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  "cells": [
    {
      "cell_type": "markdown",
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      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/introduction/01_Welcome_to_Semantica.ipynb)\n",
        "\n",
        "# Welcome to Semantica\n",
        "\n",
        "## Overview\n",
        "\n",
        "This notebook introduces you to the **Semantica framework** - a comprehensive knowledge graph and semantic processing framework for building production-ready semantic AI applications.\n",
        "\n",
        "**Documentation**: [Getting Started](https://semantica.readthedocs.io/getting-started/) • [Concepts](https://semantica.readthedocs.io/concepts/) • [API Reference](https://semantica.readthedocs.io/reference/)\n",
        "\n",
        "### What You'll Learn\n",
        "\n",
        "- What Semantica is and why it's useful\n",
        "- How to install and configure the framework\n",
        "- Understanding the framework architecture\n",
        "- Key concepts and terminology\n",
        "- Next steps for getting started\n",
        "\n",
        "## What is Semantica?\n",
        "\n",
        "**Semantica** is a production-ready framework for:\n",
        "\n",
        "- **Building Knowledge Graphs**: Transform unstructured data into structured knowledge graphs\n",
        "- **Semantic Processing**: Extract entities, relationships, and meaning from text\n",
        "- **GraphRAG**: Graph-based retrieval augmented generation\n",
        "- **Temporal Analysis**: Time-aware knowledge graphs\n",
        "- **Text Processing**: Handle text and structured data\n",
        "- **Enterprise Features**: Quality assurance, conflict resolution, ontology generation\n",
        "\n",
        "### Use Cases\n",
        "\n",
        "- Threat intelligence and cybersecurity\n",
        "- Healthcare and medical research\n",
        "- Financial analysis and fraud detection\n",
        "- Supply chain optimization\n",
        "- Research and knowledge management\n",
        "- Multi-agent AI systems\n",
        "\n",
        "\n",
        "## Installation & Setup\n",
        "\n",
        "### Prerequisites\n",
        "\n",
        "Before installing Semantica, ensure you have:\n",
        "- Python 3.8 or higher\n",
        "- pip package manager\n",
        "- (Optional) Virtual environment for isolation\n",
        "\n",
        "### Installation Methods\n",
        "\n",
        "```bash\n",
        "# Method 1: Install from PyPI (Recommended)\n",
        "pip install semantica\n",
        "\n",
        "# Or install with all optional dependencies:\n",
        "pip install semantica[all]\n",
        "\n",
        "# Method 2: Install from source (development version)\n",
        "git clone https://github.com/Hawksight-AI/semantica.git\n",
        "cd semantica\n",
        "pip install -e .\n",
        "\n",
        "# Or with all optional dependencies:\n",
        "pip install -e \".[all]\"\n",
        "\n",
        "# Verify installation\n",
        "import semantica\n",
        "print(semantica.__version__)\n",
        "```\n",
        "\n",
        "### Configuration\n",
        "\n",
        "```bash\n",
        "# Set up environment variables for API keys and configuration\n",
        "# export SEMANTICA_API_KEY=your_openai_key\n",
        "# export SEMANTICA_EMBEDDING_PROVIDER=openai\n",
        "# export SEMANTICA_MODEL_NAME=gpt-4\n",
        "\n",
        "# Or use a config file (config.yaml):\n",
        "# api_keys:\n",
        "#   openai: your_key_here\n",
        "#   anthropic: your_key_here\n",
        "# embedding:\n",
        "#   provider: openai\n",
        "#   model: text-embedding-3-large\n",
        "#   dimensions: 3072\n",
        "# knowledge_graph:\n",
        "#   backend: networkx  # or neo4j, arangodb\n",
        "#   temporal: true\n",
        "```\n",
        "\n",
        "---\n",
        "\n",
        "## Framework Architecture Overview\n",
        "\n",
        "Semantica is organized into modular components, each handling a specific aspect of semantic processing:\n",
        "\n",
        "### 1. INGEST MODULE - Data Ingestion\n",
        "**Purpose**: Ingest data from various sources\n",
        "**Components**:\n",
        "- `FileIngestor`: Read files (PDF, DOCX, HTML, JSON, CSV, etc.)\n",
        "- `WebIngestor`: Scrape and ingest web pages\n",
        "- `FeedIngestor`: Process RSS/Atom feeds\n",
        "- `StreamIngestor`: Real-time data streaming (Kafka, RabbitMQ, Kinesis, Pulsar)\n",
        "- `DBIngestor`: Database queries and ingestion (PostgreSQL, MySQL, SQLite, Oracle, SQL Server)\n",
        "- `EmailIngestor`: Process email messages (IMAP, POP3)\n",
        "- `RepoIngestor`: Git repository analysis\n",
        "- `MCPIngestor`: Model Context Protocol server integration\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.ingest import FileIngestor, WebIngestor, FeedIngestor, StreamIngestor, DBIngestor, EmailIngestor, RepoIngestor, MCPIngestor\n",
        "file_ingestor = FileIngestor()\n",
        "web_ingestor = WebIngestor()\n",
        "documents = file_ingestor.ingest(\"data/\")\n",
        "web_docs = web_ingestor.ingest(\"https://example.com\")\n",
        "```\n",
        "\n",
        "### 2. PARSE MODULE - Document Parsing\n",
        "**Purpose**: Parse and extract content from various formats\n",
        "**Components**:\n",
        "- `DocumentParser`: Main parser orchestrator\n",
        "- `PDFParser`: Extract text, tables, images from PDFs\n",
        "- `DOCXParser`: Parse Word documents\n",
        "- `HTMLParser`: Extract content from HTML\n",
        "- `JSONParser`: Parse structured JSON data\n",
        "- `ExcelParser`: Process spreadsheets\n",
        "- `ImageParser`: OCR and image analysis\n",
        "- `CodeParser`: Parse source code files\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.parse import DocumentParser\n",
        "parser = DocumentParser()\n",
        "parsed_docs = parser.parse(documents)\n",
        "```\n",
        "\n",
        "### 3. NORMALIZE MODULE - Text Normalization\n",
        "**Purpose**: Clean and normalize text for processing\n",
        "**Components**:\n",
        "- `TextNormalizer`: Main normalization orchestrator\n",
        "- `TextCleaner`: Remove noise, fix encoding\n",
        "- `DataCleaner`: Clean structured data\n",
        "- `EntityNormalizer`: Normalize entity names\n",
        "- `DateNormalizer`: Standardize date formats\n",
        "- `NumberNormalizer`: Normalize numeric values\n",
        "- `LanguageDetector`: Detect document language\n",
        "- `EncodingHandler`: Handle character encoding\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.normalize import TextNormalizer\n",
        "normalizer = TextNormalizer()\n",
        "normalized = normalizer.normalize(parsed_docs)\n",
        "```\n",
        "\n",
        "### 4. SEMANTIC_EXTRACT MODULE - Entity & Relationship Extraction\n",
        "**Purpose**: Extract entities, relationships, and semantic information\n",
        "**Components**:\n",
        "- `NERExtractor`: Named Entity Recognition\n",
        "- `RelationExtractor`: Extract relationships between entities\n",
        "- `SemanticAnalyzer`: Deep semantic analysis\n",
        "- `SemanticNetworkExtractor`: Extract semantic networks\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.semantic_extract import NERExtractor, RelationExtractor\n",
        "extractor = NERExtractor()\n",
        "entities = extractor.extract(normalized_docs)\n",
        "relation_extractor = RelationExtractor()\n",
        "relationships = relation_extractor.extract(normalized_docs, entities)\n",
        "```\n",
        "\n",
        "### 5. KG MODULE - Knowledge Graph Construction\n",
        "**Purpose**: Build and manage knowledge graphs\n",
        "**Components**:\n",
        "- `GraphBuilder`: Construct knowledge graphs from entities/relationships\n",
        "- `GraphAnalyzer`: Analyze graph structure and properties\n",
        "- `GraphValidator`: Validate graph quality and consistency\n",
        "- `EntityResolver`: Resolve entity conflicts and duplicates\n",
        "- `ConflictDetector`: Detect conflicting information\n",
        "- `CentralityCalculator`: Calculate node importance metrics\n",
        "- `CommunityDetector`: Detect communities in graphs\n",
        "- `ConnectivityAnalyzer`: Analyze graph connectivity\n",
        "- `TemporalQuery`: Query temporal knowledge graphs\n",
        "- `Deduplicator`: Remove duplicate entities/relationships\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.kg import GraphBuilder, GraphAnalyzer\n",
        "builder = GraphBuilder()\n",
        "kg = builder.build(entities, relationships)\n",
        "analyzer = GraphAnalyzer()\n",
        "metrics = analyzer.analyze(kg)\n",
        "```\n",
        "\n",
        "### 6. EMBEDDINGS MODULE - Embedding Generation\n",
        "**Purpose**: Generate vector embeddings for text data\n",
        "**Components**:\n",
        "- `EmbeddingGenerator`: Main embedding orchestrator\n",
        "- `TextEmbedder`: Generate text embeddings\n",
        "- `VectorEmbeddingManager`: Manage embeddings for vector databases\n",
        "- `GraphEmbeddingManager`: Manage embeddings for graph databases\n",
        "- `ProviderAdapters`: Support for OpenAI, BGE, FastEmbed, Llama, etc.\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.embeddings import EmbeddingGenerator\n",
        "generator = EmbeddingGenerator()\n",
        "embeddings = generator.generate_embeddings(documents, data_type=\"text\")\n",
        "```\n",
        "\n",
        "### 7. VECTOR_STORE MODULE - Vector Database Operations\n",
        "**Purpose**: Store and search vector embeddings\n",
        "**Components**:\n",
        "- `VectorStore`: Main vector store interface\n",
        "- `FAISSAdapter`: FAISS integration\n",
        "- `HybridSearch`: Combine vector and keyword search\n",
        "- `VectorRetriever`: Retrieve relevant vectors\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.vector_store import VectorStore, HybridSearch\n",
        "vector_store = VectorStore()\n",
        "vector_store.store(embeddings, documents, metadata)\n",
        "hybrid_search = HybridSearch(vector_store)\n",
        "results = hybrid_search.search(query, top_k=10)\n",
        "```\n",
        "\n",
        "### 8. GRAPH_STORE MODULE - Persistent Graph Database Operations\n",
    "**Purpose**: Store and query property graphs in Neo4j or FalkorDB\n",
    "**Components**:\n",
    "- `GraphStore`: Main graph store interface\n",
    "- `Neo4jAdapter`: Neo4j integration (enterprise features)\n",
    "- `FalkorDBAdapter`: FalkorDB integration (Redis-based, ultra-fast)\n",
    "\n",
    "**Example**:\n",
    "```python\n",
    "from semantica.graph_store import GraphStore\n",
    "store = GraphStore(backend=\"neo4j\", uri=\"bolt://localhost:7687\", user=\"neo4j\", password=\"password\")\n",
        "store.connect()\n",
        "node1 = store.create_node(\n",
        "    labels=[\"Person\"],\n",
        "    properties={\"name\": \"John\", \"age\": 30}\n",
        ")\n",
        "node2 = store.create_node(\n",
        "    labels=[\"Person\"],\n",
        "    properties={\"name\": \"Jane\", \"age\": 28}\n",
        ")\n",
        "store.create_relationship(\n",
        "    start_node_id=node1[\"id\"],\n",
        "    end_node_id=node2[\"id\"],\n",
        "    rel_type=\"KNOWS\",\n",
        "    properties={\"since\": 2020}\n",
        ")\n",
        "results = store.execute_query(\"MATCH (p:Person) RETURN p.name\")\n",
        "store.close()\n",
        "```\n",
        "\n",
        "### 9. REASONING MODULE - Inference and Reasoning\n",
        "**Purpose**: Perform logical inference and reasoning\n",
        "**Components**:\n",
        "- `InferenceEngine`: Main inference orchestrator\n",
        "- `RuleManager`: Manage inference rules\n",
        "- `DeductiveReasoner`: Deductive reasoning\n",
        "- `AbductiveReasoner`: Abductive reasoning\n",
        "- `ExplanationGenerator`: Generate explanations for inferences\n",
        "- `RETEEngine`: RETE algorithm for rule matching\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.reasoning import InferenceEngine, RuleManager\n",
        "inference_engine = InferenceEngine()\n",
        "rule_manager = RuleManager()\n",
        "new_facts = inference_engine.forward_chain()\n",
        "```\n",
        "\n",
        "### 10. ONTOLOGY MODULE - Ontology Generation\n",
        "**Purpose**: Generate and manage ontologies\n",
        "**Components**:\n",
        "- `OntologyGenerator`: Generate ontologies from knowledge graphs\n",
        "- `OntologyValidator`: Validate ontology structure\n",
        "- `OWLGenerator`: Generate OWL format ontologies\n",
        "- `PropertyGenerator`: Generate ontology properties\n",
        "- `ClassInferrer`: Infer ontology classes\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.ontology import OntologyGenerator\n",
        "generator = OntologyGenerator()\n",
        "ontology = generator.generate_from_graph(kg)\n",
        "```\n",
        "\n",
        "### 11. EXPORT MODULE - Data Export\n",
        "**Purpose**: Export data in various formats\n",
        "**Components**:\n",
        "- `JSONExporter`: Export to JSON\n",
        "- `RDFExporter`: Export to RDF/XML\n",
        "- `CSVExporter`: Export to CSV\n",
        "- `GraphExporter`: Export to graph formats (GraphML, GEXF)\n",
        "- `OWLExporter`: Export to OWL\n",
        "- `VectorExporter`: Export vectors\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.export import JSONExporter, RDFExporter\n",
        "json_exporter = JSONExporter()\n",
        "json_exporter.export(kg, \"output.json\")\n",
        "```\n",
        "\n",
        "### 12. VISUALIZATION MODULE - Graph Visualization\n",
        "**Purpose**: Visualize knowledge graphs and analytics\n",
        "**Components**:\n",
        "- `KGVisualizer`: Visualize knowledge graphs\n",
        "- `EmbeddingVisualizer`: Visualize embeddings (t-SNE, PCA, UMAP)\n",
        "- `QualityVisualizer`: Visualize quality metrics\n",
        "- `AnalyticsVisualizer`: Visualize graph analytics\n",
        "- `TemporalVisualizer`: Visualize temporal data\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.visualization import KGVisualizer\n",
        "visualizer = KGVisualizer()\n",
        "visualizer.visualize(kg)\n",
        "```\n",
        "\n",
        "### 13. PIPELINE MODULE - Pipeline Orchestration\n",
        "**Purpose**: Build and execute processing pipelines\n",
        "**Components**:\n",
        "- `PipelineBuilder`: Build complex pipelines\n",
        "- `ExecutionEngine`: Execute pipelines\n",
        "- `FailureHandler`: Handle pipeline failures\n",
        "- `ParallelismManager`: Enable parallel processing\n",
        "- `ResourceScheduler`: Schedule resources\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.pipeline import PipelineBuilder\n",
        "builder = PipelineBuilder()\n",
        "pipeline = builder.add_step(\"ingest\", FileIngestor()) \\\\\n",
        "                  .add_step(\"parse\", DocumentParser()) \\\\\n",
        "                  .build()\n",
        "```\n",
        "\n",
        "### 14. CORE MODULE - Framework Orchestration\n",
        "**Purpose**: Framework initialization, configuration, lifecycle management, and plugin system\n",
        "**Components**:\n",
        "- `Semantica`: Main framework class coordinating all components\n",
        "- `ConfigManager`: Configuration loading, validation, and management\n",
        "- `Config`: Configuration data class with validation\n",
        "- `LifecycleManager`: System lifecycle management with hooks and health monitoring\n",
        "- `PluginRegistry`: Dynamic plugin discovery and loading\n",
        "- `MethodRegistry`: Registry for custom orchestration methods\n",
        "\n",
        "**Example**:\n",
        "```python\n",
        "from semantica.core import Semantica, ConfigManager\n",
        "\n",
        "# Load configuration\n",
        "config_manager = ConfigManager()\n",
        "config = config_manager.load_from_file(\"config.yaml\")\n",
        "\n",
        "# Initialize framework\n",
        "framework = Semantica(config=config)\n",
        "framework.initialize()\n",
        "\n",
        "# Build knowledge base\n",
        "result = framework.build_knowledge_base(\n",
        "    sources=[\"doc1.pdf\", \"doc2.docx\"],\n",
        "    embeddings=True,\n",
        "    graph=True\n",
        ")\n",
        "\n",
        "# Shutdown gracefully\n",
        "framework.shutdown()\n",
        "```\n",
        "\n",
        "---\n",
        "\n",
        "## Key Concepts Explained\n",
        "\n",
        "Understanding these concepts is crucial for working with Semantica:\n",
        "\n",
        "### 1. KNOWLEDGE GRAPHS\n",
        "**Definition**: A knowledge graph is a structured representation of entities (nodes) and their relationships (edges) with properties and attributes.\n",
        "\n",
        "**Structure**:\n",
        "- **Nodes**: Represent entities (people, places, concepts, events)\n",
        "- **Edges**: Represent relationships (works_for, located_in, causes)\n",
        "- **Properties**: Attributes of entities and relationships\n",
        "- **Metadata**: Additional information (sources, timestamps, confidence)\n",
        "\n",
        "**Example**:\n",
        "- Entity: \"John Doe\" (Person)\n",
        "- Relationship: \"works_for\" -> \"Acme Corp\" (Organization)\n",
        "- Properties: `{start_date: \"2020-01-01\", role: \"Engineer\"}`\n",
        "\n",
        "**Benefits**:\n",
        "- Structured representation of unstructured data\n",
        "- Enables complex queries and reasoning\n",
        "- Supports temporal tracking\n",
        "- Facilitates knowledge discovery\n",
        "\n",
        "### 2. ENTITY EXTRACTION (NER - Named Entity Recognition)\n",
        "**Definition**: The process of identifying and classifying named entities in text into predefined categories.\n",
        "\n",
        "**Entity Types**:\n",
        "- **Person**: Names of people\n",
        "- **Organization**: Companies, institutions\n",
        "- **Location**: Places, geographic entities\n",
        "- **Date/Time**: Temporal expressions\n",
        "- **Money**: Monetary values\n",
        "- **Product**: Products and services\n",
        "- **Event**: Events and occurrences\n",
        "- **Custom**: Domain-specific entities\n",
        "\n",
        "**Example**:\n",
        "Text: \"Apple Inc. was founded by Steve Jobs in Cupertino, California.\"\n",
        "Entities:\n",
        "- \"Apple Inc.\" -> Organization\n",
        "- \"Steve Jobs\" -> Person\n",
        "- \"Cupertino, California\" -> Location\n",
        "\n",
        "**Methods**:\n",
        "- Rule-based: Pattern matching\n",
        "- Machine Learning: Trained models (spaCy, transformers)\n",
        "- LLM-based: Using large language models\n",
        "\n",
        "### 3. RELATIONSHIP EXTRACTION\n",
        "**Definition**: Identifying and extracting relationships between entities in text.\n",
        "\n",
        "**Relationship Types**:\n",
        "- **Semantic**: \"works_for\", \"located_in\", \"causes\"\n",
        "- **Temporal**: \"before\", \"after\", \"during\"\n",
        "- **Causal**: \"causes\", \"results_in\", \"prevents\"\n",
        "- **Hierarchical**: \"part_of\", \"subclass_of\", \"instance_of\"\n",
        "\n",
        "**Example**:\n",
        "Text: \"John works for Acme Corp in New York.\"\n",
        "Relationships:\n",
        "- (John, works_for, Acme Corp)\n",
        "- (Acme Corp, located_in, New York)\n",
        "\n",
        "**Methods**:\n",
        "- Pattern matching\n",
        "- Dependency parsing\n",
        "- Machine learning models\n",
        "- LLM-based extraction\n",
        "\n",
        "### 4. EMBEDDINGS\n",
        "**Definition**: Dense vector representations of text, images, or other data that capture semantic meaning in a continuous vector space.\n",
        "\n",
        "**Properties**:\n",
        "- Similar entities have similar embeddings (close in vector space)\n",
        "- Enable semantic search and similarity calculations\n",
        "- Fixed or variable dimensions (typically 128-4096)\n",
        "\n",
        "**Example**:\n",
        "Text: \"machine learning\"\n",
        "Embedding: `[0.123, -0.456, 0.789, ..., 0.234]` (vector of 1536 dimensions)\n",
        "\n",
        "**Use Cases**:\n",
        "- Semantic search\n",
        "- Clustering and classification\n",
        "- Recommendation systems\n",
        "- Anomaly detection\n",
        "\n",
        "### 5. TEMPORAL GRAPHS\n",
        "**Definition**: Knowledge graphs that track changes over time, allowing queries about the state of the graph at specific time points.\n",
        "\n",
        "**Features**:\n",
        "- Timestamps on entities and relationships\n",
        "- Version history\n",
        "- Time-point queries\n",
        "- Temporal pattern detection\n",
        "\n",
        "**Example**:\n",
        "- Entity: \"Company X\"\n",
        "- Relationship: (Company X, has_CEO, Person Y)\n",
        "- Temporal: `valid_from=\"2020-01-01\", valid_to=\"2023-12-31\"`\n",
        "\n",
        "**Use Cases**:\n",
        "- Tracking organizational changes\n",
        "- Monitoring system evolution\n",
        "- Analyzing trends over time\n",
        "- Historical analysis\n",
        "\n",
        "### 6. GraphRAG (Graph-based Retrieval Augmented Generation)\n",
        "**Definition**: An advanced RAG approach that combines vector search with knowledge graph traversal to provide more accurate and contextually relevant information to LLMs.\n",
        "\n",
        "**Components**:\n",
        "- **Vector Store**: For semantic similarity search\n",
        "- **Knowledge Graph**: For structured relationship traversal\n",
        "- **Hybrid Search**: Combines both approaches\n",
        "- **LLM Integration**: Uses retrieved context for generation\n",
        "\n",
        "**Advantages over Traditional RAG**:\n",
        "- Better handling of complex queries\n",
        "- Relationship-aware retrieval\n",
        "- Reduced hallucinations\n",
        "- More accurate answers\n",
        "\n",
        "**Example Workflow**:\n",
        "1. Query: \"Who worked with John at Acme Corp?\"\n",
        "2. Vector search finds relevant documents\n",
        "3. Knowledge graph traversal finds relationships\n",
        "4. Combined context sent to LLM\n",
        "5. LLM generates accurate answer using both sources\n",
        "\n",
        "### 7. ONTOLOGY\n",
        "**Definition**: A formal specification of concepts, relationships, and constraints in a domain, typically expressed in OWL (Web Ontology Language).\n",
        "\n",
        "**Components**:\n",
        "- **Classes**: Categories of entities\n",
        "- **Properties**: Relationships and attributes\n",
        "- **Individuals**: Specific instances\n",
        "- **Axioms**: Rules and constraints\n",
        "\n",
        "**Example**:\n",
        "- Class: Person\n",
        "- SubClass: Employee, Customer\n",
        "- Property: worksFor (domain: Person, range: Organization)\n",
        "\n",
        "**Use Cases**:\n",
        "- Standardize domain knowledge\n",
        "- Enable reasoning\n",
        "- Facilitate data integration\n",
        "- Support semantic web\n",
        "\n",
        "### 8. QUALITY ASSURANCE\n",
        "**Definition**: Processes and metrics to ensure knowledge graph quality, including completeness, consistency, and accuracy.\n",
        "\n",
        "**Metrics**:\n",
        "- **Completeness**: Percentage of entities with required properties\n",
        "- **Consistency**: Absence of contradictions\n",
        "- **Accuracy**: Correctness of extracted information\n",
        "- **Coverage**: Breadth of domain coverage\n",
        "\n",
        "**Methods**:\n",
        "- Validation rules\n",
        "- Automated quality checks\n",
        "- Conflict detection\n",
        "- Source verification\n",
        "---\n",
        "\n",
        "## Next Steps\n",
        "\n",
        "Now that you understand the basics, here are recommended next steps:\n",
        "\n",
        "1. **Your First Knowledge Graph** (`01_Your_First_Knowledge_Graph.ipynb`)\n",
        "   - Build your first knowledge graph from a document\n",
        "   - Learn the basic workflow\n",
        "\n",
        "2. **Configuration Basics** (`02_Configuration_Basics.ipynb`)\n",
        "   - Set up configuration files\n",
        "   - Configure API keys and providers\n",
        "\n",
        "3. **Core Workflows** (`01_core_workflows/`)\n",
        "   - Learn common patterns and workflows\n",
        "   - Start with \"From Unstructured to Structured\"\n",
        "\n",
        "4. **Use Cases** (`03_use_cases/`)\n",
        "   - Explore domain-specific applications\n",
        "   - Find examples relevant to your domain\n",
        "\n",
        "---\n",
        "\n",
        "## Best Practices\n",
        "\n",
        "### 1. START SMALL\n",
        "- Begin with simple documents\n",
        "- Validate each step before moving forward\n",
        "- Build incrementally\n",
        "\n",
        "### 2. CONFIGURE PROPERLY\n",
        "- Use environment variables for sensitive data\n",
        "- Set up proper logging\n",
        "- Configure appropriate model sizes\n",
        "\n",
        "### 3. VALIDATE DATA\n",
        "- Always validate extracted entities\n",
        "- Check relationship quality\n",
        "- Use quality assurance tools\n",
        "\n",
        "### 4. HANDLE ERRORS\n",
        "- Implement error handling\n",
        "- Use retry mechanisms\n",
        "- Log errors for debugging\n",
        "\n",
        "### 5. OPTIMIZE PERFORMANCE\n",
        "- Use batch processing for large datasets\n",
        "- Enable parallel processing where possible\n",
        "- Cache embeddings and results\n",
        "\n",
        "### 6. DOCUMENT YOUR WORKFLOWS\n",
        "- Document data sources\n",
        "- Track processing steps\n",
        "- Maintain metadata\n",
        "\n",
        "---\n",
        "\n",
        "## Troubleshooting\n",
        "\n",
        "Common issues and solutions:\n",
        "\n",
        "### Issue 1: Import Errors\n",
        "**Solution**:\n",
        "- Ensure Semantica is properly installed\n",
        "- Check Python version (3.8+)\n",
        "- Verify virtual environment is activated\n",
        "- Install missing dependencies: `pip install -r requirements.txt`\n",
        "\n",
        "### Issue 2: API Key Errors\n",
        "**Solution**:\n",
        "- Set environment variables: `export SEMANTICA_API_KEY=your_key`\n",
        "- Check config file for correct key format\n",
        "- Verify API key is valid and has sufficient credits\n",
        "\n",
        "### Issue 3: Memory Issues\n",
        "**Solution**:\n",
        "- Process documents in batches\n",
        "- Use smaller embedding models\n",
        "- Enable garbage collection\n",
        "- Consider using streaming for large datasets\n",
        "\n",
        "### Issue 4: Low Quality Extractions\n",
        "**Solution**:\n",
        "- Preprocess and normalize text\n",
        "- Use domain-specific models\n",
        "- Adjust extraction parameters\n",
        "- Validate and clean extracted entities\n",
        "\n",
        "### Issue 5: Slow Processing\n",
        "**Solution**:\n",
        "- Enable parallel processing\n",
        "- Use GPU acceleration if available\n",
        "- Cache intermediate results\n",
        "- Optimize batch sizes\n",
        "\n"
      ]
    }
  ],
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