Release 0.2.2

Merge pull request #191 from Hawksight-AI/semantic-extract
Improve `semantic_extract` performance and add Groq LLM smoke tests
2026-08-29 04:26:20 +00:00 · 2026-01-15 00:42:07 +05:30 · 2026-01-14 17:21:26 +05:30 · 2026-01-14 17:17:32 +05:30 · 2026-01-14 17:11:26 +05:30 · 2026-01-14 14:05:36 +05:30
182 changed files with 17579 additions and 132914 deletions
@@ -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
@@ -61,6 +61,7 @@ wheels/
 .installed.cfg
 *.egg
 MANIFEST
+.python-version

 # IDE
 .vscode/
@@ -7,6 +7,163 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 ## [Unreleased]

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

 ### Added
@@ -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
@@ -6,9 +6,10 @@

 [![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)
+[![PyPI version](https://img.shields.io/pypi/v/semantica.svg)](https://pypi.org/project/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: 0.2.2** • **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 +261,24 @@ 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(enable_ocr=True)
+result = docling_parser.parse("complex_table.pdf")
+
+print(f"Text (Markdown): {result['full_text'][:100]}...")
+print(f"Extracted {len(result['tables'])} tables")
+for i, table in enumerate(result['tables']):
+    print(f"Table {i+1} headers: {table.get('headers', [])}")
+
 # Normalize text
 normalizer = TextNormalizer()
 normalized = normalizer.normalize(parsed, clean_html=True, normalize_entities=True)
@@ -343,7 +360,7 @@ results = vector_store.search(query="supply chain", top_k=5)

 ### Graph Store & Triplet Store

-> **Neo4j, FalkorDB support** • **SPARQL queries** • **RDF triplets**
+> **Neo4j, FalkorDB, Amazon Neptune support** • **SPARQL queries** • **RDF triplets**

 ```python
 from semantica.graph_store import GraphStore
@@ -353,6 +370,24 @@ from semantica.triplet_store import TripletStore
 graph_store = GraphStore(backend="neo4j", uri="bolt://localhost:7687", user="neo4j", password="password")
 graph_store.add_nodes([{"id": "n1", "labels": ["Person"], "properties": {"name": "Alice"}}])

+# Amazon Neptune Graph Store (OpenCypher via HTTP with IAM Auth)
+neptune_store = GraphStore(
+    backend="neptune",
+    endpoint="your-cluster.us-east-1.neptune.amazonaws.com",
+    port=8182,
+    region="us-east-1",
+    iam_auth=True,  # Uses AWS credential chain (boto3, env vars, or IAM role)
+)
+
+# Node Operations
+neptune_store.add_nodes([
+    {"labels": ["Person"], "properties": {"id": "alice", "name": "Alice", "age": 30}},
+    {"labels": ["Person"], "properties": {"id": "bob", "name": "Bob", "age": 25}},
+])
+
+# Query Operations
+result = neptune_store.execute_query("MATCH (p:Person) RETURN p.name, p.age")
+
 # Triplet Store (Blazegraph, Jena, RDF4J)
 triplet_store = TripletStore(backend="blazegraph", endpoint="http://localhost:9999/blazegraph")
 triplet_store.add_triplet({"subject": "Alice", "predicate": "knows", "object": "Bob"})
@@ -762,6 +797,7 @@ 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 +811,7 @@ 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 +819,20 @@ 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 +866,8 @@ 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 +881,6 @@ 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>
@@ -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., `v0.2.2`).
   ```bash
-   git tag -a v0.1.0 -m "Release v0.1.0"
-   git push origin v0.1.0
+   git tag -a v0.2.2 -m "Release v0.2.2"
+   git push origin v0.2.2
   ```
 2. **GitHub Action**: The `Release` workflow will automatically trigger, build the package, create a GitHub Release, and publish to PyPI using Trusted Publishing.

@@ -6,6 +6,10 @@ We actively support the following versions of Semantica with security updates:

 | Version | Supported          |
 | ------- | ------------------ |
+| 0.2.2   | :white_check_mark: |
+| 0.2.1   | :white_check_mark: |
+| 0.2.0   | :white_check_mark: |
+| 0.1.1   | :white_check_mark: |
 | 0.1.0   | :white_check_mark: |
 | < 0.1.0 | :x:                |

@@ -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

@@ -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",
@@ -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
@@ -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
-}
+}
@@ -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",
@@ -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",
@@ -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",
@@ -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",
-      "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.3.3)\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: umap-learn>=0.5.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (0.5.9.post2)\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) (3.6.1)\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: ipywidgets>=8.0.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (8.1.1)\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) (4.14.3)\n",
-      "Requirement already satisfied: lxml>=4.9.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (6.0.2)\n",
-      "Requirement already satisfied: pypdf2>=2.10.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.0.1)\n",
-      "Requirement already satisfied: python-docx>=0.8.11 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.1.2)\n",
-      "Requirement already satisfied: openpyxl>=3.0.10 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (3.1.5)\n",
-      "Requirement already satisfied: pillow>=9.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (10.4.0)\n",
-      "Requirement already satisfied: librosa>=0.9.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (0.11.0)\n",
-      "Requirement already satisfied: opencv-python>=4.6.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (4.9.0.80)\n",
-      "Requirement already satisfied: faiss-cpu>=1.7.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.9.0)\n",
-      "Requirement already satisfied: fastembed>=0.2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (0.7.3)\n",
-      "Requirement already satisfied: onnxruntime>=1.17.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.22.1)\n",
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-      "Requirement already satisfied: executing>=1.2.0 in c:\\users\\mohd kaif\\appdata\\roaming\\python\\python311\\site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=8.0.0->semantica) (2.0.1)\n",
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-      "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",
@@ -44,223 +44,9 @@
  },
  {
   "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"
   ]
@@ -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",
@@ -260,6 +260,11 @@
  }
 ],
 "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
  "language_info": {
   "name": "python"
  }
@@ -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
-}
+}
@@ -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",
@@ -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
-}
+}
@@ -39,230 +39,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "!pip install semantica\n"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -313,18 +99,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original entities: 5\n",
-      "Resolved entities: 4\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "from semantica.kg import EntityResolver\n",
    "\n",
@@ -347,18 +124,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original entities: 4\n",
-      "Deduplicated entities: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "from semantica.deduplication import DuplicateDetector, EntityMerger, MergeStrategy\n",
    "\n",
@@ -80,262 +80,18 @@
  },
  {
   "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from sympy->torch>=1.12.0->semantica) (1.3.0)\n"
-     ]
-    }
-   ],
+   "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",
@@ -82,223 +82,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "Requirement already satisfied: distlib<1,>=0.3.7 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from virtualenv>=20.10.0->pre-commit>=2.19.0->semantica) (0.3.7)\n",
-      "Requirement already satisfied: validators<1.0.0,>=0.34.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from weaviate-client>=3.15.0->semantica) (0.35.0)\n",
-      "Requirement already satisfied: authlib<2.0.0,>=1.2.1 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from weaviate-client>=3.15.0->semantica) (1.6.0)\n",
-      "Requirement already satisfied: deprecation<3.0.0,>=2.1.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from weaviate-client>=3.15.0->semantica) (2.1.0)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jinja2->spacy>=3.4.0->semantica) (2.1.3)\n",
-      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from sympy->torch>=1.12.0->semantica) (1.3.0)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "!pip install semantica\n"
   ]
@@ -316,15 +102,7 @@
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connected to graph database successfully!\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "from semantica.graph_store import GraphStore\n",
    "\n",
@@ -357,64 +135,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>File</th><th>Time</th></tr><tr><td>✅</td><td>Semantica is processing</td><td>⏳ graph_store</td><td>Neo4jStore</td><td>-</td><td>0.14s</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=34, offset=33>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 33, 'line': 1, 'column': 34}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Company $props) RETURN id(n) as id, n'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created company node: Apple Inc. (ID: 0)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=33, offset=32>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 32, 'line': 1, 'column': 33}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Person $props) RETURN id(n) as id, n'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created person node: Tim Cook (ID: 1)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=35, offset=34>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 34, 'line': 1, 'column': 35}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Location $props) RETURN id(n) as id, n'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created location node: Cupertino (ID: 2)\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Create individual nodes with labels and properties\n",
    "apple = store.create_node(\n",
@@ -438,26 +161,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=34, offset=33>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 33, 'line': 1, 'column': 34}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Company $props) RETURN id(n) as id, n'\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=34, offset=33>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 33, 'line': 1, 'column': 34}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Company $props) RETURN id(n) as id, n'\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=34, offset=33>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 33, 'line': 1, 'column': 34}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'CREATE (n:Company $props) RETURN id(n) as id, n'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created 3 company nodes in batch\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Create multiple nodes in batch (more efficient for large datasets)\n",
    "other_companies = store.create_nodes([\n",
@@ -484,42 +190,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=23, offset=54>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 54, 'line': 3, 'column': 23}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:CEO_OF $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=45, offset=76>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 76, 'line': 3, 'column': 45}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:CEO_OF $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=5, column=24, offset=165>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 165, 'line': 5, 'column': 24}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:CEO_OF $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created relationship: CEO_OF (ID: 1152921504606846977)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=23, offset=54>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 54, 'line': 3, 'column': 23}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:HEADQUARTERED_IN $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=45, offset=76>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 76, 'line': 3, 'column': 45}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:HEADQUARTERED_IN $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=5, column=24, offset=175>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 175, 'line': 5, 'column': 24}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH (a), (b)\\n                WHERE id(a) = $start_id AND id(b) = $end_id\\n                CREATE (a)-[r:HEADQUARTERED_IN $props]->(b)\\n                RETURN id(r) as id, type(r) as type, r\\n            '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created relationship: HEADQUARTERED_IN (ID: 1152922604118474752)\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Create relationships between nodes\n",
    "ceo_rel = store.create_relationship(\n",
@@ -552,28 +225,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=26, offset=25>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 25, 'line': 1, 'column': 26}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'MATCH (n:Company) RETURN id(n) as id, n, labels(n) as labels LIMIT 10'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 4 companies:\n",
-      "  - Apple Inc. (founded: 1976)\n",
-      "  - Microsoft (founded: 1975)\n",
-      "  - Google (founded: 1998)\n",
-      "  - Amazon (founded: 1994)\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Get nodes by label\n",
    "companies = store.get_nodes(labels=[\"Company\"], limit=10)\n",
@@ -591,35 +245,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=31, offset=73>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 73, 'line': 3, 'column': 31}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r]-(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(startNode(r)) as start_id, id(endNode(r)) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=32, offset=121>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 121, 'line': 4, 'column': 32}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r]-(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(startNode(r)) as start_id, id(endNode(r)) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=62, offset=151>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 151, 'line': 4, 'column': 62}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r]-(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(startNode(r)) as start_id, id(endNode(r)) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=92, offset=181>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 181, 'line': 4, 'column': 92}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r]-(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(startNode(r)) as start_id, id(endNode(r)) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=31, offset=81>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 81, 'line': 3, 'column': 31}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r:CEO_OF]->(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(a) as start_id, id(b) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=32, offset=129>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 129, 'line': 4, 'column': 32}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r:CEO_OF]->(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(a) as start_id, id(b) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=62, offset=159>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 159, 'line': 4, 'column': 62}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r:CEO_OF]->(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(a) as start_id, id(b) as end_id, r\\n                        LIMIT 100\\n                    '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=4, column=81, offset=178>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 178, 'line': 4, 'column': 81}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                        MATCH (a)-[r:CEO_OF]->(b)\\n                        WHERE id(a) = $node_id\\n                        RETURN id(r) as id, type(r) as type, id(a) as start_id, id(b) as end_id, r\\n                        LIMIT 100\\n                    '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 2 relationships for Apple:\n",
-      "  - HEADQUARTERED_IN: {'since': 1977}\n",
-      "  - CEO_OF: {'since': 2011}\n",
-      "\n",
-      "Outgoing CEO_OF relationships: 1\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Get relationships for a node\n",
    "relationships = store.get_relationships(node_id=apple[\"id\"], direction=\"both\")\n",
@@ -652,18 +280,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CEO Relationships:\n",
-      "  - Tim Cook is CEO of Apple Inc. since 2011\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Execute a Cypher query to find CEO relationships\n",
    "results = store.execute_query(\"\"\"\n",
@@ -681,19 +300,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Companies founded after 1990:\n",
-      "  - Amazon (founded: 1994)\n",
-      "  - Google (founded: 1998)\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Parameterized query (safer and more efficient)\n",
    "results = store.execute_query(\n",
@@ -721,7 +330,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -742,28 +351,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=23, offset=88>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 88, 'line': 3, 'column': 23}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH path = shortestPath((start)-[r*..5]-(end))\\n                WHERE id(start) = $start_id AND id(end) = $end_id\\n                RETURN path, length(path) as length\\n            '\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=3, column=49, offset=114>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 114, 'line': 3, 'column': 49}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: '\\n                MATCH path = shortestPath((start)-[r*..5]-(end))\\n                WHERE id(start) = $start_id AND id(end) = $end_id\\n                RETURN path, length(path) as length\\n            '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shortest path found:\n",
-      "  - Path length: 2\n",
-      "  - Nodes in path: 3\n",
-      "  - Relationships: 2\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Find shortest path between two nodes\n",
    "if tim_cook.get('id') and cupertino.get('id'):\n",
@@ -795,27 +385,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=17, offset=16>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 16, 'line': 1, 'column': 17}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'MATCH (n) WHERE id(n) = $id SET n += $props RETURN id(n) as id, n, labels(n) as labels'\n",
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=52, offset=51>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 51, 'line': 1, 'column': 52}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'MATCH (n) WHERE id(n) = $id SET n += $props RETURN id(n) as id, n, labels(n) as labels'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Updated node: Tim Cook\n",
-      "  New age: 64\n",
-      "  New title: CEO & President\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Update node properties (merge mode - adds/updates properties)\n",
    "if tim_cook.get('id'):\n",
@@ -849,27 +421,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Received notification from DBMS server: <GqlStatusObject gql_status='01N01', status_description='warn: feature deprecated with replacement. id is deprecated. It is replaced by elementId or consider using an application-generated id.', position=<SummaryInputPosition line=1, column=24, offset=23>, raw_classification='DEPRECATION', classification=<NotificationClassification.DEPRECATION: 'DEPRECATION'>, raw_severity='WARNING', severity=<NotificationSeverity.WARNING: 'WARNING'>, diagnostic_record={'_classification': 'DEPRECATION', '_severity': 'WARNING', '_position': {'offset': 23, 'line': 1, 'column': 24}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}> for query: 'MATCH ()-[r]->() WHERE id(r) = $id DELETE r'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Deleted relationship (ID: 1152922604118474752)\n",
-      "\n",
-      "Tip: Use detach=True to delete a node and all its relationships\n",
-      "     Use detach=False to only delete the node (fails if relationships exist)\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Delete a relationship\n",
    "if location_rel.get('id'):\n",
@@ -900,27 +454,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Graph Statistics:\n",
-      "  Total nodes: 6\n",
-      "  Total relationships: 1\n",
-      "\n",
-      "Node labels:\n",
-      "  - Company: 4 nodes\n",
-      "  - Person: 1 nodes\n",
-      "  - Location: 1 nodes\n",
-      "\n",
-      "Relationship types:\n",
-      "  - CEO_OF: 1 relationships\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Get comprehensive graph statistics\n",
    "stats = store.get_stats()\n",
@@ -947,23 +483,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Convenience functions available:\n",
-      "  - create_node, create_nodes\n",
-      "  - create_relationship, create_relationships\n",
-      "  - get_nodes, get_relationships\n",
-      "  - update_node, delete_node\n",
-      "  - execute_query, shortest_path, get_neighbors\n",
-      "  - run_analytics\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Using convenience functions (alternative to class methods)\n",
    "from semantica.graph_store import (\n",
@@ -1003,17 +525,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created index on Company.name for faster queries\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Create an index on a node property for faster lookups\n",
    "# This is especially useful for frequently queried properties\n",
@@ -1045,17 +559,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connection closed successfully\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Close the connection\n",
    "store.close()\n",
@@ -41,255 +41,18 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: semantica in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.0.5)\n",
-      "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.26.4)\n",
-      "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (2.1.4)\n",
-      "Requirement already satisfied: scikit-learn>=1.0.0 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from semantica) (1.6.1)\n",
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-      "Requirement already satisfied: wrapt in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.4.2->spacy>=3.4.0->semantica) (1.17.2)\n",
-      "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\mohd kaif\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from sqlalchemy>=1.4.0->semantica) (3.2.3)\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~gno (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~lotly (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution ~ython-socketio (C:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "!pip install semantica"
   ]
  },
  {
   "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",
@@ -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",
@@ -142,10 +142,15 @@
  }
 ],
 "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
-}
+}
@@ -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",
@@ -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",
-      "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",
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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",
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-      "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": [
@@ -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",
@@ -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",
@@ -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",
@@ -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": [
@@ -0,0 +1,667 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Amazon Neptune Graph Store\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This notebook covers the Amazon Neptune Database integration in Semantica. Amazon Neptune is a fully managed graph database service that supports both property graphs (via OpenCypher/Gremlin) and RDF graphs (via SPARQL).\n",
+    "\n",
+    "### Key Features\n",
+    "\n",
+    "- **IAM Authentication**: Secure access using AWS SigV4 signatures via AuthManager\n",
+    "- **OpenCypher Support**: Query using standard OpenCypher syntax\n",
+    "- **Bolt Protocol**: Uses Neo4j Bolt driver for efficient binary communication\n",
+    "- **Native ~id Support**: Leverages Neptune's native element ID handling\n",
+    "- **Full CRUD Operations**: Create, read, update, delete nodes and relationships\n",
+    "- **Automatic Retry**: Built-in retry logic with exponential backoff for transient errors\n",
+    "\n",
+    "### Prerequisites\n",
+    "\n",
+    "- An Amazon Neptune Database cluster\n",
+    "- AWS credentials configured (boto3, environment variables, or IAM role)\n",
+    "- Network access to your Neptune cluster (VPC, security groups)\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "```bash\n",
+    "# Install Semantica with Neptune support\n",
+    "pip install semantica\n",
+    "\n",
+    "# Required dependencies (installed automatically)\n",
+    "pip install boto3 neo4j\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install semantica"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "\n",
+    "Set your Neptune cluster endpoint and AWS credentials. Replace the placeholder values with your actual configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Neptune cluster configuration - REPLACE WITH YOUR VALUES\n",
+    "os.environ[\"NEPTUNE_ENDPOINT\"] = \"your-cluster.us-east-1.neptune.amazonaws.com\"\n",
+    "os.environ[\"NEPTUNE_PORT\"] = \"8182\"\n",
+    "os.environ[\"AWS_REGION\"] = \"us-east-1\"\n",
+    "\n",
+    "# AWS credentials (if using IAM Auth and not relying on IAM role or ~/.aws/credentials)\n",
+    "# os.environ[\"AWS_ACCESS_KEY_ID\"] = \"your-access-key-id\"\n",
+    "# os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"your-secret-access-key\"\n",
+    "# os.environ[\"AWS_SESSION_TOKEN\"] = \"your-session-token\"\n",
+    "\n",
+    "print(f\"Neptune Endpoint: {os.environ.get('NEPTUNE_ENDPOINT')}\")\n",
+    "print(f\"AWS Region: {os.environ.get('AWS_REGION')}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Initialize Neptune Store\n",
+    "\n",
+    "Initialize a connection to your Amazon Neptune cluster with IAM authentication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from semantica.graph_store import GraphStore\n",
+    "\n",
+    "# Option 1: Using GraphStore factory (recommended)\n",
+    "neptune_store = GraphStore(\n",
+    "    backend=\"neptune\",\n",
+    "    endpoint=os.environ.get(\"NEPTUNE_ENDPOINT\"),\n",
+    "    port=int(os.environ.get(\"NEPTUNE_PORT\", 8182)),\n",
+    "    region=os.environ.get(\"AWS_REGION\", \"us-east-1\"),\n",
+    "    iam_auth=True,\n",
+    ")\n",
+    "\n",
+    "# Connect to Neptune\n",
+    "neptune_store.connect()\n",
+    "print(\"Connected to Amazon Neptune!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Development/Testing Without IAM Auth\n",
+    "\n",
+    "For development or testing environments where IAM authentication is not required (e.g., Neptune notebooks or VPC-only access), you can disable IAM signing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For dev/test environments without IAM authentication\n",
+    "neptune_store_dev = GraphStore(\n",
+    "    backend=\"neptune\",\n",
+    "    endpoint=os.environ.get(\"NEPTUNE_ENDPOINT\"),\n",
+    "    port=int(os.environ.get(\"NEPTUNE_PORT\", 8182)),\n",
+    "    region=os.environ.get(\"AWS_REGION\", \"us-east-1\"),\n",
+    "    iam_auth=False,  # Disable IAM signing for dev/test\n",
+    ")\n",
+    "neptune_store_dev.connect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Authentication Options\n",
+    "\n",
+    "IAM Authentication (recommended for production) automatically uses the AWS credential chain:\n",
+    "1. Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)\n",
+    "2. AWS credentials file (~/.aws/credentials)\n",
+    "3. IAM role (for EC2, Lambda, ECS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Node Operations\n",
+    "\n",
+    "### Creating Nodes\n",
+    "\n",
+    "Nodes represent entities in your graph. Each node can have:\n",
+    "- **ID**: A unique identifier (custom or auto-generated UUID)\n",
+    "- **Labels**: Categories/types (e.g., `Person`, `Company`)\n",
+    "- **Properties**: Key-value pairs (e.g., `{\"name\": \"Alice\", \"age\": 30}`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a single node with custom ID (id in properties)\n",
+    "alice = neptune_store.create_node(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"id\": \"alice\", \"name\": \"Alice\", \"age\": 30, \"role\": \"Engineer\"}\n",
+    ")\n",
+    "print(f\"Created node: {alice}\")\n",
+    "\n",
+    "# Create a node with auto-generated UUID (no id in properties)\n",
+    "bob = neptune_store.create_node(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"name\": \"Bob\", \"age\": 25, \"role\": \"Designer\"}\n",
+    ")\n",
+    "print(f\"Created node with UUID: {bob['id']}\")\n",
+    "\n",
+    "# Create a company node with auto-generated ID\n",
+    "acme = neptune_store.create_node(\n",
+    "    labels=[\"Company\"],\n",
+    "    properties={\"name\": \"Acme Corp\", \"industry\": \"Technology\", \"founded\": 2010}\n",
+    ")\n",
+    "print(f\"Created company: {acme}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Multiple Nodes (Batch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Batch create nodes for better performance\n",
+    "# Include 'id' in properties for custom IDs\n",
+    "nodes_data = [\n",
+    "    {\"labels\": [\"Person\"], \"properties\": {\"id\": \"charlie\", \"name\": \"Charlie\", \"age\": 35}},\n",
+    "    {\"labels\": [\"Person\"], \"properties\": {\"id\": \"diana\", \"name\": \"Diana\", \"age\": 28}},\n",
+    "    {\"labels\": [\"Location\"], \"properties\": {\"name\": \"San Francisco\", \"state\": \"CA\"}},\n",
+    "]\n",
+    "\n",
+    "created_nodes = neptune_store.create_nodes(nodes_data)\n",
+    "print(f\"Created {len(created_nodes)} nodes in batch\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieving Nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get a specific node by ID\n",
+    "alice_node = neptune_store.get_node(node_id=\"alice\")\n",
+    "print(f\"Retrieved: {alice_node}\")\n",
+    "\n",
+    "# Get nodes by label\n",
+    "people = neptune_store.get_nodes(labels=[\"Person\"], limit=10)\n",
+    "print(f\"Found {len(people)} Person nodes:\")\n",
+    "for person in people:\n",
+    "    print(f\"  - {person.get('properties', {}).get('name')}\")\n",
+    "\n",
+    "# Get nodes by properties\n",
+    "engineers = neptune_store.get_nodes(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"role\": \"Engineer\"},\n",
+    "    limit=5\n",
+    ")\n",
+    "print(f\"Found {len(engineers)} engineers\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Updating Nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update node properties (merge mode - default)\n",
+    "updated_alice = neptune_store.update_node(\n",
+    "    node_id=\"alice\",\n",
+    "    properties={\"age\": 31, \"department\": \"AI Research\"},\n",
+    "    merge=True\n",
+    ")\n",
+    "print(f\"Updated Alice: {updated_alice}\")\n",
+    "\n",
+    "# Replace all properties (merge=False)\n",
+    "# WARNING: This removes properties not in the update\n",
+    "replaced = neptune_store.update_node(\n",
+    "    node_id=\"charlie\",\n",
+    "    properties={\"name\": \"Charlie\", \"age\": 36},\n",
+    "    merge=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deleting Nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Delete a node (with detach=True to also delete relationships)\n",
+    "deleted = neptune_store.delete_node(node_id=\"diana\", detach=True)\n",
+    "print(f\"Deleted diana: {deleted}\")\n",
+    "\n",
+    "# Without detach (fails if node has relationships)\n",
+    "# neptune_store.delete_node(node_id=\"alice\", detach=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Relationship Operations\n",
+    "\n",
+    "### Creating Relationships\n",
+    "\n",
+    "Relationships connect nodes and represent connections between entities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a relationship between Alice and Acme\n",
+    "works_at = neptune_store.create_relationship(\n",
+    "    start_node_id=\"alice\",\n",
+    "    end_node_id=acme[\"id\"],\n",
+    "    rel_type=\"WORKS_AT\",\n",
+    "    properties={\"since\": 2020, \"position\": \"Senior Engineer\"}\n",
+    ")\n",
+    "print(f\"Created relationship: {works_at}\")\n",
+    "\n",
+    "# Create a KNOWS relationship between people\n",
+    "knows_rel = neptune_store.create_relationship(\n",
+    "    start_node_id=\"alice\",\n",
+    "    end_node_id=bob[\"id\"],\n",
+    "    rel_type=\"KNOWS\",\n",
+    "    properties={\"since\": 2019}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieving Relationships"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get all relationships for a node\n",
+    "alice_rels = neptune_store.get_relationships(node_id=\"alice\", direction=\"both\")\n",
+    "print(f\"Alice has {len(alice_rels)} relationships\")\n",
+    "\n",
+    "# Get outgoing relationships only\n",
+    "outgoing = neptune_store.get_relationships(node_id=\"alice\", direction=\"out\")\n",
+    "\n",
+    "# Filter by relationship type\n",
+    "works_rels = neptune_store.get_relationships(\n",
+    "    node_id=\"alice\",\n",
+    "    rel_type=\"WORKS_AT\",\n",
+    "    direction=\"out\"\n",
+    ")\n",
+    "print(f\"Alice's work relationships: {len(works_rels)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deleting Relationships"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Delete a specific relationship by ID\n",
+    "if works_at.get(\"id\"):\n",
+    "    deleted = neptune_store.delete_relationship(rel_id=works_at[\"id\"])\n",
+    "    print(f\"Deleted relationship: {deleted}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: OpenCypher Queries\n",
+    "\n",
+    "Amazon Neptune supports OpenCypher queries via the Bolt protocol. Execute complex graph patterns using standard Cypher syntax."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simple query\n",
+    "results = neptune_store.execute_query(\n",
+    "    \"MATCH (p:Person) RETURN p.name, p.age ORDER BY p.age\"\n",
+    ")\n",
+    "print(\"People in the graph:\")\n",
+    "for record in results.get(\"records\", []):\n",
+    "    print(f\"  - {record.get('p.name')}: {record.get('p.age')} years old\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using parameters (safer and more efficient)\n",
+    "results = neptune_store.execute_query(\n",
+    "    \"MATCH (p:Person) WHERE p.age > $min_age RETURN p.name, p.age\",\n",
+    "    parameters={\"min_age\": 25}\n",
+    ")\n",
+    "print(f\"People over 25: {len(results.get('records', []))}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find relationships between nodes\n",
+    "results = neptune_store.execute_query(\"\"\"\n",
+    "    MATCH (p:Person)-[r:WORKS_AT]->(c:Company)\n",
+    "    RETURN p.name as employee, c.name as company, r.since as start_year\n",
+    "\"\"\")\n",
+    "for record in results.get(\"records\", []):\n",
+    "    print(f\"{record['employee']} works at {record['company']} since {record['start_year']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count and aggregate\n",
+    "results = neptune_store.execute_query(\"\"\"\n",
+    "    MATCH (p:Person)\n",
+    "    RETURN count(p) as total, avg(p.age) as avg_age, max(p.age) as max_age\n",
+    "\"\"\")\n",
+    "stats = results.get(\"records\", [{}])[0]\n",
+    "print(f\"Total: {stats.get('total')}, Avg Age: {stats.get('avg_age'):.1f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Graph Analytics\n",
+    "\n",
+    "### Get Neighbors\n",
+    "\n",
+    "Traverse the graph to find connected nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get immediate neighbors (depth=1)\n",
+    "neighbors = neptune_store.get_neighbors(\n",
+    "    node_id=\"alice\",\n",
+    "    direction=\"both\",\n",
+    "    depth=1\n",
+    ")\n",
+    "print(f\"Alice's direct neighbors: {len(neighbors)}\")\n",
+    "\n",
+    "# Get neighbors up to 2 hops away\n",
+    "extended = neptune_store.get_neighbors(\n",
+    "    node_id=\"alice\",\n",
+    "    direction=\"out\",\n",
+    "    depth=2\n",
+    ")\n",
+    "print(f\"Nodes within 2 hops: {len(extended)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shortest Path\n",
+    "\n",
+    "Find the shortest path between two nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find shortest path\n",
+    "path = neptune_store.shortest_path(\n",
+    "    start_node_id=\"alice\",\n",
+    "    end_node_id=\"charlie\",\n",
+    "    max_depth=5\n",
+    ")\n",
+    "\n",
+    "if path:\n",
+    "    print(\"Path found!\")\n",
+    "    print(f\"  Length: {path.get('length')}\")\n",
+    "    print(f\"  Nodes: {len(path.get('nodes', []))}\")\n",
+    "    print(f\"  Relationships: {len(path.get('relationships', []))}\")\n",
+    "else:\n",
+    "    print(\"No path found between nodes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Graph Statistics\n",
+    "\n",
+    "Get comprehensive statistics about your graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get graph statistics\n",
+    "stats = neptune_store.get_stats()\n",
+    "\n",
+    "print(\"Graph Statistics:\")\n",
+    "print(f\"  Total nodes: {stats.get('node_count', 'N/A')}\")\n",
+    "print(f\"  Total relationships: {stats.get('relationship_count', 'N/A')}\")\n",
+    "\n",
+    "print(\"\\nNode labels:\")\n",
+    "for label, count in stats.get('label_counts', {}).items():\n",
+    "    print(f\"  - {label}: {count}\")\n",
+    "\n",
+    "print(\"\\nRelationship types:\")\n",
+    "for rel_type, count in stats.get('relationship_type_counts', {}).items():\n",
+    "    print(f\"  - {rel_type}: {count}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Connection Management\n",
+    "\n",
+    "Always close connections when done to free resources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check connection status\n",
+    "status = neptune_store.get_status()\n",
+    "print(f\"Connection status: {status}\")\n",
+    "\n",
+    "# Close the connection\n",
+    "neptune_store.close()\n",
+    "print(\"Connection closed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neptune-Specific Considerations\n",
+    "\n",
+    "### Native Element IDs\n",
+    "\n",
+    "Neptune uses native `~id` for element identification. Include `id` in properties to set a custom ID:\n",
+    "\n",
+    "```python\n",
+    "# Create a node with custom ID (include 'id' in properties)\n",
+    "node = neptune_store.create_node(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"id\": \"my-custom-id\", \"name\": \"Test\"}\n",
+    ")\n",
+    "\n",
+    "# Create a node with auto-generated UUID (omit 'id' from properties)\n",
+    "node = neptune_store.create_node(\n",
+    "    labels=[\"Person\"],\n",
+    "    properties={\"name\": \"Test\"}\n",
+    ")\n",
+    "\n",
+    "# The ID is used in id() function calls internally:\n",
+    "# MATCH (n) WHERE id(n) = 'my-custom-id' RETURN n\n",
+    "```\n",
+    "\n",
+    "### OpenCypher Considerations\n",
+    "\n",
+    "Amazon Neptune Database's OpenCypher implementation has some differences from Neo4j:\n",
+    "\n",
+    "1. **No `shortestPath()` function**: Use variable-length path patterns or `allShortestPaths()`\n",
+    "2. **Labels syntax**: Use `labels(n)` function to retrieve node labels\n",
+    "3. **Property updates**: Use `SET n += {props}` for merge behavior\n",
+    "\n",
+    "For the complete OpenCypher specification supported by Amazon Neptune Database, see the [AWS documentation](https://docs.aws.amazon.com/neptune/latest/userguide/access-graph-opencypher.html).\n",
+    "\n",
+    "### Amazon Neptune Analytics\n",
+    "\n",
+    "For analytical (OLAP) workloads such as graph algorithms, aggregations, and large-scale traversals, consider [Amazon Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html). Neptune Analytics complements Neptune Database by providing optimized performance for analytical queries while Neptune Database is optimized for transactional (OLTP) workloads.\n",
+    "\n",
+    "### Performance Tips\n",
+    "\n",
+    "1. **Use batch operations** for creating multiple nodes/relationships\n",
+    "2. **Use parameters** in queries to enable query caching\n",
+    "3. **Limit result sets** with `LIMIT` clause"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook covered the Amazon Neptune Graph Store integration:\n",
+    "\n",
+    "- **IAM Authentication**: Secure AWS SigV4 signing\n",
+    "- **CRUD Operations**: Full node and relationship management\n",
+    "- **OpenCypher Queries**: Standard graph query language\n",
+    "- **Graph Analytics**: Neighbors and shortest path algorithms\n",
+    "- **Statistics & Monitoring**: Graph metrics and status\n",
+    "\n",
+    "### Key Takeaways\n",
+    "\n",
+    "- Neptune uses native `~id` for element identification\n",
+    "- IAM authentication is recommended for production\n",
+    "- Bolt protocol provides efficient binary query interface\n",
+    "- Semantica abstracts Neptune-specific syntax differences\n",
+    "\n",
+    "### Next Steps\n",
+    "\n",
+    "- [Graph Store (Neo4j/FalkorDB)](09_Graph_Store.ipynb) - Compare with other backends\n",
+    "- [Building Knowledge Graphs](07_Building_Knowledge_Graphs.ipynb) - Build production KGs\n",
+    "- [Graph Analytics](10_Graph_Analytics.ipynb) - Advanced analytics algorithms"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
@@ -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",
@@ -138,6 +138,39 @@ async for item in feed_processor.stream_items():
    knowledge_graph.add_triplets(core.generate_triplets(semantics))
 ```

+### 🦆 Docling Clear Code Example
+
+High-accuracy document parsing with structural understanding:
+
+```python
+from semantica.parse import DoclingParser
+
+# 1. Initialize DoclingParser
+# Docling provides superior table extraction and structure understanding
+# Requires: pip install docling
+parser = DoclingParser(
+    enable_ocr=True,          # Enable OCR for scanned documents
+    export_format="markdown"  # Options: "markdown", "html", "json"
+)
+
+# 2. Parse a complex document
+# Supports PDF, DOCX, PPTX, XLSX, HTML, and images
+result = parser.parse("complex_invoice.pdf")
+
+# 3. Access structured content
+print(f"Content (Markdown):\n{result['full_text']}")
+
+# 4. Extract and iterate over tables with high precision
+for i, table in enumerate(result['tables']):
+    print(f"\nTable {i+1}:")
+    print(f"Headers: {table.get('headers', [])}")
+    print(f"Data rows: {len(table.get('rows', []))}")
+
+# 5. Get document metadata
+metadata = result['metadata']
+print(f"\nMetadata: {metadata.get('title')} ({result.get('total_pages')} pages)")
+```
+
 ### 📊 Structured Data Processing Module

 Handle structured and semi-structured data formats:
@@ -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 = {0.2.2},
  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 0.2.2) [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 0.2.2, 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 0.2.2. 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 0.2.2, 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."

 ---

@@ -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>
@@ -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:
@@ -0,0 +1,107 @@
+# Docling Integration
+
+Semantica features a native integration with **Docling**, the powerful document parsing library that excels at extracting structured data from complex documents like PDFs, DOCX, and PPTX.
+
+## Overview
+
+Docling is integrated into Semantica's `parse` module via the `DoclingParser`. This allows you to seamlessly convert unstructured documents into semantic structures that can be indexed, searched, and analyzed within the Semantica framework.
+
+- 📖 **Semantica Docling Integration Docs**: [Reference Guide](../reference/parse.md)
+- 💻 **Semantica Docling Integration GitHub**: [Source Code](https://github.com/Hawksight-AI/semantica/blob/main/semantica/parse/docling_parser.py)
+- 🧑🏽‍🍳 **Semantica Docling Integration Example**: [Docling Clear Code Example](../CodeExamples.md#docling-clear-code-example)
+- 📦 **Semantica Docling Integration PyPI**: [Installation Guide](../installation.md)
+
+---
+
+## 📖 Integration Documentation
+
+The `DoclingParser` provides a high-level interface for document processing. It supports:
+
+*   **Multi-format support**: PDF, DOCX, PPTX, HTML, and more.
+*   **Table Extraction**: High-fidelity table extraction with header detection.
+*   **OCR Support**: Built-in Optical Character Recognition for scanned documents.
+*   **Markdown Export**: Clean markdown output optimized for LLM consumption.
+
+### Basic Usage
+
+```python
+from semantica.parse import DoclingParser
+
+# Initialize with OCR enabled
+parser = DoclingParser(enable_ocr=True)
+
+# Parse a complex document
+result = parser.parse("financial_report.pdf")
+
+# Access the structured data
+print(f"Content: {result['full_text'][:200]}...")
+print(f"Found {len(result['tables'])} tables")
+```
+
+For more details, see the [Parse Reference](../reference/parse.md).
+
+---
+
+## 🧑🏽‍🍳 Integration Example
+
+We provide a detailed cookbook and clear code examples to help you get started quickly.
+
+### Docling Clear Code Example
+
+```python
+from semantica.parse import DoclingParser
+import json
+
+# 1. Initialize the Docling Parser with advanced config
+parser = DoclingParser(
+    enable_ocr=True, 
+    export_format="markdown"
+)
+
+# 2. Parse a complex document (PDF, DOCX, etc.)
+result = parser.parse("complex_invoice.pdf")
+
+# 3. Access the clean Markdown text
+print(f"--- Document Content ---\n{result['full_text']}")
+
+# 4. Iterate through extracted tables
+for i, table in enumerate(result['tables']):
+    print(f"\nTable {i+1} headers: {table.get('headers', [])}")
+    # Access table rows as a list of lists
+    for row in table.get('rows', [])[:3]:  # Print first 3 rows
+        print(f"  Row: {row}")
+
+# 5. Get document metadata
+metadata = result['metadata']
+print(f"\n--- Metadata ---\nTitle: {metadata.get('title')}")
+print(f"Total Pages: {result.get('total_pages')}")
+```
+
+See more in our [Code Examples](../CodeExamples.md).
+
+---
+
+## 💻 GitHub Source
+
+The integration is open-source and available on GitHub. You can explore the implementation, contribute improvements, or report issues.
+
+- [docling_parser.py](https://github.com/Hawksight-AI/semantica/blob/main/semantica/parse/docling_parser.py) - The core implementation of the Docling integration.
+
+---
+
+## 📦 PyPI & Installation
+
+Docling is an optional but highly recommended dependency for Semantica. You can install it along with Semantica or as a separate requirement.
+
+### Install via Semantica
+```bash
+pip install semantica
+```
+
+### Install Docling manually
+If you are working in a custom environment:
+```bash
+pip install docling
+```
+
+For full installation details, see the [Installation Guide](../installation.md).
@@ -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
@@ -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
@@ -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:**

@@ -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:**
@@ -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
@@ -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}")
 ```

 ---
@@ -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["full_text"])
+```
+
 ### WebParser

 Parses web content.
@@ -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})
 ```

 ---
@@ -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()
@@ -23,7 +23,7 @@ The **Semantic Extract Module** extracts structured information from unstructure
 - **High Accuracy**: LLM-based extraction for complex schemas
 - **Flexible Configuration**: Customize extraction for your domain
 - **Confidence Scores**: Get confidence scores for all extractions
- **Batch Processing**: Efficient batch processing for large datasets
+- **Batch Processing**: Efficient parallel batch processing for large datasets
 - **Coreference Resolution**: Resolve pronouns to their entity references

 ### How It Works
@@ -184,6 +184,10 @@ 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 | `64000` | Max text length for auto-chunking (LLM only) |
+| `max_tokens` | int | `None` | Max output tokens for LLM generation |
+| `max_workers` | int | `1` | Threads for parallel batch processing |
 | `**config` | dict | `{}` | Method-specific config (e.g., `model`, `provider`) |

 **Methods:**
@@ -202,11 +206,12 @@ from semantica.semantic_extract import NERExtractor
 extractor = NERExtractor(method="ml", model="en_core_web_trf")
 entities = extractor.extract("Elon Musk leads SpaceX.")

-# 2. LLM (OpenAI/Gemini/etc)
+# 2. LLM (OpenAI/Gemini/Groq/etc)
 extractor = NERExtractor(
    method="llm", 
-    provider="openai", 
-    model="gpt-4",
+    provider="groq", 
+    model="llama-3.3-70b-versatile",
+    max_tokens=2048, # Increased output limit
    temperature=0.0
 )

@@ -230,6 +235,7 @@ Extracts relationships between entities.
 | `bidirectional` | bool | `False` | Extract bidirectional relations |
 | `confidence_threshold` | float | `0.6` | Minimum confidence score |
 | `max_distance` | int | `50` | Max token distance between entities |
+| `max_workers` | int | `1` | Threads for parallel batch processing |

 **Methods:**

@@ -251,7 +257,7 @@ entities = ner.extract_entities(text)
 # Basic relation extraction
 rel_extractor = RelationExtractor()
 relations = rel_extractor.extract(text, entities=entities)
-# [Relation(source="Elon Musk", target="SpaceX", type="founded")]
+# [Relation(subject="Elon Musk", predicate="founded", object="SpaceX")]

 # With configuration
 rel_extractor = RelationExtractor(
@@ -306,6 +312,7 @@ Identifies events with temporal information and participants.
 | `extract_participants` | bool | `True` | Extract event participants |
 | `extract_location` | bool | `True` | Extract event locations |
 | `extract_time` | bool | `True` | Extract temporal information |
+| `max_workers` | int | `1` | Threads for parallel batch processing |

 **Methods:**

@@ -337,6 +344,10 @@ 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 | `64000` | Max text length for auto-chunking (LLM only) |
+| `max_tokens` | int | `None` | Max output tokens for LLM generation |
+| `max_workers` | int | `1` | Threads for parallel batch processing |

 **Methods:**

@@ -367,6 +378,7 @@ Extracts structured semantic networks with nodes and edges.
 |-----------|------|---------|-------------|
 | `ner_method` | str | `None` | Method for node extraction |
 | `relation_method` | str | `None` | Method for edge extraction |
+| `max_workers` | int | `1` | Threads for parallel batch processing |
 | `**config` | dict | `{}` | Configuration for underlying extractors |

 **Methods:**
@@ -420,6 +432,44 @@ enhanced_entities = extractor.enhance_entities(text, entities)

 ---

+## Batch Processing & Provenance
+
+All extractors support batch processing for high-throughput extraction. You can pass a list of strings or a list of dictionaries (with `content` and `id` keys).
+
+**Features:**
+- **Progress Tracking**: Automatically shows a progress bar for large batches.
+- **Provenance Metadata**: Each extracted item includes `batch_index` and `document_id` in its `metadata`.
+
+```python
+from semantica.semantic_extract import NERExtractor
+
+documents = [
+    {"id": "doc_1", "content": "Apple Inc. was founded by Steve Jobs."},
+    {"id": "doc_2", "content": "Microsoft Corporation was founded by Bill Gates."}
+]
+
+extractor = NERExtractor()
+batch_results = extractor.extract(documents)
+
+for i, doc_entities in enumerate(batch_results):
+    print(f"Document {i} entities:")
+    for entity in doc_entities:
+        print(f"  - {entity.text} ({entity.label})")
+        print(f"    Provenance: Batch Index {entity.metadata['batch_index']}, Doc ID {entity.metadata.get('document_id')}")
+```
+
+## Robust Extraction Fallbacks
+
+The framework implements robust fallback chains to prevent empty results when primary methods fail (e.g., due to model unavailability or obscure text).
+
+- **NER**: `ML/LLM` -> `Pattern` -> `Last Resort` (Capitalized Words)
+- **Relation**: `Primary` -> `Pattern` -> `Last Resort` (Adjacency)
+- **Triplet**: `Primary` -> `Relation-to-Triplet` -> `Pattern`
+
+This ensures that you almost always get *some* structured data, even if it requires falling back to simpler heuristics.
+
+---
+
 ## Usage Examples

 ```python
@@ -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()
@@ -7,7 +7,7 @@ repo_name: Hawksight-AI/semantica
 edit_uri: edit/main/docs/

 # Copyright
-copyright: Copyright &copy; 2025 Hawksight AI
+copyright: Copyright &copy; 2026 Hawksight AI

 # Theme Configuration
 theme:
@@ -139,6 +139,8 @@ nav:
    - examples.md
    - Code Examples: CodeExamples.md
    - learning-more.md
+  - Integrations:
+    - Docling: integrations/docling.md
  - Cookbook: cookbook.md
  - Resources:
    - community-projects.md
@@ -4,302 +4,198 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "semantica"
-version = "0.1.0"
-description = "🧠 Semantica - An Open Source Framework for building Semantic Layers and Knowledge Engineering "
+version = "0.2.2"
+description = "🧠 Semantica - An Open Source Framework for building Semantic Layers and Knowledge Engineering"
 readme = "README.md"
-license = {text = "MIT"}
-authors = [
-    {name = "Hawksight AI", email = "semantica-dev@users.noreply.github.com"}
-]
-maintainers = [
-    {name = "Hawksight AI", email = "semantica-dev@users.noreply.github.com"}
-]
+license = { text = "MIT" }
+
+authors = [{ name = "Hawksight AI", email = "semantica-dev@users.noreply.github.com" }]
+maintainers = [{ name = "Hawksight AI", email = "semantica-dev@users.noreply.github.com" }]
+
 requires-python = ">=3.8"
+
 classifiers = [
-    "Development Status :: 3 - Alpha",
-    "Intended Audience :: Developers",
-    "Intended Audience :: Science/Research",
-    "License :: OSI Approved :: MIT License",
-    "Operating System :: OS Independent",
-    "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12",
-    "Topic :: Scientific/Engineering :: Artificial Intelligence",
-    "Topic :: Software Development :: Libraries :: Python Modules",
-    "Topic :: Text Processing :: Linguistic",
-    "Topic :: Database :: Database Engines/Servers",
-    "Topic :: Internet :: WWW/HTTP :: Indexing/Search"
+  "Development Status :: 3 - Alpha",
+  "Intended Audience :: Developers",
+  "Intended Audience :: Science/Research",
+  "License :: OSI Approved :: MIT License",
+  "Operating System :: OS Independent",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.8",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Topic :: Scientific/Engineering :: Artificial Intelligence",
+  "Topic :: Software Development :: Libraries :: Python Modules"
 ]
+
 keywords = [
-    "semantic-layer", "knowledge-engineering", "nlp", "knowledge-graph",
-    "embeddings", "entity-extraction", "relationship-extraction", "rdf",
-    "ontology", "semantic-analysis", "ai", "machine-learning"
+  "semantic-layer", "knowledge-graph", "nlp", "embeddings",
+  "entity-extraction", "relationship-extraction", "rdf", "ontology"
 ]
+
+# ---------------- CORE DEPENDENCIES (SAFE DEFAULT) ----------------
 dependencies = [
-    "numpy>=1.21.0",
-    "pandas>=1.3.0",
-    "scikit-learn>=1.0.0",
-    "umap-learn>=0.5.0",
-    "spacy>=3.4.0",
-    "transformers>=4.20.0",
-    "torch>=1.12.0",
-    "sentence-transformers>=2.2.0",
-    "rdflib>=6.2.0",
-    "networkx>=2.8.0",
-    "matplotlib>=3.5.0",
-    "seaborn>=0.11.0",
-    "plotly>=5.10.0",
-    "ipywidgets>=8.0.0",
-    "requests>=2.28.0",
-    "beautifulsoup4>=4.11.0",
-    "lxml>=4.9.0",
-    "pypdf2>=2.10.0",
-    "python-docx>=0.8.11",
-    "docling>=1.0.0",
-    "openpyxl>=3.0.10",
-    "pillow>=9.2.0",
-    "librosa>=0.9.0",
-    "opencv-python>=4.6.0",
-    "faiss-cpu>=1.7.0",
-    "fastembed>=0.2.0",
-    "onnxruntime>=1.17.0",
-    "tokenizers>=0.15.0",
-    "weaviate-client>=3.15.0",
-    "qdrant-client>=1.3.0",
-    "neo4j>=5.0.0",
-    "falkordb>=1.0.0",
-    "pymongo>=4.2.0",
-    "sqlalchemy>=1.4.0",
-    "psycopg2-binary>=2.9.0",
-    "pymysql>=1.0.0",
-    "redis>=4.3.0",
-    "celery>=5.2.0",
-    "kafka-python>=2.0.0",
-    "pulsar-client>=3.0.0",
-    "pika>=1.3.0",
-    "boto3>=1.24.0",
-    "azure-storage-blob>=12.12.0",
-    "google-cloud-storage>=2.5.0",
-    "pydantic>=2.0.0",
-    "fastmcp>=0.1.0",
-    "groq>=0.4.0",
-    "openai>=1.0.0",
-    "litellm>=1.0.0",
-    "click>=8.1.0",
-    "rich>=12.5.0",
-    "tqdm>=4.64.0",
-    "pyyaml>=6.0",
-    "toml>=0.10.0",
-    "python-dotenv>=0.20.0",
-    "loguru>=0.6.0",
-    "structlog>=22.1.0",
-    "prometheus-client>=0.14.0",
-    "opentelemetry-api>=1.12.0",
-    "opentelemetry-sdk>=1.12.0",
-    "opentelemetry-instrumentation",
-    "fastapi>=0.78.0",
-    "uvicorn>=0.18.0",
-    "pytest>=7.1.0",
-    "pytest-cov>=3.0.0",
-    "pytest-asyncio>=0.19.0",
-    "black>=22.6.0",
-    "isort>=5.10.0",
-    "flake8>=4.0.0",
-    "mypy>=0.971",
-    "pre-commit>=2.19.0"
+  "numpy>=1.21.0",
+  "pandas>=1.3.0",
+  "scikit-learn>=1.0.0",
+  "umap-learn>=0.5.0",
+  "spacy>=3.4.0",
+  "transformers>=4.20.0",
+  "torch>=1.12.0",
+  "sentence-transformers>=2.2.0",
+  "rdflib>=6.2.0",
+  "networkx>=2.8.0",
+  "matplotlib>=3.5.0",
+  "seaborn>=0.11.0",
+  "plotly>=5.10.0",
+  "ipywidgets>=8.0.0",
+  "requests>=2.28.0",
+  "GitPython>=3.1.30",
+  "chardet>=5.1.0",
+  "protobuf>=5.29.1,<7.0",
+  "grpcio>=1.71.2",
+  "beautifulsoup4>=4.11.0",
+  "lxml>=4.9.0",
+  "pypdf2>=2.10.0",
+  "python-docx>=0.8.11",
+  "openpyxl>=3.0.10",
+  "pillow>=9.2.0",
+  "librosa>=0.9.0",
+  "opencv-python>=4.6.0",
+  "faiss-cpu>=1.7.0",
+  "fastembed>=0.2.0",
+  "onnxruntime>=1.17.0",
+  "tokenizers>=0.15.0",
+  "pydantic>=2.0.0",
+  "click>=8.1.0",
+  "rich>=12.5.0",
+  "tqdm>=4.64.0",
+  "pyyaml>=6.0",
+  "toml>=0.10.0",
+  "python-dotenv>=0.20.0",
+  "loguru>=0.6.0",
+  "structlog>=22.1.0"
 ]
-[project.urls]
-Homepage = "https://github.com/Hawksight-AI/semantica"
-Repository = "https://github.com/Hawksight-AI/semantica"
-"Bug Tracker" = "https://github.com/Hawksight-AI/semantica/issues"
-Discussions = "https://github.com/Hawksight-AI/semantica/discussions"

+# ---------------- OPTIONAL DEPENDENCIES ----------------
 [project.optional-dependencies]
-dev = [
-    "pytest>=7.1.0",
-    "pytest-cov>=3.0.0",
-    "pytest-asyncio>=0.19.0",
-    "black>=22.6.0",
-    "isort>=5.10.0",
-    "flake8>=4.0.0",
-    "mypy>=0.971",
-    "pre-commit>=2.19.0",
-    "jupyter>=1.0.0",
-    "ipykernel>=6.15.0",
-    "notebook>=6.4.0"
-]
-viz = [
-    "pyvis>=0.3.0",
-    "graphviz>=0.20.0",
-    "umap-learn>=0.5.0",
-    "d3blocks>=1.0.0"
-]
-gpu = [
-    "torch>=1.12.0",
-    "faiss-gpu>=1.7.0",
-    "cupy>=10.0.0"
-]
-cloud = [
-    "boto3>=1.24.0",
-    "azure-storage-blob>=12.12.0",
-    "google-cloud-storage>=2.5.0",
-    "kubernetes>=24.0.0",
-    "helm>=3.10.0"
-]
-monitoring = [
-    "prometheus-client>=0.14.0",
-    "opentelemetry-api>=1.12.0",
-    "opentelemetry-sdk>=1.12.0",
-    "opentelemetry-instrumentation>=0.32.0",
-    "grafana-api>=1.0.0",
-    "elasticsearch>=8.5.0"
-]
-llm-openai = [
-    "openai>=1.0.0"
-]
-llm-gemini = [
-    "google-generativeai>=0.3.0"
-]
-llm-groq = [
-    "groq>=0.4.0"
-]
-llm-anthropic = [
-    "anthropic>=0.18.0"
-]
-llm-ollama = [
-    "ollama>=0.1.0"
-]
-llm-litellm = [
-    "litellm>=1.0.0"
-]
+
+# ---- LLM Providers ----
+llm-openai = ["openai>=1.0.0"]
+llm-groq = ["groq>=0.4.0"]
+llm-gemini = ["google-genai>=0.1.0"]
+llm-anthropic = ["anthropic>=0.18.0"]
+llm-ollama = ["ollama>=0.1.0"]
+llm-deepseek = ["deepseek>=0.1.0"]
+llm-litellm = ["litellm>=1.0.0"]
+llm-instructor = ["instructor>=1.0.0"]
+
 llm-all = [
-    "semantica[llm-openai,llm-gemini,llm-groq,llm-anthropic,llm-ollama,llm-litellm]"
-]
-models-huggingface = [
-    "transformers>=4.20.0",
-    "torch>=1.12.0"
-]
-split-tiktoken = [
-    "tiktoken>=0.5.0"
-]
-split-community = [
-    "python-louvain>=0.16"
-]
-split-topic = [
-    "bertopic>=0.15.0",
-    "gensim>=4.3.0"
-]
-split-all = [
-    "semantica[split-tiktoken,split-community,split-topic]"
-]
-graph-neo4j = [
-    "neo4j>=5.0.0"
-]
-graph-falkordb = [
-    "falkordb>=1.0.0",
-    "redis>=4.3.0"
-]
-graph-all = [
-    "semantica[graph-neo4j,graph-falkordb]"
-]
-parse-docling = [
-    "docling>=1.0.0"
-]
-all = [
-    "semantica[dev,viz,gpu,cloud,monitoring,llm-all,models-huggingface,split-all,graph-all,parse-docling]"
+  "semantica[llm-openai,llm-groq,llm-gemini,llm-anthropic,llm-ollama,llm-deepseek,llm-litellm,llm-instructor]"
 ]

+# ---- Document Parsing ----
+parse-docling = ["docling>=1.0.0"]
+
+# ---- Embedding / Models ----
+models-huggingface = [
+  "transformers>=4.20.0",
+  "torch>=1.12.0"
+]
+
+# ---- Graph Backends ----
+graph-neo4j = ["neo4j>=5.0.0"]
+graph-falkordb = ["falkordb>=1.0.0", "redis>=4.3.0"]
+graph-amazon-neptune = ["boto3>=1.24.0", "neo4j>=5.0.0"]
+
+graph-all = [
+  "semantica[graph-neo4j,graph-falkordb,graph-amazon-neptune]"
+]
+
+# ---- Infra / Queues / Workers ----
+infra = [
+  "redis>=4.3.0",
+  "celery>=5.2.0",
+  "kafka-python>=2.0.0",
+  "pulsar-client>=3.0.0",
+  "pika>=1.3.0"
+]
+
+# ---- Cloud Providers ----
+cloud = [
+  "boto3>=1.24.0",
+  "azure-storage-blob>=12.12.0",
+  "google-cloud-storage>=2.5.0"
+]
+
+# ---- Monitoring (FIXED) ----
+monitoring = [
+  "prometheus-client>=0.14.0",
+  "opentelemetry-api>=1.30.0,<2.0.0",
+  "opentelemetry-sdk>=1.30.0,<2.0.0",
+  "opentelemetry-semantic-conventions>=0.58b0,<0.61b0",
+  "opentelemetry-instrumentation>=0.58b0,<0.61b0"
+]
+
+# ---- Visualization ----
+viz = [
+  "pyvis>=0.3.0",
+  "graphviz>=0.20.0",
+  "d3blocks>=1.0.0"
+]
+
+# ---- GPU ----
+gpu = [
+  "faiss-gpu>=1.7.0",
+  "cupy>=10.0.0"
+]
+
+# ---- Splitting / Chunking ----
+split-tiktoken = ["tiktoken>=0.5.0"]
+split-community = ["python-louvain>=0.16"]
+split-topic = ["bertopic>=0.15.0", "gensim>=4.3.0"]
+
+split-all = [
+  "semantica[split-tiktoken,split-community,split-topic]"
+]
+
+# ---- Dev ----
+dev = [
+  "pytest>=7.1.0",
+  "pytest-cov>=3.0.0",
+  "pytest-asyncio>=0.19.0",
+  "black>=22.6.0",
+  "isort>=5.10.0",
+  "flake8>=4.0.0",
+  "mypy>=0.971",
+  "pre-commit>=2.19.0",
+  "jupyter>=1.0.0",
+  "ipykernel>=6.15.0"
+]
+
+# ---- Everything ----
+all = [
+  "semantica[dev,viz,gpu,infra,cloud,monitoring,llm-all,models-huggingface,split-all,graph-all,parse-docling]"
+]
+
+# ---------------- ENTRYPOINTS ----------------
 [project.scripts]
 semantica = "semantica.cli:main"
 semantica-server = "semantica.server:main"
 semantica-worker = "semantica.worker:main"

+# ---------------- TOOLING ----------------
 [tool.setuptools.packages.find]
 where = ["."]
 include = ["semantica*"]
-exclude = ["tests*", "docs*", "examples*"]
-
-[tool.setuptools.package-data]
-semantica = ["*.yaml", "*.yml", "*.json", "*.toml", "*.txt", "*.md"]

 [tool.black]
 line-length = 88
-target-version = ['py38', 'py39', 'py310', 'py311', 'py312']
-include = '\.pyi?$'
-extend-exclude = '''
-/(
-  # directories
-  \.eggs
-  | \.git
-  | \.hg
-  | \.mypy_cache
-  | \.tox
-  | \.venv
-  | build
-  | dist
-)/
-'''

 [tool.isort]
 profile = "black"
-multi_line_output = 3
-line_length = 88
-known_first_party = ["semantica"]
-known_third_party = ["numpy", "pandas", "scikit-learn", "spacy", "transformers", "torch"]
-
-[tool.mypy]
-python_version = "3.9"
-warn_return_any = true
-warn_unused_configs = true
-disallow_untyped_defs = true
-disallow_incomplete_defs = true
-check_untyped_defs = true
-disallow_untyped_decorators = true
-no_implicit_optional = true
-warn_redundant_casts = true
-warn_unused_ignores = true
-warn_no_return = true
-warn_unreachable = true
-strict_equality = true
-show_error_codes = true

 [tool.pytest.ini_options]
-minversion = "7.0"
-addopts = "-ra -q --strict-markers --strict-config"
 testpaths = ["tests"]
-python_files = ["test_*.py", "*_test.py"]
-python_classes = ["Test*"]
-python_functions = ["test_*"]
-markers = [
-    "slow: marks tests as slow (deselect with '-m \"not slow\"')",
-    "integration: marks tests as integration tests",
-    "unit: marks tests as unit tests",
-    "gpu: marks tests that require GPU",
-    "cloud: marks tests that require cloud services"
-]
-
-[tool.coverage.run]
-source = ["semantica"]
-omit = [
-    "*/tests/*",
-    "*/test_*",
-    "*/__pycache__/*",
-    "*/migrations/*"
-]
-
-[tool.coverage.report]
-exclude_lines = [
-    "pragma: no cover",
-    "def __repr__",
-    "if self.debug:",
-    "if settings.DEBUG",
-    "raise AssertionError",
-    "raise NotImplementedError",
-    "if 0:",
-    "if __name__ == .__main__.:",
-    "class .*\\bProtocol\\):",
-    "@(abc\\.)?abstractmethod"
-]
@@ -10,7 +10,7 @@ Main exports:
    - Config: Configuration management
 """

-__version__ = "0.1.0"
+__version__ = "0.2.2"
 __author__ = "Semantica Contributors"
 __license__ = "MIT"

@@ -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
@@ -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")
@@ -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:
@@ -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:
@@ -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
@@ -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"
            )
@@ -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:
        """
@@ -4,7 +4,7 @@ Semantica Evals Module
 Coming Soon
 """

-__version__ = "0.1.0"
+__version__ = "0.1.1"
 __status__ = "coming_soon"
 __all__ = []

@@ -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"
            )
@@ -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:
@@ -1,48 +1,71 @@
 """
 Graph Store Module

-This module provides comprehensive property graph database integration for the
-Semantica framework, supporting multiple graph database backends including Neo4j
-and FalkorDB for storing and querying knowledge graphs.
+This module provides comprehensive property graph database integration for
+the Semantica framework, supporting multiple graph database backends including
+Neo4j and FalkorDB for storing and querying knowledge graphs.

 Algorithms Used:

 Graph Store Management:
-    - Store Registration: Store type detection, store factory pattern, configuration management, default store selection
-    - Backend Pattern: Unified interface for multiple backends (Neo4j, FalkorDB), backend instantiation, backend-specific operation delegation
-    - Store Selection: Default store resolution, store ID lookup, store validation
+    - Store Registration: Store type detection, store factory pattern,
+      configuration management, default store selection
+    - Backend Pattern: Unified interface for multiple backends (Neo4j,
+      FalkorDB), backend instantiation, backend-specific operation delegation
+    - Store Selection: Default store resolution, store ID lookup,
+      store validation

 Node and Relationship Operations:
-    - Node Creation: Single node insertion, batch node insertion, property validation, label management, backend delegation
-    - Node Retrieval: Pattern matching (label/property filtering), Cypher query construction, result extraction, node reconstruction
-    - Node Update: Property update, label modification, atomic update operations, conflict detection
-    - Node Deletion: Node matching, cascade deletion (optional), deletion operation delegation, result verification
-    - Relationship Creation: Single relationship insertion, batch insertion, property validation, type management
+    - Node Creation: Single node insertion, batch node insertion,
+      property validation, label management, backend delegation
+    - Node Retrieval: Pattern matching (label/property filtering),
+      Cypher query construction, result extraction, node reconstruction
+    - Node Update: Property update, label modification, atomic update
+      operations, conflict detection
+    - Node Deletion: Node matching, cascade deletion (optional),
+      deletion operation delegation, result verification
+    - Relationship Creation: Single relationship insertion, batch insertion,
+      property validation, type management
    - Relationship Retrieval: Pattern matching, path queries, traversal queries
    - Relationship Update: Property update, type modification
-    - Relationship Deletion: Relationship matching, deletion operation delegation
+    - Relationship Deletion: Relationship matching, deletion operation
+      delegation

 Graph Query Execution:
-    - Cypher Query: Full Cypher query language support for Neo4j and FalkorDB (OpenCypher)
-    - Pattern Matching: Node and relationship pattern matching, variable binding, path matching
-    - Graph Traversal: BFS/DFS traversal, shortest path algorithms, path finding
+    - Cypher Query: Full Cypher query language support for Neo4j and
+      FalkorDB (OpenCypher)
+    - Pattern Matching: Node and relationship pattern matching, variable
+      binding, path matching
+    - Graph Traversal: BFS/DFS traversal, shortest path algorithms,
+      path finding
    - Aggregation: COUNT, SUM, AVG, MIN, MAX operations, GROUP BY support
-    - Query Optimization: Query caching, execution plan analysis, index utilization
+    - Query Optimization: Query caching, execution plan analysis,
+      index utilization

 Graph Analytics:
-    - Centrality Algorithms: Degree centrality, betweenness centrality, PageRank, closeness centrality
-    - Community Detection: Label propagation, Louvain modularity, connected components
-    - Path Algorithms: Shortest path, all shortest paths, Dijkstra, A* pathfinding
+    - Centrality Algorithms: Degree centrality, betweenness centrality,
+      PageRank, closeness centrality
+    - Community Detection: Label propagation, Louvain modularity,
+      connected components
+    - Path Algorithms: Shortest path, all shortest paths, Dijkstra,
+      A* pathfinding
    - Similarity: Node similarity, Jaccard similarity, cosine similarity

 Store Backends:
-    - Neo4j Store: Official Neo4j Python driver, Bolt protocol communication, transaction support, multi-database support, APOC procedures
-    - FalkorDB Store: Redis-based graph database, sparse matrix representation, linear algebra queries, OpenCypher support, ultra-fast performance
+    - Neo4j Store: Official Neo4j Python driver, Bolt protocol
+      communication, transaction support, multi-database support,
+      APOC procedures
+    - FalkorDB Store: Redis-based graph database, sparse matrix
+      representation, linear algebra queries, OpenCypher support,
+      ultra-fast performance

 Bulk Operations:
-    - Batch Processing: Chunking algorithm (fixed-size batch creation), batch size optimization, memory management for large datasets
-    - Transaction Management: ACID transaction support, batch commits, rollback on failure
-    - Progress Tracking: Load progress calculation, elapsed time tracking, throughput calculation
+    - Batch Processing: Chunking algorithm (fixed-size batch creation),
+      batch size optimization, memory management for large datasets
+    - Transaction Management: ACID transaction support, batch commits,
+      rollback on failure
+    - Progress Tracking: Load progress calculation, elapsed time tracking,
+      throughput calculation

 Key Features:
    - Multi-backend property graph support (Neo4j, FalkorDB)
@@ -79,33 +102,42 @@ Convenience Functions:
    - list_available_methods: List registered graph store methods

 Example Usage:
-    >>> from semantica.graph_store import GraphStore, create_node, create_relationship, execute_query
+    >>> from semantica.graph_store import GraphStore, create_node, \
+    ...     create_relationship, execute_query
    >>> # Using convenience functions
-    >>> node_id = create_node(labels=["Person"], properties={"name": "Alice", "age": 30})
-    >>> rel_id = create_relationship(start_id=node1_id, end_id=node2_id, rel_type="KNOWS", properties={"since": 2020})
-    >>> results = execute_query("MATCH (p:Person) WHERE p.age > 25 RETURN p.name")
+    >>> node_id = create_node(labels=["Person"],
+    ...                       properties={"name": "Alice", "age": 30})
+    >>> rel_id = create_relationship(start_id=node1_id, end_id=node2_id,
+    ...                              rel_type="KNOWS",
+    ...                              properties={"since": 2020})
+    >>> results = execute_query("MATCH (p:Person) WHERE p.age > 25 "
+    ...                         "RETURN p.name")
    >>> # Using classes directly
    >>> store = GraphStore(backend="neo4j", uri="bolt://localhost:7687")
-    >>> node_id = store.create_node(labels=["Person"], properties={"name": "Bob"})
+    >>> node_id = store.create_node(labels=["Person"],
+    ...                             properties={"name": "Bob"})
    >>> results = store.execute_query("MATCH (n) RETURN n LIMIT 10")

 Author: Semantica Contributors
 License: MIT
 """

-from .config import GraphStoreConfig, graph_store_config
-from .falkordb_store import (
-    FalkorDBStore,
-    FalkorDBClient,
-    FalkorDBGraph,
+from .amazon_neptune import (
+    AmazonNeptuneStore,
+    NeptuneAuthTokenManager,
+    NeptuneDriver,
+    NeptuneSession,
+    NeptuneTransaction,
 )
+from .config import GraphStoreConfig, graph_store_config
+from .falkordb_store import FalkorDBClient, FalkorDBGraph, FalkorDBStore
 from .graph_store import (
+    GraphAnalytics,
    GraphManager,
    GraphStore,
    NodeManager,
    QueryEngine,
    RelationshipManager,
-    GraphAnalytics,
 )
 from .methods import (
    create_node,
@@ -125,11 +157,7 @@ from .methods import (
    update_node,
    update_relationship,
 )
-from .neo4j_store import (
-    Neo4jStore,
-    Neo4jDriver,
-    Neo4jTransaction,
-)
+from .neo4j_store import Neo4jDriver, Neo4jStore, Neo4jTransaction
 from .registry import MethodRegistry, method_registry

 __all__ = [
@@ -144,6 +172,12 @@ __all__ = [
    "Neo4jStore",
    "Neo4jDriver",
    "Neo4jTransaction",
+    # Amazon Neptune
+    "AmazonNeptuneStore",
+    "NeptuneAuthTokenManager",
+    "NeptuneDriver",
+    "NeptuneSession",
+    "NeptuneTransaction",
    # FalkorDB
    "FalkorDBStore",
    "FalkorDBClient",
@@ -171,4 +205,3 @@ __all__ = [
    "MethodRegistry",
    "method_registry",
 ]
-
@@ -6,7 +6,8 @@ supporting multiple configuration sources including environment variables, confi
 and programmatic configuration.

 Supported Configuration Sources:
-    - Environment variables: GRAPH_STORE_DEFAULT_BACKEND, GRAPH_STORE_NEO4J_URI, GRAPH_STORE_FALKORDB_HOST, etc.
+    - Environment variables: GRAPH_STORE_DEFAULT_BACKEND,
+      GRAPH_STORE_NEO4J_URI, GRAPH_STORE_FALKORDB_HOST, etc.
    - Config files: YAML, JSON, TOML formats
    - Programmatic: Python API for setting graph store configurations

@@ -44,7 +45,11 @@ from ..utils.logging import get_logger


 class GraphStoreConfig:
-    """Configuration manager for graph store module - supports .env files, environment variables, and programmatic config."""
+    """
+    Configuration manager for graph store module.
+
+    Supports .env files, environment variables, and programmatic config.
+    """

    def __init__(self, config_file: Optional[str] = None):
        """
@@ -124,6 +129,15 @@ class GraphStoreConfig:
            "GRAPH_STORE_FALKORDB_PORT": "falkordb_port",
            "GRAPH_STORE_FALKORDB_PASSWORD": "falkordb_password",
            "GRAPH_STORE_FALKORDB_GRAPH_NAME": "falkordb_graph_name",
+            # Amazon Neptune settings
+            "GRAPH_STORE_NEPTUNE_ENDPOINT": "neptune_endpoint",
+            "GRAPH_STORE_NEPTUNE_PORT": "neptune_port",
+            "GRAPH_STORE_NEPTUNE_REGION": "neptune_region",
+            "GRAPH_STORE_NEPTUNE_IAM_AUTH": "neptune_iam_auth",
+            "GRAPH_STORE_NEPTUNE_USE_SSL": "neptune_use_ssl",
+            "AWS_ACCESS_KEY_ID": "neptune_access_key",
+            "AWS_SECRET_ACCESS_KEY": "neptune_secret_key",
+            "AWS_SESSION_TOKEN": "neptune_session_token",
        }

        for env_var, config_key in env_mappings.items():
@@ -135,6 +149,7 @@ class GraphStoreConfig:
                    "timeout",
                    "max_retries",
                    "falkordb_port",
+                    "neptune_port",
                ]:
                    try:
                        self._config[config_key] = int(value)
@@ -142,7 +157,11 @@ class GraphStoreConfig:
                        self.logger.warning(
                            f"Invalid integer value for {env_var}: {value}"
                        )
-                elif config_key in ["neo4j_encrypted"]:
+                elif config_key in [
+                    "neo4j_encrypted",
+                    "neptune_iam_auth",
+                    "neptune_use_ssl",
+                ]:
                    self._config[config_key] = value.lower() in [
                        "true",
                        "1",
@@ -171,6 +190,15 @@ class GraphStoreConfig:
            "falkordb_port": 6379,
            "falkordb_password": None,
            "falkordb_graph_name": "default",
+            # Amazon Neptune defaults
+            "neptune_endpoint": None,
+            "neptune_port": 8182,
+            "neptune_region": None,
+            "neptune_iam_auth": True,
+            "neptune_use_ssl": True,
+            "neptune_access_key": None,
+            "neptune_secret_key": None,
+            "neptune_session_token": None,
        }

        for key, default_value in defaults.items():
@@ -269,6 +297,24 @@ class GraphStoreConfig:
            "graph_name": self._config.get("falkordb_graph_name"),
        }

+    def get_neptune_config(self) -> Dict[str, Any]:
+        """
+        Get Amazon Neptune-specific configuration.
+
+        Returns:
+            Neptune configuration dictionary
+        """
+        return {
+            "endpoint": self._config.get("neptune_endpoint"),
+            "port": self._config.get("neptune_port"),
+            "region": self._config.get("neptune_region"),
+            "iam_auth": self._config.get("neptune_iam_auth"),
+            "use_ssl": self._config.get("neptune_use_ssl"),
+            "access_key": self._config.get("neptune_access_key"),
+            "secret_key": self._config.get("neptune_secret_key"),
+            "session_token": self._config.get("neptune_session_token"),
+        }
+
    def reset(self) -> None:
        """Reset configuration to defaults."""
        self._config.clear()
@@ -278,4 +324,3 @@ class GraphStoreConfig:

 # Global configuration instance
 graph_store_config = GraphStoreConfig()
-
@@ -47,7 +47,7 @@ try:
    from falkordb import FalkorDB

    FALKORDB_AVAILABLE = True
-except ImportError:
+except (ImportError, OSError):
    FALKORDB_AVAILABLE = False
    FalkorDB = None

@@ -34,7 +34,7 @@ License: MIT

 from typing import Any, Dict, List, Optional, Tuple, Union

-from ..utils.exceptions import ProcessingError, ValidationError
+from ..utils.exceptions import ValidationError
 from ..utils.logging import get_logger
 from ..utils.progress_tracker import get_progress_tracker
 from .config import graph_store_config
@@ -214,7 +214,9 @@ class RelationshipManager:
        Returns:
            List of relationships
        """
-        return self.backend.get_relationships(node_id, rel_type, direction, limit, **options)
+        return self.backend.get_relationships(
+            node_id, rel_type, direction, limit, **options
+        )

    def delete(
        self,
@@ -290,6 +292,7 @@ class QueryEngine:
    ) -> str:
        """Generate cache key for query."""
        import hashlib
+
        key_str = f"{query}:{str(parameters)}"
        return hashlib.md5(key_str.encode()).hexdigest()

@@ -340,7 +343,9 @@ class GraphAnalytics:
        Returns:
            Path information or None
        """
-        return self.backend.shortest_path(start_node_id, end_node_id, rel_type, max_depth, **options)
+        return self.backend.shortest_path(
+            start_node_id, end_node_id, rel_type, max_depth, **options
+        )

    def get_neighbors(
        self,
@@ -363,7 +368,9 @@ class GraphAnalytics:
        Returns:
            List of neighboring nodes
        """
-        return self.backend.get_neighbors(node_id, rel_type, direction, depth, **options)
+        return self.backend.get_neighbors(
+            node_id, rel_type, direction, depth, **options
+        )

    def degree_centrality(
        self,
@@ -440,7 +447,7 @@ class GraphAnalytics:
            Component information
        """
        backend_type = type(self.backend).__name__
-        
+
        if "Neo4j" in backend_type:
            query = """
            CALL gds.wcc.stream({
@@ -452,16 +459,23 @@ class GraphAnalytics:
            """
            params = {"label": labels[0] if labels else "*"}
            result = self.backend.execute_query(query, params)
-            return [{"component": r["componentId"], "nodes": r["nodes"]} for r in result]
-            
+            return [
+                {"component": r["componentId"], "nodes": r["nodes"]} for r in result
+            ]
+
        elif "NetworkX" in backend_type:
            import networkx as nx
+
            G = self.backend.graph
            components = list(nx.connected_components(G))
-            return [{"component": i, "nodes": list(c)} for i, c in enumerate(components)]
-            
+            return [
+                {"component": i, "nodes": list(c)} for i, c in enumerate(components)
+            ]
+
        else:
-            raise NotImplementedError(f"connected_components not implemented for {backend_type}")
+            raise NotImplementedError(
+                f"connected_components not implemented for {backend_type}"
+            )


 class GraphManager:
@@ -534,7 +548,11 @@ class GraphStore:
            self.progress_tracker.enabled = True

        # Determine backend
-        self.backend = backend or config.get("backend") or graph_store_config.get("default_backend", "neo4j")
+        self.backend = (
+            backend
+            or config.get("backend")
+            or graph_store_config.get("default_backend", "neo4j")
+        )
        self.config = config

        # Initialize store backend
@@ -546,16 +564,25 @@ class GraphStore:
        """Initialize the appropriate store backend based on backend type."""
        if self.backend == "neo4j":
            from .neo4j_store import Neo4jStore
+
            neo4j_config = graph_store_config.get_neo4j_config()
            neo4j_config.update(self.config)
            self._store_backend = Neo4jStore(**neo4j_config)

        elif self.backend == "falkordb":
            from .falkordb_store import FalkorDBStore
+
            falkordb_config = graph_store_config.get_falkordb_config()
            falkordb_config.update(self.config)
            self._store_backend = FalkorDBStore(**falkordb_config)

+        elif self.backend == "neptune" or self.backend == "amazon_neptune":
+            from .amazon_neptune import AmazonNeptuneStore
+
+            neptune_config = graph_store_config.get_neptune_config()
+            neptune_config.update(self.config)
+            self._store_backend = AmazonNeptuneStore(**neptune_config)
+
        else:
            raise ValidationError(f"Unknown backend: {self.backend}")

@@ -621,7 +648,9 @@ class GraphStore:
        **options,
    ) -> List[Dict[str, Any]]:
        """Get nodes matching criteria."""
-        return self._manager.nodes.get(labels=labels, properties=properties, limit=limit, **options)
+        return self._manager.nodes.get(
+            labels=labels, properties=properties, limit=limit, **options
+        )

    def update_node(
        self,
@@ -665,7 +694,9 @@ class GraphStore:
        **options,
    ) -> List[Dict[str, Any]]:
        """Get relationships."""
-        return self._manager.relationships.get(node_id, rel_type, direction, limit, **options)
+        return self._manager.relationships.get(
+            node_id, rel_type, direction, limit, **options
+        )

    def delete_relationship(
        self,
@@ -723,7 +754,9 @@ class GraphStore:
            node_id, rel_type, direction, actual_depth, **options
        )

-    def query(self, query: str, parameters: Optional[Dict[str, Any]] = None, **options) -> List[Dict[str, Any]]:
+    def query(
+        self, query: str, parameters: Optional[Dict[str, Any]] = None, **options
+    ) -> List[Dict[str, Any]]:
        """
        Execute a query and return results (Compatibility method for ContextRetriever).

@@ -771,42 +804,46 @@ class GraphStore:
        # Convert to GraphStore format (labels, properties)
        graph_nodes = []
        for node in nodes:
-            # Extract label from type
-            labels = [node.get("type", "Entity")]
-            if isinstance(labels[0], str):
-                labels = [labels[0]] # Ensure list
+            # Extract labels - support both 'labels' array and 'type' string
+            labels = node.get("labels")
+            if not labels:
+                node_type = node.get("type", "Entity")
+                labels = [node_type] if isinstance(node_type, str) else node_type
+            if isinstance(labels, str):
+                labels = [labels]

            # Prepare properties
            props = node.get("properties", {}).copy()
-            
+
            # Ensure ID is preserved
            if "id" in node and "id" not in props:
                props["id"] = node["id"]
-                
+
            # Ensure content/text is preserved
            if "content" in node and "content" not in props:
                props["content"] = node["content"]
            if "text" in node and "text" not in props:
                props["text"] = node["text"]

-            graph_nodes.append({
-                "labels": labels,
-                "properties": props
-            })
+            graph_nodes.append({"labels": labels, "properties": props})

        # Use batch creation
-        # Note: create_nodes expects dicts with 'labels' and 'properties' keys if passed directly?
+        # Note: create_nodes expects dicts with 'labels' and 'properties'
+        # keys if passed directly?
        # Let's check create_nodes signature implementation in manager.
-        # But here I'll assume create_nodes takes a list of such dicts or similar.
+        # But here I'll assume create_nodes takes a list of such dicts
+        # or similar.
        # Actually, let's look at create_nodes wrapper in this file:
        # def create_nodes(self, nodes: List[Dict[str, Any]], **options)
        # It passes to self._manager.nodes.create_batch(nodes)
-        
+
        # If create_batch expects specific format, I should match it.
        # Assuming create_batch is smart enough or expects standard format.
-        # To be safe, let's look at NodeManager.create_batch if possible, but I can't easily.
-        # Standard expectation: List of dicts where each dict has labels and properties.
-        
+        # To be safe, let's look at NodeManager.create_batch if possible,
+        # but I can't easily.
+        # Standard expectation: List of dicts where each dict has labels
+        # and properties.
+
        result = self.create_nodes(graph_nodes, **options)
        return len(result)

@@ -827,17 +864,21 @@ class GraphStore:
            target_id = edge.get("target_id")
            rel_type = edge.get("type", "RELATED_TO")
            properties = edge.get("properties", {}).copy()
-            
+
            # Preserve weight
            if "weight" in edge:
                properties["weight"] = edge["weight"]

            if source_id and target_id:
                try:
-                    self.create_relationship(source_id, target_id, rel_type, properties, **options)
+                    self.create_relationship(
+                        source_id, target_id, rel_type, properties, **options
+                    )
                    count += 1
                except Exception as e:
-                    self.logger.warning(f"Failed to add edge {source_id}->{target_id}: {e}")
+                    self.logger.warning(
+                        f"Failed to add edge {source_id}->{target_id}: {e}"
+                    )
        return count

    def build_from_conversations(
@@ -872,27 +913,29 @@ class GraphStore:
            all_nodes = []
            all_edges = []
            seen_nodes = set()
-            
+
            for conv in conversations:
                # Load conversation if string (file path)
                conv_data = conv
                if isinstance(conv, str):
                    from pathlib import Path
+
                    from ..utils.helpers import read_json_file
+
                    conv_data = read_json_file(Path(conv))

                nodes, edges = self._process_conversation_to_elements(
-                    conv_data, 
+                    conv_data,
                    extract_intents=extract_intents,
-                    extract_sentiments=extract_sentiments
+                    extract_sentiments=extract_sentiments,
                )
-                
+
                # Add unique nodes
                for node in nodes:
                    if node["id"] not in seen_nodes:
                        all_nodes.append(node)
                        seen_nodes.add(node["id"])
-                
+
                all_edges.extend(edges)

            if link_entities:
@@ -904,13 +947,8 @@ class GraphStore:
            edge_count = self.add_edges(all_edges)

            self.progress_tracker.stop_tracking(tracking_id, status="completed")
-            
-            return {
-                "statistics": {
-                    "node_count": node_count,
-                    "edge_count": edge_count
-                }
-            }
+
+            return {"statistics": {"node_count": node_count, "edge_count": edge_count}}

        except Exception as e:
            self.progress_tracker.stop_tracking(
@@ -930,92 +968,112 @@ class GraphStore:
        """
        nodes = []
        edges = []
-        
+
        # Process entities
        for entity in entities:
            entity_id = entity.get("id") or entity.get("entity_id")
            if entity_id:
-                nodes.append({
-                    "id": entity_id,
-                    "type": entity.get("type", "entity"),
-                    "properties": {
-                        "content": entity.get("text") or entity.get("label") or entity_id,
-                        **entity
+                nodes.append(
+                    {
+                        "id": entity_id,
+                        "type": entity.get("type", "entity"),
+                        "properties": {
+                            "content": entity.get("text")
+                            or entity.get("label")
+                            or entity_id,
+                            **entity,
+                        },
                    }
-                })
+                )

        # Process relationships
        for rel in relationships:
            source = rel.get("source_id")
            target = rel.get("target_id")
            if source and target:
-                edges.append({
-                    "source_id": source,
-                    "target_id": target,
-                    "type": rel.get("type", "related_to"),
-                    "weight": rel.get("confidence", 1.0),
-                    "properties": rel
-                })
+                edges.append(
+                    {
+                        "source_id": source,
+                        "target_id": target,
+                        "type": rel.get("type", "related_to"),
+                        "weight": rel.get("confidence", 1.0),
+                        "properties": rel,
+                    }
+                )

        node_count = self.add_nodes(nodes)
        edge_count = self.add_edges(edges)
-        
+
        return {"statistics": {"node_count": node_count, "edge_count": edge_count}}

-    def _process_conversation_to_elements(self, conv_data: Dict[str, Any], **kwargs) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+    def _process_conversation_to_elements(
+        self, conv_data: Dict[str, Any], **kwargs
+    ) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
        """Helper to process conversation into nodes and edges."""
        nodes = []
        edges = []
-        
+
        conv_id = conv_data.get("id") or f"conv_{hash(str(conv_data)) % 10000}"

        # Conversation node
-        nodes.append({
-            "id": conv_id,
-            "type": "conversation",
-            "properties": {
-                "content": conv_data.get("content", "") or conv_data.get("summary", ""),
-                "timestamp": conv_data.get("timestamp")
+        nodes.append(
+            {
+                "id": conv_id,
+                "type": "conversation",
+                "properties": {
+                    "content": conv_data.get("content", "")
+                    or conv_data.get("summary", ""),
+                    "timestamp": conv_data.get("timestamp"),
+                },
            }
-        })
+        )

        name_to_id = {}
-        extract_entities = kwargs.get("extract_entities", True) # Default true if not passed?
-        # Actually ContextGraph defaults to True in init, but here we are static.
+        # Note: extract_entities option is available but not used in this
+        # implementation. Default true if not passed. ContextGraph defaults
+        # to True in init, but here we are static.
        # Let's assume True unless told otherwise or check config.
-        
+
        # Extract entities
        for entity in conv_data.get("entities", []):
            entity_id = entity.get("id") or entity.get("entity_id")
-            entity_text = entity.get("text") or entity.get("label") or entity.get("name") or entity_id
+            entity_text = (
+                entity.get("text")
+                or entity.get("label")
+                or entity.get("name")
+                or entity_id
+            )
            entity_type = entity.get("type", "entity")

            # Generate ID if missing
            if not entity_id and entity_text:
                import hashlib
-                entity_hash = hashlib.md5(f"{entity_text}_{entity_type}".encode()).hexdigest()[:12]
+
+                entity_hash = hashlib.md5(
+                    f"{entity_text}_{entity_type}".encode()
+                ).hexdigest()[:12]
                entity_id = f"{entity_type.lower()}_{entity_hash}"

            if entity_id:
                if entity_text:
                    name_to_id[entity_text] = entity_id

-                nodes.append({
-                    "id": entity_id,
-                    "type": "entity", # Normalize type?
-                    "properties": {
-                        "content": entity_text,
-                        "type": entity_type,
-                        **entity
+                nodes.append(
+                    {
+                        "id": entity_id,
+                        "type": "entity",  # Normalize type?
+                        "properties": {
+                            "content": entity_text,
+                            "type": entity_type,
+                            **entity,
+                        },
                    }
-                })
-                
+                )
+
                # Edge: Conversation -> Entity
-                edges.append({
-                    "source_id": conv_id,
-                    "target_id": entity_id,
-                    "type": "mentions"
-                })
+                edges.append(
+                    {"source_id": conv_id, "target_id": entity_id, "type": "mentions"}
+                )

        # Extract relationships
        for rel in conv_data.get("relationships", []):
@@ -1029,43 +1087,54 @@ class GraphStore:
                target = name_to_id[rel.get("target")]

            if source and target:
-                edges.append({
-                    "source_id": source,
-                    "target_id": target,
-                    "type": rel.get("type", "related_to"),
-                    "weight": rel.get("confidence", 1.0),
-                    "properties": rel
-                })
-                
+                edges.append(
+                    {
+                        "source_id": source,
+                        "target_id": target,
+                        "type": rel.get("type", "related_to"),
+                        "weight": rel.get("confidence", 1.0),
+                        "properties": rel,
+                    }
+                )
+
        return nodes, edges

-    def _link_entities_elements(self, nodes: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    def _link_entities_elements(
+        self, nodes: List[Dict[str, Any]]
+    ) -> List[Dict[str, Any]]:
        """Link similar entities."""
        edges = []
        # Lazy import to avoid circular dependency
        try:
            from ..context.entity_linker import EntityLinker
-            linker = EntityLinker() # Use default config
-        except ImportError:
+
+            linker = EntityLinker()  # Use default config
+        except (ImportError, OSError):
            return []

        entity_nodes = [n for n in nodes if n.get("type") == "entity"]
        for i, node1 in enumerate(entity_nodes):
            content1 = node1["properties"].get("content", "")
-            if not content1: continue
-            
+            if not content1:
+                continue
+
            for node2 in entity_nodes[i + 1 :]:
                content2 = node2["properties"].get("content", "")
-                if not content2: continue
-                
-                similarity = linker._calculate_text_similarity(content1.lower(), content2.lower())
+                if not content2:
+                    continue
+
+                similarity = linker._calculate_text_similarity(
+                    content1.lower(), content2.lower()
+                )
                if similarity >= linker.similarity_threshold:
-                    edges.append({
-                        "source_id": node1["id"],
-                        "target_id": node2["id"],
-                        "type": "similar_to",
-                        "weight": similarity
-                    })
+                    edges.append(
+                        {
+                            "source_id": node1["id"],
+                            "target_id": node2["id"],
+                            "type": "similar_to",
+                            "weight": similarity,
+                        }
+                    )
        return edges

    @property
@@ -1087,4 +1156,3 @@ class GraphStore:
    def analytics(self) -> GraphAnalytics:
        """Get analytics engine."""
        return self._manager.analytics
-
@@ -50,7 +50,7 @@ try:
    )

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

@@ -358,7 +358,7 @@ class FeedParser:
            from dateutil import parser

            return parser.parse(date_string)
-        except ImportError:
+        except (ImportError, OSError):
            pass

        return None
@@ -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:
    """
@@ -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
@@ -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
@@ -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"
@@ -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
@@ -36,7 +36,7 @@ from ..utils.progress_tracker import get_progress_tracker

 try:
    import pandas as pd
-except ImportError:
+except (ImportError, OSError):
    pd = None


@@ -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)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
KaifAhmad1	c6316ba4bd	Release 0.2.2	2026-01-15 00:42:07 +05:30
Mohd Kaif	b6d630fc74	Merge pull request #191 from Hawksight-AI/semantic-extract Improve `semantic_extract` performance and add Groq LLM smoke tests	2026-01-14 17:21:26 +05:30
Mohd Kaif	3f2cb49e50	Delete PR_DESCRIPTION.md	2026-01-14 17:17:32 +05:30
KaifAhmad1	c7814616a9	Improve semantic_extract performance and add Groq LLM smoke tests	2026-01-14 17:11:26 +05:30
Mohd Kaif	531014fbda	Update version and description in pyproject.toml	2026-01-14 14:05:36 +05:30
Mohd Kaif	1cf9b34e3e	Merge pull request #190 from Hawksight-AI/utils docs: update CHANGELOG.md with recent changes	2026-01-14 12:51:40 +05:30
KaifAhmad1	2e81c86489	docs: update CHANGELOG.md with recent changes	2026-01-14 12:49:29 +05:30
Mohd Kaif	1690fec3f7	Merge pull request #189 from Hawksight-AI/utils resolve dependencies, migrate Gemini SDK, and sanitize notebooks	2026-01-14 12:42:38 +05:30
KaifAhmad1	72a6ddb48f	Merge remote-tracking branch 'origin/utils' into utils	2026-01-14 12:38:44 +05:30
KaifAhmad1	a5da533d55	chore: resolve dependencies, migrate Gemini SDK, and sanitize notebooks	2026-01-14 12:37:29 +05:30
Mohd Kaif	be8856cfcf	Merge pull request #188 from Hawksight-AI/semantic-extract [SECURITY] Enhance caching security by excluding sensitive keys and using SHA-256	2026-01-14 00:25:46 +05:30
KaifAhmad1	d2e599bcb0	[SECURITY] Enhance caching security by excluding sensitive keys and using SHA-256	2026-01-14 00:22:41 +05:30
Mohd Kaif	05d0bbf86c	Merge pull request #187 from Hawksight-AI/semantic-extract Performance Bottlenecks and Scaling Limitations in semantic_extract	2026-01-14 00:15:06 +05:30
KaifAhmad1	dd7fcd3ddb	[FEATURE] Performance Bottlenecks and Scaling Limitations in semantic_extract #186 - Implemented high-throughput parallel batch processing across all core extractors (NERExtractor, RelationExtractor, TripletExtractor, EventDetector, SemanticNetworkExtractor) using ThreadPoolExecutor. - Added max_workers configuration parameter (default: 1) to all extractor extract() methods. - Implemented parallel processing for large document chunking in _extract_entities_chunked and _extract_relations_chunked. - Enhanced ProgressTracker to be thread-safe. - Optimized setUpClass in tests to reduce Groq LLM initialization overhead. - Updated documentation and usage examples.	2026-01-14 00:11:30 +05:30
Mohd Kaif	43f55e1028	Delete RELEASE_NOTES_v0.2.0.md	2026-01-13 00:33:59 +05:30
Mohd Kaif	e20c522c62	Merge pull request #185 from Hawksight-AI/docs Update Earning Call Notebook	2026-01-13 00:13:16 +05:30
KaifAhmad1	fd9f0b2526	Add all changes	2026-01-13 00:10:44 +05:30
Mohd Kaif	ccaadf6299	Merge pull request #180 from Hawksight-AI/docs Release v0.2.1: Stability Fixes	2026-01-12 17:52:43 +05:30
KaifAhmad1	428fc3b83a	chore(release): bump version to 0.2.1 and update release docs	2026-01-12 17:48:07 +05:30
Mohd Kaif	09cf3ed132	Merge pull request #179 from Hawksight-AI/docs Resolve TypeError in Earnings Call Analysis Notebook (#177)	2026-01-12 17:35:36 +05:30
KaifAhmad1	58686d409b	fix(cookbook): resolve TypeError in earnings call analysis step 7 #177	2026-01-12 17:32:16 +05:30
Mohd Kaif	6d5fbc8b63	Merge pull request #178 from Hawksight-AI/semantic-extract Resolve Incomplete Output (#176), Relax Constraints, and Add Groq Support	2026-01-12 17:18:54 +05:30
KaifAhmad1	8c3f7f1f0a	fix(semantic-extract): resolve incomplete output #176 , relax constraints, and add Groq support	2026-01-12 17:15:21 +05:30
Mohd Kaif	4acad23a4d	Merge pull request #174 from Hawksight-AI/docs Update Earnings Call Analysis Notebook (Finance Use Case)	2026-01-11 23:31:13 +05:30
KaifAhmad1	cd1435ee10	Save changes to Earnings Call Analysis notebook	2026-01-11 23:25:28 +05:30
KaifAhmad1	68f0a1d4d9	docs: Update PyPI version badge to shields.io	2026-01-10 23:44:35 +05:30
KaifAhmad1	a47274593b	docs: Add v0.2.0 release notes	2026-01-10 23:36:51 +05:30
KaifAhmad1	87a08e0240	chore: Prepare release v0.2.0	2026-01-10 23:32:10 +05:30
Mohd Kaif	1a2604255f	Merge pull request #172 from Hawksight-AI/docs Docs Update - Neptune Store & Docling Parser	2026-01-10 21:14:29 +05:30
KaifAhmad1	94b312901b	docs: Update CHANGELOG with Neptune Store and Docling Parser features - Added Amazon Neptune Graph Store support details: - IAM SigV4 signing - Robust connection handling with retries - New dependency group - Added Docling Parser integration details: - Multi-format support (PDF, DOCX, etc.) - Superior table extraction - Standalone parser architecture	2026-01-10 21:12:34 +05:30
Mohd Kaif	25fe95dd1a	Merge pull request #171 from Hawksight-AI/semantic-extract Enhanced Semantic Extraction with Robust Fallback Chains & Provenance Metadata	2026-01-10 21:02:21 +05:30
KaifAhmad1	f338b66274	feat: Add provenance metadata and robust fallback chains to semantic extraction - Implemented ML/LLM -> Pattern -> Last Resort fallback chains for NER, Relation, and Triplet extractors to prevent empty results. - Added provenance metadata (batch_index, document_id) to all extraction schemas (Entity, Relation, Triplet, etc.). - Unified batch processing API with progress tracking across all extractors. - Updated documentation (module usage and reference docs) to reflect new features. - Added robustness and batch provenance tests.	2026-01-10 20:43:09 +05:30
Mohd Kaif	8b1cd47f51	Merge pull request #167 from don-simpson/feature/amazon-neptune-graph-store feat: Add Amazon Neptune Database Graph Store Support	2026-01-09 19:27:44 +05:30
Mohd Kaif	48395b2f00	Merge pull request #170 from Hawksight-AI/docs docs: update CHANGELOG.md	2026-01-09 18:56:55 +05:30
KaifAhmad1	91ef2939c5	docs: update CHANGELOG.md and remove PR description	2026-01-09 18:54:38 +05:30
Mohd Kaif	30d84c41ad	Merge pull request #169 from Hawksight-AI/semantic-extract Semantic Extraction Empty Returns & Schema Validation	2026-01-09 18:49:27 +05:30
KaifAhmad1	a5c531fd29	Fix semantic extraction empty returns, schema validation, and update docs	2026-01-09 18:39:09 +05:30
Don Simpson	976a20496d	feat: Add Amazon Neptune Database Graph Store Support - Implement NeptuneAuthTokenManager extending Neo4j AuthManager for IAM SigV4 signing - Add automatic token refresh and security exception handling - Add retry logic with backoff for transient errors (signature expired, connection closed) - Add connection recovery with driver recreation - Add NeptuneDriver, NeptuneSession, NeptuneTransaction wrapper classes - Use native Neptune ~id via id() function for all CRUD operations - Add graph-amazon-neptune optional dependency group (boto3, neo4j) - Update cookbook with Amazon Neptune Graph Store examples - Add comprehensive tests (61 tests covering all GraphStore interface methods) Closes #151	2026-01-08 20:13:28 -05:00
Mohd Kaif	9bb94c2337	Merge pull request #165 from Hawksight-AI/parse Docling Integration & Parser Documentation Fixes	2026-01-08 21:30:10 +05:30
KaifAhmad1	957c122116	docs: add Docling integration guide, clear code example, and fix parser consistency issues	2026-01-08 21:27:48 +05:30
Mohd Kaif	31ca2e4446	Merge pull request #164 from Hawksight-AI/docs docs: update changelog with model switching fixes	2026-01-08 19:38:01 +05:30
KaifAhmad1	b08c13364b	docs: update changelog with model switching fixes and tests	2026-01-08 19:35:24 +05:30
Mohd Kaif	01dd0c97ab	Merge pull request #163 from Hawksight-AI/embeddings Fix Model Switching and Dynamic Dimension Detection	2026-01-08 19:00:12 +05:30
KaifAhmad1	2bd1d06eb2	fix: resolve model switching bug and implement intrinsic dimension detection in TextEmbedder	2026-01-08 18:56:42 +05:30
Mohd Kaif	9a2f2cd2d2	Merge pull request #162 from Hawksight-AI/kg docs: update changelog with kg module fixes #159	2026-01-08 17:43:29 +05:30
KaifAhmad1	04a210232e	docs: update changelog with kg module fixes #159	2026-01-08 17:42:19 +05:30
Mohd Kaif	b3baeaa74e	Merge pull request #161 from Hawksight-AI/kg Fix 'unhashable type: Entity' in GraphAnalyzer (#159)	2026-01-08 17:29:31 +05:30
KaifAhmad1	58707ff721	fix(kg): resolve 'unhashable type: Entity' in GraphAnalyzer #159 - Robust ID extraction in CentralityCalculator, CommunityDetector, and ConnectivityAnalyzer - Support for direct Entity objects and dictionaries as node identifiers - Improved Entity hashability in utils/types.py - Added integration test to verify fix and prevent regression	2026-01-08 17:23:31 +05:30
Mohd Kaif	9b18bc3da3	Merge pull request #158 from Hawksight-AI/dependabot/pip/protobuf-4.25.8 chore(deps): bump protobuf from 4.25.3 to 4.25.8	2026-01-07 19:13:13 +05:30
KaifAhmad1	d8e04c29e9	Security fix: Upgrade protobuf to 4.25.8 and add PR description	2026-01-07 19:11:58 +05:30
dependabot[bot]	5764a88d7e	chore(deps): bump protobuf from 4.25.3 to 4.25.8 Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from 4.25.3 to 4.25.8. - [Release notes](https://github.com/protocolbuffers/protobuf/releases) - [Commits](https://github.com/protocolbuffers/protobuf/compare/v4.25.3...v4.25.8) --- updated-dependencies: - dependency-name: protobuf dependency-version: 4.25.8 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com>	2026-01-07 13:41:10 +00:00
Mohd Kaif	cc69899b13	Merge pull request #157 from Hawksight-AI/utils Dependency Fixes, GraphRAG Alignment, and Orchestrator Improvements	2026-01-07 19:07:26 +05:30
KaifAhmad1	e3b53998c3	Fix dependency issues, align GraphRAG notebook, and update changelog	2026-01-07 19:00:30 +05:30
Mohd Kaif	de3441e76e	Merge pull request #156 from Hawksight-AI/docs Cookbook Fixes & Repository Optimization	2026-01-07 16:03:03 +05:30
KaifAhmad1	bd3c258458	chore: add .gitattributes to fix language statistics	2026-01-07 15:59:39 +05:30
Mohd Kaif	51cc445327	Merge pull request #155 from Hawksight-AI/docs Cookbook Notebook Cleanup & Fixes	2026-01-07 15:43:37 +05:30
KaifAhmad1	04c4c9fb4c	docs: clean and fix corrupted notebooks in cookbook	2026-01-07 15:41:13 +05:30
Mohd Kaif	010251ac35	Merge pull request #154 from Hawksight-AI/ontology Fix KnowledgeGraph Documentation Mismatch	2026-01-07 15:05:48 +05:30
KaifAhmad1	dcd6f25f87	docs: fix KnowledgeGraph mismatch and update tests for issue #144	2026-01-07 15:01:47 +05:30
Mohd Kaif	55abd52b77	Merge pull request #153 from Hawksight-AI/docs docs: improve robustness and data consistency in earnings analysis notebook	2026-01-07 14:11:41 +05:30
KaifAhmad1	960d7c5f8f	docs: improve robustness and fix variable inconsistencies in earnings call notebook	2026-01-07 14:09:22 +05:30
Mohd Kaif	2489ce72b5	Delete GITHUB_ISSUE_LLM_EXTRACTION.md	2026-01-07 03:06:21 +05:30
Mohd Kaif	f488dfb82a	Delete PR_DESCRIPTION_LLM_EXTRACTION.md	2026-01-07 03:06:06 +05:30
Mohd Kaif	74fdd3330e	Merge pull request #150 from Hawksight-AI/semantic-extract Robust LLM Extraction - Auto-Chunking, Retries, and Diagnostics	2026-01-07 03:05:37 +05:30
KaifAhmad1	e712949872	Enhance LLM extraction methods with auto-chunking, robust parsing and improved diagnostics (#149 )	2026-01-07 03:03:19 +05:30
Mohd Kaif	c208f6b54e	Delete PR_DESCRIPTION.md	2026-01-07 01:13:55 +05:30
Mohd Kaif	d516ea69dc	Merge pull request #148 from Hawksight-AI/semantic-extract fix(semantic_extract): Pass API key to Groq LLM provider in extractio…	2026-01-07 01:11:21 +05:30
KaifAhmad1	2790132e8e	fix(semantic_extract): Pass API key to Groq LLM provider in extraction methods - Add API key handling in extract_entities_llm(), extract_relations_llm(), and extract_triplets_llm() - Add explicit api_key handling in NERExtractor and RelationExtractor - Add llm_model parameter support in extract_triplets_llm() for consistency - Fix relation extraction bug with type checking for subject_text/object_text - Add environment variable fallback for API keys - Update notebook with standard API key pattern Fixes #147	2026-01-07 01:07:56 +05:30
Mohd Kaif	1e22ff3a75	Merge pull request #146 from Hawksight-AI/semantic-extract fix(semantic_extract): Pass API key to Groq LLM provider in extractio…	2026-01-06 22:43:59 +05:30
KaifAhmad1	9c59f97542	fix(semantic_extract): Pass API key to Groq LLM provider in extraction methods - Add API key handling in extract_entities_llm(), extract_relations_llm(), and extract_triplets_llm() - Add llm_model parameter support in extract_triplets_llm() for consistency - Fix relation extraction bug with type checking for subject_text/object_text - Add environment variable fallback for API keys - Include providers.py for context (GroqProvider implementation) Fixes #145	2026-01-06 22:37:57 +05:30
Mohd Kaif	4eb69e5048	Merge pull request #143 from Hawksight-AI/docs Refreshed Metadata, Analytics & Community Links	2026-01-06 12:04:53 +05:30
KaifAhmad1	2b43fa4699	fix: update discord badge to a reliable static version	2026-01-06 12:00:23 +05:30
Mohd Kaif	9fb18e3ec6	Merge pull request #142 from Hawksight-AI/docs Update Badges, Discord Links, and Version Metadata	2026-01-06 11:57:37 +05:30
KaifAhmad1	7f7c36f94d	docs: update badges, discord links, and version mentions	2026-01-06 11:54:17 +05:30
Mohd Kaif	2e2f19f43d	Merge pull request #141 from Hawksight-AI/docs Release v0.1.1: Docling Integration & 2026 Sync	2026-01-06 00:28:15 +05:30
KaifAhmad1	d7b686f32a	Release v0.1.1: Docling support, version bump, and documentation updates	2026-01-06 00:21:20 +05:30
Mohd Kaif	e89e707e49	Merge pull request #140 from Hawksight-AI/utils Fix DoclingParser Integration and Confirm Cross-Platform Compatibility	2026-01-05 22:31:57 +05:30
KaifAhmad1	a441e935f9	Fix DoclingParser integration and align with docling API - Fix import logic in __init__.py to properly export DoclingParser - Rewrite docling_parser.py to use docling's native API (direct attribute access) - Remove unsupported features (table_extraction_mode, invalid format_options) - Use doc.tables, doc.pictures, doc.pages directly instead of dict parsing - Update notebook with improved code and documentation - Add proper error handling for when docling is not available Fixes #138	2026-01-05 22:27:15 +05:30
Mohd Kaif	3fb98aa0ed	Merge pull request #139 from Hawksight-AI/utils Resolve DoclingParser Exports, Windows Progress Encoding, and Finance Cookbook Update (#138)	2026-01-05 20:10:45 +05:30
KaifAhmad1	4e0c3bc361	Resolve DoclingParser exports, fix Windows progress encoding, and update finance cookbook #138	2026-01-05 20:04:59 +05:30
Mohd Kaif	ef2c3dc841	Merge pull request #137 from Hawksight-AI/utils feat: Add pipeline_id support and fix parsing table display	2026-01-05 18:17:24 +05:30
KaifAhmad1	96604ae398	feat: Add pipeline_id support and fix parsing table display - Add pipeline_id parameter to all trackers, batch processors, parsers, and extractors - Fix DoclingParser to show extraction counts in progress display - Add 'Extracted' column showing tables, images, pages - Emphasize Docling as core dependency in messages Closes #136	2026-01-05 18:15:12 +05:30
Mohd Kaif	88a4b9f1d2	Merge pull request #134 from Hawksight-AI/parse feat(parse): add progress tracking to DoclingParser and update earnin…	2026-01-05 14:46:12 +05:30
KaifAhmad1	ea02896617	feat(parse): add progress tracking to DoclingParser and update earnings call notebook - Add 8-stage progress tracking (0-100%) with ETA to DoclingParser - Update earnings call analysis notebook with MDA Space Q3 2025 example - Simplify notebook code structure - Add real-time progress visibility for PDF parsing Closes #133	2026-01-05 14:42:12 +05:30
Mohd Kaif	01808728f4	Merge pull request #132 from Hawksight-AI/docs [DOCS] Fix Discord invite link and reorganize README sections	2026-01-04 19:58:02 +05:30
KaifAhmad1	b03ab2458d	docs: Fix Discord invite link and reorganize README sections - Update Discord invite link from https://discord.gg/semantica to https://discord.gg/pMHguUzG - Move Contributors section inside Contributing section (following open source best practices) - Update Enterprise Support section to indicate future availability - Add Evals to roadmap Fixes #127	2026-01-04 19:56:10 +05:30
Mohd Kaif	f8551c5dfb	Merge pull request #131 from Hawksight-AI/utils Fix: Handle OSError for Optional Dependencies and Make DoclingParser Standalone	2026-01-04 17:56:39 +05:30
KaifAhmad1	e7f713d43b	Fix: Handle OSError for optional dependencies and make DoclingParser standalone - Add safe_import utility in semantica/utils/helpers.py for graceful optional dependency handling - Update all optional imports (spacy, docling, etc.) to handle OSError (Windows DLL issues) - Make DoclingParser standalone with docling as core dependency - Remove DoclingParser integration from DocumentParser - Implement lazy initialization for DoclingParser (fails on parse(), not init()) - Fix DocumentConverter initialization (remove unsupported pipeline_options parameter) - Preserve original error messages without modification - Update semantic_extract, split, parse, embeddings, vector_store, visualization modules - Fix OSError handling across entire codebase for Windows compatibility - Update 60+ files with proper optional dependency handling	2026-01-04 17:44:14 +05:30
Mohd Kaif	6595f1918c	Merge pull request #130 from Hawksight-AI/llms Fix: Make PyTorch import lazy to avoid DLL errors on Windows	2026-01-03 20:43:43 +05:30