftOS\", \"properties\": {\"released\": \"1998-07-25\"}},\n",
+    "    {\"id\": \"prod_3\", \"type\": \"Product\", \"name\": \"SmartAI\", \"properties\": {\"released\": \"2015-01-10\"}}\n",
     "]\n",
     "\n",
+    "# 2. Define Temporal Relationships\n",
     "relationships = [\n",
-    "    {\"source\": \"e2\", \"target\": \"e1\", \"type\": \"founded\", \"properties\": {\"timestamp\": \"1976-04-01\"}}\n",
+    "    # Founding Events (Point in time)\n",
+    "    {\"source\": \"per_1\", \"target\": \"org_1\", \"type\": \"founded\", \"timestamp\": \"1980-01-01\", \"properties\": {\"timestamp\": \"1980-01-01\"}},\n",
+    "    {\"source\": \"per_2\", \"target\": \"org_2\", \"type\": \"founded\", \"timestamp\": \"1995-06-15\", \"properties\": {\"timestamp\": \"1995-06-15\"}},\n",
+    "    \n",
+    "    # Employment (Intervals)\n",
+    "    {\"source\": \"per_1\", \"target\": \"org_1\", \"type\": \"ceo_of\", \"valid_from\": \"1980-01-01\", \"valid_to\": \"2000-01-01\", \"properties\": {\"role\": \"CEO\"}},\n",
+    "    {\"source\": \"per_3\", \"target\": \"org_1\", \"type\": \"ceo_of\", \"valid_from\": \"2000-01-02\", \"valid_to\": \"2023-01-01\", \"properties\": {\"role\": \"CEO\"}},\n",
+    "    {\"source\": \"per_2\", \"target\": \"org_2\", \"type\": \"cto_of\", \"valid_from\": \"1995-06-15\", \"valid_to\": \"2010-05-01\", \"properties\": {\"role\": \"CTO\"}},\n",
+    "    \n",
+    "    # Product Launches\n",
+    "    {\"source\": \"org_1\", \"target\": \"prod_1\", \"type\": \"launched\", \"timestamp\": \"1985-11-20\", \"properties\": {\"timestamp\": \"1985-11-20\"}},\n",
+    "    {\"source\": \"org_2\", \"target\": \"prod_2\", \"type\": \"launched\", \"timestamp\": \"1998-07-25\", \"properties\": {\"timestamp\": \"1998-07-25\"}},\n",
+    "    {\"source\": \"org_3\", \"target\": \"prod_3\", \"type\": \"launched\", \"timestamp\": \"2015-01-10\", \"properties\": {\"timestamp\": \"2015-01-10\"}},\n",
+    "    \n",
+    "    # Corporate Actions\n",
+    "    {\"source\": \"org_1\", \"target\": \"org_2\", \"type\": \"acquired\", \"timestamp\": \"2010-05-01\", \"properties\": {\"amount\": \"$5B\", \"timestamp\": \"2010-05-01\"}},\n",
+    "    {\"source\": \"org_1\", \"target\": \"org_3\", \"type\": \"invested_in\", \"timestamp\": \"2012-08-15\", \"properties\": {\"amount\": \"$100M\", \"timestamp\": \"2012-08-15\"}}\n",
     "]\n",
     "\n",
+    "print(f\"Defined {len(entities)} entities and {len(relationships)} temporal relationships.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Building the Temporal Graph\n",
+    "\n",
+    "We use `GraphBuilder` with `enable_temporal=True`. This instructs the builder to index temporal properties like `timestamp`, `valid_from`, and `valid_to`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "builder = GraphBuilder(\n",
+    "    enable_temporal=True,\n",
+    "    temporal_granularity=\"day\"  # Can be 'year', 'month', 'day', 'hour'\n",
+    ")\n",
+    "\n",
     "temporal_kg = builder.build(entities, relationships)\n",
     "\n",
-    "print(f\"Built temporal knowledge graph with {len(entities)} entities\")\n"
+    "# The graph object now contains temporal indices\n",
+    "print(\"Graph built successfully.\")\n",
+    "print(f\"Nodes: {len(temporal_kg['entities'])}\")\n",
+    "print(f\"Edges: {len(temporal_kg['relationships'])}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 2: Time-Aware Queries\n",
+    "## Step 3: Advanced Temporal Querying\n",
     "\n",
-    "Query the graph at specific time points.\n"
+    "We use `TemporalGraphQuery` to ask time-sensitive questions."
    ]
   },
   {
@@ -87,25 +162,48 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "temporal_query = TemporalGraphQuery()\n",
+    "query_engine = TemporalGraphQuery()\n",
     "\n",
-    "query_result = temporal_query.query_time_range(\n",
+    "# 1. Point-in-Time Query\n",
+    "# \"Who was the CEO of TechCorp in 1990?\"\n",
+    "ceo_1990 = query_engine.query_at_time(\n",
+    "    temporal_kg,\n",
+    "    query=\"Find the CEO of TechCorp\",\n",
+    "    at_time=\"1990-06-01\"\n",
+    ")\n",
+    "print(\"CEO in 1990:\", [e['id'] for e in ceo_1990.get('entities', [])])\n",
+    "\n",
+    "# \"Who was the CEO of TechCorp in 2015?\"\n",
+    "ceo_2015 = query_engine.query_at_time(\n",
+    "    temporal_kg,\n",
+    "    query=\"Find the CEO of TechCorp\",\n",
+    "    at_time=\"2015-06-01\"\n",
+    ")\n",
+    "print(\"CEO in 2015:\", [e['id'] for e in ceo_2015.get('entities', [])])\n",
+    "\n",
+    "# 2. Temporal Path Finding\n",
+    "# \"How did Alice (Founder) connect to SmartAI (Product released in 2015)?\"\n",
+    "# This requires traversing through time: Alice -> founded TechCorp -> invested in FutureSystems -> launched SmartAI\n",
+    "paths = query_engine.find_temporal_paths(\n",
     "    graph=temporal_kg,\n",
-    "    query=\"Find entities founded in 1976\",\n",
-    "    start_time=\"1976-01-01\",\n",
-    "    end_time=\"1976-12-31\"\n",
+    "    source=\"per_1\", # Alice\n",
+    "    target=\"prod_3\", # SmartAI\n",
+    "    start_time=\"1980-01-01\",\n",
+    "    end_time=\"2020-01-01\"\n",
     ")\n",
     "\n",
-    "print(f\"Time-aware query returned {len(query_result.get('entities', []))} entities\")\n"
+    "print(f\"\\nFound {len(paths)} temporal paths from Alice to SmartAI.\")\n",
+    "for i, path in enumerate(paths):\n",
+    "    print(f\"Path {i+1}: {path}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 3: Temporal Pattern Detection\n",
+    "## Step 4: Graph Evolution Analysis\n",
     "\n",
-    "Detect temporal patterns in the graph.\n"
+    "We can analyze how the graph properties change over time using `analyze_evolution`."
    ]
   },
   {
@@ -114,24 +212,56 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pattern_detector = TemporalPatternDetector()\n",
+    "evolution_stats = query_engine.analyze_evolution(\n",
+    "    temporal_kg,\n",
+    "    start_time=\"1980-01-01\",\n",
+    "    end_time=\"2025-01-01\",\n",
+    "    metrics=[\"count\", \"diversity\", \"stability\"]\n",
+    ")\n",
     "\n",
-    "patterns = pattern_detector.detect_temporal_patterns(\n",
+    "print(\"\\nEvolution Statistics (1980-2025):\")\n",
+    "print(f\"Total Relationships: {evolution_stats.get('count', 'N/A')}\")\n",
+    "print(f\"Relationship Diversity: {evolution_stats.get('diversity', 'N/A')}\")\n",
+    "print(f\"Graph Stability: {evolution_stats.get('stability', 'N/A')}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Temporal Pattern Detection\n",
+    "\n",
+    "We use `TemporalPatternDetector` to automatically find recurring structures, such as sequences (A -> B -> C) or cycles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector = TemporalPatternDetector()\n",
+    "\n",
+    "# Detect sequential patterns (e.g., Founded -> Launched -> Acquired)\n",
+    "sequences = detector.detect_temporal_patterns(\n",
     "    temporal_kg,\n",
     "    pattern_type=\"sequence\",\n",
     "    min_frequency=1\n",
     ")\n",
     "\n",
-    "print(f\"Detected {len(patterns)} temporal patterns\")\n"
+    "print(f\"\\nDetected {len(sequences)} sequential patterns.\")\n",
+    "for seq in sequences[:3]: # Show top 3\n",
+    "    print(f\"Pattern: {seq.get('pattern')}\")\n",
+    "    print(f\"Support: {seq.get('support')}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 4: Version Management\n",
+    "## Step 6: Version Management & Comparisons\n",
     "\n",
-    "Manage temporal versions and snapshots.\n"
+    "In real-world scenarios, KGs are updated in batches. `TemporalVersionManager` handles these versions."
    ]
   },
   {
@@ -142,19 +272,25 @@
    "source": [
     "version_manager = TemporalVersionManager()\n",
     "\n",
-    "snapshot = version_manager.create_snapshot(temporal_kg, timestamp=datetime.now())\n",
+    "# Create explicit versions\n",
+    "v1_1990 = version_manager.create_version(temporal_kg, timestamp=\"1990-01-01\", version_label=\"v1.0 (Early Days)\")\n",
+    "v2_2010 = version_manager.create_version(temporal_kg, timestamp=\"2010-01-01\", version_label=\"v2.0 (Post-Merger)\")\n",
     "\n",
-    "print(f\"Created temporal snapshot at {snapshot.get('timestamp', 'N/A')}\")\n",
-    "print(f\"Snapshot contains {len(snapshot.get('entities', []))} entities\")\n"
+    "# Compare versions\n",
+    "diff = version_manager.compare_versions(v1_1990, v2_2010)\n",
+    "\n",
+    "print(f\"\\nComparing {v1_1990['label']} vs {v2_2010['label']}:\")\n",
+    "print(f\"New Entities: {diff.get('entities_added', 0)}\")\n",
+    "print(f\"New Relationships: {diff.get('relationships_added', 0)}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 5: Temporal Visualization\n",
+    "## Step 7: Visualizing the Timeline\n",
     "\n",
-    "Visualize temporal data.\n"
+    "Finally, `TemporalVisualizer` brings the data to life. We will create an interactive timeline and a snapshot comparison."
    ]
   },
   {
@@ -163,9 +299,46 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "temporal_visualizer = TemporalVisualizer()\n",
+    "visualizer = TemporalVisualizer()\n",
     "\n",
-    "visualization = temporal_visualizer.visualize_timeline(temporal_kg, output=\"interactive\")\n"
+    "# 1. Interactive Timeline\n",
+    "# Prepare events for visualization (extract from KG)\n",
+    "def extract_events(graph):\n",
+    "    events = []\n",
+    "    for rel in graph['relationships']:\n",
+    "        # Point events\n",
+    "        if rel.get('timestamp'):\n",
+    "            events.append({\n",
+    "                'timestamp': rel['timestamp'],\n",
+    "                'type': rel['type'],\n",
+    "                'label': f\"{rel['source']} -> {rel['target']}\",\n",
+    "                'entity': rel['source']\n",
+    "            })\n",
+    "        # Interval events (start)\n",
+    "        if rel.get('valid_from'):\n",
+    "             events.append({\n",
+    "                'timestamp': rel['valid_from'],\n",
+    "                'type': f\"{rel['type']} (start)\",\n",
+    "                'label': f\"{rel['source']} -> {rel['target']}\",\n",
+    "                'entity': rel['source']\n",
+    "            })\n",
+    "    return {'events': events}\n",
+    "\n",
+    "temporal_data = extract_events(temporal_kg)\n",
+    "timeline_fig = visualizer.visualize_timeline(temporal_data, output=\"interactive\")\n",
+    "# In a notebook, this would render a Plotly figure. \n",
+    "timeline_fig.show()\n",
+    "\n",
+    "# 2. Version History Visualization\n",
+    "history = [\n",
+    "    {\"version\": \"v1.0\", \"timestamp\": \"1990-01-01\", \"changes\": \"Founding Era\"},\n",
+    "    {\"version\": \"v2.0\", \"timestamp\": \"2010-01-01\", \"changes\": \"Expansion Era\"},\n",
+    "    {\"version\": \"v3.0\", \"timestamp\": \"2020-01-01\", \"changes\": \"AI Era\"}\n",
+    "]\n",
+    "history_fig = visualizer.visualize_version_history(history, output=\"interactive\")\n",
+    "history_fig.show()\n",
+    "\n",
+    "print(\"Visualizations generated (render requires Jupyter environment).\")"
    ]
   },
   {
@@ -174,66 +347,38 @@
    "source": [
     "## Summary\n",
     "\n",
-    "You've learned advanced temporal knowledge graph capabilities:\n",
+    "In this deep dive, we:\n",
+    "1.  **modeled** a complex corporate history with temporal metadata.\n",
+    "2.  **Built** a time-aware knowledge graph using `GraphBuilder`.\n",
+    "3.  **Queried** specific time slices and intervals to reconstruct history.\n",
+    "4.  **Traced** temporal paths to understand indirect connections.\n",
+    "5.  **Analyzed** the graph's evolution metrics.\n",
+    "6.  **Managed** versions and visualized the timeline.\n",
+    "7.  **Visualized** the data with `TemporalVisualizer`.\n",
     "\n",
-    "- **TemporalGraphQuery**: Time-aware graph querying\n",
-    "- **TemporalPatternDetector**: Temporal pattern detection\n",
-    "- **TemporalVersionManager**: Temporal versioning and snapshots\n",
-    "- **TemporalVisualizer**: Temporal data visualization\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Snapshot Comparison and Version History\n",
-    "\n",
-    "Compare graph snapshots across time and visualize version history."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create multiple versions\n",
-    "version_manager = TemporalVersionManager()\n",
-    "version_2020 = version_manager.create_version(temporal_kg, timestamp=\"2020-01-01\", version_label=\"v2020\")\n",
-    "# Simulate changes for 2023\n",
-    "temporal_kg_updated = {\n",
-    "    \"entities\": temporal_kg.get(\"entities\", []),\n",
-    "    \"relationships\": temporal_kg.get(\"relationships\", []) + [\n",
-    "        {\"source\": \"e1\", \"target\": \"e2\", \"type\": \"collaborated_with\", \"valid_from\": \"2023-01-01\"}\n",
-    "    ]\n",
-    "}\n",
-    "version_2023 = version_manager.create_version(temporal_kg_updated, timestamp=\"2023-01-01\", version_label=\"v2023\")\n",
-    "\n",
-    "# Build snapshots dict for comparison\n",
-    "snapshots = {\n",
-    "    version_2020[\"timestamp\"]: version_2020,\n",
-    "    version_2023[\"timestamp\"]: version_2023\n",
-    "}\n",
-    "\n",
-    "# Visualize snapshot comparison\n",
-    "fig_snapshots = temporal_visualizer.visualize_snapshot_comparison(snapshots, output=\"interactive\")\n",
-    "\n",
-    "# Build version history list\n",
-    "version_history = [\n",
-    "    {\"version\": version_2020.get(\"label\", \"v2020\"), \"timestamp\": version_2020.get(\"timestamp\"), \"changes\": f\"Entities: {len(version_2020.get('entities', []))}, Relationships: {len(version_2020.get('relationships', []))}\"},\n",
-    "    {\"version\": version_2023.get(\"label\", \"v2023\"), \"timestamp\": version_2023.get(\"timestamp\"), \"changes\": f\"Entities: {len(version_2023.get('entities', []))}, Relationships: {len(version_2023.get('relationships', []))}\"}\n",
-    "]\n",
-    "\n",
-    "# Visualize version history\n",
-    "fig_versions = temporal_visualizer.visualize_version_history(version_history, output=\"interactive\")\n"
+    "This workflow forms the backbone of temporal intelligence applications in Semantica."
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 4
+}

cookbook/advanced/12_Unstructured_to_Ontology.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/advanced/12_Unstructured_to_Ontology.ipynb)\n",
     "\n",
-    "# Advanced: Unstructured Text to Ontology\n",
+    "# Unstructured Text to Ontology\n",
     "\n",
     "Welcome to the advanced guide on extracting structured ontologies from unstructured text. This notebook explores two powerful paradigms available in Semantica:\n",
     "\n",
@@ -25,10 +25,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "9c21e116",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !pip install semantica[all]\n",
+    "!pip install -qU semantica"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
     "\n",
     "from semantica.utils.logging import get_logger\n",
     "\n",
@@ -82,6 +92,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "75f896b9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,19 +106,65 @@
     "# 2. Extract Entities\n",
     "print(\"Extracting entities...\")\n",
     "entities = ner.extract(text_corpus)\n",
-    "print(f\"Found {len(entities)} entities: {[e['text'] for e in entities]}\")\n",
+    "\n",
+    "# Note: entities are returned as Entity objects (dataclasses), not dictionaries.\n",
+    "# We access properties using dot notation (e.g., entity.text, entity.label).\n",
+    "print(f\"Found {len(entities)} entities.\")\n",
+    "for e in entities[:5]:\n",
+    "    print(f\" - {e.text} ({e.label}) [Conf: {e.confidence}]\")\n",
     "\n",
     "# 3. Extract Relationships\n",
-    "print(\"Extracting relationships...\")\n",
+    "print(\"\\nExtracting relationships...\")\n",
     "relationships = re.extract(text_corpus, entities)\n",
+    "\n",
+    "# Note: relationships are returned as Relation objects.\n",
+    "print(f\"Found {len(relationships)} relationships.\")\n",
     "for r in relationships:\n",
-    "    print(f\" - {r['source']} -> {r['type']} -> {r['target']}\")\n",
+    "    print(f\" - {r.subject.text} -> {r.predicate} -> {r.object.text}\")\n",
     "\n",
-    "# 4. Generate Structure\n",
+    "# 4. Prepare Data for Ontology Generation\n",
+    "# The OntologyGenerator expects dictionaries, so we convert our objects.\n",
+    "# We also ensure we handle both object attributes and potential dictionary keys for robustness.\n",
+    "entities_data = []\n",
+    "for e in entities:\n",
+    "    if hasattr(e, 'to_dict'):\n",
+    "        entities_data.append(e.to_dict())\n",
+    "    else:\n",
+    "        # Manual conversion for dataclasses without to_dict\n",
+    "        entities_data.append({\n",
+    "            \"id\": getattr(e, \"text\", str(e)),\n",
+    "            \"text\": getattr(e, \"text\", str(e)),\n",
+    "            \"type\": getattr(e, \"label\", getattr(e, \"type\", \"Unknown\")),\n",
+    "            \"confidence\": getattr(e, \"confidence\", 1.0)\n",
+    "        })\n",
+    "\n",
+    "relationships_data = []\n",
+    "for r in relationships:\n",
+    "    if hasattr(r, 'to_dict'):\n",
+    "        relationships_data.append(r.to_dict())\n",
+    "    else:\n",
+    "        # Manual conversion for dataclasses without to_dict\n",
+    "        # Handle nested Entity objects in subject/object fields\n",
+    "        subj = r.subject\n",
+    "        obj = r.object\n",
+    "        subj_text = getattr(subj, \"text\", str(subj))\n",
+    "        obj_text = getattr(obj, \"text\", str(obj))\n",
+    "        \n",
+    "        relationships_data.append({\n",
+    "            \"source\": subj_text,\n",
+    "            \"target\": obj_text,\n",
+    "            \"type\": getattr(r, \"predicate\", getattr(r, \"type\", \"related_to\")),\n",
+    "            \"confidence\": getattr(r, \"confidence\", 1.0)\n",
+    "        })\n",
+    "\n",
+    "# 5. Generate Structure\n",
     "generator = OntologyGenerator()\n",
-    "nlp_ontology = generator.generate_ontology({\"entities\": entities, \"relationships\": relationships}, name=\"QuantumOntologyNLP\")\n",
+    "nlp_ontology = generator.generate_ontology(\n",
+    "    {\"entities\": entities_data, \"relationships\": relationships_data},\n",
+    "    name=\"QuantumOntologyNLP\"\n",
+    ")\n",
     "\n",
-    "# 5. Optimize (Clean up)\n",
+    "# 6. Optimize (Clean up)\n",
     "optimizer = OntologyOptimizer()\n",
     "nlp_ontology = optimizer.optimize_ontology(nlp_ontology, remove_redundancy=True)\n",
     "\n",

cookbook/advanced/Advanced_Triplet_Store.ipynb

@@ -1,173 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Advanced Triplet Store Guide\n",
-    "\n",
-    "This guide explores the advanced capabilities of the Semantica Triplet Store module, focusing on RDF data management, SPARQL querying, and multi-backend support (Blazegraph, Jena, RDF4J).\n",
-    "\n",
-    "## Key Features\n",
-    "- Unified interface for Blazegraph, Jena, and RDF4J\n",
-    "- Bulk loading with progress tracking\n",
-    "- SPARQL query execution and optimization\n",
-    "- Transaction support (backend-dependent)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from semantica.triplet_store import TripletStore\n",
-    "from semantica.semantic_extract.triplet_extractor import Triplet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Initialization\n",
-    "\n",
-    "Initialize the Triplet Store with your preferred backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize Blazegraph store\n",
-    "store = TripletStore(\n",
-    "    backend=\"blazegraph\",\n",
-    "    endpoint=\"http://localhost:9999/blazegraph\"\n",
-    ")\n",
-    "\n",
-    "# Or Jena\n",
-    "# store = TripletStore(backend=\"jena\", endpoint=\"http://localhost:3030/ds\")\n",
-    "\n",
-    "# Or RDF4J\n",
-    "# store = TripletStore(backend=\"rdf4j\", endpoint=\"http://localhost:8080/rdf4j-server/repositories/myrepo\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Adding Triplets\n",
-    "\n",
-    "Add individual triplets or batch load them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Single triplet\n",
-    "triplet = Triplet(\n",
-    "    subject=\"http://example.org/Alice\",\n",
-    "    predicate=\"http://xmlns.com/foaf/0.1/knows\",\n",
-    "    object=\"http://example.org/Bob\"\n",
-    ")\n",
-    "\n",
-    "store.add_triplet(triplet)\n",
-    "\n",
-    "# Bulk load\n",
-    "triplets = [\n",
-    "    Triplet(\n",
-    "        subject=\"http://example.org/Bob\",\n",
-    "        predicate=\"http://xmlns.com/foaf/0.1/knows\",\n",
-    "        object=\"http://example.org/Charlie\"\n",
-    "    ),\n",
-    "    Triplet(\n",
-    "        subject=\"http://example.org/Charlie\",\n",
-    "        predicate=\"http://xmlns.com/foaf/0.1/knows\",\n",
-    "        object=\"http://example.org/Alice\"\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "result = store.add_triplets(triplets, batch_size=100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. SPARQL Querying\n",
-    "\n",
-    "Execute SPARQL queries to retrieve data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"\"\"\n",
-    "SELECT ?s ?p ?o\n",
-    "WHERE {\n",
-    "  ?s ?p ?o\n",
-    "}\n",
-    "LIMIT 10\n",
-    "\"\"\"\n",
-    "\n",
-    "results = store.execute_query(query)\n",
-    "for result in results.get(\"results\", {}).get(\"bindings\", []):\n",
-    "    print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Deleting and Updating\n",
-    "\n",
-    "Manage triplet lifecycle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "store.delete_triplet(triplet)\n",
-    "\n",
-    "# Update is delete + add\n",
-    "new_triplet = Triplet(\n",
-    "    subject=\"http://example.org/Alice\",\n",
-    "    predicate=\"http://xmlns.com/foaf/0.1/knows\",\n",
-    "    object=\"http://example.org/David\"\n",
-    ")\n",
-    "store.update_triplet(triplet, new_triplet)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

cookbook/advanced/Advanced_Vector_Store_and_Search.ipynb

@@ -69,7 +69,7 @@
     "\n",
     "### Simple Rule\n",
     "- Less than 10,000 items? Use **Flat**\n",
-    "- Between 10,000 and 1 million? Use **HNSW** \u2705 (recommended)\n",
+    "- Between 10,000 and 1 million? Use **HNSW** ✅ (recommended)\n",
     "- More than 1 million? Use **IVF**"
    ]
   },
@@ -90,14 +90,14 @@
     "\n",
     "# HNSW Index - Best for most cases\n",
     "index = adapter.create_index(index_type=\"hnsw\", metric=\"L2\", m=16)\n",
-    "adapter.add_vectors(index, vectors, ids=[f\"doc_{i}\" for i in range(len(vectors))])\n",
+    "adapter.add_vectors(vectors, ids=[f\"doc_{i}\" for i in range(len(vectors))])\n",
     "\n",
     "# Search for similar vectors\n",
-    "distances, indices = adapter.search(index, query, k=5)\n",
+    "results = adapter.search_similar(query, k=5)\n",
     "\n",
     "print(\"Found 5 most similar documents:\")\n",
-    "for i, (dist, idx) in enumerate(zip(distances, indices), 1):\n",
-    "    print(f\"  {i}. Document {idx} (distance: {dist:.3f})\")"
+    "for i, result in enumerate(results, 1):\n",
+    "    print(f\"  {i}. Document {result['id']} (distance: {result['distance']:.3f})\")"
    ]
   },
   {
@@ -286,7 +286,7 @@
     "# Small dataset (< 10,000 items)\n",
     "index = adapter.create_index(index_type=\"flat\", metric=\"L2\")\n",
     "\n",
-    "# Medium dataset (10,000 - 1,000,000 items) \u2705 RECOMMENDED\n",
+    "# Medium dataset (10,000 - 1,000,000 items) ✅ RECOMMENDED\n",
     "index = adapter.create_index(index_type=\"hnsw\", metric=\"L2\", m=16)\n",
     "\n",
     "# Large dataset (> 1,000,000 items)\n",
@@ -340,10 +340,10 @@
     "\n",
     "You've learned:\n",
     "\n",
-    "1. \u2705 **Index Selection**: Use HNSW for most cases\n",
-    "2. \u2705 **Smart Filtering**: Combine vector search with metadata\n",
-    "3. \u2705 **Result Fusion**: Merge searches from different sources\n",
-    "4. \u2705 **Data Isolation**: Keep users' data separate\n",
+    "1. ✅ **Index Selection**: Use HNSW for most cases\n",
+    "2. ✅ **Smart Filtering**: Combine vector search with metadata\n",
+    "3. ✅ **Result Fusion**: Merge searches from different sources\n",
+    "4. ✅ **Data Isolation**: Keep users' data separate\n",
     "\n",
     "### Next Steps\n",
     "\n",
@@ -377,4 +377,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

cookbook/advanced/knowledge_graph.ttl

@@ -0,0 +1,20 @@
+<e1> a <Person> ;
+    semantica:text "" ;
+    semantica:confidence 1.0 .
+
+<e2> a <Person> ;
+    semantica:text "" ;
+    semantica:confidence 1.0 .
+
+<e3> a <Organization> ;
+    semantica:text "" ;
+    semantica:confidence 1.0 .
+
+<e4> a <Project> ;
+    semantica:text "" ;
+    semantica:confidence 1.0 .
+
+<e1> <reports_to> <e2> .
+<e1> <works_for> <e3> .
+<e2> <works_for> <e3> .
+<e1> <works_on> <e4> .

cookbook/advanced/quantum_ontology.ttl

@@ -0,0 +1,21 @@
+@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
+@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
+@prefix owl: <http://www.w3.org/2002/07/owl#> .
+@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
+@prefix ont: <https://semantica.dev/ontology/> .
+
+<https://semantica.dev/ontology/> a owl:Ontology ;
+    rdfs:label "QuantumOntologyNLP" ;
+    owl:versionInfo "1.0" .
+
+<> a owl:Class ;
+    rdfs:label "Org" .
+    rdfs:comment "Class representing org entities" .
+
+<> a owl:Class ;
+    rdfs:label "Person" .
+    rdfs:comment "Class representing person entities" .
+
+<> a owl:Class ;
+    rdfs:label "Gpe" .
+    rdfs:comment "Class representing gpe entities" .

cookbook/introduction/02_Data_Ingestion.ipynb

@@ -44,223 +44,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: semantica in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.0.5)\n",
-      "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.26.4)\n",
-      "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.1.4)\n",
-      "Requirement already satisfied: scikit-learn>=1.0.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.6.1)\n",
-      "Requirement already satisfied: spacy>=3.4.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.8.11)\n",
-      "Requirement already satisfied: transformers>=4.20.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.53.2)\n",
-      "Requirement already satisfied: torch>=1.12.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.2.1)\n",
-      "Requirement already satisfied: sentence-transformers>=2.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.2.1)\n",
-      "Requirement already satisfied: rdflib>=6.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (7.4.0)\n",
-      "Requirement already satisfied: networkx>=2.8.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.5)\n",
-      "Requirement already satisfied: matplotlib>=3.5.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.10.1)\n",
-      "Requirement already satisfied: seaborn>=0.11.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (0.13.2)\n",
-      "Requirement already satisfied: plotly>=5.10.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (5.17.0)\n",
-      "Requirement already satisfied: requests>=2.28.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.32.5)\n",
-      "Requirement already satisfied: beautifulsoup4>=4.11.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.12.3)\n",
-      "Requirement already satisfied: lxml>=4.9.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (6.0.2)\n",
-      "Requirement already satisfied: pypdf2>=2.10.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.0.1)\n",
-      "Requirement already satisfied: python-docx>=0.8.11 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.1.2)\n",
-      "Requirement already satisfied: openpyxl>=3.0.10 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.1.5)\n",
-      "Requirement already satisfied: pillow>=9.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (10.4.0)\n",
-      "Requirement already satisfied: librosa>=0.9.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (0.11.0)\n",
-      "Requirement already satisfied: opencv-python>=4.6.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.9.0.80)\n",
-      "Requirement already satisfied: faiss-cpu>=1.7.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.9.0)\n",
-      "Requirement already satisfied: weaviate-client>=3.15.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.18.1)\n",
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-      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jinja2->spacy>=3.4.0->semantica) (2.1.3)\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install semantica\n"
    ]
@@ -276,17 +62,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detected Type: txt\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -734,4 +512,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

cookbook/introduction/03_Document_Parsing.ipynb

@@ -260,10 +260,15 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "name": "python"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

cookbook/introduction/04_Data_Normalization.ipynb

@@ -204,8 +204,8 @@
     "\n",
     "text_samples = [\n",
     "    \"Hello, this is English text.\",\n",
-    "    \"Bonjour, ceci est du texte fran\u00e7ais.\",\n",
-    "    \"Hola, este es texto en espa\u00f1ol.\"\n",
+    "    \"Bonjour, ceci est du texte français.\",\n",
+    "    \"Hola, este es texto en español.\"\n",
     "]\n",
     "\n",
     "for text in text_samples:\n",
@@ -238,10 +238,15 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "name": "python"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

cookbook/introduction/05_Entity_Extraction.ipynb

@@ -54,223 +54,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install semantica\n"
    ]
@@ -294,45 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>NERExtractor</td><td>-</td><td>1.08s</td></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>NamedEntityRecognizer</td><td>-</td><td>2.50s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Extracted 13 entities:\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      " 1. Apple Inc.                     | Type: ORG          | Confidence: 1.00\n",
-      " 2. Steve Jobs                     | Type: PERSON       | Confidence: 1.00\n",
-      " 3. Steve Wozniak                  | Type: PERSON       | Confidence: 1.00\n",
-      " 4. Ronald Wayne                   | Type: PERSON       | Confidence: 1.00\n",
-      " 5. Cupertino                      | Type: GPE          | Confidence: 1.00\n",
-      " 6. California                     | Type: GPE          | Confidence: 1.00\n",
-      " 7. April 1, 1976                  | Type: DATE         | Confidence: 1.00\n",
-      " 8. Tim Cook                       | Type: PERSON       | Confidence: 1.00\n",
-      " 9. Steve Jobs                     | Type: PERSON       | Confidence: 1.00\n",
-      "10. August 2011                    | Type: DATE         | Confidence: 1.00\n",
-      "11. Apple                          | Type: ORG          | Confidence: 1.00\n",
-      "12. One                            | Type: CARDINAL     | Confidence: 1.00\n",
-      "13. Cupertino                      | Type: GPE          | Confidence: 1.00\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import NERExtractor\n",
     "\n",
@@ -403,43 +153,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Entity Visualization:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "CARDINAL:\n",
-      "  • One\n",
-      "\n",
-      "DATE:\n",
-      "  • August 2011\n",
-      "  • April 1, 1976\n",
-      "\n",
-      "GPE:\n",
-      "  • California\n",
-      "  • Cupertino\n",
-      "\n",
-      "ORG:\n",
-      "  • Apple\n",
-      "  • Apple Inc.\n",
-      "\n",
-      "PERSON:\n",
-      "  • Steve Wozniak\n",
-      "  • Ronald Wayne\n",
-      "  • Steve Jobs\n",
-      "  • Tim Cook\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def highlight_entities(text, entities):\n",
     "    \"\"\"\n",
@@ -493,47 +209,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Comparing Extraction Methods:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      " Method: PATTERN\n",
-      "----------------------------------------\n",
-      "Found 4 entities:\n",
-      "  • Apple Inc (PERSON)\n",
-      "  • Steve Jobs (PERSON)\n",
-      "  • Apple Inc (ORG)\n",
-      "  • 1976 (DATE)\n",
-      "\n",
-      " Method: REGEX\n",
-      "----------------------------------------\n",
-      "Found 8 entities:\n",
-      "  • Apple Inc (PERSON)\n",
-      "  • was founded by Steve Jobs in Cupertino (PERSON)\n",
-      "  • California in (PERSON)\n",
-      "  • Apple Inc (ORG)\n",
-      "  • Apple Inc (GPE)\n",
-      "\n",
-      " Method: ML\n",
-      "----------------------------------------\n",
-      "Found 5 entities:\n",
-      "  • Apple Inc. (ORG)\n",
-      "  • Steve Jobs (PERSON)\n",
-      "  • Cupertino (GPE)\n",
-      "  • California (GPE)\n",
-      "  • 1976 (DATE)\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import NERExtractor\n",
     "\n",
@@ -583,49 +261,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Advanced Entity Recognition Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      " Text 1: Tim Cook is the CEO of Apple Inc., based in Cupertino....\n",
-      "   Found 3 entities:\n",
-      "     • Tim Cook                  | PERSON     | Confidence: 1.00\n",
-      "     • Apple Inc.                | ORG        | Confidence: 1.00\n",
-      "     • Cupertino                 | GPE        | Confidence: 1.00\n",
-      "\n",
-      " Text 2: Microsoft Corporation, founded by Bill Gates, is headquarter...\n",
-      "   Found 4 entities:\n",
-      "     • Microsoft Corporation     | ORG        | Confidence: 1.00\n",
-      "     • Bill Gates                | PERSON     | Confidence: 1.00\n",
-      "     • Redmond                   | GPE        | Confidence: 1.00\n",
-      "     • Washington                | GPE        | Confidence: 1.00\n",
-      "\n",
-      " Text 3: Amazon was founded by Jeff Bezos in Seattle in 1994....\n",
-      "   Found 4 entities:\n",
-      "     • Amazon                    | ORG        | Confidence: 1.00\n",
-      "     • Jeff Bezos                | PERSON     | Confidence: 1.00\n",
-      "     • Seattle                   | GPE        | Confidence: 1.00\n",
-      "     • 1994                      | DATE       | Confidence: 1.00\n",
-      "\n",
-      " Text 4: Google was started by Larry Page and Sergey Brin at Stanford...\n",
-      "   Found 4 entities:\n",
-      "     • Google                    | ORG        | Confidence: 1.00\n",
-      "     • Larry Page                | PERSON     | Confidence: 1.00\n",
-      "     • Sergey Brin               | PERSON     | Confidence: 1.00\n",
-      "     • Stanford University       | ORG        | Confidence: 1.00\n",
-      "\n",
-      " Total entities extracted: 15\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import NamedEntityRecognizer\n",
     "\n",
@@ -685,48 +323,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "️  Entity Classification Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "DATE (1 entities):\n",
-      "----------------------------------------\n",
-      "  • 1994\n",
-      "\n",
-      "GPE (4 entities):\n",
-      "----------------------------------------\n",
-      "  • Cupertino\n",
-      "  • Redmond\n",
-      "  • Seattle\n",
-      "  • Washington\n",
-      "\n",
-      "ORG (5 entities):\n",
-      "----------------------------------------\n",
-      "  • Amazon\n",
-      "  • Apple Inc.\n",
-      "  • Google\n",
-      "  • Microsoft Corporation\n",
-      "  • Stanford University\n",
-      "\n",
-      "PERSON (5 entities):\n",
-      "----------------------------------------\n",
-      "  • Bill Gates\n",
-      "  • Jeff Bezos\n",
-      "  • Larry Page\n",
-      "  • Sergey Brin\n",
-      "  • Tim Cook\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import EntityClassifier\n",
     "\n",
@@ -774,32 +373,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Entity Confidence Scoring:\n",
-      "\n",
-      "================================================================================\n",
-      " High Confidence (≥0.8): 15 entities\n",
-      "️  Medium Confidence (0.5-0.8): 0 entities\n",
-      " Low Confidence (<0.5): 0 entities\n",
-      "\n",
-      " Confidence Distribution:\n",
-      "----------------------------------------\n",
-      "\n",
-      "High Confidence Examples:\n",
-      "  • Tim Cook (PERSON) - 1.00\n",
-      "  • Apple Inc. (ORG) - 1.00\n",
-      "  • Cupertino (GPE) - 1.00\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import EntityConfidenceScorer\n",
     "\n",
@@ -865,34 +441,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Custom Entity Detection:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "EMAIL:\n",
-      "  • support@apple.com\n",
-      "  • tech@apple.com\n",
-      "\n",
-      "PHONE:\n",
-      "  • 800-692-7753\n",
-      "\n",
-      "PRODUCT_CODE:\n",
-      "  • SKU-12345\n",
-      "\n",
-      "URL:\n",
-      "  • https://store.apple.com.\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import CustomEntityDetector\n",
     "import re\n",
@@ -948,55 +499,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Batch Processing Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      " Document 1:\n",
-      "   Text: Apple Inc. released the iPhone 15 in September 202...\n",
-      "   Entities: 2\n",
-      "\n",
-      " Document 2:\n",
-      "   Text: Microsoft announced Azure AI updates at Build 2023...\n",
-      "   Entities: 3\n",
-      "\n",
-      " Document 3:\n",
-      "   Text: Google's Sundar Pichai spoke at I/O 2023 in Mounta...\n",
-      "   Entities: 5\n",
-      "\n",
-      " Document 4:\n",
-      "   Text: Tesla's Elon Musk unveiled the Cybertruck in Austi...\n",
-      "   Entities: 5\n",
-      "\n",
-      " Document 5:\n",
-      "   Text: Amazon Web Services launched new features in North...\n",
-      "   Entities: 2\n",
-      "\n",
-      " Batch Processing Summary:\n",
-      "----------------------------------------\n",
-      "Documents processed: 5\n",
-      "Total entities: 17\n",
-      "Average per document: 3.4\n",
-      "\n",
-      " Entity Type Distribution:\n",
-      "  ORG: 5\n",
-      "  GPE: 5\n",
-      "  DATE: 3\n",
-      "  PERSON: 2\n",
-      "  PRODUCT: 1\n",
-      "  LOC: 1\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Sample document collection\n",
     "documents = [\n",

cookbook/introduction/06_Relation_Extraction.ipynb

@@ -23,7 +23,7 @@
     "-  Configure extraction parameters for optimal results\n",
     "-  Extract RDF triplets with `TripletExtractor`\n",
     "-  Serialize triplets to RDF formats with `RDFSerializer`\n",
-    "-  Build complete entity \u2192 relation \u2192 triplet pipelines\n",
+    "-  Build complete entity → relation → triplet pipelines\n",
     "\n",
     "###  What You'll Learn\n",
     "\n",
@@ -164,7 +164,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## \ufe0f Step 2: Different Extraction Methods\n",
+    "## ️ Step 2: Different Extraction Methods\n",
     "\n",
     "Semantica supports multiple relation extraction methods:\n",
     "\n",
@@ -172,11 +172,11 @@
     "\n",
     "| Method | Speed | Accuracy | Use Case | Requires |\n",
     "|--------|-------|----------|----------|----------|\n",
-    "| **pattern** |  | \u2b50\u2b50\u2b50 | Common relations | Nothing |\n",
-    "| **dependency** |  | \u2b50\u2b50\u2b50\u2b50 | Grammatical relations | spaCy |\n",
-    "| **cooccurrence** |  | \u2b50\u2b50 | Proximity-based | Nothing |\n",
-    "| **huggingface** |  | \u2b50\u2b50\u2b50\u2b50\u2b50 | Domain-specific | HF model |\n",
-    "| **llm** |  | \u2b50\u2b50\u2b50\u2b50\u2b50 | Complex, custom | API key |"
+    "| **pattern** |  | ⭐⭐⭐ | Common relations | Nothing |\n",
+    "| **dependency** |  | ⭐⭐⭐⭐ | Grammatical relations | spaCy |\n",
+    "| **cooccurrence** |  | ⭐⭐ | Proximity-based | Nothing |\n",
+    "| **huggingface** |  | ⭐⭐⭐⭐⭐ | Domain-specific | HF model |\n",
+    "| **llm** |  | ⭐⭐⭐⭐⭐ | Complex, custom | API key |"
    ]
   },
   {
@@ -214,10 +214,10 @@
     "            source_text = source.get('text', str(source)) if isinstance(source, dict) else getattr(source, 'text', str(source))\n",
     "            target_text = target.get('text', str(target)) if isinstance(target, dict) else getattr(target, 'text', str(target))\n",
     "            \n",
-    "            print(f\"  \u2022 {source_text} --[{rel_type}]--> {target_text}\")\n",
+    "            print(f\"  • {source_text} --[{rel_type}]--> {target_text}\")\n",
     "            \n",
     "    except Exception as e:\n",
-    "        print(f\"  \ufe0f  Method '{method_name}' not available: {str(e)[:50]}\")\n",
+    "        print(f\"  ️  Method '{method_name}' not available: {str(e)[:50]}\")\n",
     "\n",
     "print(\"\\n\" + \"=\" * 80)"
    ]
@@ -279,7 +279,7 @@
     "        source_text = source.get('text', str(source)) if isinstance(source, dict) else getattr(source, 'text', str(source))\n",
     "        target_text = target.get('text', str(target)) if isinstance(target, dict) else getattr(target, 'text', str(target))\n",
     "        \n",
-    "        print(f\"     \u2022 {source_text} --[{rel_type}]--> {target_text} (conf: {confidence:.2f})\")\n",
+    "        print(f\"     • {source_text} --[{rel_type}]--> {target_text} (conf: {confidence:.2f})\")\n",
     "\n",
     "print(\"\\n\" + \"=\" * 80)"
    ]
@@ -288,7 +288,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## \ufe0f Step 4: Relation Classification\n",
+    "## ️ Step 4: Relation Classification\n",
     "\n",
     "Group and classify extracted relations by their predicate type."
    ]
@@ -309,7 +309,7 @@
     "# Classify relations\n",
     "classified_relations = advanced_extractor.classify_relations(all_relations)\n",
     "\n",
-    "print(\"\ufe0f  Relation Classification:\\n\")\n",
+    "print(\"️  Relation Classification:\\n\")\n",
     "print(\"=\" * 80)\n",
     "\n",
     "for rel_type, rel_list in sorted(classified_relations.items()):\n",
@@ -323,7 +323,7 @@
     "        source_text = source.get('text', str(source)) if isinstance(source, dict) else getattr(source, 'text', str(source))\n",
     "        target_text = target.get('text', str(target)) if isinstance(target, dict) else getattr(target, 'text', str(target))\n",
     "        \n",
-    "        print(f\"  \u2022 {source_text} \u2192 {target_text}\")\n",
+    "        print(f\"  • {source_text} → {target_text}\")\n",
     "\n",
     "print(\"\\n\" + \"=\" * 80)"
    ]
@@ -455,7 +455,7 @@
    "source": [
     "##  Step 7: Complete Extraction Pipeline\n",
     "\n",
-    "Let's build a complete pipeline: **Entities \u2192 Relations \u2192 Triplets**\n",
+    "Let's build a complete pipeline: **Entities → Relations → Triplets**\n",
     "\n",
     "This demonstrates the full workflow for knowledge graph construction."
    ]
@@ -534,7 +534,7 @@
     "3. **Try co-occurrence** for exploratory analysis\n",
     "4. **Consider LLM** for complex, domain-specific relations\n",
     "\n",
-    "### \ufe0f Optimizing Extraction\n",
+    "### ️ Optimizing Extraction\n",
     "\n",
     "- **Set confidence thresholds** (0.6-0.7 for production)\n",
     "- **Specify relation_types** to focus extraction\n",
@@ -585,7 +585,7 @@
     "2. **Multiple methods available**: Choose based on accuracy vs speed needs\n",
     "3. **Configuration is powerful**: Tune parameters for your domain\n",
     "4. **Triplets are standardized**: Use RDF for interoperability\n",
-    "5. **Pipelines are efficient**: Extract entities \u2192 relations \u2192 triplets in sequence\n",
+    "5. **Pipelines are efficient**: Extract entities → relations → triplets in sequence\n",
     "\n",
     "### Next Steps\n",
     "\n",
@@ -624,4 +624,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

cookbook/introduction/07_Building_Knowledge_Graphs.ipynb

@@ -39,230 +39,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install semantica\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,18 +99,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original entities: 5\n",
-      "Resolved entities: 4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.kg import EntityResolver\n",
     "\n",
@@ -347,18 +124,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original entities: 4\n",
-      "Deduplicated entities: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.deduplication import DuplicateDetector, EntityMerger, MergeStrategy\n",
     "\n",

cookbook/introduction/08_Your_First_Knowledge_Graph.ipynb

@@ -80,262 +80,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-     "output_type": "stream",
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-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
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-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install semantica"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "File: sample_document.txt\n",
-      "Content length: 281 characters\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is ingesting</td><td>📥 ingest</td><td>FileIngestor</td><td>sample_document.txt</td><td>0.02s</td></tr><tr><td>✅</td><td>Semantica is parsing</td><td>🔍 parse</td><td>DocumentParser</td><td>sample_document.txt</td><td>0.02s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  File name: sample_document.txt\n",
-      "  File type: txt\n",
-      "  Content available: True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.ingest import FileIngestor\n",
     "from pathlib import Path\n",
@@ -375,18 +131,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Parsed content length: 0 characters\n",
-      "  Preview: N/A...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.parse import DocumentParser\n",
     "\n",
@@ -412,25 +159,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Text: ...\n",
-      "  - Apple Inc. (Organization)\n",
-      "  - Steve Jobs (Person)\n",
-      "  - Steve Wozniak (Person)\n",
-      "  - Ronald Wayne (Person)\n",
-      "  - 1976 (Date)\n",
-      "  - Cupertino, California (Location)\n",
-      "  - Tim Cook (Person)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import NamedEntityRecognizer, NERExtractor\n",
     "\n",
@@ -465,31 +196,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Nodes (entities): 7\n",
-      "  Edges (relationships): 6\n",
-      "  Node: Apple Inc. (Organization)\n",
-      "  Node: Steve Jobs (Person)\n",
-      "  Node: Steve Wozniak (Person)\n",
-      "  Node: Ronald Wayne (Person)\n",
-      "  Node: 1976 (Date)\n",
-      "  Node: Cupertino, California (Location)\n",
-      "  Node: Tim Cook (Person)\n",
-      "  Apple Inc. --[founded_by]--> Steve Jobs\n",
-      "  Apple Inc. --[founded_by]--> Steve Wozniak\n",
-      "  Apple Inc. --[founded_by]--> Ronald Wayne\n",
-      "  Apple Inc. --[founded_in]--> 1976\n",
-      "  Apple Inc. --[located_in]--> Cupertino, California\n",
-      "  Tim Cook --[ceo_of]--> Apple Inc.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.kg import GraphBuilder\n",
     "import networkx as nx\n",
@@ -546,26 +255,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Total entities: 7\n",
-      "  Total relationships: 6\n",
-      "  - Organization: 1\n",
-      "  - Person: 4\n",
-      "  - Date: 1\n",
-      "  - Location: 1\n",
-      "  - founded_by: 3\n",
-      "  - founded_in: 1\n",
-      "  - located_in: 1\n",
-      "  - ceo_of: 1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.visualization import KGVisualizer\n",
     "\n",

cookbook/introduction/09_Graph_Store.ipynb

@@ -82,223 +82,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "Requirement already satisfied: authlib<2.0.0,>=1.2.1 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from weaviate-client>=3.15.0->semantica) (1.6.0)\n",
-      "Requirement already satisfied: deprecation<3.0.0,>=2.1.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from weaviate-client>=3.15.0->semantica) (2.1.0)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jinja2->spacy>=3.4.0->semantica) (2.1.3)\n",
-      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from sympy->torch>=1.12.0->semantica) (1.3.0)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install semantica\n"
    ]
@@ -316,15 +102,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connected to graph database successfully!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.graph_store import GraphStore\n",
     "\n",
@@ -357,64 +135,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is processing</td><td>⏳ graph_store</td><td>Neo4jStore</td><td>-</td><td>0.14s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=34, offset=33>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 33, 'line': 1, 'column': 34}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Company $props) RETURN id(n) as id, n'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created company node: Apple Inc. (ID: 0)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=33, offset=32>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 32, 'line': 1, 'column': 33}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Person $props) RETURN id(n) as id, n'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created person node: Tim Cook (ID: 1)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=35, offset=34>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 34, 'line': 1, 'column': 35}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Location $props) RETURN id(n) as id, n'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created location node: Cupertino (ID: 2)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create individual nodes with labels and properties\n",
     "apple = store.create_node(\n",
@@ -438,26 +161,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=34, offset=33>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 33, 'line': 1, 'column': 34}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Company $props) RETURN id(n) as id, n'\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=34, offset=33>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 33, 'line': 1, 'column': 34}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Company $props) RETURN id(n) as id, n'\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=34, offset=33>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 33, 'line': 1, 'column': 34}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Company $props) RETURN id(n) as id, n'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created 3 company nodes in batch\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create multiple nodes in batch (more efficient for large datasets)\n",
     "other_companies = store.create_nodes([\n",
@@ -484,42 +190,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=23, offset=54>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 54, 'line': 3, 'column': 23}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:CEO_OF $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=45, offset=76>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 76, 'line': 3, 'column': 45}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:CEO_OF $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=5, column=24, offset=165>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 165, 'line': 5, 'column': 24}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:CEO_OF $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created relationship: CEO_OF (ID: 1152921504606846977)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=23, offset=54>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 54, 'line': 3, 'column': 23}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:HEADQUARTERED_IN $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=45, offset=76>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 76, 'line': 3, 'column': 45}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:HEADQUARTERED_IN $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=5, column=24, offset=175>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 175, 'line': 5, 'column': 24}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:HEADQUARTERED_IN $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created relationship: HEADQUARTERED_IN (ID: 1152922604118474752)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create relationships between nodes\n",
     "ceo_rel = store.create_relationship(\n",
@@ -552,28 +225,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=26, offset=25>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 25, 'line': 1, 'column': 26}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'MATCH (n:Company) RETURN id(n) as id, n, labels(n) as labels LIMIT 10'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 4 companies:\n",
-      "  - Apple Inc. (founded: 1976)\n",
-      "  - Microsoft (founded: 1975)\n",
-      "  - Google (founded: 1998)\n",
-      "  - Amazon (founded: 1994)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get nodes by label\n",
     "companies = store.get_nodes(labels=[\"Company\"], limit=10)\n",
@@ -591,35 +245,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=31, offset=73>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 73, 'line': 3, 'column': 31}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r]-(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(startNode(r)) as start_id, id(endNode(r)) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=32, offset=121>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 121, 'line': 4, 'column': 32}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r]-(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(startNode(r)) as start_id, id(endNode(r)) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=62, offset=151>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 151, 'line': 4, 'column': 62}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r]-(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(startNode(r)) as start_id, id(endNode(r)) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=92, offset=181>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 181, 'line': 4, 'column': 92}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r]-(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(startNode(r)) as start_id, id(endNode(r)) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=31, offset=81>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 81, 'line': 3, 'column': 31}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r:CEO_OF]->(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(a) as start_id, id(b) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=32, offset=129>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 129, 'line': 4, 'column': 32}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r:CEO_OF]->(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(a) as start_id, id(b) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=62, offset=159>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 159, 'line': 4, 'column': 62}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r:CEO_OF]->(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(a) as start_id, id(b) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=81, offset=178>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 178, 'line': 4, 'column': 81}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r:CEO_OF]->(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(a) as start_id, id(b) as end_id, r\\n                        LIMIT 100\\n                    '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 2 relationships for Apple:\n",
-      "  - HEADQUARTERED_IN: {'since': 1977}\n",
-      "  - CEO_OF: {'since': 2011}\n",
-      "\n",
-      "Outgoing CEO_OF relationships: 1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get relationships for a node\n",
     "relationships = store.get_relationships(node_id=apple[\"id\"], direction=\"both\")\n",
@@ -652,18 +280,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CEO Relationships:\n",
-      "  - Tim Cook is CEO of Apple Inc. since 2011\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Execute a Cypher query to find CEO relationships\n",
     "results = store.execute_query(\"\"\"\n",
@@ -681,19 +300,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Companies founded after 1990:\n",
-      "  - Amazon (founded: 1994)\n",
-      "  - Google (founded: 1998)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Parameterized query (safer and more efficient)\n",
     "results = store.execute_query(\n",
@@ -721,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -742,28 +351,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=23, offset=88>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 88, 'line': 3, 'column': 23}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH path = shortestPath((start)-[r*..5]-(end))\\n                WHERE id(start) = $start_id AND id(end) = $end_id\\n                RETURN path, length(path) as length\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=49, offset=114>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 114, 'line': 3, 'column': 49}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH path = shortestPath((start)-[r*..5]-(end))\\n                WHERE id(start) = $start_id AND id(end) = $end_id\\n                RETURN path, length(path) as length\\n            '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shortest path found:\n",
-      "  - Path length: 2\n",
-      "  - Nodes in path: 3\n",
-      "  - Relationships: 2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Find shortest path between two nodes\n",
     "if tim_cook.get('id') and cupertino.get('id'):\n",
@@ -795,27 +385,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=17, offset=16>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 16, 'line': 1, 'column': 17}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'MATCH (n) WHERE id(n) = $id SET n += $props RETURN id(n) as id, n, labels(n) as labels'\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=52, offset=51>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 51, 'line': 1, 'column': 52}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'MATCH (n) WHERE id(n) = $id SET n += $props RETURN id(n) as id, n, labels(n) as labels'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Updated node: Tim Cook\n",
-      "  New age: 64\n",
-      "  New title: CEO & President\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Update node properties (merge mode - adds/updates properties)\n",
     "if tim_cook.get('id'):\n",
@@ -849,27 +421,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=24, offset=23>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 23, 'line': 1, 'column': 24}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'MATCH ()-[r]->() WHERE id(r) = $id DELETE r'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Deleted relationship (ID: 1152922604118474752)\n",
-      "\n",
-      "Tip: Use detach=True to delete a node and all its relationships\n",
-      "     Use detach=False to only delete the node (fails if relationships exist)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Delete a relationship\n",
     "if location_rel.get('id'):\n",
@@ -900,27 +454,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Graph Statistics:\n",
-      "  Total nodes: 6\n",
-      "  Total relationships: 1\n",
-      "\n",
-      "Node labels:\n",
-      "  - Company: 4 nodes\n",
-      "  - Person: 1 nodes\n",
-      "  - Location: 1 nodes\n",
-      "\n",
-      "Relationship types:\n",
-      "  - CEO_OF: 1 relationships\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get comprehensive graph statistics\n",
     "stats = store.get_stats()\n",
@@ -947,23 +483,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Convenience functions available:\n",
-      "  - create_node, create_nodes\n",
-      "  - create_relationship, create_relationships\n",
-      "  - get_nodes, get_relationships\n",
-      "  - update_node, delete_node\n",
-      "  - execute_query, shortest_path, get_neighbors\n",
-      "  - run_analytics\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using convenience functions (alternative to class methods)\n",
     "from semantica.graph_store import (\n",
@@ -1003,17 +525,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created index on Company.name for faster queries\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create an index on a node property for faster lookups\n",
     "# This is especially useful for frequently queried properties\n",
@@ -1045,17 +559,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connection closed successfully\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Close the connection\n",
     "store.close()\n",

cookbook/introduction/10_Graph_Analytics.ipynb

@@ -41,255 +41,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: semantica in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.0.5)\n",
-      "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.26.4)\n",
-      "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.1.4)\n",
-      "Requirement already satisfied: scikit-learn>=1.0.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.6.1)\n",
-      "Requirement already satisfied: spacy>=3.4.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.8.11)\n",
-      "Requirement already satisfied: transformers>=4.20.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.53.2)\n",
-      "Requirement already satisfied: torch>=1.12.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.2.1)\n",
-      "Requirement already satisfied: sentence-transformers>=2.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.2.1)\n",
-      "Requirement already satisfied: rdflib>=6.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (7.4.0)\n",
-      "Requirement already satisfied: networkx>=2.8.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.5)\n",
-      "Requirement already satisfied: matplotlib>=3.5.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.10.1)\n",
-      "Requirement already satisfied: seaborn>=0.11.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (0.13.2)\n",
-      "Requirement already satisfied: plotly>=5.10.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (5.17.0)\n",
-      "Requirement already satisfied: requests>=2.28.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.32.5)\n",
-      "Requirement already satisfied: beautifulsoup4>=4.11.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.12.3)\n",
-      "Requirement already satisfied: lxml>=4.9.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (6.0.2)\n",
-      "Requirement already satisfied: pypdf2>=2.10.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.0.1)\n",
-      "Requirement already satisfied: python-docx>=0.8.11 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.1.2)\n",
-      "Requirement already satisfied: openpyxl>=3.0.10 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.1.5)\n",
-      "Requirement already satisfied: pillow>=9.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (10.4.0)\n",
-      "Requirement already satisfied: librosa>=0.9.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (0.11.0)\n",
-      "Requirement already satisfied: opencv-python>=4.6.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.9.0.80)\n",
-      "Requirement already satisfied: faiss-cpu>=1.7.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.9.0)\n",
-      "Requirement already satisfied: weaviate-client>=3.15.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.18.1)\n",
-      "Requirement already satisfied: qdrant-client>=1.3.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.12.2)\n",
-      "Requirement already satisfied: neo4j>=5.0.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (6.0.3)\n",
-      "Requirement already satisfied: falkordb>=1.0.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.2.2)\n",
-      "Requirement already satisfied: pymongo>=4.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.15.4)\n",
-      "Requirement already satisfied: sqlalchemy>=1.4.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.0.23)\n",
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-      "Requirement already satisfied: pymysql>=1.0.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.1.2)\n",
-      "Requirement already satisfied: redis>=4.3.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (6.4.0)\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install semantica"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is building</td><td>🧠 kg</td><td>GraphBuilder</td><td>-</td><td>0.08s</td></tr><tr><td>🔄</td><td>Semantica is building</td><td>🧠 kg</td><td>EntityResolver</td><td>-</td><td>131.06s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>DuplicateDetector</td><td>-</td><td>0.04s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>SimilarityCalculator</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is resolving</td><td>⚠️ conflicts</td><td>ConflictDetector</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is building</td><td>🧠 kg</td><td>CentralityCalculator</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is building</td><td>🧠 kg</td><td>CommunityDetector</td><td>-</td><td>0.01s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Graph metrics:\n",
-      "  Entities: 3\n",
-      "  Relationships: 2\n",
-      "  Density: 0.667\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.kg import GraphBuilder, GraphAnalyzer\n",
     "from semantica.semantic_extract import NERExtractor, RelationExtractor\n",
@@ -329,20 +92,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Centrality scores:\n",
-      "  e2: 0.500\n",
-      "  e1: 1.000\n",
-      "  e3: 0.500\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.kg import CentralityCalculator\n",
     "\n",
@@ -367,18 +119,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detected 1 communities\n",
-      "  Community 1: 3 entities\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.kg import CommunityDetector\n",
     "\n",

cookbook/introduction/11_Chunking_and_Splitting.ipynb

@@ -54,25 +54,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install -q semantica\n"
    ]
@@ -96,72 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Split into 8 chunks using recursive method\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Chunk 1:\n",
-      "  Length: 181 characters\n",
-      "  Start: 0, End: 184\n",
-      "  Text: Apple Inc. is a technology company founded by Steve Jobs, Steve Wozniak, and Ronald Wayne \n",
-      "in Cupert...\n",
-      "\n",
-      "Chunk 2:\n",
-      "  Length: 144 characters\n",
-      "  Start: 134, End: 281\n",
-      "  Text: The company's current CEO is Tim Cook, who took \n",
-      "over from Steve Jobs in August 2011. Apple is headq...\n",
-      "\n",
-      "Chunk 3:\n",
-      "  Length: 150 characters\n",
-      "  Start: 231, End: 383\n",
-      "  Text: eadquartered at One Apple Park Way in Cupertino.\n",
-      "\n",
-      "Apple develops and sells consumer electronics, com...\n",
-      "\n",
-      "Chunk 4:\n",
-      "  Length: 151 characters\n",
-      "  Start: 333, End: 486\n",
-      "  Text: ter software, and online services. The company's \n",
-      "hardware products include the iPhone smartphone, t...\n",
-      "\n",
-      "Chunk 5:\n",
-      "  Length: 176 characters\n",
-      "  Start: 436, End: 614\n",
-      "  Text: iPad tablet computer, the Mac personal computer, \n",
-      "the iPod portable media player, the Apple Watch sm...\n",
-      "\n",
-      "Chunk 6:\n",
-      "  Length: 152 characters\n",
-      "  Start: 564, End: 718\n",
-      "  Text: al media player, and the \n",
-      "HomePod smart speaker.\n",
-      "\n",
-      "Apple's software includes the macOS and iOS operat...\n",
-      "\n",
-      "Chunk 7:\n",
-      "  Length: 150 characters\n",
-      "  Start: 668, End: 820\n",
-      "  Text: systems, the iTunes media player, the Safari web \n",
-      "browser, and the iLife and iWork creativity and pr...\n",
-      "\n",
-      "Chunk 8:\n",
-      "  Length: 126 characters\n",
-      "  Start: 770, End: 896\n",
-      "  Text: uctivity suites. Its online services include the \n",
-      "iTunes Store, the iOS App Store and Mac App Store,...\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import TextSplitter\n",
     "\n",
@@ -222,39 +143,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Comparing Standard Splitting Methods:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Method: RECURSIVE\n",
-      "----------------------------------------\n",
-      "  Chunks created: 8\n",
-      "  Avg chunk size: 154 chars\n",
-      "  First chunk: Apple Inc. is a technology company founded by Steve Jobs, Steve Wozniak, and Ron...\n",
-      "\n",
-      "Method: SENTENCE\n",
-      "----------------------------------------\n",
-      "  Chunks created: 6\n",
-      "  Avg chunk size: 148 chars\n",
-      "  First chunk: Apple Inc. is a technology company founded by Steve Jobs, Steve Wozniak, and Ron...\n",
-      "\n",
-      "Method: PARAGRAPH\n",
-      "----------------------------------------\n",
-      "  Chunks created: 3\n",
-      "  Avg chunk size: 297 chars\n",
-      "  First chunk: Apple Inc. is a technology company founded by Steve Jobs, Steve Wozniak, and Ron...\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Compare different methods\n",
     "methods = [\"recursive\", \"sentence\", \"paragraph\"]\n",
@@ -297,136 +188,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Token-Based Splitting Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Chunk 1:\n",
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-      "  Ratio: 4.61 chars/token\n",
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-      "  Ratio: 4.00 chars/token\n",
-      "\n",
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-      "  Ratio: 4.11 chars/token\n",
-      "\n",
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-      "  Ratio: 4.12 chars/token\n",
-      "\n",
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-      "  Ratio: 4.00 chars/token\n",
-      "\n",
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-      "  Ratio: 4.20 chars/token\n",
-      "\n",
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-      "\n",
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-      "\n",
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-      "\n",
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-      "\n",
-      "Chunk 14:\n",
-      "  Tokens: 10\n",
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-      "  Ratio: 4.10 chars/token\n",
-      "\n",
-      "Chunk 15:\n",
-      "  Tokens: 9\n",
-      "  Characters: 37\n",
-      "  Ratio: 4.11 chars/token\n",
-      "\n",
-      "Chunk 16:\n",
-      "  Tokens: 8\n",
-      "  Characters: 33\n",
-      "  Ratio: 4.12 chars/token\n",
-      "\n",
-      "Chunk 17:\n",
-      "  Tokens: 7\n",
-      "  Characters: 27\n",
-      "  Ratio: 3.86 chars/token\n",
-      "\n",
-      "Chunk 18:\n",
-      "  Tokens: 6\n",
-      "  Characters: 26\n",
-      "  Ratio: 4.33 chars/token\n",
-      "\n",
-      "Chunk 19:\n",
-      "  Tokens: 5\n",
-      "  Characters: 20\n",
-      "  Ratio: 4.00 chars/token\n",
-      "\n",
-      "Chunk 20:\n",
-      "  Tokens: 4\n",
-      "  Characters: 14\n",
-      "  Ratio: 3.50 chars/token\n",
-      "\n",
-      "Chunk 21:\n",
-      "  Tokens: 3\n",
-      "  Characters: 13\n",
-      "  Ratio: 4.33 chars/token\n",
-      "\n",
-      "Chunk 22:\n",
-      "  Tokens: 2\n",
-      "  Characters: 9\n",
-      "  Ratio: 4.50 chars/token\n",
-      "\n",
-      "Chunk 23:\n",
-      "  Tokens: 1\n",
-      "  Characters: 2\n",
-      "  Ratio: 2.00 chars/token\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import split_by_tokens\n",
     "\n",
@@ -470,69 +234,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is splitting</td><td>✂️ split</td><td>SemanticChunker</td><td>-</td><td>0.13s</td></tr><tr><td>✅</td><td>Semantica is splitting</td><td>✂️ split</td><td>EntityAwareChunker</td><td>-</td><td>0.98s</td></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>NERExtractor</td><td>-</td><td>0.47s</td></tr><tr><td>✅</td><td>Semantica is splitting</td><td>✂️ split</td><td>RelationAwareChunker</td><td>-</td><td>1.39s</td></tr><tr><td>✅</td><td>Semantica is extracting</td><td>🎯 semantic_extract</td><td>RelationExtractor</td><td>-</td><td>0.45s</td></tr><tr><td>✅</td><td>Semantica is splitting</td><td>✂️ split</td><td>StructuralChunker</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is splitting</td><td>✂️ split</td><td>HierarchicalChunker</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is splitting</td><td>✂️ split</td><td>SlidingWindowChunker</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is splitting</td><td>✂️ split</td><td>TableChunker</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is splitting</td><td>✂️ split</td><td>ChunkValidator</td><td>-</td><td>0.01s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Semantic Chunking Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Chunk 1:\n",
-      "  Length: 133 chars\n",
-      "  Coherence: N/A\n",
-      "  Text: Apple Inc. is a technology company founded by Steve Jobs, Steve Wozniak, and Ronald Wayne \n",
-      "in Cupert...\n",
-      "\n",
-      "Chunk 2:\n",
-      "  Length: 194 chars\n",
-      "  Coherence: N/A\n",
-      "  Text: Wayne \n",
-      "in Cupertino, California on April 1, 1976. The company's current CEO is Tim Cook, who took \n",
-      "o...\n",
-      "\n",
-      "Chunk 3:\n",
-      "  Length: 136 chars\n",
-      "  Coherence: N/A\n",
-      "  Text: headquartered at One Apple Park Way in Cupertino. Apple develops and sells consumer electronics, com...\n",
-      "\n",
-      "Chunk 4:\n",
-      "  Length: 295 chars\n",
-      "  Coherence: N/A\n",
-      "  Text: ectronics, computer software, and online services. The company's \n",
-      "hardware products include the iPho...\n",
-      "\n",
-      "Chunk 5:\n",
-      "  Length: 223 chars\n",
-      "  Coherence: N/A\n",
-      "  Text: ital media player, and the \n",
-      "HomePod smart speaker. Apple's software includes the macOS and iOS opera...\n",
-      "\n",
-      "Chunk 6:\n",
-      "  Length: 159 chars\n",
-      "  Coherence: N/A\n",
-      "  Text: Life and iWork creativity and productivity suites. Its online services include the \n",
-      "iTunes Store, th...\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import SemanticChunker\n",
     "\n",
@@ -576,26 +280,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Entity-Aware Chunking Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Chunk 1:\n",
-      "  Length: 892 chars\n",
-      "  Entities: 28\n",
-      "  Sample entities: [\"Entity(text='Apple Inc.', label='ORG', start_char=1, end_char=11, confidence=1.0, metadata={'extraction_method': 'ml', 'model': 'en_core_web_sm', 'lemma': 'Apple Inc.'})\", \"Entity(text='Steve Jobs', label='PERSON', start_char=47, end_char=57, confidence=1.0, metadata={'extraction_method': 'ml', 'model': 'en_core_web_sm', 'lemma': 'Steve Jobs'})\", \"Entity(text='Steve Wozniak', label='PERSON', start_char=59, end_char=72, confidence=1.0, metadata={'extraction_method': 'ml', 'model': 'en_core_web_sm', 'lemma': 'Steve Wozniak'})\"]\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import EntityAwareChunker\n",
     "\n",
@@ -643,51 +330,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Relation-Aware Chunking Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Chunk 1:\n",
-      "  Length: 133 chars\n",
-      "  Triplets: 2\n",
-      "  Relationships: 2\n",
-      "\n",
-      "Chunk 2:\n",
-      "  Length: 144 chars\n",
-      "  Triplets: 0\n",
-      "  Relationships: 0\n",
-      "\n",
-      "Chunk 3:\n",
-      "  Length: 86 chars\n",
-      "  Triplets: 0\n",
-      "  Relationships: 0\n",
-      "\n",
-      "Chunk 4:\n",
-      "  Length: 244 chars\n",
-      "  Triplets: 0\n",
-      "  Relationships: 0\n",
-      "\n",
-      "Chunk 5:\n",
-      "  Length: 172 chars\n",
-      "  Triplets: 0\n",
-      "  Relationships: 0\n",
-      "\n",
-      "Chunk 6:\n",
-      "  Length: 108 chars\n",
-      "  Triplets: 0\n",
-      "  Relationships: 0\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import RelationAwareChunker\n",
     "\n",
@@ -732,50 +377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Structural Chunking Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Chunk 1:\n",
-      "  Section: N/A\n",
-      "  Level: N/A\n",
-      "  Text: # Apple Inc....\n",
-      "\n",
-      "Chunk 2:\n",
-      "  Section: N/A\n",
-      "  Level: N/A\n",
-      "  Text: ## History\n",
-      "\n",
-      "Apple Inc. was founded by Steve Jobs, Steve Wozniak, and Ronald Wayn...\n",
-      "\n",
-      "Chunk 3:\n",
-      "  Section: N/A\n",
-      "  Level: N/A\n",
-      "  Text: ## Products\n",
-      "\n",
-      "### Hardware\n",
-      "\n",
-      "- iPhone\n",
-      "- iPad\n",
-      "- Mac\n",
-      "\n",
-      "### Software\n",
-      "\n",
-      "- macOS\n",
-      "- iOS\n",
-      "- ...\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import StructuralChunker\n",
     "\n",
@@ -842,39 +446,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hierarchical Chunking Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Chunk 1:\n",
-      "  Level: N/A\n",
-      "  Length: 278 chars\n",
-      "  Parent: None (root)\n",
-      "  Children: 0\n",
-      "\n",
-      "Chunk 2:\n",
-      "  Level: N/A\n",
-      "  Length: 331 chars\n",
-      "  Parent: None (root)\n",
-      "  Children: 0\n",
-      "\n",
-      "Chunk 3:\n",
-      "  Level: N/A\n",
-      "  Length: 281 chars\n",
-      "  Parent: None (root)\n",
-      "  Children: 0\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import HierarchicalChunker\n",
     "\n",
@@ -921,66 +495,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sliding Window Chunking Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Window 1:\n",
-      "  Position: 0-134\n",
-      "  Length: 133 chars\n",
-      "  Overlap with previous: 0 chars\n",
-      "\n",
-      "Window 2:\n",
-      "  Position: 100-220\n",
-      "  Length: 120 chars\n",
-      "  Overlap with previous: 34 chars\n",
-      "\n",
-      "Window 3:\n",
-      "  Position: 200-279\n",
-      "  Length: 79 chars\n",
-      "  Overlap with previous: 20 chars\n",
-      "\n",
-      "Window 4:\n",
-      "  Position: 300-383\n",
-      "  Length: 81 chars\n",
-      "  Overlap with previous: 0 chars\n",
-      "\n",
-      "Window 5:\n",
-      "  Position: 400-486\n",
-      "  Length: 83 chars\n",
-      "  Overlap with previous: 0 chars\n",
-      "\n",
-      "Window 6:\n",
-      "  Position: 500-612\n",
-      "  Length: 112 chars\n",
-      "  Overlap with previous: 0 chars\n",
-      "\n",
-      "Window 7:\n",
-      "  Position: 600-718\n",
-      "  Length: 116 chars\n",
-      "  Overlap with previous: 12 chars\n",
-      "\n",
-      "Window 8:\n",
-      "  Position: 700-786\n",
-      "  Length: 86 chars\n",
-      "  Overlap with previous: 18 chars\n",
-      "\n",
-      "Window 9:\n",
-      "  Position: 800-896\n",
-      "  Length: 95 chars\n",
-      "  Overlap with previous: 0 chars\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import SlidingWindowChunker\n",
     "\n",
@@ -1027,29 +544,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Table Chunking Results:\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Chunk 1:\n",
-      "  Type: Text\n",
-      "  Content: Product | Category | Release Year\n",
-      "--- | --- | ---\n",
-      "iPhone | Smartphone | 2007\n",
-      "iPad | Tablet | 2010\n",
-      "Ma...\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.split import TableChunker\n",
     "\n",
@@ -1114,29 +611,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chunk class fields: {'text': <class 'str'>, 'start_index': <class 'int'>, 'end_index': <class 'int'>, 'metadata': typing.Dict[str, typing.Any], 'id': typing.Optional[str]}\n",
-      "Provenance Tracking Results:\n",
-      "\n",
-      "================================================================================\n",
-      "Chunk ID: f0bddbe2-b717-47c7-8bdb-631a61660373\n",
-      "\n",
-      "Lineage for Chunk 1:\n",
-      "  Source Document: apple_doc_001\n",
-      "  File Path: data/apple.txt\n",
-      "  Method: recursive\n",
-      "  Timestamp: None\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import sys\n",
     "import os\n",
@@ -1228,26 +705,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Method Comparison:\n",
-      "\n",
-      "================================================================================\n",
-      "Method          Chunks     Avg Size     Time (ms)   \n",
-      "--------------------------------------------------------------------------------\n",
-      "recursive       8          154          0.00        \n",
-      "sentence        6          148          519.51      \n",
-      "paragraph       3          297          0.00        \n",
-      "token           51         129          0.00        \n",
-      "================================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import time\n",
     "\n",

cookbook/introduction/12_Embedding_Generation.ipynb

@@ -142,10 +142,15 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "name": "python"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

cookbook/introduction/13_Vector_Store.ipynb

@@ -55,25 +55,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install -q semantica\n"
    ]
@@ -97,30 +81,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is indexing</td><td>📊 vector_store</td><td>VectorStore</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is indexing</td><td>📊 vector_store</td><td>FAISSStore</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is indexing</td><td>📊 vector_store</td><td>HybridSearch</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is indexing</td><td>📊 vector_store</td><td>MetadataStore</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is indexing</td><td>📊 vector_store</td><td>NamespaceManager</td><td>-</td><td>0.00s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stored 100 vectors\n",
-      "First 3 IDs: ['vec_0', 'vec_2', 'vec_4']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import VectorStore\n",
     "from semantica.embeddings import TextEmbedder\n",
@@ -166,18 +129,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index search found 10 results\n",
-      "Distances: [0.         0.59663105 0.6359793  0.66132385 0.6909249 ]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import VectorIndexer, FAISSStore \n",
     "import numpy as np \n",
@@ -228,18 +182,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index search found 10 results\n",
-      "Distances: [0.         0.59663105 0.6359793  0.66132385 0.6909249 ]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import VectorIndexer, FAISSStore\n",
     "\n",
@@ -282,24 +227,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hybrid search found 10 results\n",
-      "\n",
-      "Filtered results (science, year > 2021):\n",
-      "1. Category: science, Year: 2020, Score: 1.000\n",
-      "2. Category: technology, Year: 2021, Score: 0.702\n",
-      "3. Category: science, Year: 2022, Score: 0.682\n",
-      "4. Category: technology, Year: 2023, Score: 0.669\n",
-      "5. Category: science, Year: 2022, Score: 0.655\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import HybridSearch, MetadataFilter \n",
     "import numpy as np\n",
@@ -351,19 +281,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 5 vectors with category='science'\n",
-      "\n",
-      "Metadata validation: True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import MetadataStore, MetadataSchema\n",
     "\n",
@@ -410,21 +330,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fused results using RRF:\n",
-      "1. ID: vec_2, Fused Score: 0.033\n",
-      "2. ID: vec_1, Fused Score: 0.032\n",
-      "3. ID: vec_4, Fused Score: 0.016\n",
-      "4. ID: vec_3, Fused Score: 0.016\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import SearchRanker\n",
     "\n",
@@ -469,21 +377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tenant 1: 5 vectors\n",
-      "Tenant 2: 5 vectors\n",
-      "\n",
-      "User1 can write: True\n",
-      "User2 can write: False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import NamespaceManager\n",
     "\n",

cookbook/introduction/15_Export.ipynb

@@ -76,245 +76,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install semantica"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>🔄</td><td>Semantica is building</td><td>🧠 kg</td><td>EntityResolver</td><td>-</td><td>762.30s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>DuplicateDetector</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is resolving</td><td>⚠️ conflicts</td><td>ConflictDetector</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is exporting</td><td>💾 export</td><td>JSONExporter</td><td>output.json</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is exporting</td><td>💾 export</td><td>RDFExporter</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is exporting</td><td>💾 export</td><td>GraphExporter</td><td>output.graphml</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is generating</td><td>📚 ontology</td><td>OntologyGenerator</td><td>-</td><td>0.04s</td></tr><tr><td>✅</td><td>Semantica is generating</td><td>📚 ontology</td><td>ClassInferrer</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is generating</td><td>📚 ontology</td><td>PropertyGenerator</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is exporting</td><td>💾 export</td><td>OWLExporter</td><td>output.owl</td><td>0.01s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.export import JSONExporter\n",
     "from semantica.kg import GraphBuilder\n",
@@ -350,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [

cookbook/introduction/17_Conflict_Detection_and_Resolution.ipynb

@@ -26,7 +26,7 @@
     "\n",
     "- Use `ConflictDetector.detect_value_conflicts(entities, property_name=...)` when you want to check one field.\n",
     "- Use `ConflictDetector.detect_conflicts(entities)` when you want a broader scan (value/type/temporal/etc.).\n",
-    "- If you're starting from a `KnowledgeGraph`, pull entities via `kg.get(\"entities\", [])` (not `kg.entities`).\n",
+    "- If you're starting from a Knowledge Graph dictionary (built via `GraphBuilder`), pull entities via `kg.get(\"entities\", [])`.\n",
     "\n",
     "### Key Capabilities\n",
     "\n",
@@ -64,32 +64,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install -q semantica "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,17 +109,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 3 records for Employee 001\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 1. Define source metadata\n",
     "sources_metadata = {\n",
@@ -199,20 +175,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Registering sources...\n",
-      "  - Registered 'hr_db' with credibility 0.95\n",
-      "  - Registered 'linkedin_scrape' with credibility 0.6\n",
-      "  - Registered 'public_dir' with credibility 0.4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.conflicts import SourceTracker\n",
     "\n",
@@ -255,45 +220,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is resolving</td><td>⚠️ conflicts</td><td>ConflictDetector</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is resolving</td><td>⚠️ conflicts</td><td>ConflictAnalyzer</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is resolving</td><td>⚠️ conflicts</td><td>ConflictResolver</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is resolving</td><td>⚠️ conflicts</td><td>InvestigationGuideGenerator</td><td>-</td><td>0.00s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Value conflict detected: emp_001.birth_date has conflicting values: ['1980-05-15', '1982-05-15']\n",
-      "Value conflict detected: emp_001.department has conflicting values: ['Software Engineering', 'Engineering']\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detected 2 conflicts:\n",
-      "- value_conflict: birth_date for emp_001\n",
-      "  Values: ['1980-05-15', '1980-05-15', '1982-05-15']\n",
-      "  Severity: medium\n",
-      "--- \n",
-      "- value_conflict: department for emp_001\n",
-      "  Values: ['Engineering', 'Software Engineering', 'Engineering']\n",
-      "  Severity: medium\n",
-      "--- \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.conflicts import ConflictDetector\n",
     "\n",
@@ -339,20 +268,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conflict Analysis Summary:\n",
-      "Total Conflicts: 2\n",
-      "By Type: {'value_conflict': 2}\n",
-      "By Severity: {'medium': 2}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.conflicts import ConflictAnalyzer\n",
     "\n",
@@ -401,23 +319,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--- Resolution: Voting ---\n",
-      "Property: birth_date      | Resolved Value: 1980-05-15\n",
-      "Property: department      | Resolved Value: Engineering\n",
-      "\n",
-      "--- Resolution: Credibility Weighted ---\n",
-      "Property: birth_date      | Resolved Value: 1980-05-15 (Confidence: 0.79)\n",
-      "Property: department      | Resolved Value: Engineering (Confidence: 0.69)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.conflicts import ConflictResolver\n",
     "\n",
@@ -462,39 +366,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=== Investigation: emp_001_birth_date_conflict ===\n",
-      "Summary: Conflict Type: value_conflict | Entity: emp_001 | Property: birth_date | Conflicting Values: 1980-05-15, 1982-05-15 | Severity: medium | Sources: public_dir, hr_db, linkedin_scrape\n",
-      "\n",
-      "Investigation Steps:\n",
-      "1. Review conflict details and context\n",
-      "   Action: Examine the conflict summary and identify the conflicting values\n",
-      "2. Identify all source documents\n",
-      "   Action: Locate and review the following source documents: public_dir, hr_db, linkedin_scrape\n",
-      "3. Compare conflicting information across sources\n",
-      "   Action: Review each source document and note the specific value and context for each\n",
-      "4. Assess source credibility and reliability\n",
-      "   Action: Review source credibility scores and document metadata\n",
-      "5. Determine resolution approach\n",
-      "   Action: Choose resolution strategy based on source credibility, recency, and business rules\n",
-      "6. Document resolution decision\n",
-      "   Action: Record the resolved value, resolution method, and rationale\n",
-      "\n",
-      "Recommended Actions:\n",
-      "[ ] Compare source documents side-by-side\n",
-      "[ ] Check for data entry errors or typos\n",
-      "[ ] Verify if values represent different time periods\n",
-      "[ ] Review 3 conflicting source documents\n",
-      "[ ] Identify most authoritative source\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.conflicts import InvestigationGuideGenerator\n",
     "\n",

cookbook/introduction/18_Deduplication.ipynb

@@ -21,25 +21,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Install Semantica\n",
     "!pip install -q semantica"
@@ -61,17 +45,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created 6 sample entities.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "entities = [\n",
     "    # Apple Variations\n",
@@ -140,34 +116,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>SimilarityCalculator</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>DuplicateDetector</td><td>-</td><td>0.17s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>EntityMerger</td><td>-</td><td>0.19s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>MergeStrategyManager</td><td>-</td><td>0.01s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Similarity between 'Apple Inc.' and 'Apple Inc':\n",
-      "  Total Score: 0.7218\n",
-      "  Breakdown:   {'string': 0.98, 'property': 0.7728205128205129, 'relationship': 0.0}\n",
-      "\n",
-      "Similarity between 'Apple Inc.' and 'Microsoft':\n",
-      "  Total Score: 0.1816\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.deduplication import SimilarityCalculator, SimilarityResult\n",
     "# Initialize calculator with custom weights\n",
@@ -206,19 +157,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 2 duplicate pairs:\n",
-      "  - Microsoft Corp <==> Microsoft (Score: 0.80)\n",
-      "  - Apple Inc. <==> Apple Inc (Score: 0.74)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import specific classes for Duplicate Detection\n",
     "from semantica.deduplication import DuplicateDetector, DuplicateCandidate, DuplicateGroup\n",
@@ -247,17 +188,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "New matches found: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "existing_db = entities[:3] # The Apple entities\n",
     "new_data = [entities[4]]   # Microsoft\n",
@@ -279,22 +212,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 2 clusters:\n",
-      "  Cluster 1: ['Apple Inc.', 'Apple Inc']\n",
-      "  Cluster 2: ['Microsoft Corp', 'Microsoft']\n",
-      "Found 2 clusters:\n",
-      "  Cluster 1: ['Apple Inc.', 'Apple Inc']\n",
-      "  Cluster 2: ['Microsoft Corp', 'Microsoft']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import specific classes for Clustering\n",
     "from semantica.deduplication import ClusterBuilder, Cluster, ClusterResult\n",
@@ -331,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,25 +261,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Performed 2 merge operations.\n",
-      "\n",
-      "--- Merged Results ---\n",
-      "Merged 2 entities into: 'Microsoft Corp'\n",
-      "  - Final Properties: {'industry': 'Software', 'hq': 'Redmond'}\n",
-      "  - Final Relationships: 0\n",
-      "Merged 2 entities into: 'Apple Inc.'\n",
-      "  - Final Properties: {'industry': 'Technology', 'hq': 'Cupertino', 'founded': 1976}\n",
-      "  - Final Relationships: 1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "merger = EntityMerger()\n",
     "\n",
@@ -394,25 +298,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1. Detecting duplicates...\n",
-      "2. Merging entities...\n",
-      "\n",
-      "Original Size: 6\n",
-      "Cleaned Size:  4\n",
-      "\n",
-      "Final Entity Names:\n",
-      "  - Microsoft Corp\n",
-      "  - Apple Inc.\n",
-      "  - Apple\n",
-      "  - Google LLC\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def deduplicate_dataset(raw_entities):\n",
     "    print(\"1. Detecting duplicates...\")\n",

cookbook/introduction/19_Context_Module.ipynb

@@ -23,26 +23,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "257bad40",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install -q semantica"
    ]
@@ -59,31 +43,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "8c845a94",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sentence_transformers\\cross_encoder\\CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
-      "  from tqdm.autonotebook import tqdm, trange\n",
-      "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n",
-      "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "('inmemory', 384)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import VectorStore\n",
     "\n",
@@ -107,25 +70,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "f4d788b5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'retention_days': 30,\n",
-       " 'max_memories': 10000,\n",
-       " 'use_graph_expansion': True,\n",
-       " 'max_expansion_hops': 2,\n",
-       " 'hybrid_alpha': 0.5}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.context import AgentContext, ContextGraph\n",
     "\n",
@@ -147,38 +95,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "5d65eb00",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is processing</td><td>🔗 context</td><td>AgentMemory</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is embedding</td><td>💾 embeddings</td><td>TextEmbedder</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is indexing</td><td>📊 vector_store</td><td>VectorStore</td><td>-</td><td>0.00s</td></tr><tr><td>✅</td><td>Semantica is processing</td><td>🔗 context</td><td>ContextRetriever</td><td>-</td><td>0.02s</td></tr><tr><td>✅</td><td>Semantica is processing</td><td>🔗 context</td><td>EntityLinker</td><td>-</td><td>0.00s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'id': None,\n",
-       " 'content': 'User prefers short answers about Python.',\n",
-       " 'timestamp': '2025-12-18T20:46:17.265703',\n",
-       " 'metadata': {'type': 'preference',\n",
-       "  'conversation_id': 'conv_1',\n",
-       "  'user_id': 'user_1'}}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "memory_id = context.store(\n",
     "    \"User prefers short answers about Python.\",\n",
@@ -192,35 +112,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "3c1be718",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Mohd Kaif\\semantica\\semantica\\vector_store\\vector_store.py:480: RuntimeWarning: invalid value encountered in divide\n",
-      "  similarities = np.dot(vectors, query_vector) / (vector_norms * query_norm)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'content': 'User prefers short answers about Python.',\n",
-       "  'score': 1.0,\n",
-       "  'source': 'short_term',\n",
-       "  'metadata': {'type': 'preference',\n",
-       "   'conversation_id': 'conv_1',\n",
-       "   'user_id': 'user_1'},\n",
-       "  'related_entities': []}]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "context.store(\n",
     "    \"User is working on Semantica context module examples.\",\n",
@@ -234,21 +129,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "485acf33",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "context.conversation(\"conv_1\", max_items=10)"
    ]
@@ -265,21 +149,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "a264ef4d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'{\\n  \"exported_at\": \"2025-12-18T20:46:27.455602\",\\n  \"count\": 2,\\n  \"memories\": [\\n    {\\n      \"memory_id\": \"mem_5c7dba9a7373\",\\n      \"content\": \"User prefers short answers about Python.\",\\n      \"timestamp\": \"2025-12-18T20:46:17.265703\",\\n      \"metadata\": {\\n        \"type\": \"preference\",\\n        \"convers'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "export_json = context.export(conversation_id=\"conv_1\", format=\"json\")\n",
     "export_json[:300]"
@@ -287,24 +160,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "b62d1859",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'conversation_id': 'conv_1',\n",
-       " 'message_count': 0,\n",
-       " 'first_message': None,\n",
-       " 'last_message': None}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tempfile\n",
     "\n",
@@ -327,24 +186,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "72930ae7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'stored_count': 2,\n",
-       " 'memory_ids': ['mem_b467da01b18a', 'mem_e3dbd310355d'],\n",
-       " 'graph_nodes': 0,\n",
-       " 'graph_edges': 0}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "documents = [\n",
     "    {\n",
@@ -415,54 +260,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "df2e5fcd",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'node': {'id': 'e_ml',\n",
-       "   'type': 'CONCEPT',\n",
-       "   'properties': {'content': 'Machine Learning',\n",
-       "    'id': 'e_ml',\n",
-       "    'text': 'Machine Learning',\n",
-       "    'type': 'CONCEPT'}},\n",
-       "  'score': 1.0,\n",
-       "  'content': 'Machine Learning'}]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kg.query(\"machine learning\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "0836feeb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'id': 'e_ml',\n",
-       "  'type': 'CONCEPT',\n",
-       "  'content': 'Machine Learning',\n",
-       "  'relationship': 'used_for',\n",
-       "  'weight': 0.9,\n",
-       "  'hop': 1}]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kg.get_neighbors(\"e_python\", hops=2)"
    ]
@@ -479,22 +290,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "24b011c0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('e1', 'https://semantica.dev/entity/python#programming_language', 1),\n",
-       " ('e2', 'https://semantica.dev/entity/pytorch#framework', 0)]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.context import EntityLinker\n",
     "\n",
@@ -511,22 +310,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "a282de3a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[EntityLink(source_entity_id='e1', target_entity_id='e_py', link_type='same_as', confidence=1.0, source=None, metadata={'similarity': 1.0}),\n",
-       " EntityLink(source_entity_id='e1', target_entity_id='e2', link_type='related_to', confidence=0.8, source=None, metadata={})]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "linker.link_entities(\"e1\", \"e2\", link_type=\"related_to\", confidence=0.8)\n",
     "linker.get_entity_links(\"e1\")[:2]"
@@ -554,22 +341,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "3791d6c6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Python powers Semantica.', 'short_term', 1.0),\n",
-       " ('Python', 'graph:e_python', 0.5)]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.context import AgentMemory, ContextRetriever\n",
     "\n",
@@ -594,21 +369,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "896e7001",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'7_days'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.context.config import context_config\n",
     "\n",
@@ -618,21 +382,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "e925a2e0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'graph': ['entities_relationships', 'conversations', 'hybrid', 'custom_demo']}"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.context.methods import build_context_graph\n",
     "from semantica.context.registry import method_registry\n",
@@ -650,21 +403,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "21fa6cb3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'nodes': [], 'edges': [], 'statistics': {'node_count': 0, 'edge_count': 0}}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "build_context_graph(\n",
     "    entities=[{\"id\": \"e1\", \"text\": \"Python\", \"type\": \"PROGRAMMING_LANGUAGE\"}],\n",

cookbook/introduction/20_Triplet_Store.ipynb

@@ -21,25 +21,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import sys\n",
     "import os\n",
@@ -53,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [

cookbook/introduction/21_Amazon_Neptune_Store.ipynb

@@ -0,0 +1,667 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Amazon Neptune Graph Store\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This notebook covers the Amazon Neptune Database integration in Semantica. Amazon Neptune is a fully managed graph database service that supports both property graphs (via OpenCypher/Gremlin) and RDF graphs (via SPARQL).\n",
+    "\n",
+    "### Key Features\n",
+    "\n",
+    "- **IAM Authentication**: Secure access using AWS SigV4 signatures via AuthManager\n",
+    "- **OpenCypher Support**: Query using standard OpenCypher syntax\n",
+    "- **Bolt Protocol**: Uses Neo4j Bolt driver for efficient binary communication\n",
+    "- **Native ~id Support**: Leverages Neptune's native element ID handling\n",
+    "- **Full CRUD Operations**: Create, read, update, delete nodes and relationships\n",
+    "- **Automatic Retry**: Built-in retry logic with exponential backoff for transient errors\n",
+    "\n",
+    "### Prerequisites\n",
+    "\n",
+    "- An Amazon Neptune Database cluster\n",
+    "- AWS credentials configured (boto3, environment variables, or IAM role)\n",
+    "- Network access to your Neptune cluster (VPC, security groups)\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "```bash\n",
+    "# Install Semantica with Neptune support\n",
+    "pip install semantica\n",
+    "\n",
+    "# Required dependencies (installed automatically)\n",
+    "pip install boto3 neo4j\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install semantica"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "\n",
+    "Set your Neptune cluster endpoint and AWS credentials. Replace the placeholder values with your actual configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Neptune cluster configuration - REPLACE WITH YOUR VALUES\n",
+    "os.environ[\"NEPTUNE_ENDPOINT\"] = \"your-cluster.us-east-1.neptune.amazonaws.com\"\n",
+    "os.environ[\"NEPTUNE_PORT\"] = \"8182\"\n",
+    "os.environ[\"AWS_REGION\"] = \"us-east-1\"\n",
+    "\n",
+    "# AWS credentials (if using IAM Auth and not relying on IAM role or ~/.aws/credentials)\n",
+    "# os.environ[\"AWS_ACCESS_KEY_ID\"] = \"your-access-key-id\"\n",
+    "# os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"your-secret-access-key\"\n",
+    "# os.environ[\"AWS_SESSION_TOKEN\"] = \"your-session-token\"\n",
+    "\n",
+    "print(f\"Neptune Endpoint: {os.environ.get('NEPTUNE_ENDPOINT')}\")\n",
+    "print(f\"AWS Region: {os.environ.get('AWS_REGION')}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Initialize Neptune Store\n",
+    "\n",
+    "Initialize a connection to your Amazon Neptune cluster with IAM authentication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from semantica.graph_store import GraphStore\n",
+    "\n",
+    "# Option 1: Using GraphStore factory (recommended)\n",
+    "neptune_store = GraphStore(\n",
+    "    backend=\"neptune\",\n",
+    "    endpoint=os.environ.get(\"NEPTUNE_ENDPOINT\"),\n",
+    "    port=int(os.environ.get(\"NEPTUNE_PORT\", 8182)),\n",
+    "    region=os.environ.get(\"AWS_REGION\", \"us-east-1\"),\n",
+    "    iam_auth=True,\n",
+    ")\n",
+    "\n",
+    "# Connect to Neptune\n",
+    "neptune_store.connect()\n",
+    "print(\"Connected to Amazon Neptune!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Development/Testing Without IAM Auth\n",
+    "\n",
+    "For development or testing environments where IAM authentication is not required (e.g., Neptune notebooks or VPC-only access), you can disable IAM signing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For dev/test environments without IAM authentication\n",
+    "neptune_store_dev = GraphStore(\n",
+    "    backend=\"neptune\",\n",
+    "    endpoint=os.environ.get(\"NEPTUNE_ENDPOINT\"),\n",
+    "    port=int(os.environ.get(\"NEPTUNE_PORT\", 8182)),\n",
+    "    region=os.environ.get(\"AWS_REGION\", \"us-east-1\"),\n",
+    "    iam_auth=False,  # Disable IAM signing for dev/test\n",
+    ")\n",
+    "neptune_store_dev.connect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Authentication Options\n",
+    "\n",
+    "IAM Authentication (recommended for production) automatically uses the AWS credential chain:\n",
+    "1. Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)\n",
+    "2. AWS credentials file (~/.aws/credentials)\n",
+    "3. IAM role (for EC2, Lambda, ECS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Node Operations\n",
+    "\n",
+    "### Creating Nodes\n",
+    "\n",
+    "Nodes represent entities in your graph. Each node can have:\n",
+    "- **ID**: A unique identifier (custom or auto-generated UUID)\n",
+    "- **Labels**: Categories/types (e.g., `Person`, `Company`)\n",
+    "- **Properties**: Key-value pairs (e.g., `{\"name\": \"Alice\", \"age\": 30}`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a single node with custom ID (id in properties)\n",
+    "alice = neptune_store.create_node(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"id\": \"alice\", \"name\": \"Alice\", \"age\": 30, \"role\": \"Engineer\"}\n",
+    ")\n",
+    "print(f\"Created node: {alice}\")\n",
+    "\n",
+    "# Create a node with auto-generated UUID (no id in properties)\n",
+    "bob = neptune_store.create_node(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"name\": \"Bob\", \"age\": 25, \"role\": \"Designer\"}\n",
+    ")\n",
+    "print(f\"Created node with UUID: {bob['id']}\")\n",
+    "\n",
+    "# Create a company node with auto-generated ID\n",
+    "acme = neptune_store.create_node(\n",
+    "    labels=[\"Company\"],\n",
+    "    properties={\"name\": \"Acme Corp\", \"industry\": \"Technology\", \"founded\": 2010}\n",
+    ")\n",
+    "print(f\"Created company: {acme}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Multiple Nodes (Batch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Batch create nodes for better performance\n",
+    "# Include 'id' in properties for custom IDs\n",
+    "nodes_data = [\n",
+    "    {\"labels\": [\"Person\"], \"properties\": {\"id\": \"charlie\", \"name\": \"Charlie\", \"age\": 35}},\n",
+    "    {\"labels\": [\"Person\"], \"properties\": {\"id\": \"diana\", \"name\": \"Diana\", \"age\": 28}},\n",
+    "    {\"labels\": [\"Location\"], \"properties\": {\"name\": \"San Francisco\", \"state\": \"CA\"}},\n",
+    "]\n",
+    "\n",
+    "created_nodes = neptune_store.create_nodes(nodes_data)\n",
+    "print(f\"Created {len(created_nodes)} nodes in batch\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieving Nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get a specific node by ID\n",
+    "alice_node = neptune_store.get_node(node_id=\"alice\")\n",
+    "print(f\"Retrieved: {alice_node}\")\n",
+    "\n",
+    "# Get nodes by label\n",
+    "people = neptune_store.get_nodes(labels=[\"Person\"], limit=10)\n",
+    "print(f\"Found {len(people)} Person nodes:\")\n",
+    "for person in people:\n",
+    "    print(f\"  - {person.get('properties', {}).get('name')}\")\n",
+    "\n",
+    "# Get nodes by properties\n",
+    "engineers = neptune_store.get_nodes(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"role\": \"Engineer\"},\n",
+    "    limit=5\n",
+    ")\n",
+    "print(f\"Found {len(engineers)} engineers\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Updating Nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update node properties (merge mode - default)\n",
+    "updated_alice = neptune_store.update_node(\n",
+    "    node_id=\"alice\",\n",
+    "    properties={\"age\": 31, \"department\": \"AI Research\"},\n",
+    "    merge=True\n",
+    ")\n",
+    "print(f\"Updated Alice: {updated_alice}\")\n",
+    "\n",
+    "# Replace all properties (merge=False)\n",
+    "# WARNING: This removes properties not in the update\n",
+    "replaced = neptune_store.update_node(\n",
+    "    node_id=\"charlie\",\n",
+    "    properties={\"name\": \"Charlie\", \"age\": 36},\n",
+    "    merge=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deleting Nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Delete a node (with detach=True to also delete relationships)\n",
+    "deleted = neptune_store.delete_node(node_id=\"diana\", detach=True)\n",
+    "print(f\"Deleted diana: {deleted}\")\n",
+    "\n",
+    "# Without detach (fails if node has relationships)\n",
+    "# neptune_store.delete_node(node_id=\"alice\", detach=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Relationship Operations\n",
+    "\n",
+    "### Creating Relationships\n",
+    "\n",
+    "Relationships connect nodes and represent connections between entities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a relationship between Alice and Acme\n",
+    "works_at = neptune_store.create_relationship(\n",
+    "    start_node_id=\"alice\",\n",
+    "    end_node_id=acme[\"id\"],\n",
+    "    rel_type=\"WORKS_AT\",\n",
+    "    properties={\"since\": 2020, \"position\": \"Senior Engineer\"}\n",
+    ")\n",
+    "print(f\"Created relationship: {works_at}\")\n",
+    "\n",
+    "# Create a KNOWS relationship between people\n",
+    "knows_rel = neptune_store.create_relationship(\n",
+    "    start_node_id=\"alice\",\n",
+    "    end_node_id=bob[\"id\"],\n",
+    "    rel_type=\"KNOWS\",\n",
+    "    properties={\"since\": 2019}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieving Relationships"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get all relationships for a node\n",
+    "alice_rels = neptune_store.get_relationships(node_id=\"alice\", direction=\"both\")\n",
+    "print(f\"Alice has {len(alice_rels)} relationships\")\n",
+    "\n",
+    "# Get outgoing relationships only\n",
+    "outgoing = neptune_store.get_relationships(node_id=\"alice\", direction=\"out\")\n",
+    "\n",
+    "# Filter by relationship type\n",
+    "works_rels = neptune_store.get_relationships(\n",
+    "    node_id=\"alice\",\n",
+    "    rel_type=\"WORKS_AT\",\n",
+    "    direction=\"out\"\n",
+    ")\n",
+    "print(f\"Alice's work relationships: {len(works_rels)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deleting Relationships"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Delete a specific relationship by ID\n",
+    "if works_at.get(\"id\"):\n",
+    "    deleted = neptune_store.delete_relationship(rel_id=works_at[\"id\"])\n",
+    "    print(f\"Deleted relationship: {deleted}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: OpenCypher Queries\n",
+    "\n",
+    "Amazon Neptune supports OpenCypher queries via the Bolt protocol. Execute complex graph patterns using standard Cypher syntax."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simple query\n",
+    "results = neptune_store.execute_query(\n",
+    "    \"MATCH (p:Person) RETURN p.name, p.age ORDER BY p.age\"\n",
+    ")\n",
+    "print(\"People in the graph:\")\n",
+    "for record in results.get(\"records\", []):\n",
+    "    print(f\"  - {record.get('p.name')}: {record.get('p.age')} years old\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using parameters (safer and more efficient)\n",
+    "results = neptune_store.execute_query(\n",
+    "    \"MATCH (p:Person) WHERE p.age > $min_age RETURN p.name, p.age\",\n",
+    "    parameters={\"min_age\": 25}\n",
+    ")\n",
+    "print(f\"People over 25: {len(results.get('records', []))}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find relationships between nodes\n",
+    "results = neptune_store.execute_query(\"\"\"\n",
+    "    MATCH (p:Person)-[r:WORKS_AT]->(c:Company)\n",
+    "    RETURN p.name as employee, c.name as company, r.since as start_year\n",
+    "\"\"\")\n",
+    "for record in results.get(\"records\", []):\n",
+    "    print(f\"{record['employee']} works at {record['company']} since {record['start_year']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count and aggregate\n",
+    "results = neptune_store.execute_query(\"\"\"\n",
+    "    MATCH (p:Person)\n",
+    "    RETURN count(p) as total, avg(p.age) as avg_age, max(p.age) as max_age\n",
+    "\"\"\")\n",
+    "stats = results.get(\"records\", [{}])[0]\n",
+    "print(f\"Total: {stats.get('total')}, Avg Age: {stats.get('avg_age'):.1f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Graph Analytics\n",
+    "\n",
+    "### Get Neighbors\n",
+    "\n",
+    "Traverse the graph to find connected nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get immediate neighbors (depth=1)\n",
+    "neighbors = neptune_store.get_neighbors(\n",
+    "    node_id=\"alice\",\n",
+    "    direction=\"both\",\n",
+    "    depth=1\n",
+    ")\n",
+    "print(f\"Alice's direct neighbors: {len(neighbors)}\")\n",
+    "\n",
+    "# Get neighbors up to 2 hops away\n",
+    "extended = neptune_store.get_neighbors(\n",
+    "    node_id=\"alice\",\n",
+    "    direction=\"out\",\n",
+    "    depth=2\n",
+    ")\n",
+    "print(f\"Nodes within 2 hops: {len(extended)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shortest Path\n",
+    "\n",
+    "Find the shortest path between two nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find shortest path\n",
+    "path = neptune_store.shortest_path(\n",
+    "    start_node_id=\"alice\",\n",
+    "    end_node_id=\"charlie\",\n",
+    "    max_depth=5\n",
+    ")\n",
+    "\n",
+    "if path:\n",
+    "    print(\"Path found!\")\n",
+    "    print(f\"  Length: {path.get('length')}\")\n",
+    "    print(f\"  Nodes: {len(path.get('nodes', []))}\")\n",
+    "    print(f\"  Relationships: {len(path.get('relationships', []))}\")\n",
+    "else:\n",
+    "    print(\"No path found between nodes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Graph Statistics\n",
+    "\n",
+    "Get comprehensive statistics about your graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get graph statistics\n",
+    "stats = neptune_store.get_stats()\n",
+    "\n",
+    "print(\"Graph Statistics:\")\n",
+    "print(f\"  Total nodes: {stats.get('node_count', 'N/A')}\")\n",
+    "print(f\"  Total relationships: {stats.get('relationship_count', 'N/A')}\")\n",
+    "\n",
+    "print(\"\\nNode labels:\")\n",
+    "for label, count in stats.get('label_counts', {}).items():\n",
+    "    print(f\"  - {label}: {count}\")\n",
+    "\n",
+    "print(\"\\nRelationship types:\")\n",
+    "for rel_type, count in stats.get('relationship_type_counts', {}).items():\n",
+    "    print(f\"  - {rel_type}: {count}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Connection Management\n",
+    "\n",
+    "Always close connections when done to free resources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check connection status\n",
+    "status = neptune_store.get_status()\n",
+    "print(f\"Connection status: {status}\")\n",
+    "\n",
+    "# Close the connection\n",
+    "neptune_store.close()\n",
+    "print(\"Connection closed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neptune-Specific Considerations\n",
+    "\n",
+    "### Native Element IDs\n",
+    "\n",
+    "Neptune uses native `~id` for element identification. Include `id` in properties to set a custom ID:\n",
+    "\n",
+    "```python\n",
+    "# Create a node with custom ID (include 'id' in properties)\n",
+    "node = neptune_store.create_node(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"id\": \"my-custom-id\", \"name\": \"Test\"}\n",
+    ")\n",
+    "\n",
+    "# Create a node with auto-generated UUID (omit 'id' from properties)\n",
+    "node = neptune_store.create_node(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"name\": \"Test\"}\n",
+    ")\n",
+    "\n",
+    "# The ID is used in id() function calls internally:\n",
+    "# MATCH (n) WHERE id(n) = 'my-custom-id' RETURN n\n",
+    "```\n",
+    "\n",
+    "### OpenCypher Considerations\n",
+    "\n",
+    "Amazon Neptune Database's OpenCypher implementation has some differences from Neo4j:\n",
+    "\n",
+    "1. **No `shortestPath()` function**: Use variable-length path patterns or `allShortestPaths()`\n",
+    "2. **Labels syntax**: Use `labels(n)` function to retrieve node labels\n",
+    "3. **Property updates**: Use `SET n += {props}` for merge behavior\n",
+    "\n",
+    "For the complete OpenCypher specification supported by Amazon Neptune Database, see the [AWS documentation](https://docs.aws.amazon.com/neptune/latest/userguide/access-graph-opencypher.html).\n",
+    "\n",
+    "### Amazon Neptune Analytics\n",
+    "\n",
+    "For analytical (OLAP) workloads such as graph algorithms, aggregations, and large-scale traversals, consider [Amazon Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html). Neptune Analytics complements Neptune Database by providing optimized performance for analytical queries while Neptune Database is optimized for transactional (OLTP) workloads.\n",
+    "\n",
+    "### Performance Tips\n",
+    "\n",
+    "1. **Use batch operations** for creating multiple nodes/relationships\n",
+    "2. **Use parameters** in queries to enable query caching\n",
+    "3. **Limit result sets** with `LIMIT` clause"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook covered the Amazon Neptune Graph Store integration:\n",
+    "\n",
+    "- **IAM Authentication**: Secure AWS SigV4 signing\n",
+    "- **CRUD Operations**: Full node and relationship management\n",
+    "- **OpenCypher Queries**: Standard graph query language\n",
+    "- **Graph Analytics**: Neighbors and shortest path algorithms\n",
+    "- **Statistics & Monitoring**: Graph metrics and status\n",
+    "\n",
+    "### Key Takeaways\n",
+    "\n",
+    "- Neptune uses native `~id` for element identification\n",
+    "- IAM authentication is recommended for production\n",
+    "- Bolt protocol provides efficient binary query interface\n",
+    "- Semantica abstracts Neptune-specific syntax differences\n",
+    "\n",
+    "### Next Steps\n",
+    "\n",
+    "- [Graph Store (Neo4j/FalkorDB)](09_Graph_Store.ipynb) - Compare with other backends\n",
+    "- [Building Knowledge Graphs](07_Building_Knowledge_Graphs.ipynb) - Build production KGs\n",
+    "- [Graph Analytics](10_Graph_Analytics.ipynb) - Advanced analytics algorithms"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

cookbook/use_cases/advanced_rag/data/sample_graphrag_paper.txt

@@ -0,0 +1,13 @@
+Graph Retrieval-Augmented Generation (GraphRAG): A New Era for Intelligent Search
+
+GraphRAG is an advanced technique that combines the retrieval capabilities of vector databases with the structural reasoning of knowledge graphs. Unlike traditional RAG, which relies solely on vector similarity, GraphRAG leverages the relationships between entities to provide more contextually accurate and comprehensive answers.
+
+Key Components:
+1. Knowledge Graph: A structured representation of data where nodes represent entities and edges represent relationships.
+2. Vector Search: Finds semantically similar text chunks.
+3. Graph Traversal: Navigates the knowledge graph to find related entities that might not be semantically similar but are structurally relevant.
+
+Benefits:
+- Improved Context: By following relationships, the system can understand the broader context of a query.
+- Multi-hop Reasoning: Can answer complex questions that require connecting multiple pieces of information.
+- Reduced Hallucinations: Grounding answers in a verified knowledge structure reduces the likelihood of generating false information.

cookbook/use_cases/advanced_rag/expert_guide.txt

@@ -0,0 +1,5 @@
+
+RETINOL CLINICAL GUIDE
+Mechanism: Binds to retinoic acid receptors to increase cellular turnover.
+Precautions: Should not be used with high-concentration AHA/BHA exfoliants.
+Synergy: Highly effective when paired with Niacinamide to offset potential erythema.

cookbook/use_cases/advanced_rag/expert_skincare_guide.txt

@@ -0,0 +1,6 @@
+
+RETINOL CLINICAL GUIDE v2.1
+Mechanism: Binds to retinoic acid receptors (RAR) to increase cellular turnover.
+Precautions: Should not be used with high-concentration AHA/BHA exfoliants.
+Synergy: Highly effective when paired with Niacinamide to offset potential erythema.
+Target: Stratum corneum thickening and dermal collagen synthesis.

cookbook/use_cases/advanced_rag/graphrag_kg.graphml

@@ -0,0 +1,254 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+
+  <key id="type" for="node" attr.name="type" attr.type="string"/>
+  <key id="confidence" for="node" attr.name="confidence" attr.type="double"/>
+
+  <graph id="G" edgedefault="directed">
+
+    <node id="makeup_and_beauty_blog">
+      <data key="label">Makeup and Beauty Blog</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="monday_poll">
+      <data key="label">Monday Poll</data>
+      <data key="type">EVENT</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="2007">
+      <data key="label">2007</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="rosacea">
+      <data key="label">Rosacea</data>
+      <data key="type">CONCEPT</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="dr._bailey">
+      <data key="label">Dr. Bailey</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="green_tea_antioxidant_skin_therapy">
+      <data key="label">Green Tea Antioxidant Skin Therapy</data>
+      <data key="type">PRODUCT</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="vol._892">
+      <data key="label">Vol. 892</data>
+      <data key="type">EVENT</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="laneige">
+      <data key="label">Laneige</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="sausalito">
+      <data key="label">Sausalito</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="ulta">
+      <data key="label">Ulta</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="december_15,_2025">
+      <data key="label">December 15, 2025</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="jo_malone">
+      <data key="label">Jo Malone</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1</data>
+    </node>
+    <node id="trader_joe">
+      <data key="label">Trader Joe</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1</data>
+    </node>
+    <node id="hawaii">
+      <data key="label">hawaii</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="benzoyl_peroxide_cream">
+      <data key="label">Benzoyl Peroxide Cream</data>
+      <data key="type">PRODUCT</data>
+      <data key="confidence">1</data>
+    </node>
+    <node id="facial_dandruff">
+      <data key="label">Facial dandruff</data>
+      <data key="type">CONCEPT</data>
+      <data key="confidence">1</data>
+    </node>
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+</graphml>

cookbook/use_cases/advanced_rag/graphrag_kg.json

@@ -0,0 +1,678 @@
+{
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+      "type": "can_be_combined_with",
+      "attributes": {
+        "confidence": 0.9
+      }
+    },
+    {
+      "source": "Omega Enriched Face Booster Oil",
+      "target": "castor seed oil",
+      "type": "contains",
+      "attributes": {
+        "confidence": 0.9
+      }
+    },
+    {
+      "source": "Omega Enriched Face Booster Oil",
+      "target": "sea buckthorn",
+      "type": "contains",
+      "attributes": {
+        "confidence": 0.9
+      }
+    },
+    {
+      "source": "Daily Moisturizing Face Cream",
+      "target": "Omega Enriched Face Booster Oil",
+      "type": "can_be_replaced_with",
+      "attributes": {
+        "confidence": 0.9
+      }
+    }
+  ],
+  "metadata": {
+    "num_entities": 20,
+    "num_relationships": 35,
+    "temporal_enabled": false,
+    "timestamp": "2025-12-24T12:46:41.535755",
+    "entity_resolution_applied": true
+  }
+}

cookbook/use_cases/advanced_rag/ground_truth.json

@@ -0,0 +1 @@
+{"entities": [{"id": "python_org", "name": "Python Software Foundation", "type": "Organization"}, {"id": "guido_van_rossum", "name": "Guido van Rossum", "type": "Person"}], "relationships": [{"source": "guido_van_rossum", "target": "python_org", "type": "FOUNDED"}]}

cookbook/use_cases/advanced_rag/skincare_base.json

@@ -0,0 +1,38 @@
+{
+  "entities": [
+    {
+      "id": "hyaluronic_acid",
+      "name": "Hyaluronic Acid",
+      "type": "Ingredient",
+      "properties": {
+        "role": "Humectant"
+      }
+    },
+    {
+      "id": "retinol",
+      "name": "Retinol",
+      "type": "Ingredient",
+      "properties": {
+        "role": "Anti-aging actives"
+      }
+    },
+    {
+      "id": "niacinamide",
+      "name": "Niacinamide",
+      "type": "Ingredient",
+      "properties": {
+        "role": "Barrier repair"
+      }
+    }
+  ],
+  "relationships": [
+    {
+      "source": "hyaluronic_acid",
+      "target": "niacinamide",
+      "type": "COMPLEMENTS",
+      "properties": {
+        "benefit": "Hydration + Barrier"
+      }
+    }
+  ]
+}

cookbook/use_cases/biomedical/data/chunks/chunk_0.txt

@@ -0,0 +1 @@
+Apoptotic signatures allow early and rapid screening of drug-induced liver injury to accelerate drug discovery

cookbook/use_cases/biomedical/data/chunks/chunk_1.txt

@@ -0,0 +1 @@
+SynergyGraph: predicting cell line specific drug combination synergy scores using knowledge graph representation and hypergraph modeling

cookbook/use_cases/biomedical/data/chunks/chunk_10.txt

@@ -0,0 +1 @@
+Unraveling the mechanism of curcumin in coronary slow flow phenomenon through network pharmacology and molecular docking

cookbook/use_cases/biomedical/data/chunks/chunk_11.txt

@@ -0,0 +1 @@
+Penicillium chrysogenum originated chloro-diydropyridyl-oxopropanimidic acid derivative as a potent EPSP synthase-targeted bioherbicide against invasive weed species

cookbook/use_cases/biomedical/data/chunks/chunk_12.txt

@@ -0,0 +1 @@
+Enhancing the anti-cancer potential of resveratrol through cocrystal technology in colorectal cancerous rats

cookbook/use_cases/biomedical/data/chunks/chunk_13.txt

@@ -0,0 +1 @@
+Synthesis, spectral, thermal, and biological characterization of Se(IV) nanocomplexes derived from vitamin E and amino acid mixed ligands as a metal-drug model

cookbook/use_cases/biomedical/data/chunks/chunk_14.txt

@@ -0,0 +1 @@
+BT-11 targets the LANCL2 pathway to attenuate cognitive deficits and hippocampal pathology in Alzheimer’s transgenic rats

cookbook/use_cases/biomedical/data/chunks/chunk_15.txt

@@ -0,0 +1 @@
+Saffron as a natural modulator of reverse cholesterol transport genes in atherosclerotic rabbits, with molecular docking insights

cookbook/use_cases/biomedical/data/chunks/chunk_16.txt

@@ -0,0 +1 @@
+Simultaneous targeting of KRAS and CDK4 synergistically induces durable growth arrest in pancreatic cancer cells

cookbook/use_cases/biomedical/data/chunks/chunk_17.txt

@@ -0,0 +1 @@
+Antiproliferative activity of Moringa oleifera (L.) Lam. and Moringa stenopetala (Bak.) Cufod. leaves extract against selected cancer cells in primary cell culture

cookbook/use_cases/biomedical/data/chunks/chunk_18.txt

@@ -0,0 +1 @@
+Applications of Sombor topological indices and entropy measures for QSPR modeling of anticancer drugs: a Python-based methodology

cookbook/use_cases/biomedical/data/chunks/chunk_19.txt

@@ -0,0 +1 @@
+In-silico studies, synthesis, and pharmacological screening of novel multitarget diphenylpyrazole scaffold as EGFR/BRAF and cyclooxygenase-2 inhibitors

cookbook/use_cases/biomedical/data/chunks/chunk_2.txt

@@ -0,0 +1 @@
+Rational design and in silico characterization of a multiepitope mRNA vaccine candidate against human metapneumovirus (hMPV) using reverse vaccinology and immunoinformatics approaches

cookbook/use_cases/biomedical/data/chunks/chunk_20.txt

@@ -0,0 +1 @@
+Unveiling novel potential drug targets for lung cancer through Mendelian randomization analysis

cookbook/use_cases/biomedical/data/chunks/chunk_21.txt

@@ -0,0 +1 @@
+Comparative analysis of OECD guideline data and Tox21 assays to improve reproductive and developmental toxicity prediction

cookbook/use_cases/biomedical/data/chunks/chunk_22.txt

@@ -0,0 +1 @@
+High-throughput triazole-based combinatorial click chemistry for the synthesis and identification of functional metal complexes

cookbook/use_cases/biomedical/data/chunks/chunk_23.txt

@@ -0,0 +1 @@
+Efficacy and safety assessment of gelatin hemostatic matrix in a burr hole neurosurgical beagle model

cookbook/use_cases/biomedical/data/chunks/chunk_24.txt

@@ -0,0 +1 @@
+Biological and computer-aided evaluation of 3-methoxy-13α-estrone-16α-diphenylphosphine oxide as a new antiestrogenic agent

cookbook/use_cases/biomedical/data/chunks/chunk_25.txt

@@ -0,0 +1 @@
+Design and optimization of intranasal aripiprazole-loaded nanostructured lipid carriers for enhanced brain targeting in schizophrenia: in vitro and ex vivo evaluation

cookbook/use_cases/biomedical/data/chunks/chunk_26.txt

@@ -0,0 +1 @@
+Endosome-phagophore linking assemblies for the degradation of membrane/extracellular proteins

cookbook/use_cases/biomedical/data/chunks/chunk_27.txt

@@ -0,0 +1 @@
+Protodioscin enhances Methotrexate-induced senescence and senolytic activity in HepG2 liver cancer spheroids by modulating cell cycle regulators and the TGF-β/p-Smad2-4 signaling pathway

cookbook/use_cases/biomedical/data/chunks/chunk_28.txt

@@ -0,0 +1 @@
+Morpholino-RNA duplex exhibits robust, sustained, and safe steric-block antisense activity by intracerebroventricular and intrathecal injection

cookbook/use_cases/biomedical/data/chunks/chunk_29.txt

@@ -0,0 +1 @@
+Pushing the limits of hydrogen/deuterium exchange mass spectrometry to study protein:fragment low affinity interactions

cookbook/use_cases/biomedical/data/chunks/chunk_3.txt

@@ -0,0 +1 @@
+Cannabidiol perturbs macrophage polarization by interfering with the metabolic flux and PI3K/Akt pathway

cookbook/use_cases/biomedical/data/chunks/chunk_4.txt

@@ -0,0 +1 @@
+Bidirectional reinforcement learning neural network for constrained molecular design

cookbook/use_cases/biomedical/data/chunks/chunk_5.txt

@@ -0,0 +1 @@
+Phytochemical investigation and antibacterial activities of Cordia africana roots extracts

cookbook/use_cases/biomedical/data/chunks/chunk_6.txt

@@ -0,0 +1 @@
+Similar short-term efficacy of oral levosulpiride and intravitreal ranibizumab in patients with diabetic macular oedema

cookbook/use_cases/biomedical/data/chunks/chunk_7.txt

@@ -0,0 +1 @@
+SIMD: Synergistic integration mutualistic platform based on single-cell and proteotranscriptomics for drug repositioning

cookbook/use_cases/biomedical/data/chunks/chunk_8.txt

@@ -0,0 +1 @@
+Elucidating the anti-fibrotic mechanisms of Abrus cantoniensis in hepatic fibrosis using network pharmacology and proteomics

cookbook/use_cases/biomedical/data/chunks/chunk_9.txt

@@ -0,0 +1 @@
+Identification of bioactive phytoconstituents as promising ABL2 inhibitors using virtual screening and molecular dynamics simulation

cookbook/use_cases/biomedical/data/sample_drugs.txt

@@ -0,0 +1,9 @@
+
+    Aspirin (acetylsalicylic acid) is a medication used to reduce pain, fever, or inflammation. 
+    It targets cyclooxygenase enzymes COX-1 and COX-2. Aspirin is commonly used for cardiovascular protection.
+    Ibuprofen is a nonsteroidal anti-inflammatory drug (NSAID) that targets COX-1 and COX-2 enzymes.
+    Metformin is an antidiabetic medication that targets AMP-activated protein kinase (AMPK).
+    Insulin targets the insulin receptor (INSR) to regulate glucose metabolism.
+    Warfarin is an anticoagulant that targets vitamin K epoxide reductase complex subunit 1 (VKORC1).
+    Atorvastatin is a statin medication that targets HMG-CoA reductase.
+    

cookbook/use_cases/biomedical/data/variants.txt

@@ -0,0 +1,11 @@
+
+Variant rs699 is located in the AGT gene and associated with hypertension.
+Variant rs7412 in APOE gene is linked to Alzheimer's disease risk.
+BRCA1 variant c.5266dupC increases breast cancer susceptibility.
+CFTR variant F508del causes cystic fibrosis.
+Variant rs1800566 in NAT2 gene affects drug metabolism.
+Variant rs1042713 in ADRB2 gene is associated with asthma response.
+TP53 variant R273H is linked to multiple cancer types.
+Variant rs1799853 in CYP2C9 gene affects warfarin metabolism.
+Variant rs1057910 in CYP2C9 affects phenytoin metabolism.
+Variant rs9923231 in VKORC1 gene influences warfarin dosing.

cookbook/use_cases/biomedical/drug_target_kg.graphml

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+
+  <key id="type" for="node" attr.name="type" attr.type="string"/>
+  <key id="confidence" for="node" attr.name="confidence" attr.type="double"/>
+
+  <graph id="G" edgedefault="directed">
+
+
+  </graph>
+</graphml>

cookbook/use_cases/biomedical/genomic_variant_kg.graphml

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+
+  <key id="type" for="node" attr.name="type" attr.type="string"/>
+  <key id="confidence" for="node" attr.name="confidence" attr.type="double"/>
+
+  <graph id="G" edgedefault="directed">
+
+
+  </graph>
+</graphml>

cookbook/use_cases/blockchain/data/transactions.txt

@@ -0,0 +1,9 @@
+
+    Transaction 0x123 transfers 1000 ETH from wallet 0xABC to wallet 0xDEF at block 18500000.
+    Transaction 0x456 transfers 500 BTC from wallet 0xGHI to wallet 0xJKL at block 18500001.
+    Large transaction 0x789 moves 10000 ETH (whale movement) from wallet 0xMNO to wallet 0xPQR at block 18500002.
+    Transaction 0xabc transfers 200 USDT from wallet 0xSTU to wallet 0xVWX at block 18500003.
+    Transaction 0xdef transfers 5000 ETH from wallet 0xYZA to wallet 0xBCD at block 18500004.
+    Transaction 0x111 transfers 3000 DAI from wallet 0xEFG to wallet 0xHIJ at block 18500005.
+    Transaction 0x222 transfers 1500 USDC from wallet 0xKLM to wallet 0xNOP at block 18500006.
+    

cookbook/use_cases/blockchain/defi_protocol_kg.graphml

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+
+  <key id="type" for="node" attr.name="type" attr.type="string"/>
+  <key id="confidence" for="node" attr.name="confidence" attr.type="double"/>
+
+  <graph id="G" edgedefault="directed">
+
+
+  </graph>
+</graphml>

cookbook/use_cases/blockchain/transaction_network.dot

@@ -0,0 +1,5 @@
+digraph G {
+  rankdir=LR;
+
+
+}

cookbook/use_cases/blockchain/transaction_network.gexf

@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version="1.2">
+  <graph mode="static" defaultedgetype="directed">
+    <nodes>
+    </nodes>
+    <edges>
+    </edges>
+  </graph>
+</gexf>