fix: resolve model switching bug and implement intrinsic dimension detection in TextEmbedder

docs: update changelog with kg module fixes #159

.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

CHANGELOG.md

@@ -7,6 +7,41 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Fixed
+- Fixed `TypeError: unhashable type: 'Entity'` in `GraphAnalyzer` when processing graphs with raw `Entity` objects or dictionaries in relationships (#159).
+- Robustified ID extraction across `CentralityCalculator`, `CommunityDetector`, and `ConnectivityAnalyzer` to handle various entity formats.
+- Improved `Entity` class hashability and equality logic in `utils/types.py`.
+- Added end-to-end integration test suite for Knowledge Graph pipeline validation (GraphBuilder -> EntityResolver -> GraphAnalyzer).
+- 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==4.25.3` and `grpcio==1.67.1`.
+- Added missing dependencies `GitPython` and `chardet` to `pyproject.toml`.
+- Verified and aligned `FileObject.text` property usage in GraphRAG notebooks for consistent content decoding.
+
+### Added
+- **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.
+
+### Fixed
+- 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

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

README.md

@@ -7,8 +7,9 @@
 [![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![PyPI version](https://badge.fury.io/py/semantica.svg)](https://pypi.org/project/semantica/)
-[![Downloads](https://pepy.tech/badge/semantica)](https://pepy.tech/project/semantica)
-[![Discord](https://img.shields.io/discord/semantica?color=7289da&label=discord)](https://discord.gg/semantica)
+[![Monthly Downloads](https://img.shields.io/pypi/dm/semantica)](https://pypi.org/project/semantica/)
+[![Total Downloads](https://static.pepy.tech/badge/semantica)](https://pepy.tech/project/semantica)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-7289da?style=flat&logo=discord&logoColor=white)](https://discord.gg/pMHguUzG)
 [![CI](https://github.com/Hawksight-AI/semantica/workflows/CI/badge.svg)](https://github.com/Hawksight-AI/semantica/actions)
 
 <p align="center">
@@ -27,9 +28,9 @@
 
 *The missing fabric between raw data and AI engineering. A comprehensive open-source framework for building semantic layers and knowledge engineering systems that transform unstructured data into AI-ready knowledge — powering Knowledge Graph-Powered RAG (GraphRAG), AI Agents, Multi-Agent Systems, and AI applications with structured semantic knowledge.*
 
-**100% Open Source** • **MIT Licensed** • **Production Ready** • **Community Driven**
+**100% Open Source** • **MIT Licensed** • **Latest Version: 0.1.1** • **Production Ready** • **Community Driven**
 
-[**Discord**](https://discord.gg/semantica)
+[**Discord**](https://discord.gg/pMHguUzG)
 
 </div>
 
@@ -115,7 +116,7 @@ Semantica operates through three integrated layers that transform raw data into
 
 **Universal Data Ingestion** — Handles multiple formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams) with unified pipeline, no custom parsers needed.
 
-**Automated Semantic Extraction** — NER, relationship extraction, and triplet generation with LLM enhancement discovers entities and relationships automatically.
+**Automated Semantic Extraction** — NER, relationship extraction, and triplet generation with LLM enhancement. Includes **auto-chunking** for long documents and **robust error handling** with automatic retry logic.
 
 **Knowledge Graph Construction** — Production-ready graphs with entity resolution, temporal support, and graph analytics. Queryable knowledge ready for AI applications.
 
@@ -134,7 +135,7 @@ Semantica operates through three integrated layers that transform raw data into
 | **Feature Category** | **Capabilities** | **Key Benefits** |
 |:---------------------|:-----------------|:------------------|
 | **Data Ingestion** | Multiple formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams, archives) | Universal ingestion, no custom parsers needed |
-| **Semantic Extraction** | NER, relationship extraction, triplet generation, LLM enhancement | Automated discovery of entities and relationships |
+| **Semantic Extraction** | NER, relations, triplets, LLM enhancement, **auto-chunking** | Automated discovery with robust error handling |
 | **Knowledge Graphs** | Entity resolution, temporal support, graph analytics, query interface | Production-ready, queryable knowledge structures |
 | **Ontology Generation** | 6-stage LLM pipeline, OWL generation, HermiT/Pellet validation | Automated ontology creation from documents |
 | **GraphRAG** | Hybrid vector + graph retrieval, multi-hop reasoning, LLM-generated responses | 91% accuracy, 30% improvement over vector-only, reasoning traces |
@@ -184,12 +185,18 @@ pip install semantica
 # Or install with optional dependencies
 pip install semantica[all]
 
+# GitHub Workaround (if PyPI version has issues)
+pip install git+https://github.com/Hawksight-AI/semantica.git@main
+
 # Verify installation
-python -c "import semantica; print(semantica.__version__)"
+python -c "from semantica.parse import DoclingParser; DoclingParser(); print('✓ Semantica ready')"
 ```
 
 **Current Version:** [![PyPI version](https://badge.fury.io/py/semantica.svg)](https://pypi.org/project/semantica/) • [View on PyPI](https://pypi.org/project/semantica/)
 
+!!! info "Windows PyTorch Note"
+    If you encounter PyTorch DLL errors on Windows, ensure you have the [Microsoft Visual C++ Redistributable](https://aka.ms/vs/17/release/vc_redist.x64.exe) installed. This is a common environment-specific issue with PyTorch on Windows and not a bug in Semantica.
+
 
 
 ### Install from Source (Development)
@@ -254,14 +261,20 @@ print(f" Ingested {len(sources)} sources")
 > **Multi-format parsing** • **Text normalization** • **Intelligent chunking**
 
 ```python
-from semantica.parse import DocumentParser
+from semantica.parse import DocumentParser, DoclingParser
 from semantica.normalize import TextNormalizer
 from semantica.split import TextSplitter
 
-# Parse documents
+# Standard parsing
 parser = DocumentParser()
 parsed = parser.parse("document.pdf", format="auto")
 
+# Enhanced parsing with Docling (recommended for complex layouts/tables)
+# Requires: pip install docling
+docling_parser = DoclingParser()
+docling_result = docling_parser.parse("complex_table.pdf")
+print(f"Extracted {len(docling_result.tables)} tables")
+
 # Normalize text
 normalizer = TextNormalizer()
 normalized = normalizer.normalize(parsed, clean_html=True, normalize_entities=True)
@@ -762,6 +775,7 @@ print(f"Found {len(results)} results")
 - [x] Advanced reasoning v2 (Rete, Forward/Backward Chaining)
 - [ ] Quality assurance features and Quality Assurance module
 - [ ] Enhanced multi-language support
+- [ ] Evals
 - [ ] Real-time streaming improvements
 
 ### Q2 2026
@@ -775,7 +789,7 @@ print(f"Found {len(results)} results")
 
 | **Channel** | **Purpose** |
 |:-----------:|:-----------|
-| [**Discord**](https://discord.gg/semantica) | Real-time help, showcases |
+| [**Discord**](https://discord.gg/pMHguUzG) | Real-time help, showcases |
 | [**GitHub Discussions**](https://github.com/Hawksight-AI/semantica/discussions) | Q&A, feature requests |
 
 ### Learning Resources
@@ -783,14 +797,20 @@ print(f"Found {len(results)} results")
 
 ### Enterprise Support
 
-| **Tier** | **Features** | **SLA** | **Price** |
-|:--------:|:-----------|:-------:|:--------:|
-| **Community** | Public support | Best effort | Free |
-| **Professional** | Email support | 48h | Contact |
-| **Enterprise** | 24/7 support | 4h | Contact |
-| **Premium** | Phone, custom dev | 1h | Contact |
+Enterprise support, professional services, and commercial licensing will be available in the future. For now, we offer community support through Discord and GitHub Discussions.
 
-**Contact:** [GitHub Issues](https://github.com/Hawksight-AI/semantica/issues) with "[Enterprise]" prefix
+**Current Support:**
+- **Community Support** - Free support via [Discord](https://discord.gg/pMHguUzG) and [GitHub Discussions](https://github.com/Hawksight-AI/semantica/discussions)
+- **Bug Reports** - [GitHub Issues](https://github.com/Hawksight-AI/semantica/issues)
+
+**Future Enterprise Offerings:**
+- Professional support with SLA
+- Enterprise licensing
+- Custom development services
+- Priority feature requests
+- Dedicated support channels
+
+Stay tuned for updates!
 
 ## 🤝 Contributing
 
@@ -824,15 +844,8 @@ git push origin feature/your-feature
 3. **Bug Reports** - [Create issue](https://github.com/Hawksight-AI/semantica/issues/new)
 4. **Feature Requests** - [Request feature](https://github.com/Hawksight-AI/semantica/issues/new)
 
-### Recognition
 
-Contributors receive:
-- Recognition in [CONTRIBUTORS.md](https://github.com/Hawksight-AI/semantica/blob/main/CONTRIBUTORS.md)
-- GitHub badges
-- Semantica swag
-- Featured showcases
-
-## 🏆 Contributors
+###  Contributors
 
 <a href="https://github.com/Hawksight-AI/semantica/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=Hawksight-AI/semantica" alt="Contributors" />
@@ -846,6 +859,6 @@ Semantica is licensed under the **MIT License** - see the [LICENSE](https://gith
 
 **Built by the Semantica Community**
 
-[GitHub](https://github.com/Hawksight-AI/semantica) • [Discord](https://discord.gg/semantica)
+[GitHub](https://github.com/Hawksight-AI/semantica) • [Discord](https://discord.gg/pMHguUzG)
 
 </div>

RELEASE.md

@@ -26,10 +26,10 @@ Before releasing, ensure:
 
 The project uses GitHub Actions for automated releases to PyPI.
 
-1. **Tag the commit**: Create a new git tag for the version (e.g., `v0.1.0`).
+1. **Tag the commit**: Create a new git tag for the version (e.g., `v0.1.1`).
    ```bash
-   git tag -a v0.1.0 -m "Release v0.1.0"
-   git push origin v0.1.0
+   git tag -a v0.1.1 -m "Release v0.1.1"
+   git push origin v0.1.1
    ```
 2. **GitHub Action**: The `Release` workflow will automatically trigger, build the package, create a GitHub Release, and publish to PyPI using Trusted Publishing.
 

SECURITY.md

@@ -6,6 +6,7 @@ We actively support the following versions of Semantica with security updates:
 
 | Version | Supported          |
 | ------- | ------------------ |
+| 0.1.1   | :white_check_mark: |
 | 0.1.0   | :white_check_mark: |
 | < 0.1.0 | :x:                |
 

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

@@ -30,32 +30,16 @@
   },
   {
    "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": [
@@ -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>Reasoner</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is reasoning</td><td>🤔 reasoning</td><td>Reasoner</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",
@@ -118,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,19 +115,9 @@
   },
   {
    "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)\n",
-      " - lives_in(alice, california)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Perform forward chaining to derive new facts\n",
     "# The Reasoner can infer facts directly from the knowledge graph or a list of facts\n",
@@ -176,17 +137,9 @@
   },
   {
    "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",
@@ -209,18 +162,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Explanation for backward chaining proof:\n",
-      "Given the premises: parent_of(alice, bob), parent_of(bob, charlie), we conclude: grandparent_of(alice, charlie).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "generator = ExplanationGenerator()\n",
     "\n",
@@ -265,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,18 +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",
@@ -327,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,19 +276,9 @@
   },
   {
    "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",
@@ -367,20 +292,9 @@
   },
   {
    "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",
@@ -394,18 +308,9 @@
   },
   {
    "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",

cookbook/advanced/09_Semantic_Layer_Construction.ipynb

@@ -30,32 +30,16 @@
   },
   {
    "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 -qU semantica\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,22 +58,9 @@
   },
   {
    "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 building</td><td>🧠 kg</td><td>GraphBuilder</td><td>-</td><td>0.13s</td></tr><tr><td>🔄</td><td>Semantica is building</td><td>🧠 kg</td><td>EntityResolver</td><td>-</td><td>7.37s</td></tr><tr><td>✅</td><td>Semantica is deduplicating</td><td>🔄 deduplication</td><td>DuplicateDetector</td><td>-</td><td>0.09s</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 generating</td><td>📚 ontology</td><td>OntologyGenerator</td><td>-</td><td>0.06s</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>RDFExporter</td><td>-</td><td>0.01s</td></tr><tr><td>✅</td><td>Semantica is storing</td><td>🗄️ triplet_store</td><td>BulkLoader</td><td>-</td><td>3.01s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "builder = GraphBuilder()\n",
     "\n",
@@ -119,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,17 +159,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exported knowledge graph to knowledge_graph.ttl\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "exporter = RDFExporter()\n",
     "# Export Knowledge Graph\n",

cookbook/advanced/11_Advanced_Context_Engineering.ipynb

@@ -42,53 +42,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "88491af5",
    "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",
-      "C:\\Users\\Mohd Kaif\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\utils\\_process_win32.py:124: ResourceWarning: unclosed file <_io.BufferedWriter name=3>\n",
-      "  return process_handler(cmd, _system_body)\n",
-      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-      "C:\\Users\\Mohd Kaif\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\utils\\_process_win32.py:124: ResourceWarning: unclosed file <_io.BufferedReader name=4>\n",
-      "  return process_handler(cmd, _system_body)\n",
-      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-      "C:\\Users\\Mohd Kaif\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\utils\\_process_win32.py:124: ResourceWarning: unclosed file <_io.BufferedReader name=5>\n",
-      "  return process_handler(cmd, _system_body)\n",
-      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install -qU semantica "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "d6401d91",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Libraries imported successfully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import sys\n",
     "import os\n",
@@ -129,25 +96,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "812158a5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "VectorStore initialized (Backend: FAISS)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "try:\n",
     "    # Initialize FAISS Vector Store\n",
@@ -173,28 +125,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "7c2aa896",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Connectivity check failed: Couldn't connect to localhost:7687 (resolved to ('[::1]:7687', '127.0.0.1:7687')):\n",
-      "Failed to establish connection to ResolvedIPv6Address(('::1', 7687, 0, 0)) (reason [WinError 10061] No connection could be made because the target machine actively refused it)\n",
-      "Failed to establish connection to ResolvedIPv4Address(('127.0.0.1', 7687)) (reason [WinError 10061] No connection could be made because the target machine actively refused it)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GraphStore Connection Failed: Failed to connect to Neo4j: Could not verify connectivity to Neo4j\n",
-      "   Switching to in-memory ContextGraph (Non-persistent fallback)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "try:\n",
     "    # Initialize Neo4j Graph Store\n",
@@ -243,18 +177,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "f3b2eff6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Agent Context is online and ready.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if vs:\n",
     "    context = AgentContext(\n",
@@ -293,18 +219,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "71adf433",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Memory Stored: mem_2ec8206c1601\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "user_id = \"user_123\"\n",
     "session_id = \"session_alpha\"\n",
@@ -332,18 +250,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "3a98ca53",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Knowledge Ingestion Stats: {'stored_count': 2, 'memory_ids': ['mem_4e6d93ce75a2', 'mem_50eecb14c339'], 'graph_nodes': 2, 'graph_edges': 0}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "documents = [\n",
     "    {\n",
@@ -393,27 +303,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "970b605c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\neo4j\\_sync\\driver.py:515: ResourceWarning: unclosed  BoltDriver: <neo4j._sync.driver.BoltDriver object at 0x00000221C7B9FB90>.\n",
-      "  _unclosed_resource_warn(self)\n",
-      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Manual Graph Build Complete: {'node_count': 6, 'edge_count': 0}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 1. Define Nodes\n",
     "entities = [\n",
@@ -450,19 +343,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "dc61c324",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Neighbors of 'project_apollo':\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Helper to print graph neighbors\n",
     "def inspect_node(node_id):\n",
@@ -508,41 +392,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "69381e8c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Asking: 'Who is responsible for the Python web framework project?'...\n",
-      "\n"
-     ]
-    },
-    {
-     "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Retrieved 3 context items:\n",
-      "\n",
-      "1. [Score: 0.54] Project Apollo is a next-gen web framework designed for high scalability....\n",
-      "\n",
-      "2. [Score: 0.32] Python 3.12 introduces significant performance improvements for async workloads....\n",
-      "\n",
-      "3. [Score: 0.21] I am working on a new project called 'Project Apollo' which uses Python and React....\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "query = \"Who is responsible for the Python web framework project?\"\n",
     "print(f\"Asking: '{query}'...\\n\")\n",
@@ -590,18 +443,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "0574b1b7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Chat History for session_alpha:\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get recent chat history for context window\n",
     "history = context.conversation(\n",
@@ -624,21 +469,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "cd472495",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "System Vital Signs:\n",
-      "   - Total Memories: 3\n",
-      "   - Graph Nodes:    N/A\n",
-      "   - Graph Edges:    N/A\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "stats = context.stats()\n",
     "print(\"System Vital Signs:\")\n",

cookbook/advanced/Advanced_Vector_Store_and_Search.ipynb

@@ -24,297 +24,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "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)"
-     ]
-    },
-    {
-     "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\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",
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-      "Requirement already satisfied: asttokens>=2.1.0 in c:\\users\\mohd kaif\\appdata\\roaming\\python\\python311\\site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=8.0.0->semantica) (2.4.1)\n",
-      "Requirement already satisfied: pure-eval in c:\\users\\mohd kaif\\appdata\\roaming\\python\\python311\\site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=8.0.0->semantica) (0.2.2)\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->onnxruntime>=1.17.0->semantica) (1.3.0)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install semantica\n"
    ]
@@ -330,25 +42,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Selected model: BAAI/bge-small-en-v1.5\n",
-      "Embedding dimension: 128\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.embeddings import TextEmbedder\n",
     "\n",
@@ -379,37 +75,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\threading.py:986: ResourceWarning: unclosed file <_io.BufferedWriter name=3>\n",
-      "  del self._target, self._args, self._kwargs\n",
-      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-      "c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\threading.py:986: ResourceWarning: unclosed file <_io.BufferedReader name=4>\n",
-      "  del self._target, self._args, self._kwargs\n",
-      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-      "c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\threading.py:986: ResourceWarning: unclosed file <_io.BufferedReader name=5>\n",
-      "  del self._target, self._args, self._kwargs\n",
-      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 5 most similar documents:\n",
-      "  1. Document doc_1935 (distance: 110.917)\n",
-      "  2. Document doc_3860 (distance: 111.535)\n",
-      "  3. Document doc_277 (distance: 113.270)\n",
-      "  4. Document doc_1903 (distance: 113.371)\n",
-      "  5. Document doc_2959 (distance: 113.612)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import FAISSStore\n",
     "import numpy as np\n",
@@ -449,29 +117,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Technology articles from 2024:\n",
-      "  - AI in Healthcare\n",
-      "  - Business Strategy\n",
-      "  - Data Science Guide\n",
-      "  - Marketing Tips\n",
-      "  - Machine Learning Basics\n",
-      "\n",
-      "All articles from 2024:\n",
-      "  - AI in Healthcare (Technology)\n",
-      "  - Business Strategy (Business)\n",
-      "  - Data Science Guide (Technology)\n",
-      "  - Marketing Tips (Business)\n",
-      "  - Machine Learning Basics (Technology)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import HybridSearch, MetadataFilter\n",
     "import numpy as np\n",
@@ -529,25 +177,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Combined results (fair ranking):\n",
-      "  1. Data Science Guide (score: 0.033)\n",
-      "  2. Machine Learning Basics (score: 0.016)\n",
-      "  3. AI in Healthcare (score: 0.016)\n",
-      "\n",
-      "Combined results (prefer recent):\n",
-      "  1. Data Science Guide (score: 0.929)\n",
-      "  2. AI in Healthcare (score: 0.630)\n",
-      "  3. Business Strategy (score: 0.595)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import SearchRanker\n",
     "\n",
@@ -605,21 +237,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Company A has 10 documents\n",
-      "Company B has 15 documents\n",
-      "\n",
-      "Admin can delete: True\n",
-      "User can delete: False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import NamespaceManager\n",
     "\n",

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",
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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",

cookbook/introduction/03_Document_Parsing.ipynb

@@ -260,6 +260,11 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "name": "python"
   }

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",
-     "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",
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-      "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",
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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",
-     "text": [
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-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
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-      "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"
-     ]
-    },
-    {
-     "name": "stdout",
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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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-     ]
-    },
-    {
-     "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",
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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",
-      "  Tokens: 100\n",
-      "  Characters: 461\n",
-      "  Ratio: 4.61 chars/token\n",
-      "\n",
-      "Chunk 2:\n",
-      "  Tokens: 100\n",
-      "  Characters: 501\n",
-      "  Ratio: 5.01 chars/token\n",
-      "\n",
-      "Chunk 3:\n",
-      "  Tokens: 31\n",
-      "  Characters: 150\n",
-      "  Ratio: 4.84 chars/token\n",
-      "\n",
-      "Chunk 4:\n",
-      "  Tokens: 20\n",
-      "  Characters: 78\n",
-      "  Ratio: 3.90 chars/token\n",
-      "\n",
-      "Chunk 5:\n",
-      "  Tokens: 19\n",
-      "  Characters: 76\n",
-      "  Ratio: 4.00 chars/token\n",
-      "\n",
-      "Chunk 6:\n",
-      "  Tokens: 18\n",
-      "  Characters: 74\n",
-      "  Ratio: 4.11 chars/token\n",
-      "\n",
-      "Chunk 7:\n",
-      "  Tokens: 17\n",
-      "  Characters: 70\n",
-      "  Ratio: 4.12 chars/token\n",
-      "\n",
-      "Chunk 8:\n",
-      "  Tokens: 16\n",
-      "  Characters: 64\n",
-      "  Ratio: 4.00 chars/token\n",
-      "\n",
-      "Chunk 9:\n",
-      "  Tokens: 15\n",
-      "  Characters: 63\n",
-      "  Ratio: 4.20 chars/token\n",
-      "\n",
-      "Chunk 10:\n",
-      "  Tokens: 14\n",
-      "  Characters: 59\n",
-      "  Ratio: 4.21 chars/token\n",
-      "\n",
-      "Chunk 11:\n",
-      "  Tokens: 13\n",
-      "  Characters: 55\n",
-      "  Ratio: 4.23 chars/token\n",
-      "\n",
-      "Chunk 12:\n",
-      "  Tokens: 12\n",
-      "  Characters: 51\n",
-      "  Ratio: 4.25 chars/token\n",
-      "\n",
-      "Chunk 13:\n",
-      "  Tokens: 11\n",
-      "  Characters: 45\n",
-      "  Ratio: 4.09 chars/token\n",
-      "\n",
-      "Chunk 14:\n",
-      "  Tokens: 10\n",
-      "  Characters: 41\n",
-      "  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": [
-    {
-     "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",
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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/use_cases/finance/01_Financial_Data_Integration_MCP.ipynb

@@ -57,32 +57,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\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 -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq sentence-transformers scikit-learn\n"
    ]
@@ -96,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,23 +97,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ingesting from 8 feed sources...\n",
-      "🧠 Semantica is ingesting: 429 Client Error: Too Many Requests for url: https://feeds.finance.yahoo.com/rss/2.0/headline ❌📥 (1.0s) | 🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥  [2/8] Financial Times: 10 documents\n",
-      "🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested 30 items |███████████████| 100.0% ✅📥  [3/8] Bloomberg: 30 documents\n",
-      "🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.marketwatch.com/404?origin=feeds ❌📥 (3.2s) | 🧠 Semantica is ingesting: Ingested 30 items |███████████████| 100.0% ✅📥  [5/8] Seeking Alpha: 30 documents\n",
-      "🧠 Semantica is ingesting: Ingested 30 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥  [6/8] Investing.com: 10 documents\n",
-      "🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.fnlondon.com/rss ❌📥 (0.5s) | 🧠 Semantica is ingesting: Ingested 20 items |███████████████| 100.0% ✅📥  [8/8] Wall Street Journal: 20 documents\n",
-      "Ingested 100 documents\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.ingest import MCPIngestor, WebIngestor, FeedIngestor, FileIngestor\n",
     "import os\n",
@@ -220,17 +183,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 6 seed data items for market foundation\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.seed import SeedDataManager\n",
     "\n",
@@ -270,19 +225,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parsing 100 documents...\n",
-      "🧠 Semantica is parsing: Document file not found: Jonathan Ferro, Lisa Abramowicz and Annmarie Hordern speak daily with leaders and decision makers from Wall Street to Washington and beyond. No other program better positions investors and executives for the trading day. (Source: Bloomberg) ❌🔍 (0.0s) | 🧠 Semantica is parsing: Document file not found: <p>A look at some of the year’s notable trades.<\\p> ❌🔍 (0.0s)ay. (Source: Bloomberg) ❌🔍 (0.0s)  Parsed 50/100 documents...\n",
-      "🧠 Semantica is parsing: Document file not found: Maverick Natural Resources has operations in Texas and Oklahoma. ❌🔍 (0.0s) | 🧠 Semantica is parsing: Document file not found: The president has told Bank of America and JPMorgan Chase to stop cutting conservatives off from doing business. ❌🔍 (0.0s)  Parsed 100/100 documents...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.parse import DocumentParser\n",
     "\n",
@@ -314,23 +259,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Normalizing 100 documents...\n",
-      "🧠 Semantica is parsing: Document file not found: The president has told Bank of America and JPMorgan Chase to stop cutting conservatives off from doing business. ❌🔍 (0.0s) | 🧠 Normalizing text 🔄🔧 (0.0s)  Normalized 50/100 documents...\n",
-      "🧠 Semantica is parsing: Document file not found: The president has told Bank of America and JPMorgan Chase to stop cutting conservatives off from doing business. ❌🔍 (0.0s) | 🧠 Normalizing text 🔄🔧 (0.0s)  Normalized 100/100 documents...\n",
-      "Chunking 100 documents...\n",
-      "  Chunked 50/100 documents (50 chunks so far)\n",
-      "  Chunked 100/100 documents (100 chunks so far)\n",
-      "Created 100 chunks from 100 documents\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.normalize import TextNormalizer\n",
     "from semantica.split import TextSplitter\n",
@@ -380,23 +311,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting entities from 100 chunks...\n",
-      "🧠 Normalizing text 🔄🔧 (0.0s) | 🧠 Semantica is extracting: Extracted 4 entities using ml |███████████████| 100.0% ✅🎯  Processed 20/100 chunks (56 entities found)\n",
-      "🧠 Normalizing text 🔄🔧 (0.0s) | 🧠 Semantica is extracting: Extracted 5 entities using ml |███████████████| 100.0% ✅🎯�  Processed 40/100 chunks (160 entities found)\n",
-      "🧠 Normalizing text 🔄🔧 (0.0s) | 🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯  Processed 60/100 chunks (182 entities found)\n",
-      "🧠 Normalizing text 🔄🔧 (0.0s) | 🧠 Semantica is extracting: Extracted 1 entities using ml |███████████████| 100.0% ✅🎯  Processed 80/100 chunks (215 entities found)\n",
-      "🧠 Normalizing text 🔄🔧 (0.0s) | 🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯  Processed 100/100 chunks (264 entities found)\n",
-      "Extracted 66 companies/organizations, 59 markets/locations, 8 prices, 24 metrics\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import NERExtractor\n",
     "\n",
@@ -436,23 +353,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting relationships from 100 chunks...\n",
-      "🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 4 relations using dependency |███████████████| 100.0% ✅🎯  Processed 20/100 chunks (49 relationships found)\n",
-      "🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 1 relations using dependency |███████████████| 100.0% ✅🎯  Processed 40/100 chunks (111 relationships found)\n",
-      "🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 0 relations |███████████████| 100.0% ✅🎯█████| 100.0% ✅🎯  Processed 60/100 chunks (121 relationships found)\n",
-      "🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 0 relations |███████████████| 100.0% ✅🎯█████| 100.0% ✅🎯  Processed 80/100 chunks (135 relationships found)\n",
-      "🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 3 relations using dependency |███████████████| 100.0% ✅🎯  Processed 100/100 chunks (175 relationships found)\n",
-      "Extracted 175 relationships\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.semantic_extract import RelationExtractor\n",
     "\n",
@@ -500,19 +403,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detecting temporal conflicts in 264 entities...\n",
-      "🧠 Semantica is extracting: Extracted 3 relations using dependency |███████████████| 100.0% ✅🎯 | 🧠 Semantica is resolving: Detected 0 temporal conflicts |███████████████| 100.0% [165/193] ✅⚠️ (223.9/s)████████░░░| 85.5% [165/193] 🔄⚠️ (ETA: 0.1s | 225.1/s)Detected 0 temporal conflicts\n",
-      "No conflicts detected\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.conflicts import ConflictDetector, ConflictResolver\n",
     "\n",
@@ -551,20 +444,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting 264 entities to dictionaries...\n",
-      "Resolving duplicates in 264 entities...\n",
-      "🧠 Semantica is deduplicating: Merging groups... 1/1 (remaining: 0) |███████████████| 100.0% [1/1] 🔄🔄 (16.4/s) | 🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (258.3/s) 🔄🔄 (ETA: 0.0s | 374.6/s)333.9/s)Converting 36 resolved entities back to Entity objects...\n",
-      "Deduplicated 264 entities to 36 unique entities\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.kg import EntityResolver\n",
     "from semantica.semantic_extract import Entity\n",
@@ -605,27 +487,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Building knowledge graph...\n",
-      "🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (258.3/s) | 🧠 Semantica is building: Processing relationships... 175/175 |███████████████| 100.0% [175/175] 🔄🧠 (27889.8/s) 15937.0/s)Building graph structure...\n",
-      "✅ Graph structure built (0.00s)\n",
-      "🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (258.3/s) | 🧠 Semantica is building: Processing relationships... 175/175 |███████████████| 100.0% [175/175] 🔄🧠 (27889.8/s)\n",
-      "============================================================\n",
-      "✅ Knowledge Graph Build Complete\n",
-      "   Entities: 36\n",
-      "   Relationships: 175\n",
-      "   Total time: 0.86s\n",
-      "============================================================\n",
-      "Graph: 36 entities, 175 relationships\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.kg import GraphBuilder\n",
     "\n",
@@ -653,25 +517,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generating embeddings for 66 companies and 59 markets...\n",
-      "Generated 66 company embeddings and 59 market embeddings\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.embeddings import EmbeddingGenerator\n",
     "\n",
@@ -699,25 +547,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Storing 66 company vectors and 59 market vectors...\n",
-      "🧠 Semantica is building: Processing relationships... 175/175 |███████████████| 100.0% [175/175] 🔄🧠 (27889.8/s) | 🧠 Semantica is indexing: Storing 66 vectors 🔄📊 (0.0s)Stored 66 company vectors and 59 market vectors\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.vector_store import VectorStore\n",
     "\n",
@@ -746,21 +578,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🧠 Semantica is indexing: Storing 66 vectors 🔄📊 (0.0s) | 🧠 Semantica is building: Calculating degree centrality 🔄🧠 (0.0s)Graph analytics:\n",
-      "  - Graph density: 0.000\n",
-      "  - Central nodes (degree): 4\n",
-      "  - Total entities: 36\n",
-      "  - Total relationships: 175\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.kg import GraphAnalyzer, CentralityCalculator\n",
     "\n",
@@ -806,75 +626,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "GraphRAG Query: What technology companies are in the market and what are their key relationships?\n",
-      "================================================================================\n",
-      "\n",
-      "🧠 Semantica is embedding: Generated embedding (dim: 128) |███████████████| 100.0% ✅💾 | 🧠 Semantica is processing: Ranking results... 🔄🔗 (0.0s)technology companies are in the market?... 🔄🔗 (0.0s)🔄🔗 (0.0s)s)ket?... 🔄🔗 (0.0s)"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Embedding generation failed: Text cannot be empty or whitespace-only\n",
-      "Using random fallback embedding\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "Generated Answer (with Multi-hop Reasoning):\n",
-      "================================================================================\n",
-      "Based on the retrieved context and reasoning paths, I can identify some technology companies and their key relationships. However, the context provided does not directly mention specific technology companies. \n",
-      "\n",
-      "One entity that is mentioned as a GPE (Geopolitical Entity) is Russia, which is related to the year through the reasoning path \"Russia --[be]--> the year\" (Path 1). This connection suggests that Russia is associated with a specific year, but the year is not explicitly stated.\n",
-      "\n",
-      "Another entity mentioned is Ukraine, which is also a GPE. It is related to the year through the reasoning path \"Ukraine --[be]--> the year\" (Path 1), similar to Russia. \n",
-      "\n",
-      "The only technology company mentioned in the context is Nvidia, but it is not related to any other entities or relationships in the context.\n",
-      "\n",
-      "To answer the question, I would say that there are limited technology companies mentioned in the context, and their relationships are not explicitly stated. However, based on the reasoning paths, we can infer that Russia and Ukraine are associated with a specific year.\n",
-      "\n",
-      "Key relationships:\n",
-      "\n",
-      "- Russia and Ukraine are associated with a specific year.\n",
-      "- The year is related to Russia and Ukraine through the reasoning path \"Russia --[be]--> the year\" (Path 1) and \"Ukraine --[be]--> the year\" (Path 1).\n",
-      "\n",
-      "Note: The context does not provide sufficient information to identify specific technology companies and their relationships.\n",
-      "\n",
-      "================================================================================\n",
-      "\n",
-      "Reasoning Details:\n",
-      "- Confidence: 0.636\n",
-      "- Sources: 15\n",
-      "- Reasoning Paths: 49\n",
-      "- Total entities in graph: 36\n",
-      "- Total relationships in graph: 175\n",
-      "\n",
-      "Top Sources:\n",
-      "  1. Score: 0.736\n",
-      "     the final days of the year is a DATE. is outstriped by Warren Buffett. is rallyed by Masayoshi Son’s. say Russia (GPE). buy Donald Trump’s (PERSON), New York City (GPE). is selled by Jonathan Ferro....\n",
-      "  2. Score: 0.728\n",
-      "     2026 is a DATE. is looked by Bloomberg’s Skylar Montgomery Koning....\n",
-      "  3. Score: 0.651\n",
-      "     the year is a DATE. is beed by Russia (GPE). sell the Christmas holiday, Ukraine (GPE), Pinnacle Group and 1 more. rally the Christmas holiday, Ukraine (GPE)....\n",
-      "  4. Score: 0.597\n",
-      "     Ukraine is a GPE. position the trading day. is selled by the year (DATE). is broadened by Joe Weisenthal. is rallyed by the year (DATE)....\n",
-      "  5. Score: 0.591\n",
-      "     Nvidia is a GPE....\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.context import AgentContext\n",
     "from semantica.llms import Groq\n",
@@ -937,17 +691,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🧠 Semantica is processing: Ranking results... 🔄🔗 (0.0s) | 🧠 Semantica is exporting: Exporting graph to json: financial_data_kg.json 🔄💾 (0.0s)Exported financial knowledge graph to JSON, GraphML, and CSV formats\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from semantica.export import GraphExporter\n",
     "\n",

cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb

@@ -6,87 +6,67 @@
    "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/use_cases/finance/03_Earnings_Call_Analysis.ipynb)\n",
     "\n",
-    "# Earnings Call Transcript Analysis with Docling and Semantica\n",
+    "# Earnings Call Analysis with Docling and Semantica: MDA Space Q3 2025\n",
     "\n",
-    "## 📄 Earnings Call Transcript PDF\n",
+    "##  Data Sources\n",
     "\n",
-    "**Example:** Download a single earnings call transcript PDF from [SEC EDGAR](https://www.sec.gov/edgar/searchedgar/companysearch.html) or company investor relations pages. Use one PDF file for this analysis.\n",
+    "This analysis dual-tracks two critical documents from **MDA Space Ltd.** for the third quarter of 2025 to provide a holistic view of the company's performance and strategy:\n",
+    "1. **Press Release:** A structured summary of financial results, strategic milestones, and executive commentary.\n",
+    "2. **Earnings Transcript:** A detailed record of the management presentation and the subsequent Q&A session with analysts, providing deeper qualitative context.\n",
+    "\n",
+    "###  Q3 2025 Strategic Highlights\n",
+    "* **Robust Backlog:** The company maintained a significant backlog at quarter-end, providing strong revenue visibility and supporting long-term growth objectives.\n",
+    "* **Strong Revenue Growth:** Substantial year-over-year revenue expansion driven by successful execution across major programs and satellite system deliveries.\n",
+    "* **Profitability & Margins:** Strong operational performance led to a significant increase in Adjusted EBITDA and healthy overall margin expansion.\n",
+    "* **Earnings Performance:** Noteworthy growth in adjusted net income and earnings per share, reflecting scaled operations and improved bottom-line efficiency.\n",
+    "* **Cash Flow & Capital Structure:** Positive operating cash flow and a healthy balance sheet with a very low leverage ratio, positioning the company well for future investment.\n",
     "\n",
     "---\n",
     "\n",
     "## Overview\n",
     "\n",
-    "Extract insights from earnings call transcripts by building a knowledge graph with entity extraction, relationship mapping, and GraphRAG-powered Q&A.\n",
+    "This notebook demonstrates how to extract deep insights from unstructured financial documents. By combining **Docling** for high-fidelity parsing with **Semantica** for knowledge engineering, we build a structured semantic layer that enables complex analysis and advanced GraphRAG capabilities.\n",
     "\n",
-    "**Workflow:** `PDF → Parse → Extract Entities/Relations → Build KG → GraphRAG → Answers`\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "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"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install -qU semantica \n"
+    "**Workflow:** `Dual PDF Input → Docling Parsing → Entity & Relation Extraction → Knowledge Graph Construction → GraphRAG → Strategic Q&A`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "OSError",
-     "evalue": "[WinError 1114] A dynamic link library (DLL) initialization routine failed. Error loading \"c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\lib\\c10.dll\" or one of its dependencies.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[2], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Initialize Groq LLM provider\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msemantica\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mllms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Groq\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m      5\u001b[0m GROQ_API_KEY \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgsk_7BADli5sBUZ4NFBmeIreWGdyb3FYinqEphDW4FUZ0Lg4jV7WXr85\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\semantica\\llms\\__init__.py:40\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;124;03mLLM Providers Module\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[38;5;124;03mLicense: MIT\u001b[39;00m\n\u001b[0;32m     38\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroq\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Groq\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mopenai\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m OpenAI\n\u001b[0;32m     42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhuggingface\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m HuggingFaceLLM\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\semantica\\llms\\groq.py:9\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;124;03mGroq LLM Provider\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03mWrapper for Groq API provider with clean interface.\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, Optional\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msemantic_extract\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mproviders\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m GroqProvider\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ProcessingError\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlogging\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_logger\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\semantica\\semantic_extract\\__init__.py:51\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, List, Optional, Union\n\u001b[0;32m     50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Config, config\n\u001b[1;32m---> 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcoreference_resolver\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     52\u001b[0m     CoreferenceChain,\n\u001b[0;32m     53\u001b[0m     CoreferenceChainBuilder,\n\u001b[0;32m     54\u001b[0m     CoreferenceResolver,\n\u001b[0;32m     55\u001b[0m     EntityCoreferenceDetector,\n\u001b[0;32m     56\u001b[0m     Mention,\n\u001b[0;32m     57\u001b[0m     PronounResolver,\n\u001b[0;32m     58\u001b[0m )\n\u001b[0;32m     59\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mevent_detector\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     60\u001b[0m     Event,\n\u001b[0;32m     61\u001b[0m     EventClassifier,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     64\u001b[0m     TemporalEventProcessor,\n\u001b[0;32m     65\u001b[0m )\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mextraction_validator\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ExtractionValidator, ValidationResult\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\semantica\\semantic_extract\\coreference_resolver.py:67\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlogging\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_logger\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprogress_tracker\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_progress_tracker\n\u001b[1;32m---> 67\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mner_extractor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Entity\n\u001b[0;32m     70\u001b[0m \u001b[38;5;129m@dataclass\u001b[39m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mMention\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Mention representation.\"\"\"\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\semantica\\semantic_extract\\ner_extractor.py:75\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprogress_tracker\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_progress_tracker\n\u001b[0;32m     74\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 75\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mspacy\u001b[39;00m\n\u001b[0;32m     77\u001b[0m     SPACY_AVAILABLE \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\spacy\\__init__.py:6\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, Iterable, Union\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# set library-specific custom warning handling before doing anything else\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01merrors\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m setup_default_warnings\n\u001b[0;32m      8\u001b[0m setup_default_warnings()  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# These are imported as part of the API\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\spacy\\errors.py:3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Literal\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mErrorsWithCodes\u001b[39;00m(\u001b[38;5;28mtype\u001b[39m):\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;28mself\u001b[39m, code):\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\spacy\\compat.py:5\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"Helpers for Python and platform compatibility.\"\"\"\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mthinc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m copy_array\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m      8\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mcPickle\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpickle\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\thinc\\__init__.py:5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mabout\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m __version__\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m registry\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# fmt: off\u001b[39;00m\n\u001b[0;32m      8\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mregistry\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     10\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__version__\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     11\u001b[0m ]\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\thinc\\config.py:5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mconfection\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mconfection\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m VARIABLE_RE, Config, ConfigValidationError, Promise\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtypes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Decorator\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mregistry\u001b[39;00m(confection\u001b[38;5;241m.\u001b[39mregistry):\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;66;03m# fmt: off\u001b[39;00m\n\u001b[0;32m     10\u001b[0m     optimizers: Decorator \u001b[38;5;241m=\u001b[39m catalogue\u001b[38;5;241m.\u001b[39mcreate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthinc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptimizers\u001b[39m\u001b[38;5;124m\"\u001b[39m, entry_points\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\thinc\\types.py:27\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpydantic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m GetCoreSchemaHandler\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpydantic_core\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m core_schema\n\u001b[1;32m---> 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m cupy, has_cupy\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_cupy:\n\u001b[0;32m     30\u001b[0m     get_array_module \u001b[38;5;241m=\u001b[39m cupy\u001b[38;5;241m.\u001b[39mget_array_module\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\thinc\\compat.py:35\u001b[0m\n\u001b[0;32m     31\u001b[0m     has_cupy_gpu \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m     34\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[1;32m---> 35\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m     36\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdlpack\u001b[39;00m\n\u001b[0;32m     38\u001b[0m     has_torch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\__init__.py:281\u001b[0m\n\u001b[0;32m    277\u001b[0m                     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m    279\u001b[0m         kernel32\u001b[38;5;241m.\u001b[39mSetErrorMode(prev_error_mode)\n\u001b[1;32m--> 281\u001b[0m     \u001b[43m_load_dll_libraries\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    282\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m _load_dll_libraries\n\u001b[0;32m    285\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_get_cuda_dep_paths\u001b[39m(path: \u001b[38;5;28mstr\u001b[39m, lib_folder: \u001b[38;5;28mstr\u001b[39m, lib_name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[0;32m    286\u001b[0m     \u001b[38;5;66;03m# Libraries can either be in\u001b[39;00m\n\u001b[0;32m    287\u001b[0m     \u001b[38;5;66;03m# path/nvidia/lib_folder/lib or\u001b[39;00m\n\u001b[0;32m    288\u001b[0m     \u001b[38;5;66;03m# path/nvidia/cuXX/lib (since CUDA 13.0) or\u001b[39;00m\n\u001b[0;32m    289\u001b[0m     \u001b[38;5;66;03m# path/lib_folder/lib\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\__init__.py:264\u001b[0m, in \u001b[0;36m_load_dll_libraries\u001b[1;34m()\u001b[0m\n\u001b[0;32m    260\u001b[0m     err \u001b[38;5;241m=\u001b[39m ctypes\u001b[38;5;241m.\u001b[39mWinError(last_error)\n\u001b[0;32m    261\u001b[0m     err\u001b[38;5;241m.\u001b[39mstrerror \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    262\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m Error loading \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdll\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m or one of its dependencies.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    263\u001b[0m     )\n\u001b[1;32m--> 264\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m    265\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m res \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    266\u001b[0m     is_loaded \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "\u001b[1;31mOSError\u001b[0m: [WinError 1114] A dynamic link library (DLL) initialization routine failed. Error loading \"c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\lib\\c10.dll\" or one of its dependencies."
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "!pip install -qU semantica docling pdfplumber groq\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Initialize Groq LLM provider\n",
     "from semantica.llms import Groq\n",
     "import os\n",
     "\n",
-    "GROQ_API_KEY = \"\"\n",
+    "GROQ_API_KEY = os.getenv(\"GROQ_API_KEY\", \"\")\n",
+    "\n",
+    "if not GROQ_API_KEY:\n",
+    "    try:\n",
+    "        from google.colab import userdata\n",
+    "        GROQ_API_KEY = userdata.get(\"GROQ_API_KEY\", \"\")\n",
+    "    except ImportError:\n",
+    "        pass\n",
+    "\n",
+    "if not GROQ_API_KEY:\n",
+    "    raise ValueError(\"GROQ_API_KEY not found. Please set it as an environment variable or update this cell.\")\n",
+    "\n",
+    "os.environ[\"GROQ_API_KEY\"] = GROQ_API_KEY\n",
     "    \n",
     "groq_llm = Groq(\n",
-    "        model=\"llama-3.1-8b-instant\",\n",
-    "        api_key=os.getenv(\"GROQ_API_KEY\", GROQ_API_KEY))\n",
+    "    model=\"llama-3.1-8b-instant\",\n",
+    "    api_key=GROQ_API_KEY\n",
+    ")\n",
     "\n",
     "print(f\"✓ Groq LLM initialized: {groq_llm.model}\")\n"
    ]
@@ -106,35 +86,55 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Step 1: Parse PDF with Docling\n",
+    "import requests\n",
+    "from pathlib import Path\n",
     "from semantica.parse import DoclingParser\n",
     "\n",
-    "parser = DoclingParser(\n",
-    "    export_format=\"markdown\",\n",
-    "    enable_ocr=False,\n",
-    "    table_extraction_mode=\"auto\"\n",
-    ")\n",
+    "# Initialize DoclingParser with default settings\n",
+    "# Optional: configure with export_format=\"html\" or enable_ocr=True if needed\n",
+    "parser = DoclingParser()\n",
     "\n",
-    "# Example parsed document (replace with actual PDF parsing)\n",
+    "# PDF URLs for MDA Space Q3 2025 earnings documents\n",
+    "press_release_url = \"https://filecache.investorroom.com/mr5ircnw_mda/677/MDA_Space_Ltd_Q3_2025_Press_Release_Nov_14_2025_FINAL.pdf\"\n",
+    "transcript_url = \"https://filecache.investorroom.com/mr5ircnw_mda/681/MDA%20Space%20Ltd.%20Q3%202025%20Earnings%20Conference%20Call%20Transcript%20%28November%2014%202025%29.pdf\"\n",
+    "\n",
+    "# Setup download directory\n",
+    "download_dir = Path(\"downloads\")\n",
+    "download_dir.mkdir(exist_ok=True)\n",
+    "\n",
+    "press_release_pdf = download_dir / \"mda_space_q3_2025_press_release.pdf\"\n",
+    "transcript_pdf = download_dir / \"mda_space_q3_2025_transcript.pdf\"\n",
+    "\n",
+    "# Download PDFs if they don't exist\n",
+    "if not press_release_pdf.exists():\n",
+    "    press_release_pdf.write_bytes(requests.get(press_release_url).content)\n",
+    "\n",
+    "if not transcript_pdf.exists():\n",
+    "    transcript_pdf.write_bytes(requests.get(transcript_url).content)\n",
+    "\n",
+    "# Parse documents using DoclingParser\n",
+    "try:\n",
+    "    press_release = parser.parse(press_release_pdf)\n",
+    "    transcript = parser.parse(transcript_pdf)\n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️ Parsing failed: {e}\")\n",
+    "    print(\"Using fallback empty documents for demonstration.\")\n",
+    "    press_release = {\"full_text\": \"\", \"tables\": []}\n",
+    "    transcript = {\"full_text\": \"\", \"tables\": []}\n",
+    "\n",
+    "# Combine parsed documents\n",
     "parsed_doc = {\n",
-    "    \"full_text\": \"\"\"Q1 2024 Earnings Call Transcript\n",
-    "\n",
-    "Company: TechCorp Inc.\n",
-    "Date: January 25, 2024\n",
-    "\n",
-    "Prepared Remarks:\n",
-    "Our revenue for Q1 2024 was $2.5 billion, representing 15% year-over-year growth. \n",
-    "EPS was $1.25 per share. We expect Q2 revenue to be between $2.6 and $2.8 billion.\n",
-    "\n",
-    "Q&A Session:\n",
-    "Analyst: What's your guidance for the full year?\n",
-    "CEO: We're maintaining our full-year guidance of $10.5 to $11 billion in revenue.\"\"\",\n",
-    "    \"tables\": [],\n",
-    "    \"metadata\": {\"title\": \"Q1 2024 Earnings Call\"}\n",
+    "    \"full_text\": f\"# Press Release\\n\\n{press_release['full_text']}\\n\\n# Transcript\\n\\n{transcript['full_text']}\",\n",
+    "    \"tables\": press_release['tables'] + transcript['tables'],\n",
+    "    \"metadata\": {\n",
+    "        \"title\": \"MDA Space Ltd. Q3 2025 Earnings Analysis\",\n",
+    "        \"company\": \"MDA Space Ltd.\",\n",
+    "        \"quarter\": \"Q3 2025\",\n",
+    "        \"date\": \"November 14, 2025\"\n",
+    "    }\n",
     "}\n",
     "\n",
-    "print(f\"✓ Document parsed: {parsed_doc['metadata'].get('title', 'Unknown')}\")\n",
-    "print(f\"  Text length: {len(parsed_doc['full_text'])} characters\")\n"
+    "print(f\"✓ Parsed {len(parsed_doc['tables'])} tables from {len(press_release['tables']) + len(transcript['tables'])} documents\")\n"
    ]
   },
   {
@@ -152,24 +152,65 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Step 2: Normalize text with Semantica\n",
+    "# Step 2: Normalize full document text (run ONCE)\n",
     "from semantica.normalize import TextNormalizer\n",
+    "# Initialize normalizer\n",
+    "normalizer = TextNormalizer()\n",
     "\n",
-    "text_normalizer = TextNormalizer()\n",
-    "normalized_text = text_normalizer.normalize(\n",
+    "normalized_text = normalizer.normalize(\n",
     "    parsed_doc[\"full_text\"],\n",
-    "    case=\"lower\",\n",
-    "    remove_extra_whitespace=True\n",
+    "    clean_html=False,\n",
+    "    remove_extra_whitespace=False,\n",
+    "    lowercase=False        # preserve casing for entities & finance terms\n",
     ")\n",
     "\n",
-    "print(f\"✓ Text normalized: {len(normalized_text)} characters\")\n"
+    "print(f\"Text normalized: {len(normalized_text)} characters\")\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 3: Extract Entities\n",
+    "## Step 3: Split Text into Chunks\n",
+    "\n",
+    "Split the normalized text into overlapping chunks to enable scalable and accurate entity and relation extraction.\n",
+    "This step prepares the text for LLM-based semantic processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from semantica.split import TextSplitter\n",
+    "CHUNK_SIZE = 1000\n",
+    "CHUNK_OVERLAP = 250\n",
+    "\n",
+    "splitter = TextSplitter(\n",
+    "    method=\"recursive\",   # safest for long PDFs\n",
+    "    chunk_size=CHUNK_SIZE,\n",
+    "    chunk_overlap=CHUNK_OVERLAP\n",
+    ")\n",
+    "\n",
+    "chunks = splitter.split(normalized_text)\n",
+    "\n",
+    "print(f\"✓ Created {len(chunks)} chunks\")\n",
+    "\n",
+    "# Version-safe access to chunk text\n",
+    "def get_chunk_text(chunk):\n",
+    "    return getattr(chunk, \"content\", getattr(chunk, \"text\", \"\"))\n",
+    "\n",
+    "# Inspect one chunk\n",
+    "print(\"Sample chunk:\\n\", get_chunk_text(chunks[0])[:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Extract Entities\n",
     "\n",
     "Extract entities (organizations, people, financial terms) using NERExtractor with Groq LLM.\n"
    ]
@@ -180,31 +221,60 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Step 3: Extract entities using NERExtractor with Groq\n",
-    "from semantica.semantic_extract import NERExtractor\n",
+    "# Step 4: Extract entities from ALL chunks using NERExtractor (Groq)\n",
     "\n",
+    "from semantica.semantic_extract import NERExtractor\n",
+    "import os\n",
+    "\n",
+    "# Initialize NER extractor\n",
     "ner = NERExtractor(\n",
     "    method=\"llm\",\n",
     "    provider=\"groq\",\n",
     "    llm_model=\"llama-3.1-8b-instant\",\n",
-    "    min_confidence=0.7\n",
+    "    min_confidence=0.5,\n",
+    "    temperature=0.0,\n",
+    "    api_key=os.getenv(\"GROQ_API_KEY\")\n",
     ")\n",
     "\n",
-    "entities = ner.extract_entities(\n",
-    "    normalized_text,\n",
-    "    entity_types=[\"ORG\", \"PERSON\", \"MONEY\", \"DATE\", \"PERCENT\"]\n",
-    ")\n",
+    "entity_types = [\n",
+    "    \"ORGANIZATION\", \"ORG\", \"PERSON\", \"MONEY\", \"CURRENCY\",\n",
+    "    \"PERCENT\", \"PERCENTAGE\", \"DATE\", \"TIME\", \"PRODUCT\",\n",
+    "    \"LOCATION\", \"GPE\", \"EVENT\", \"QUANTITY\", \"CARDINAL\"\n",
+    "]\n",
     "\n",
-    "print(f\"✓ Entities extracted: {len(entities)}\")\n",
-    "if entities:\n",
-    "    print(f\"  Sample: {entities[0].text} ({entities[0].label})\")\n"
+    "# Version-safe chunk text accessor\n",
+    "def get_chunk_text(chunk):\n",
+    "    return getattr(chunk, \"content\", getattr(chunk, \"text\", \"\"))\n",
+    "\n",
+    "all_entities = []\n",
+    "\n",
+    "for i, chunk in enumerate(chunks, 1):\n",
+    "    text = get_chunk_text(chunk)\n",
+    "\n",
+    "    if not text.strip():\n",
+    "        continue\n",
+    "\n",
+    "    try:\n",
+    "        entities = ner.extract_entities(\n",
+    "            text,\n",
+    "            entity_types=entity_types\n",
+    "        )\n",
+    "        all_entities.extend(entities)\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(f\"⚠️ Chunk {i} failed: {e}\")\n",
+    "\n",
+    "    if i % 10 == 0 or i == len(chunks):\n",
+    "        print(f\"Processed {i}/{len(chunks)} chunks\")\n",
+    "\n",
+    "print(f\"✓ Total entities extracted: {len(all_entities)}\")\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 4: Extract Financial Metrics\n",
+    "## Step 5: Extract Financial Metrics\n",
     "\n",
     "Extract financial metrics (money, percentages, dates) from text and tables.\n"
    ]
@@ -215,19 +285,60 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Step 4: Extract financial metrics using NERExtractor\n",
-    "financial_entities = ner.extract_entities(\n",
-    "    normalized_text,\n",
-    "    entity_types=[\"MONEY\", \"PERCENT\", \"DATE\"]\n",
-    ")\n",
+    "# Step 5: Extract financial metrics from ALL chunks (explicit + safe)\n",
     "\n",
-    "financial_metrics = {}\n",
-    "for entity in financial_entities:\n",
-    "    if entity.label == \"MONEY\":\n",
-    "        financial_metrics[entity.text] = entity.text\n",
+    "financial_entity_types = [\n",
+    "    \"MONEY\", \"CURRENCY\", \"PERCENT\", \"PERCENTAGE\",\n",
+    "    \"QUANTITY\", \"CARDINAL\"\n",
+    "]\n",
     "\n",
-    "print(f\"✓ Financial entities: {len(financial_entities)}\")\n",
-    "print(f\"  Financial metrics: {len(financial_metrics)}\")\n"
+    "def get_chunk_text(chunk):\n",
+    "    return getattr(chunk, \"content\", getattr(chunk, \"text\", \"\"))\n",
+    "\n",
+    "financial_entities = []\n",
+    "\n",
+    "total_chunks = len(chunks)\n",
+    "print(f\"Processing {total_chunks} chunks for financial entities...\")\n",
+    "\n",
+    "for idx, chunk in enumerate(chunks, start=1):\n",
+    "    text = get_chunk_text(chunk)\n",
+    "\n",
+    "    # Skip empty chunks but still count them\n",
+    "    if not text.strip():\n",
+    "        print(f\"  Skipping empty chunk {idx}/{total_chunks}\")\n",
+    "        continue\n",
+    "\n",
+    "    # ALWAYS run NER per chunk\n",
+    "    entities = ner.extract_entities(\n",
+    "        text,\n",
+    "        entity_types=financial_entity_types\n",
+    "    )\n",
+    "\n",
+    "    financial_entities.extend(entities)\n",
+    "\n",
+    "    if idx % 10 == 0 or idx == total_chunks:\n",
+    "        print(f\"  Processed {idx}/{total_chunks} chunks\")\n",
+    "\n",
+    "# Aggregate results\n",
+    "financial_metrics = {\"money\": [], \"percentages\": [], \"quantities\": []}\n",
+    "\n",
+    "for e in financial_entities:\n",
+    "    label = e.label.lower()\n",
+    "    if \"money\" in label or \"currency\" in label:\n",
+    "        financial_metrics[\"money\"].append(e.text)\n",
+    "    elif \"percent\" in label:\n",
+    "        financial_metrics[\"percentages\"].append(e.text)\n",
+    "    elif \"quantity\" in label or \"cardinal\" in label:\n",
+    "        financial_metrics[\"quantities\"].append(e.text)\n",
+    "\n",
+    "print(f\"✓ Financial entity mentions extracted: {len(financial_entities)}\")\n",
+    "print(f\"  Money/Currency: {len(financial_metrics['money'])}\")\n",
+    "print(f\"  Percentages: {len(financial_metrics['percentages'])}\")\n",
+    "print(f\"  Quantities: {len(financial_metrics['quantities'])}\")\n",
+    "\n",
+    "if financial_entities:\n",
+    "    sample = financial_entities[0]\n",
+    "    print(f\"  Sample: {sample.text} ({sample.label})\")\n"
    ]
   },
   {
@@ -245,26 +356,50 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Step 5: Extract relationships using RelationExtractor with Groq LLM\n",
+    "# Step 6: Extract relationships from all chunks (minimal handling)\n",
+    "\n",
     "from semantica.semantic_extract import RelationExtractor\n",
+    "import os\n",
     "\n",
     "relation_extractor = RelationExtractor(\n",
     "    method=\"llm\",\n",
-    "    confidence_threshold=0.6,\n",
-    "    relation_types=[\"HAS_REVENUE\", \"HAS_EPS\", \"STATES\", \"PROVIDES_GUIDANCE\", \"OPERATES_IN\"]\n",
+    "    confidence_threshold=0.5,\n",
+    "    relation_types=[\n",
+    "        \"HAS_REVENUE\", \"HAS_EPS\", \"HAS_MARGIN\", \"HAS_PROFIT\", \"HAS_GROWTH\",\n",
+    "        \"PROVIDES_GUIDANCE\", \"STATES\", \"ANNOUNCES\", \"REPORTS\", \"EXPECTS\",\n",
+    "        \"OPERATES_IN\", \"LOCATED_IN\", \"PARTNERS_WITH\", \"SERVES\",\n",
+    "        \"COMPARED_TO\", \"INCREASED_BY\", \"DECREASED_BY\", \"CHANGED_BY\",\n",
+    "        \"DURING\", \"IN_QUARTER\", \"FOR_PERIOD\",\n",
+    "        \"RELATED_TO\", \"PART_OF\", \"AFFECTS\"\n",
+    "    ],\n",
+    "    api_key=os.getenv(\"GROQ_API_KEY\")\n",
     ")\n",
     "\n",
-    "relationships = relation_extractor.extract_relations(\n",
-    "    normalized_text,\n",
-    "    entities=entities,\n",
-    "    provider=\"groq\",\n",
-    "    llm_model=\"llama-3.1-8b-instant\"\n",
-    ")\n",
+    "def get_chunk_text(chunk):\n",
+    "    return getattr(chunk, \"content\", getattr(chunk, \"text\", \"\"))\n",
+    "\n",
+    "relationships = []\n",
+    "\n",
+    "for chunk in chunks:\n",
+    "    text = get_chunk_text(chunk)\n",
+    "    if not text.strip():\n",
+    "        continue\n",
+    "\n",
+    "    relationships.extend(\n",
+    "        relation_extractor.extract_relations(\n",
+    "            text,\n",
+    "            entities=all_entities,\n",
+    "            provider=\"groq\",\n",
+    "            llm_model=\"llama-3.1-8b-instant\",\n",
+    "            temperature=0.0\n",
+    "        )\n",
+    "    )\n",
     "\n",
     "print(f\"✓ Relationships extracted: {len(relationships)}\")\n",
+    "\n",
     "if relationships:\n",
-    "    rel = relationships[0]\n",
-    "    print(f\"  Sample: {rel.subject.text} → {rel.predicate} → {rel.object.text}\")\n"
+    "    sample = relationships[0]\n",
+    "    print(f\"Sample: {sample.subject.text} → {sample.predicate} → {sample.object.text}\")\n"
    ]
   },
   {
@@ -282,29 +417,51 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Step 6: Extract RDF triplets using TripletExtractor with Groq LLM\n",
+    "# Step 7: Extract RDF triplets from all chunks (minimal handling)\n",
+    "\n",
     "from semantica.semantic_extract import TripletExtractor\n",
+    "import os\n",
     "\n",
     "triplet_extractor = TripletExtractor(\n",
     "    method=\"llm\",\n",
     "    include_temporal=True,\n",
-    "    include_provenance=True\n",
-    ")\n",
-    "\n",
-    "triplets = triplet_extractor.extract_triplets(\n",
-    "    normalized_text,\n",
-    "    entities=entities,\n",
-    "    relations=relationships,\n",
+    "    include_provenance=True,\n",
     "    provider=\"groq\",\n",
-    "    llm_model=\"llama-3.1-8b-instant\"\n",
+    "    llm_model=\"llama-3.1-8b-instant\",\n",
+    "    temperature=0.0,\n",
+    "    api_key=os.getenv(\"GROQ_API_KEY\")\n",
     ")\n",
     "\n",
-    "validated_triplets = triplet_extractor.validate_triplets(triplets)\n",
+    "def get_chunk_text(chunk):\n",
+    "    return getattr(chunk, \"content\", getattr(chunk, \"text\", \"\"))\n",
     "\n",
-    "print(f\"✓ RDF triplets extracted: {len(triplets)}\")\n",
-    "if triplets:\n",
-    "    t = triplets[0]\n",
-    "    print(f\"  Sample: {t.subject} → {t.predicate} → {t.object}\")\n"
+    "triplets = []\n",
+    "\n",
+    "for chunk in chunks:\n",
+    "    text = get_chunk_text(chunk)\n",
+    "    if not text.strip():\n",
+    "        continue\n",
+    "\n",
+    "    triplets.extend(\n",
+    "        triplet_extractor.extract_triplets(\n",
+    "            text,\n",
+    "            entities=all_entities,\n",
+    "            relations=relationships if relationships else None\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "# Optional validation (if available)\n",
+    "validated_triplets = (\n",
+    "    triplet_extractor.validate_triplets(triplets)\n",
+    "    if hasattr(triplet_extractor, \"validate_triplets\")\n",
+    "    else triplets\n",
+    ")\n",
+    "\n",
+    "print(f\"✓ RDF triplets extracted: {len(validated_triplets)}\")\n",
+    "\n",
+    "if validated_triplets:\n",
+    "    t = validated_triplets[0]\n",
+    "    print(f\"Sample: {t.subject} → {t.predicate} → {t.object}\")"
    ]
   },
   {
@@ -332,7 +489,7 @@
     ")\n",
     "\n",
     "# Track sources for entities\n",
-    "for entity in entities:\n",
+    "for entity in all_entities:\n",
     "    entity_id = getattr(entity, 'id', None) or getattr(entity, 'text', '')\n",
     "    entity_name = getattr(entity, 'text', '')\n",
     "    source_tracker.track_property_source(\n",
@@ -348,7 +505,7 @@
     "\n",
     "# Detect value conflicts\n",
     "value_conflicts = conflict_detector.detect_value_conflicts(\n",
-    "    [{'id': getattr(e, 'id', ''), 'name': getattr(e, 'text', '')} for e in entities],\n",
+    "    [{'id': getattr(e, 'id', ''), 'name': getattr(e, 'text', '')} for e in all_entities],\n",
     "    property_name='name'\n",
     ")\n",
     "\n",
@@ -384,7 +541,7 @@
     ")\n",
     "\n",
     "# Resolve value conflicts\n",
-    "resolved_entities = list(entities)\n",
+    "resolved_entities = list(all_entities)\n",
     "resolved_conflicts = []\n",
     "for conflict in value_conflicts:\n",
     "    resolution = conflict_resolver.resolve_conflict(conflict, strategy='voting')\n",
@@ -476,7 +633,7 @@
     "\n",
     "# Convert triplets to relationships format\n",
     "triplet_relationships = []\n",
-    "for triplet in triplets:\n",
+    "for triplet in validated_triplets:\n",
     "    triplet_relationships.append({\n",
     "        \"source\": triplet.subject,\n",
     "        \"predicate\": triplet.predicate,\n",
@@ -490,7 +647,7 @@
     "kg_data = {\n",
     "    \"entities\": merged_entities,\n",
     "    \"relationships\": all_relationships,\n",
-    "    \"triplets\": triplets,\n",
+    "    \"triplets\": validated_triplets,\n",
     "    \"metadata\": {\n",
     "        \"source\": \"earnings_call_transcript\",\n",
     "        \"financial_metrics\": financial_metrics,\n",
@@ -735,7 +892,7 @@
     "memory_ids = []\n",
     "memory_contents = [\n",
     "    f\"Earnings call transcript: {parsed_doc['metadata'].get('title', 'Q1 2024')}\",\n",
-    "    f\"Financial metrics extracted: {len(financial_metrics)} metrics\",\n",
+    "    f\"Financial metrics extracted: {sum(len(v) for v in financial_metrics.values())} metrics\",\n",
     "    f\"Key entities identified: {len(merged_entities)} entities\"\n",
     "]\n",
     "\n",
@@ -933,7 +1090,7 @@
     "analysis_summary = {\n",
     "    \"financial_metrics\": financial_metrics,\n",
     "    \"extraction_stats\": {\n",
-    "        \"entities\": len(entities),\n",
+    "        \"entities\": len(all_entities),\n",
     "        \"relationships\": len(relationships),\n",
     "        \"triplets\": len(triplets),\n",
     "        \"provider\": f\"Groq LLM (semantica.llms module) - {groq_llm.model}\"\n",

docs/citation.md

@@ -7,29 +7,27 @@ How to cite Semantica in academic papers and research.
 ## BibTeX
 
 ```bibtex
-@software{semantica2025,
+@software{semantica2026,
   title = {Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering},
   author = {Hawksight AI},
-  year = {2025},
-  url = {https://github.com/Hawksight-AI/semantica},
-  version = {0.1.0},
-  year = {2025},
+  year = {2026},
   url = {https://github.com/Hawksight-AI/semantica},
+  version = {0.1.1},
   doi = {10.5281/zenodo.XXXXXXX}
 }
 ```
 
 ### APA
-Hawksight AI. (2025). *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering* (Version 0.1.0) [Computer software]. https://github.com/Hawksight-AI/semantica
+Hawksight AI. (2026). *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering* (Version 0.1.1) [Computer software]. https://github.com/Hawksight-AI/semantica
 
 ### MLA
-Hawksight AI. *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering*. Version 0.1.0, GitHub, 2025, https://github.com/Hawksight-AI/semantica.
+Hawksight AI. *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering*. Version 0.1.1, GitHub, 2026, https://github.com/Hawksight-AI/semantica.
 
 ### Chicago
-Hawksight AI. *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering*. Version 0.1.0. GitHub, 2025. https://github.com/Hawksight-AI/semantica.
+Hawksight AI. *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering*. Version 0.1.1. GitHub, 2026. https://github.com/Hawksight-AI/semantica.
 
 ### IEEE
-Hawksight AI, "Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering," Version 0.1.0, GitHub, 2025. [Online]. Available: https://github.com/Hawksight-AI/semantica
+Hawksight AI, "Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering," Version 0.1.1, GitHub, 2026. [Online]. Available: https://github.com/Hawksight-AI/semantica
 
 ---
 
@@ -37,7 +35,7 @@ Hawksight AI, "Semantica: An Open Source Framework for Semantic Layers and Knowl
 
 If you use Semantica in your work:
 
-> "This work uses Semantica (Hawksight AI, 2025), an open-source framework for semantic layer construction and knowledge engineering."
+> "This work uses Semantica (Hawksight AI, 2026), an open-source framework for semantic layer construction and knowledge engineering."
 
 ---
 

docs/index.md

@@ -6,9 +6,10 @@
   <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="Python 3.8+"></a>
   <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a>
   <a href="https://badge.fury.io/py/semantica"><img src="https://badge.fury.io/py/semantica.svg" alt="PyPI version"></a>
-  <a href="https://pepy.tech/project/semantica"><img src="https://pepy.tech/badge/semantica" alt="Downloads"></a>
+  <a href="https://pypi.org/project/semantica/"><img src="https://img.shields.io/pypi/dm/semantica" alt="Monthly Downloads"></a>
+  <a href="https://pepy.tech/project/semantica"><img src="https://static.pepy.tech/badge/semantica" alt="Total Downloads"></a>
   <a href="https://semantica.readthedocs.io/"><img src="https://img.shields.io/badge/docs-latest-brightgreen.svg" alt="Documentation"></a>
-  <a href="https://discord.gg/semantica"><img src="https://img.shields.io/discord/semantica?color=7289da&label=discord" alt="Discord"></a>
+  <a href="https://discord.gg/pMHguUzG"><img src="https://img.shields.io/badge/Discord-Join%20Us-7289da?style=flat&logo=discord&logoColor=white" alt="Discord"></a>
   
   <p><strong>Open Source Framework for Semantic Layer & Knowledge Engineering</strong></p>
   
@@ -16,7 +17,7 @@
   
   <p><em>The missing fabric between raw data and AI engineering. A comprehensive open-source framework for building semantic layers and knowledge engineering systems that transform unstructured data into AI-ready knowledge — powering Knowledge Graph-Powered RAG (GraphRAG), AI Agents, Multi-Agent Systems, and AI applications with structured semantic knowledge.</em></p>
   
-  <p>🆓 <strong>100% Open Source</strong> • 📜 <strong>MIT Licensed</strong> • 🚀 <strong>Production Ready</strong> • 🌍 <strong>Community Driven</strong></p>
+  <p>🆓 <strong>100% Open Source</strong> • 📜 <strong>MIT Licensed</strong> • 🚀 <strong>Latest Version: 0.1.1</strong> • 🚀 <strong>Production Ready</strong> • 🌍 <strong>Community Driven</strong></p>
   
   <p>
     <a href="getting-started/" class="md-button md-button--primary">Get Started</a>

docs/installation.md

@@ -28,6 +28,14 @@ pip install semantica
 
 This installs Semantica with all core dependencies.
 
+### GitHub Workaround
+
+If you encounter issues with the PyPI version, you can install directly from the main branch:
+
+```bash
+pip install git+https://github.com/Hawksight-AI/semantica.git@main
+```
+
 !!! tip "Virtual Environment"
     We recommend installing Semantica in a virtual environment to avoid dependency conflicts. Use `python -m venv venv` to create one, then activate it before installing.
 
@@ -36,12 +44,15 @@ This installs Semantica with all core dependencies.
 Verify that Semantica is installed correctly:
 
 ```bash
-python -c "import semantica; print(semantica.__version__)"
+python -c "from semantica.parse import DoclingParser; DoclingParser(); print('✓ Semantica ready')"
 ```
 
+!!! info "Windows PyTorch Note"
+    If you encounter PyTorch DLL errors on Windows, ensure you have the [Microsoft Visual C++ Redistributable](https://aka.ms/vs/17/release/vc_redist.x64.exe) installed. This is a common environment-specific issue with PyTorch on Windows and not a bug in Semantica.
+
 Expected output:
 ```
-0.1.0
+✓ Semantica ready
 ```
 
 You can also check the installation:

docs/js/version-selector.js

@@ -9,7 +9,8 @@ document.addEventListener("DOMContentLoaded", function () {
 
         // Define versions
         var versions = [
-            { name: "0.1.0", url: "#", current: true }
+            { name: "0.1.1", url: "#", current: true },
+            { name: "0.1.0", url: "#", current: false }
         ];
 
         // Create the scrollable list

docs/license.md

@@ -9,7 +9,7 @@ Semantica is released under the MIT License.
 ```
 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

docs/modules.md

@@ -213,6 +213,8 @@ These modules form the intelligence core—extracting meaning, building relation
 - Multi-language support
 - Semantic network extraction
 - Coreference resolution
+- **Auto-chunking**: Automatic text splitting for long documents
+- **Robust Error Handling**: Standardized LLM provider diagnostics and retry logic
 
 **Components:**
 

docs/quickstart.md

@@ -36,32 +36,44 @@ See the [Installation Guide](installation.md) for detailed instructions.
 
 Building a knowledge graph involves these key steps:
 
-1. **Ingest** your documents using `` `FileIngestor` ``
-2. **Parse** documents to extract text using `` `DocumentParser` ``
-3. **Extract** entities and relationships using `` `NERExtractor` `` and `` `RelationExtractor` ``
-4. **Build** the graph using `` `GraphBuilder` ``
-5. **Generate** embeddings (optional) using `` `TextEmbedder` ``
+1. **Ingest** your documents using `FileIngestor`
+2. **Parse** documents to extract text using `DocumentParser` or `DoclingParser` (for enhanced layout support)
+3. **Extract** entities and relationships using `NERExtractor` and `RelationExtractor`
+4. **Build** the graph using `GraphBuilder`
+5. **Generate** embeddings (optional) using `TextEmbedder`
 
 **Quick Example:**
 ```python
 from semantica.ingest import FileIngestor
-from semantica.parse import DocumentParser
+from semantica.parse import DocumentParser, DoclingParser
 from semantica.semantic_extract import NERExtractor, RelationExtractor
 from semantica.kg import GraphBuilder
 
-# Build your first knowledge graph
+# 1. Ingest document
 ingestor = FileIngestor()
-parser = DocumentParser()
-ner = NERExtractor()
-rel_extractor = RelationExtractor()
-builder = GraphBuilder()
+sources = ingestor.ingest("data/sample.pdf")
 
-# Process document and build graph
-doc = ingestor.ingest_file("document.pdf")
-parsed = parser.parse_document("document.pdf")
-entities = ner.extract_entities(parsed.get("full_text", ""))
-relationships = rel_extractor.extract_relations(parsed.get("full_text", ""), entities=entities)
-kg = builder.build_graph(entities=entities, relationships=relationships)
+# 2. Parse (choose your parser)
+# Option A: Standard parser
+parser = DocumentParser()
+parsed_content = parser.parse(sources[0])
+
+# Option B: Enhanced Docling parser (recommended for complex tables)
+# docling_parser = DoclingParser()
+# parsed_content = docling_parser.parse(sources[0])
+
+# 3. Extract entities and relations
+ner = NERExtractor()
+entities = ner.extract(parsed_content)
+
+relations = RelationExtractor()
+relationships = relations.extract(parsed_content, entities=entities)
+
+# 4. Build graph
+builder = GraphBuilder()
+graph = builder.build(entities=entities, relationships=relationships)
+
+print(f"Built knowledge graph with {len(graph.nodes)} nodes and {len(graph.edges)} edges")
 ```
 
 **For complete step-by-step examples with detailed explanations, see:**

docs/reference/llms.md

@@ -14,7 +14,7 @@ The LLM Providers module provides:
 - **Easy Provider Switching**: Change providers without code changes
 - **Multiple Model Support**: Access to 100+ LLMs through LiteLLM
 - **GraphRAG Integration**: Seamless integration with GraphRAG reasoning features
-- **Structured Output**: Generate structured data from LLM responses
+- **Structured Output**: Generate structured data with robust JSON parsing and automatic retries (3 attempts).
 
 ### Why Use the LLM Providers Module?
 
@@ -237,6 +237,18 @@ except ProcessingError as e:
 
 If a provider is not available (library not installed, API key missing), a `ProcessingError` is raised with a helpful message.
 
+### Built-in Retries
+The `generate_structured` method includes built-in retry logic for all providers:
+- **Attempts**: 3
+- **Wait Time**: 1 second base with exponential backoff
+- **Triggers**: Network errors, rate limits, and malformed JSON responses.
+
+### Robust JSON Parsing
+`BaseProvider` uses an enhanced `_parse_json` method that can handle:
+- Markdown code blocks (e.g., ```json ... ```)
+- Extra text before or after the JSON object
+- Common LLM formatting inconsistencies.
+
 ## Examples
 
 ### Basic Text Generation

docs/reference/ontology.md

@@ -222,17 +222,31 @@ ontology:
 
 ```python
 from semantica.ontology import OntologyEngine
-from semantica.kg import KnowledgeGraph
+from semantica.kg import GraphBuilder, GraphValidator
 
 # 1. Generate Ontology from Sample Data
 engine = OntologyEngine()
 ontology = engine.from_data(sample_data)
 
-# 2. Initialize KG with Ontology
-kg = KnowledgeGraph(schema=ontology)
+# 2. Extract schema for validation
+schema = {
+    "entity_types": [c["name"] for c in ontology["classes"]],
+    "relationship_types": [p["name"] for p in ontology["properties"]]
+}
 
-# 3. Add Data (Validated against Ontology)
-kg.add_entities(full_dataset)  # Will raise error if violates schema
+# 3. Initialize Validator and Builder
+validator = GraphValidator(schema=schema, strict=True)
+builder = GraphBuilder()
+
+# 4. Build Knowledge Graph
+kg = builder.build(full_dataset)
+
+# 5. Validate against Ontology Schema
+validation_result = validator.validate(kg)
+if validation_result.is_valid:
+    print("Knowledge Graph matches the ontology schema!")
+else:
+    print(f"Validation issues found: {validation_result.issues}")
 ```
 
 ---

docs/reference/parse.md

@@ -87,6 +87,7 @@ The **Parse Module** extracts structured content from raw files and data sources
 
 ### Document Parsing
 - **PDF**: `pdfplumber` for precise layout preservation, table extraction, and image handling. Fallback to `PyPDF2`.
+- **Docling**: `docling` integration for advanced document structure understanding and high-accuracy table extraction across PDF, DOCX, PPTX, XLSX, and HTML.
 - **Office (DOCX/PPTX/XLSX)**: XML-based parsing of OpenXML formats to extract text, styles, and properties.
 - **OCR**: Tesseract-based optical character recognition for image-based PDFs and image files.
 
@@ -128,6 +129,43 @@ print(doc.get("metadata", {}).get("title"))
 print(doc.get("full_text", "")[:100])
 ```
 
+### DoclingParser
+
+Specialized parser using Docling for enhanced layout understanding and table extraction. It is independent of `DocumentParser` and optimized for complex document structures.
+
+**Methods:**
+
+| Method | Description |
+|--------|-------------|
+| `` `parse(path)` `` | Full parse including text, tables, and metadata |
+| `` `parse_batch(paths)` `` | Parse multiple documents in parallel |
+| `` `extract_text(path)` `` | Extract clean text in Markdown/HTML/JSON format |
+| `` `extract_tables(path)` `` | High-accuracy table extraction |
+| `` `extract_metadata(path)` `` | Extract document properties |
+
+**Configuration:**
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `export_format` | `str` | `"markdown"` | Output format: `"markdown"`, `"html"`, `"json"` |
+| `enable_ocr` | `bool` | `False` | Enable OCR for scanned documents |
+
+**Example:**
+
+```python
+from semantica.parse import DoclingParser
+
+parser = DoclingParser()
+result = parser.parse("complex_table.pdf")
+
+# Access high-accuracy tables
+for table in result.tables:
+    print(table.headers)
+    
+# Get markdown representation
+print(result.markdown)
+```
+
 ### WebParser
 
 Parses web content.

docs/reference/reasoning.md

@@ -190,8 +190,8 @@ if proof:
 ### Knowledge Graph Enrichment
 
 ```python
-from semantica.reasoning import Reasoner, Rule
-from semantica.kg import KnowledgeGraph
+from semantica.reasoning import Reasoner
+from semantica.kg import GraphBuilder
 
 # 1. Define Rules
 rules = [
@@ -199,14 +199,19 @@ rules = [
     "IF Ancestor(?x, ?y) AND Ancestor(?y, ?z) THEN Ancestor(?x, ?z)"
 ]
 
-# 2. Load Graph and Run Inference
-kg = KnowledgeGraph()
-reasoner = Reasoner()
-inferred = reasoner.infer_facts(kg.get_all_triplets(), rules)
+# 2. Build Graph and Run Inference
+builder = GraphBuilder()
+kg = builder.build(sources=data)
 
-# 3. Update Graph
+reasoner = Reasoner()
+# Infer new facts from entities and relationships
+inferred = reasoner.infer_facts(kg["entities"] + kg["relationships"], rules)
+
+# 3. Update Graph with Inferred Facts
 for fact_str in inferred:
-    kg.add_fact_from_string(fact_str)
+    # Add new inferred facts back to the graph
+    # For a production app, you'd parse these into entities/relationships
+    kg["entities"].append({"type": "InferredFact", "name": fact_str})
 ```
 
 ---

docs/reference/seed.md

@@ -189,7 +189,6 @@ seed:
 ```python
 from semantica.seed import SeedDataManager
 from semantica.ingest import Ingestor
-from semantica.kg import KnowledgeGraph
 
 # 1. Load Foundation (Seed)
 seed_manager = SeedDataManager()

docs/reference/semantic_extract.md

@@ -184,6 +184,8 @@ Core entity extraction implementation used by notebooks and lower-level integrat
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
 | `method` | str or list | `"ml"` | Method(s): "ml", "llm", "pattern", "regex", "huggingface" |
+| `silent_fail` | bool | `False` | Return empty list on error instead of raising (LLM only) |
+| `max_text_length` | int | `None` | Max text length for auto-chunking (LLM only) |
 | `**config` | dict | `{}` | Method-specific config (e.g., `model`, `provider`) |
 
 **Methods:**
@@ -337,6 +339,8 @@ Extracts RDF triplets (Subject-Predicate-Object).
 | `include_temporal` | bool | `False` | Include time information |
 | `include_provenance` | bool | `False` | Track source sentences |
 | `method` | str | `"pattern"` | Extraction method ("pattern", "rules", "huggingface", "llm") |
+| `silent_fail` | bool | `False` | Return empty list on error instead of raising (LLM only) |
+| `max_text_length` | int | `None` | Max text length for auto-chunking (LLM only) |
 
 **Methods:**
 

docs/reference/visualization.md

@@ -253,12 +253,12 @@ visualization:
 ### Exploratory Data Analysis (EDA)
 
 ```python
-from semantica.ingest import Ingestor
-from semantica.kg import KnowledgeGraph
+from semantica.kg import GraphBuilder
 from semantica.visualization import KGVisualizer, AnalyticsVisualizer
 
-# 1. Load Data
-kg = KnowledgeGraph.load("my_graph")
+# 1. Build Knowledge Graph
+builder = GraphBuilder()
+kg = builder.build(sources=sample_data)
 
 # 2. Visualize Structure
 kg_viz = KGVisualizer()

mkdocs.yml

@@ -7,7 +7,7 @@ repo_name: Hawksight-AI/semantica
 edit_uri: edit/main/docs/
 
 # Copyright
-copyright: Copyright © 2025 Hawksight AI
+copyright: Copyright © 2026 Hawksight AI
 
 # Theme Configuration
 theme:

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "semantica"
-version = "0.1.0"
+version = "0.1.1"
 description = "🧠 Semantica - An Open Source Framework for building Semantic Layers and Knowledge Engineering "
 readme = "README.md"
 license = {text = "MIT"}
@@ -54,6 +54,10 @@ dependencies = [
     "plotly>=5.10.0",
     "ipywidgets>=8.0.0",
     "requests>=2.28.0",
+    "GitPython>=3.1.30",
+    "chardet>=5.1.0",
+    "protobuf==4.25.8",
+    "grpcio==1.67.1",
     "beautifulsoup4>=4.11.0",
     "lxml>=4.9.0",
     "pypdf2>=2.10.0",
@@ -172,11 +176,14 @@ llm-anthropic = [
 llm-ollama = [
     "ollama>=0.1.0"
 ]
+llm-deepseek = [
+    "deepseek>=0.1.0"
+]
 llm-litellm = [
     "litellm>=1.0.0"
 ]
 llm-all = [
-    "semantica[llm-openai,llm-gemini,llm-groq,llm-anthropic,llm-ollama,llm-litellm]"
+    "semantica[llm-openai,llm-gemini,llm-groq,llm-anthropic,llm-ollama,llm-deepseek,llm-litellm]"
 ]
 models-huggingface = [
     "transformers>=4.20.0",

semantica/__init__.py

@@ -10,7 +10,7 @@ Main exports:
     - Config: Configuration management
 """
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 __author__ = "Semantica Contributors"
 __license__ = "MIT"
 

semantica/conflicts/source_tracker.py

@@ -549,3 +549,122 @@ class SourceTracker:
             List of traceability records
         """
         return self.get_traceability_chain(entity_id, property_name)
+
+    def track_sources_batch(
+        self,
+        source_data: List[Dict[str, Any]],
+        pipeline_id: Optional[str] = None,
+    ) -> Dict[str, int]:
+        """
+        Track sources for multiple entities, properties, or relationships in batch.
+
+        Args:
+            source_data: List of source tracking dictionaries, each containing:
+                - type: "entity", "property", or "relationship"
+                - entity_id: Entity identifier (required for all types)
+                - property_name: Property name (required for "property" type)
+                - value: Property value (required for "property" type)
+                - relationship_id: Relationship identifier (required for "relationship" type)
+                - source: SourceReference object or dict to create SourceReference
+                - metadata: Optional metadata dictionary
+            pipeline_id: Optional pipeline ID for progress tracking
+
+        Returns:
+            dict: Statistics with keys:
+                - entities_tracked: Number of entities tracked
+                - properties_tracked: Number of properties tracked
+                - relationships_tracked: Number of relationships tracked
+                - total_tracked: Total number of items tracked
+        """
+        if not source_data:
+            return {
+                "entities_tracked": 0,
+                "properties_tracked": 0,
+                "relationships_tracked": 0,
+                "total_tracked": 0,
+            }
+
+        tracking_id = self.progress_tracker.start_tracking(
+            module="conflicts",
+            submodule="SourceTracker",
+            message=f"Tracking sources for {len(source_data)} items",
+            pipeline_id=pipeline_id,
+        )
+
+        stats = {
+            "entities_tracked": 0,
+            "properties_tracked": 0,
+            "relationships_tracked": 0,
+            "total_tracked": 0,
+        }
+
+        try:
+            for i, item in enumerate(source_data):
+                item_type = item.get("type", "property")
+                source_ref = item.get("source")
+                metadata = item.get("metadata", {})
+
+                # Convert source dict to SourceReference if needed
+                if isinstance(source_ref, dict):
+                    source_ref = SourceReference(**source_ref)
+                elif not isinstance(source_ref, SourceReference):
+                    self.logger.warning(f"Invalid source reference in item {i}: {item}")
+                    continue
+
+                if item_type == "entity":
+                    entity_id = item.get("entity_id")
+                    if entity_id:
+                        self.track_entity_source(entity_id, source_ref, **metadata)
+                        stats["entities_tracked"] += 1
+                        stats["total_tracked"] += 1
+
+                elif item_type == "property":
+                    entity_id = item.get("entity_id")
+                    property_name = item.get("property_name")
+                    value = item.get("value")
+                    if entity_id and property_name is not None:
+                        self.track_property_source(
+                            entity_id, property_name, value, source_ref, **metadata
+                        )
+                        stats["properties_tracked"] += 1
+                        stats["total_tracked"] += 1
+
+                elif item_type == "relationship":
+                    relationship_id = item.get("relationship_id")
+                    if relationship_id:
+                        self.track_relationship_source(
+                            relationship_id, source_ref, **metadata
+                        )
+                        stats["relationships_tracked"] += 1
+                        stats["total_tracked"] += 1
+
+                else:
+                    self.logger.warning(
+                        f"Unknown type '{item_type}' in item {i}, skipping"
+                    )
+
+                # Update progress
+                self.progress_tracker.update_progress(
+                    tracking_id,
+                    processed=i + 1,
+                    total=len(source_data),
+                    message=f"Tracking sources {i+1}/{len(source_data)}...",
+                )
+
+            message = (
+                f"Tracked {stats['total_tracked']} items: "
+                f"{stats['entities_tracked']} entities, "
+                f"{stats['properties_tracked']} properties, "
+                f"{stats['relationships_tracked']} relationships"
+            )
+
+            self.progress_tracker.stop_tracking(
+                tracking_id, status="completed", message=message
+            )
+            return stats
+
+        except Exception as e:
+            self.progress_tracker.stop_tracking(
+                tracking_id, status="failed", message=str(e)
+            )
+            raise

semantica/context/agent_context.py

@@ -178,7 +178,7 @@ class AgentContext:
             vs_path = os.path.join(path, "vector_store")
             self.vector_store.save(vs_path)
 
-        # 3. Save KnowledgeGraph state
+        # 3. Save Knowledge Graph state
         if self.knowledge_graph:
             if hasattr(self.knowledge_graph, "save_to_file"):
                 # JSON export for ContextGraph
@@ -214,7 +214,7 @@ class AgentContext:
             if os.path.exists(vs_path):
                 self.vector_store.load(vs_path)
 
-        # 3. Load KnowledgeGraph state
+        # 3. Load Knowledge Graph state
         if self.knowledge_graph:
             kg_json_path = os.path.join(path, "knowledge_graph.json")
             kg_dir_path = os.path.join(path, "knowledge_graph")

semantica/core/orchestrator.py

@@ -116,7 +116,7 @@ class Semantica:
                 self._modules["embedding_generator"] = EmbeddingGenerator(
                     config=self.config.get("embedding", {})
                 )
-            except ImportError as e:
+            except (ImportError, OSError) as e:
                 self.logger.warning(f"Could not import EmbeddingGenerator: {e}")
                 raise ProcessingError(f"Embeddings module not available: {e}")
         return self._modules["embedding_generator"]
@@ -133,11 +133,87 @@ class Semantica:
             try:
                 from ..reasoning import GraphReasoner
                 self._modules["reasoner"] = GraphReasoner(config=self.config)
-            except ImportError as e:
+            except (ImportError, OSError) as e:
                 self.logger.warning(f"Could not import GraphReasoner: {e}")
                 raise ProcessingError(f"Reasoning module not available: {e}")
         return self._modules["reasoner"]
 
+    @property
+    def graph_builder(self) -> Any:
+        """
+        Get the framework's knowledge graph builder.
+
+        Returns:
+            GraphBuilder instance
+        """
+        if "graph_builder" not in self._modules:
+            try:
+                from ..kg import GraphBuilder
+                self._modules["graph_builder"] = GraphBuilder(
+                    config=self.config.get("kg", {})
+                )
+            except (ImportError, OSError) as e:
+                self.logger.warning(f"Could not import GraphBuilder: {e}")
+                raise ProcessingError(f"KG module not available: {e}")
+        return self._modules["graph_builder"]
+
+    @property
+    def document_parser(self) -> Any:
+        """
+        Get the framework's document parser.
+
+        Returns:
+            DocumentParser instance
+        """
+        if "document_parser" not in self._modules:
+            try:
+                from ..parse import DocumentParser
+                self._modules["document_parser"] = DocumentParser(
+                    config=self.config.get("parse", {})
+                )
+            except (ImportError, OSError) as e:
+                self.logger.warning(f"Could not import DocumentParser: {e}")
+                raise ProcessingError(f"Parse module not available: {e}")
+        return self._modules["document_parser"]
+
+    @property
+    def file_ingestor(self) -> Any:
+        """
+        Get the framework's file ingestor.
+
+        Returns:
+            FileIngestor instance
+        """
+        if "file_ingestor" not in self._modules:
+            try:
+                from ..ingest import FileIngestor
+                self._modules["file_ingestor"] = FileIngestor(
+                    config=self.config.get("ingest", {})
+                )
+            except (ImportError, OSError) as e:
+                self.logger.warning(f"Could not import FileIngestor: {e}")
+                raise ProcessingError(f"Ingest module not available: {e}")
+        return self._modules["file_ingestor"]
+
+    @property
+    def pipeline_builder(self) -> Any:
+        """
+        Get the framework's pipeline builder.
+
+        Returns:
+            PipelineBuilder instance
+        """
+        if "pipeline_builder" not in self._modules:
+            try:
+                from ..pipeline import PipelineBuilder
+                self._modules["pipeline_builder"] = PipelineBuilder(
+                    config=self.config.get("pipeline", {})
+                )
+            except (ImportError, OSError) as e:
+                self.logger.warning(f"Could not import PipelineBuilder: {e}")
+                raise ProcessingError(f"Pipeline module not available: {e}")
+        return self._modules["pipeline_builder"]
+
     @log_execution_time
     def initialize(self) -> None:
         """
@@ -203,6 +279,7 @@ class Semantica:
             sources: List of data sources (files, URLs, streams)
             **kwargs: Additional processing options:
                 - pipeline: Custom pipeline configuration
+                - pipeline_id: Optional pipeline ID for progress tracking
                 - embeddings: Whether to generate embeddings
                 - graph: Whether to build knowledge graph
                 - normalize: Whether to normalize data
@@ -220,11 +297,19 @@ class Semantica:
         # Auto-initialize if not already initialized
         self._ensure_initialized()
 
+        # Extract or generate pipeline_id
+        pipeline_id = kwargs.get("pipeline_id")
+        if not pipeline_id:
+            # Generate a unique pipeline ID
+            import uuid
+            pipeline_id = f"kb_build_{uuid.uuid4().hex[:8]}"
+
         # Start overall progress tracking
         overall_tracking_id = self.progress_tracker.start_tracking(
             module="core",
             submodule="Semantica",
             message=f"Building knowledge base from {len(sources)} sources",
+            pipeline_id=pipeline_id,
         )
 
         try:
@@ -236,6 +321,23 @@ class Semantica:
             # Create processing pipeline
             pipeline_config = kwargs.get("pipeline", {})
             pipeline = self._create_pipeline(pipeline_config)
+            
+            # Register pipeline modules for progress tracking
+            if hasattr(pipeline, 'steps') and pipeline.steps:
+                module_list = []
+                module_order = {}
+                for idx, step in enumerate(pipeline.steps):
+                    module_name = getattr(step, 'module', None) or getattr(step, 'name', None) or str(step)
+                    if module_name and module_name not in module_list:
+                        module_list.append(module_name)
+                        module_order[module_name] = idx
+                
+                if module_list:
+                    self.progress_tracker.register_pipeline_modules(
+                        pipeline_id=pipeline_id,
+                        module_list=module_list,
+                        module_order=module_order
+                    )
 
             # Process sources
             results = []
@@ -251,6 +353,7 @@ class Semantica:
                         module="core",
                         submodule="build_knowledge_base",
                         message=f"Processing {Path(file_str).name if file_str else 'source'}",
+                        pipeline_id=pipeline_id,
                     )
                     try:
                         result = self.run_pipeline(pipeline, source)
@@ -271,7 +374,7 @@ class Semantica:
                     # Update overall progress
                     if idx % update_interval == 0 or idx == total_sources:
                         self.progress_tracker.update_progress(
-                            tracking_id,
+                            overall_tracking_id,
                             processed=idx,
                             total=total_sources,
                             message=f"Processing sources... {idx}/{total_sources}"
@@ -308,6 +411,9 @@ class Semantica:
                 message=f"Processed {len(results)} sources",
             )
 
+            # Clear pipeline context when complete
+            self.progress_tracker.clear_pipeline_context(pipeline_id)
+
             # Show summary
             self.progress_tracker.show_summary()
 
@@ -319,6 +425,7 @@ class Semantica:
                 "metadata": {
                     "sources": validated_sources,
                     "pipeline": pipeline_config,
+                    "pipeline_id": pipeline_id,
                 },
             }
 
@@ -326,6 +433,8 @@ class Semantica:
             self.progress_tracker.stop_tracking(
                 overall_tracking_id, status="failed", message=str(e)
             )
+            # Clear pipeline context on failure
+            self.progress_tracker.clear_pipeline_context(pipeline_id)
             self.logger.error(f"Failed to build knowledge base: {e}")
             raise ProcessingError(f"Failed to build knowledge base: {e}")
 
@@ -371,7 +480,7 @@ class Semantica:
 
                 if isinstance(pipeline, Pipeline):
                     execution_engine = ExecutionEngine()
-            except ImportError:
+            except (ImportError, OSError):
                 execution_engine = None
 
             if execution_engine is None and not hasattr(pipeline, "execute"):
@@ -528,7 +637,7 @@ class Semantica:
             from ..pipeline import PipelineBuilder
 
             self.logger.debug("Framework modules verified and available")
-        except ImportError as e:
+        except (ImportError, OSError) as e:
             # Log but don't fail - modules may be optional or not installed
             self.logger.warning(
                 f"Some framework modules could not be imported: {e}. "
@@ -632,13 +741,12 @@ class Semantica:
             Pipeline object or configuration dict (if pipeline module not available)
         """
         try:
-            from ..pipeline import PipelineBuilder
-
-            pipeline_builder = PipelineBuilder()
+            # Use the lazy property
+            builder = self.pipeline_builder
 
             if not pipeline_config:
-                pipeline_builder.add_step("default_step", "default")
-                return pipeline_builder.build("default_pipeline")
+                builder.add_step("default_step", "default")
+                return builder.build("default_pipeline")
 
             steps_config = pipeline_config.get("steps")
 
@@ -655,14 +763,14 @@ class Semantica:
                 }
                 if "parallelism" in pipeline_config:
                     normalized_config["parallelism"] = pipeline_config["parallelism"]
-                return pipeline_builder.build_pipeline(normalized_config)
+                return builder.build_pipeline(normalized_config)
 
             if "steps" in pipeline_config:
-                return pipeline_builder.build_pipeline(pipeline_config)
+                return builder.build_pipeline(pipeline_config)
 
-            pipeline_builder.add_step("default_step", "default")
-            return pipeline_builder.build("default_pipeline")
-        except ImportError:
+            builder.add_step("default_step", "default")
+            return builder.build("default_pipeline")
+        except (ImportError, OSError, ProcessingError):
             self.logger.debug("Pipeline module not available, using config directly")
             return pipeline_config
 
@@ -692,8 +800,6 @@ class Semantica:
             Dictionary containing knowledge graph structure
         """
         try:
-            from ..kg import GraphBuilder
-
             # Extract entities and relationships from results
             graph_sources = []
             for result in results:
@@ -718,11 +824,8 @@ class Semantica:
             )
 
             try:
-                # Build knowledge graph
-                graph_builder = GraphBuilder(
-                    merge_entities=True, resolve_conflicts=True
-                )
-                knowledge_graph = graph_builder.build(graph_sources)
+                # Build knowledge graph using the lazy property
+                knowledge_graph = self.graph_builder.build(graph_sources)
                 self.progress_tracker.stop_tracking(kg_tracking_id, status="completed")
                 return knowledge_graph
             except Exception as e:
@@ -731,7 +834,7 @@ class Semantica:
                 )
                 raise
 
-        except ImportError:
+        except (ImportError, OSError, ProcessingError):
             self.logger.warning("KG module not available, returning placeholder")
             return {"status": "placeholder", "results": results}
 
@@ -749,8 +852,6 @@ class Semantica:
             Dictionary containing generated embeddings
         """
         try:
-            from ..embeddings import EmbeddingGenerator
-
             # Extract text content from results
             texts_to_embed = []
             for result in results:
@@ -778,9 +879,8 @@ class Semantica:
             )
 
             try:
-                # Generate embeddings
-                embedding_generator = EmbeddingGenerator()
-                embeddings_result = embedding_generator.process_batch(texts_to_embed)
+                # Generate embeddings using the lazy property
+                embeddings_result = self.embedding_generator.process_batch(texts_to_embed)
                 self.progress_tracker.stop_tracking(
                     embed_tracking_id,
                     status="completed",
@@ -801,7 +901,7 @@ class Semantica:
                 )
                 raise
 
-        except ImportError:
+        except (ImportError, OSError, ProcessingError):
             self.logger.warning(
                 "Embeddings module not available, returning placeholder"
             )
@@ -905,7 +1005,7 @@ class Semantica:
                 # CPU percent may not be available immediately
                 pass
 
-        except ImportError:
+        except (ImportError, OSError):
             # psutil not available, use basic metrics
             self.logger.debug("psutil not available, using basic metrics")
         except Exception as e:

semantica/embeddings/embedding_generator.py

@@ -258,13 +258,14 @@ class EmbeddingGenerator:
         return max(0.0, min(1.0, similarity))
 
     def process_batch(
-        self, data_items: List[Union[str, Path]], **options
+        self, data_items: List[Union[str, Path]], pipeline_id: Optional[str] = None, **options
     ) -> Dict[str, Any]:
         """
         Process multiple data items for embedding generation.
 
         Args:
             data_items: List of data items
+            pipeline_id: Optional pipeline ID for progress tracking
             **options: Processing options
 
         Returns:
@@ -275,6 +276,7 @@ class EmbeddingGenerator:
             module="embeddings",
             submodule="EmbeddingGenerator",
             message=f"Batch of {len(data_items)} items",
+            pipeline_id=pipeline_id,
         )
 
         try:

semantica/embeddings/methods.py

@@ -349,21 +349,21 @@ def check_available_providers() -> Dict[str, bool]:
     try:
         import sentence_transformers
         providers["sentence_transformers"] = True
-    except ImportError:
+    except (ImportError, OSError):
         providers["sentence_transformers"] = False
 
     # Check FastEmbed
     try:
         import fastembed
         providers["fastembed"] = True
-    except ImportError:
+    except (ImportError, OSError):
         providers["fastembed"] = False
 
     # Check OpenAI
     try:
         import openai
         providers["openai"] = True
-    except ImportError:
+    except (ImportError, OSError):
         providers["openai"] = False
 
     return providers

semantica/embeddings/provider_stores.py

@@ -66,7 +66,7 @@ class OpenAIStore(ProviderStore):
                 from openai import OpenAI
 
                 self.client = OpenAI(api_key=self.api_key)
-            except ImportError:
+            except (ImportError, OSError):
                 self.logger.warning("OpenAI library not installed")
 
     def embed(self, text: str, **options) -> np.ndarray:
@@ -114,7 +114,7 @@ class BGEStore(ProviderStore):
 
             self.model = SentenceTransformer(self.model_name)
             self.logger.info(f"Loaded BGE model: {self.model_name}")
-        except ImportError:
+        except (ImportError, OSError):
             self.logger.warning("sentence-transformers not available for BGE")
         except Exception as e:
             self.logger.warning(f"Failed to load BGE model: {e}")
@@ -210,7 +210,7 @@ class FastEmbedStore(ProviderStore):
 
             self.model = TextEmbedding(model_name=self.model_name)
             self.logger.info(f"Loaded FastEmbed model: {self.model_name}")
-        except ImportError:
+        except (ImportError, OSError):
             self.logger.warning(
                 "fastembed not available. Install with: pip install fastembed"
             )

semantica/embeddings/text_embedder.py

@@ -34,14 +34,14 @@ try:
     from sentence_transformers import SentenceTransformer
 
     SENTENCE_TRANSFORMERS_AVAILABLE = True
-except ImportError:
+except (ImportError, OSError):
     SENTENCE_TRANSFORMERS_AVAILABLE = False
 
 try:
     from fastembed import TextEmbedding
 
     FASTEMBED_AVAILABLE = True
-except ImportError:
+except (ImportError, OSError):
     FASTEMBED_AVAILABLE = False
 
 
@@ -107,6 +107,7 @@ class TextEmbedder:
         # Initialize models (will be None if unavailable)
         self.model = None
         self.fastembed_model = None
+        self.embedding_dimension = None
 
         # Initialize progress tracker
         self.progress_tracker = get_progress_tracker()
@@ -121,6 +122,11 @@ class TextEmbedder:
         If unavailable or loading fails, falls back to hash-based embedding method.
         Logs warnings but doesn't raise errors to allow graceful degradation.
         """
+        # Clear previous models to avoid conflicts when switching
+        self.model = None
+        self.fastembed_model = None
+        self.embedding_dimension = None
+
         if self.method == "fastembed":
             if FASTEMBED_AVAILABLE:
                 try:
@@ -129,8 +135,17 @@ class TextEmbedder:
                         "fastembed_model_name", self.model_name
                     )
                     self.fastembed_model = TextEmbedding(model_name=fastembed_model_name)
+                    
+                    # Detect dimension from model
+                    try:
+                        # FastEmbed doesn't expose dimension directly, so we generate a test embedding
+                        test_emb = list(self.fastembed_model.embed(["test"]))[0]
+                        self.embedding_dimension = len(test_emb)
+                    except Exception:
+                        self.embedding_dimension = 384
+                        
                     self.logger.info(
-                        f"Loaded FastEmbed model: {fastembed_model_name}"
+                        f"Loaded FastEmbed model: {fastembed_model_name} (dim: {self.embedding_dimension})"
                     )
                 except Exception as e:
                     self.logger.warning(
@@ -149,9 +164,10 @@ class TextEmbedder:
             if SENTENCE_TRANSFORMERS_AVAILABLE:
                 try:
                     self.model = SentenceTransformer(self.model_name, device=self.device)
+                    self.embedding_dimension = self.model.get_sentence_embedding_dimension()
                     self.logger.info(
                         f"Loaded sentence-transformers model: {self.model_name} "
-                        f"(device: {self.device})"
+                        f"(device: {self.device}, dim: {self.embedding_dimension})"
                     )
                 except Exception as e:
                     self.logger.warning(
@@ -165,19 +181,12 @@ class TextEmbedder:
                     "Install with: pip install sentence-transformers. "
                     "Using fallback embedding method."
                 )
-
-    def get_method(self) -> str:
-        """Get current embedding method."""
-        return self.method
-
-    def get_model_info(self) -> Dict[str, Any]:
-        """Get current model information."""
-        return {
-            "method": self.method,
-            "model_name": self.model_name,
-            "device": self.device,
-            "normalize": self.normalize
-        }
+        
+        # If no model loaded, use fallback dimension
+        if self.embedding_dimension is None:
+            self.embedding_dimension = self.config.get("dimension", 128)
+        
+        self.logger.debug(f"Initialized text embedder with dimension: {self.embedding_dimension}")
 
     def set_model(self, method: str, model_name: str, **config) -> None:
         """
@@ -190,6 +199,10 @@ class TextEmbedder:
         """
         self.method = method.lower()
         self.model_name = model_name
+        
+        # Update config with new values
+        self.config.update(config)
+        
         if "device" in config:
             self.device = config["device"]
         if "normalize" in config:
@@ -340,8 +353,8 @@ class TextEmbedder:
         hash_val = int(hashlib.sha256(text.encode("utf-8")).hexdigest(), 16)
         rng = np.random.RandomState(hash_val % (2**32))
         
-        # Determine dimension - use config or default
-        dim = self.config.get("dimension", 128)
+        # Determine dimension - use stored dimension or default
+        dim = self.embedding_dimension or 128
         embedding = rng.rand(dim).astype(np.float32)
 
         # Normalize if requested
@@ -470,18 +483,7 @@ class TextEmbedder:
             >>> dim = embedder.get_embedding_dimension()
             >>> print(f"Embedding dimension: {dim}")
         """
-        if self.fastembed_model:
-            # FastEmbed doesn't expose dimension directly, so we generate a test embedding
-            try:
-                test_emb = self._embed_with_fastembed("test")
-                return len(test_emb)
-            except Exception:
-                # Default FastEmbed dimension
-                return 384
-        elif self.model:
-            return self.model.get_sentence_embedding_dimension()
-        else:
-            return 128  # Fallback hash-based embedding dimension
+        return self.embedding_dimension or 128
 
     def get_method(self) -> str:
         """

semantica/evals/__init__.py

@@ -4,7 +4,7 @@ Semantica Evals Module
 Coming Soon
 """
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 __status__ = "coming_soon"
 __all__ = []
 

semantica/export/vector_exporter.py

@@ -287,7 +287,7 @@ class VectorExporter:
         """
         try:
             import numpy as np
-        except ImportError:
+        except (ImportError, OSError):
             raise ImportError("NumPy not installed. Install with: pip install numpy")
 
         # Extract vectors and associated data
@@ -358,7 +358,7 @@ class VectorExporter:
         """Export to binary format."""
         try:
             import numpy as np
-        except ImportError:
+        except (ImportError, OSError):
             raise ImportError("NumPy not installed. Install with: pip install numpy")
 
         # Extract vectors
@@ -419,7 +419,7 @@ class VectorExporter:
         try:
             import faiss
             import numpy as np
-        except ImportError:
+        except (ImportError, OSError):
             raise ImportError(
                 "FAISS not installed. Install with: pip install faiss-cpu or faiss-gpu"
             )

semantica/export/yaml_exporter.py

@@ -71,7 +71,7 @@ class SemanticNetworkYAMLExporter:
             import yaml
 
             self.yaml = yaml
-        except ImportError:
+        except (ImportError, OSError):
             raise ImportError("PyYAML not installed. Install with: pip install pyyaml")
 
         # Initialize progress tracker
@@ -297,7 +297,7 @@ class YAMLSchemaExporter:
             import yaml
 
             self.yaml = yaml
-        except ImportError:
+        except (ImportError, OSError):
             raise ImportError("PyYAML not installed. Install with: pip install pyyaml")
 
     def export_ontology_schema(self, ontology: Dict[str, Any], **options) -> str:

semantica/graph_store/falkordb_store.py

@@ -47,7 +47,7 @@ try:
     from falkordb import FalkorDB
 
     FALKORDB_AVAILABLE = True
-except ImportError:
+except (ImportError, OSError):
     FALKORDB_AVAILABLE = False
     FalkorDB = None
 

semantica/graph_store/graph_store.py

@@ -1046,7 +1046,7 @@ class GraphStore:
         try:
             from ..context.entity_linker import EntityLinker
             linker = EntityLinker() # Use default config
-        except ImportError:
+        except (ImportError, OSError):
             return []
 
         entity_nodes = [n for n in nodes if n.get("type") == "entity"]

semantica/graph_store/neo4j_store.py

@@ -50,7 +50,7 @@ try:
     )
 
     NEO4J_AVAILABLE = True
-except ImportError:
+except (ImportError, OSError):
     NEO4J_AVAILABLE = False
     GraphDatabase = None
     Neo4jError = Exception

semantica/ingest/api_ingestor.py

@@ -32,7 +32,7 @@ import requests
 from requests.adapters import HTTPAdapter
 try:
     from urllib3.util.retry import Retry
-except ImportError:
+except (ImportError, OSError):
     from requests.packages.urllib3.util.retry import Retry
 
 from ..utils.exceptions import ProcessingError, ValidationError

semantica/ingest/duckdb_ingestor.py

@@ -37,7 +37,7 @@ from ..utils.progress_tracker import get_progress_tracker
 
 try:
     import duckdb
-except ImportError:
+except (ImportError, OSError):
     duckdb = None
 
 
@@ -313,7 +313,7 @@ class DuckDBIngestor:
             # to read Excel and then query it
             try:
                 import pandas as pd
-            except ImportError:
+            except (ImportError, OSError):
                 raise ImportError(
                     "pandas and openpyxl are required for Excel ingestion. "
                     "Install with: pip install pandas openpyxl"

semantica/ingest/elastic_ingestor.py

@@ -34,7 +34,7 @@ from ..utils.progress_tracker import get_progress_tracker
 try:
     from elasticsearch import Elasticsearch
     from elasticsearch.helpers import scan
-except ImportError:
+except (ImportError, OSError):
     Elasticsearch = None
     scan = None
 

semantica/ingest/feed_ingestor.py

@@ -358,7 +358,7 @@ class FeedParser:
             from dateutil import parser
 
             return parser.parse(date_string)
-        except ImportError:
+        except (ImportError, OSError):
             pass
 
         return None

semantica/ingest/file_ingestor.py

@@ -54,6 +54,30 @@ class FileObject:
     metadata: Dict[str, Any] = field(default_factory=dict)
     ingested_at: datetime = field(default_factory=datetime.now)
 
+    @property
+    def text(self) -> str:
+        """
+        Get the file content as text.
+
+        Returns:
+            str: Decoded file content or empty string if no content
+        """
+        if self.content is None:
+            return ""
+
+        if isinstance(self.content, str):
+            return self.content
+
+        try:
+            # Try to decode as UTF-8
+            return self.content.decode("utf-8")
+        except UnicodeDecodeError:
+            try:
+                # Fallback to latin-1
+                return self.content.decode("latin-1")
+            except Exception:
+                return ""
+
 
 class FileTypeDetector:
     """

semantica/ingest/gdrive_ingestor.py

@@ -41,7 +41,7 @@ try:
     from googleapiclient.errors import HttpError
     from googleapiclient.http import MediaIoBaseDownload
     import io
-except ImportError:
+except (ImportError, OSError):
     Credentials = None
     InstalledAppFlow = None
     Request = None

semantica/ingest/huggingface_ingestor.py

@@ -32,7 +32,7 @@ from ..utils.progress_tracker import get_progress_tracker
 
 try:
     from datasets import load_dataset, Dataset, IterableDataset
-except ImportError:
+except (ImportError, OSError):
     load_dataset = None
     Dataset = None
     IterableDataset = None

semantica/ingest/mcp_client.py

@@ -353,7 +353,7 @@ class MCPClient:
             )
             response.raise_for_status()
             return response.json()
-        except ImportError:
+        except (ImportError, OSError):
             # Fallback to requests if httpx not available
             try:
                 import requests
@@ -366,7 +366,7 @@ class MCPClient:
                 )
                 response.raise_for_status()
                 return response.json()
-            except ImportError:
+            except (ImportError, OSError):
                 raise ProcessingError(
                     "HTTP transport requires 'httpx' or 'requests' package. "
                     "Install with: pip install httpx or pip install requests"

semantica/ingest/mongo_ingestor.py

@@ -35,7 +35,7 @@ from ..utils.progress_tracker import get_progress_tracker
 try:
     from pymongo import MongoClient
     from pymongo.errors import ConnectionFailure, OperationFailure
-except ImportError:
+except (ImportError, OSError):
     MongoClient = None
     ConnectionFailure = None
     OperationFailure = None

semantica/ingest/pandas_ingestor.py

@@ -36,7 +36,7 @@ from ..utils.progress_tracker import get_progress_tracker
 
 try:
     import pandas as pd
-except ImportError:
+except (ImportError, OSError):
     pd = None
 
 

semantica/kg/centrality_calculator.py

@@ -82,7 +82,7 @@ class CentralityCalculator:
             self.nx = nx
             self.use_networkx = True
             self.logger.debug("NetworkX available, using optimized implementations")
-        except ImportError:
+        except (ImportError, OSError):
             self.nx = None
             self.use_networkx = False
             self.logger.debug(
@@ -498,6 +498,19 @@ class CentralityCalculator:
             source = rel.get("source") or rel.get("subject")
             target = rel.get("target") or rel.get("object")
 
+            # Extract IDs if objects are passed
+            if source and not isinstance(source, (str, int, float)):
+                if isinstance(source, dict):
+                    source = source.get("id") or source.get("entity_id") or source.get("text") or str(source)
+                else:
+                    source = getattr(source, "id", getattr(source, "text", str(source)))
+            
+            if target and not isinstance(target, (str, int, float)):
+                if isinstance(target, dict):
+                    target = target.get("id") or target.get("entity_id") or target.get("text") or str(target)
+                else:
+                    target = getattr(target, "id", getattr(target, "text", str(target)))
+
             if source and target:
                 if target not in adjacency[source]:
                     adjacency[source].append(target)

semantica/kg/community_detector.py

@@ -85,7 +85,7 @@ class CommunityDetector:
             self.nx = nx
             self.use_networkx = True
             self.logger.debug("NetworkX available, using optimized implementations")
-        except ImportError:
+        except (ImportError, OSError):
             self.nx = None
             self.use_networkx = False
             self.logger.warning("NetworkX not available, using basic implementations")
@@ -474,6 +474,19 @@ class CommunityDetector:
             source = rel.get("source") or rel.get("subject")
             target = rel.get("target") or rel.get("object")
 
+            # Extract IDs if objects are passed
+            if source and not isinstance(source, (str, int, float)):
+                if isinstance(source, dict):
+                    source = source.get("id") or source.get("entity_id") or source.get("text") or str(source)
+                else:
+                    source = getattr(source, "id", getattr(source, "text", str(source)))
+            
+            if target and not isinstance(target, (str, int, float)):
+                if isinstance(target, dict):
+                    target = target.get("id") or target.get("entity_id") or target.get("text") or str(target)
+                else:
+                    target = getattr(target, "id", getattr(target, "text", str(target)))
+
             if source and target:
                 if target not in adjacency[source]:
                     adjacency[source].append(target)

semantica/kg/connectivity_analyzer.py

@@ -82,13 +82,13 @@ class ConnectivityAnalyzer:
         self.config = config
 
         # Try to use networkx if available (optional dependency)
-        try:
-            import networkx as nx
-
-            self.nx = nx
+        from ..utils.helpers import safe_import
+        networkx, nx_available = safe_import("networkx")
+        if nx_available:
+            self.nx = networkx
             self.use_networkx = True
             self.logger.debug("NetworkX available, using optimized implementations")
-        except ImportError:
+        else:
             self.nx = None
             self.use_networkx = False
             self.logger.warning("NetworkX not available, using basic implementations")
@@ -381,6 +381,19 @@ class ConnectivityAnalyzer:
             source = rel.get("source") or rel.get("subject")
             target = rel.get("target") or rel.get("object")
 
+            # Extract IDs if objects are passed
+            if source and not isinstance(source, (str, int, float)):
+                if isinstance(source, dict):
+                    source = source.get("id") or source.get("entity_id") or source.get("text") or str(source)
+                else:
+                    source = getattr(source, "id", getattr(source, "text", str(source)))
+            
+            if target and not isinstance(target, (str, int, float)):
+                if isinstance(target, dict):
+                    target = target.get("id") or target.get("entity_id") or target.get("text") or str(target)
+                else:
+                    target = getattr(target, "id", getattr(target, "text", str(target)))
+
             if source and target:
                 if target not in adjacency[source]:
                     adjacency[source].append(target)

semantica/kg/entity_resolver.py

@@ -168,15 +168,29 @@ class EntityResolver:
 
                     # Mark all source entities as processed
                     for source_entity in operation.source_entities:
-                        entity_id = source_entity.get("id") or source_entity.get(
-                            "entity_id"
+                        entity_id = (
+                            source_entity.get("id")
+                            if isinstance(source_entity, dict)
+                            else getattr(source_entity, "id", None)
+                        ) or (
+                            source_entity.get("entity_id")
+                            if isinstance(source_entity, dict)
+                            else getattr(source_entity, "entity_id", None)
                         )
                         if entity_id:
                             processed_entity_ids.add(entity_id)
 
             # Step 3: Add non-duplicate entities (entities not in any duplicate group)
             for entity in entities:
-                entity_id = entity.get("id") or entity.get("entity_id")
+                entity_id = (
+                    entity.get("id")
+                    if isinstance(entity, dict)
+                    else getattr(entity, "id", None)
+                ) or (
+                    entity.get("entity_id")
+                    if isinstance(entity, dict)
+                    else getattr(entity, "entity_id", None)
+                )
                 if entity_id and entity_id not in processed_entity_ids:
                     # This entity was not merged, add it as-is
                     merged_entities.append(entity)

semantica/kg/graph_builder.py

@@ -130,12 +130,12 @@ class GraphBuilder:
 
     def _process_item(self, item: Any, all_entities: List[Any], all_relationships: List[Any], **options):
         """Helper to process a single item and add to entities or relationships list."""
-        if hasattr(item, "text") and hasattr(item, "label"):
+        if hasattr(item, "text") and (hasattr(item, "label") or hasattr(item, "type")):
             # It's likely an Entity object
             entity_dict = {
-                "id": getattr(item, "id", item.text),
+                "id": getattr(item, "id", getattr(item, "entity_id", item.text)),
                 "name": item.text,
-                "type": item.label,
+                "type": getattr(item, "label", getattr(item, "type", "UNKNOWN")),
                 "confidence": getattr(item, "confidence", 1.0),
                 "metadata": getattr(item, "metadata", {})
             }
@@ -237,6 +237,7 @@ class GraphBuilder:
         self,
         sources: Union[List[Any], Any],
         second_arg: Optional[Any] = None,
+        pipeline_id: Optional[str] = None,
         **options,
     ) -> Dict[str, Any]:
         """
@@ -245,6 +246,7 @@ class GraphBuilder:
         Args:
             sources: Entities or sources list
             second_arg: Optional relationships list or entity_resolver (for backward compatibility)
+            pipeline_id: Optional pipeline ID for progress tracking
             **options: Additional build options
                 - extract: Whether to extract entities from text (default: True)
                 - extract_relations: Whether to extract relations from text (default: False)
@@ -307,6 +309,7 @@ class GraphBuilder:
             module="kg",
             submodule="GraphBuilder",
             message=f"Knowledge graph from {len(sources)} source(s)",
+            pipeline_id=pipeline_id,
         )
 
         try:
@@ -340,6 +343,7 @@ class GraphBuilder:
                         module="kg",
                         submodule="GraphBuilder",
                         message=f"Processing {len(entities_list)} entities",
+                        pipeline_id=pipeline_id,
                     )
                     
                     # Check if entities are already in dictionary format
@@ -347,7 +351,7 @@ class GraphBuilder:
                     is_dict_format = isinstance(sample_entity, dict) and (
                         "id" in sample_entity or "entity_id" in sample_entity or 
                         "name" in sample_entity or "text" in sample_entity
-                    )
+                    ) and not hasattr(sample_entity, "__dict__") # Ensure it's not a class instance
                     
                     if is_dict_format:
                         # Fast path: directly append dictionaries after normalizing
@@ -409,22 +413,31 @@ class GraphBuilder:
                         module="kg",
                         submodule="GraphBuilder",
                         message=f"Processing {len(relationships_list)} relationships",
+                        pipeline_id=pipeline_id,
                     )
                     
                     # Check if relationships are already in dictionary format
                     sample_rel = relationships_list[0] if relationships_list else None
                     is_dict_format = isinstance(sample_rel, dict) and (
                         "source" in sample_rel and "target" in sample_rel
-                    )
+                    ) and not hasattr(sample_rel, "__dict__") # Ensure it's not a class instance
                     
                     if is_dict_format:
-                        # Fast path: directly append dictionaries
+                        # Fast path: directly append dictionaries after normalizing source/target
                         batch_size = max(100, len(relationships_list) // 20)
                         for i in range(0, len(relationships_list), batch_size):
                             batch = relationships_list[i:i + batch_size]
                             for item in batch:
                                 if isinstance(item, dict):
-                                    all_relationships.append(item)
+                                    # Normalize source/target if they are objects
+                                    rel_dict = item.copy()
+                                    if "source" in rel_dict and not isinstance(rel_dict["source"], str):
+                                        src = rel_dict["source"]
+                                        rel_dict["source"] = getattr(src, "id", getattr(src, "text", str(src)))
+                                    if "target" in rel_dict and not isinstance(rel_dict["target"], str):
+                                        tgt = rel_dict["target"]
+                                        rel_dict["target"] = getattr(tgt, "id", getattr(tgt, "text", str(tgt)))
+                                    all_relationships.append(rel_dict)
                                 else:
                                     # Fallback to _process_item for non-dict items
                                     self._process_item(item, all_entities, all_relationships, **options)
@@ -944,7 +957,7 @@ class GraphBuilder:
             )
             return graph
 
-        except ImportError:
+        except (ImportError, OSError):
             self.progress_tracker.stop_tracking(
                 tracking_id, status="failed", message="neo4j library not available"
             )

semantica/kg/provenance_tracker.py

@@ -217,3 +217,131 @@ class ProvenanceTracker:
                   or None if entity is not tracked
         """
         return self.provenance_data.get(entity_id)
+
+    def track_entities_batch(
+        self,
+        entities: List[Dict[str, Any]],
+        source: str,
+        pipeline_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> int:
+        """
+        Track provenance for multiple entities in batch.
+
+        Args:
+            entities: List of entity dictionaries, each containing at least 'id' key
+            source: Source identifier
+            pipeline_id: Optional pipeline ID for progress tracking
+            metadata: Optional metadata dictionary to apply to all entities
+
+        Returns:
+            int: Number of entities tracked
+        """
+        if not entities:
+            return 0
+
+        tracking_id = self.progress_tracker.start_tracking(
+            module="kg",
+            submodule="ProvenanceTracker",
+            message=f"Tracking provenance for {len(entities)} entities",
+            pipeline_id=pipeline_id,
+        )
+
+        try:
+            tracked_count = 0
+            for i, entity in enumerate(entities):
+                entity_id = entity.get("id") or entity.get("entity_id")
+                if not entity_id:
+                    self.logger.warning(f"Skipping entity without ID: {entity}")
+                    continue
+
+                # Merge entity-specific metadata with batch metadata
+                entity_metadata = {**(metadata or {}), **(entity.get("metadata", {}))}
+                self.track_entity(entity_id, source, entity_metadata)
+
+                tracked_count += 1
+
+                # Update progress
+                self.progress_tracker.update_progress(
+                    tracking_id,
+                    processed=i + 1,
+                    total=len(entities),
+                    message=f"Tracking entity {i+1}/{len(entities)}...",
+                )
+
+            self.progress_tracker.stop_tracking(
+                tracking_id,
+                status="completed",
+                message=f"Tracked {tracked_count} entities",
+            )
+            return tracked_count
+
+        except Exception as e:
+            self.progress_tracker.stop_tracking(
+                tracking_id, status="failed", message=str(e)
+            )
+            raise
+
+    def track_relationships_batch(
+        self,
+        relationships: List[Dict[str, Any]],
+        source: str,
+        pipeline_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> int:
+        """
+        Track provenance for multiple relationships in batch.
+
+        Args:
+            relationships: List of relationship dictionaries, each containing at least 'id' key
+            source: Source identifier
+            pipeline_id: Optional pipeline ID for progress tracking
+            metadata: Optional metadata dictionary to apply to all relationships
+
+        Returns:
+            int: Number of relationships tracked
+        """
+        if not relationships:
+            return 0
+
+        tracking_id = self.progress_tracker.start_tracking(
+            module="kg",
+            submodule="ProvenanceTracker",
+            message=f"Tracking provenance for {len(relationships)} relationships",
+            pipeline_id=pipeline_id,
+        )
+
+        try:
+            tracked_count = 0
+            for i, relationship in enumerate(relationships):
+                relationship_id = relationship.get("id") or relationship.get("relationship_id")
+                if not relationship_id:
+                    self.logger.warning(f"Skipping relationship without ID: {relationship}")
+                    continue
+
+                # Merge relationship-specific metadata with batch metadata
+                rel_metadata = {**(metadata or {}), **(relationship.get("metadata", {}))}
+                self.track_relationship(relationship_id, source, rel_metadata)
+
+                tracked_count += 1
+
+                # Update progress
+                self.progress_tracker.update_progress(
+                    tracking_id,
+                    processed=i + 1,
+                    total=len(relationships),
+                    message=f"Tracking relationship {i+1}/{len(relationships)}...",
+                )
+
+            self.progress_tracker.stop_tracking(
+                tracking_id,
+                status="completed",
+                message=f"Tracked {tracked_count} relationships",
+            )
+            return tracked_count
+
+        except Exception as e:
+            self.progress_tracker.stop_tracking(
+                tracking_id, status="failed", message=str(e)
+            )
+            raise

semantica/llms/litellm.py

@@ -12,11 +12,13 @@ from ..utils.logging import get_logger
 
 logger = get_logger("llms.litellm")
 
-try:
+from ..utils.helpers import safe_import
+
+_litellm, LITELLM_AVAILABLE = safe_import("litellm")
+if LITELLM_AVAILABLE:
     from litellm import completion
-    LITELLM_AVAILABLE = True
-except ImportError:
-    LITELLM_AVAILABLE = False
+else:
+    completion = None
     logger.warning(
         "litellm library not installed. Install with: pip install litellm"
     )

semantica/normalize/date_normalizer.py

@@ -44,7 +44,7 @@ try:
     from dateutil.relativedelta import relativedelta
 
     HAS_DATEUTIL = True
-except ImportError:
+except (ImportError, OSError):
     HAS_DATEUTIL = False
     date_parser = None
     relativedelta = None