{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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/09_Your_First_Knowledge_Graph.ipynb)\n",
    "\n",
    "# 🚀 Your First Knowledge Graph\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook walks you through creating your first knowledge graph from a simple document. You'll learn the complete end-to-end workflow from ingesting a file to visualizing the resulting knowledge graph.\n",
    "\n",
    "> [!TIP]\n",
    "> This is the perfect starting point if you are new to Semantica. No prior knowledge of knowledge graphs is required!\n",
    "\n",
    "**Documentation**: [API Reference](https://semantica.readthedocs.io/reference/kg/)\n",
    "\n",
    "### 🎯 Learning Objectives\n",
    "\n",
    "- **Understand the Workflow**: Learn the `File → Parse → Extract → Graph` pipeline\n",
    "- **Ingest Data**: Load documents using `FileIngestor`\n",
    "- **Parse Content**: Extract text using `DocumentParser`\n",
    "- **Extract Knowledge**: Identify entities using `NERExtractor`\n",
    "- **Build Graph**: Construct a graph using `GraphBuilder`\n",
    "- **Visualize**: See your graph come to life with `KGVisualizer`\n",
    "\n",
    "## Installation\n",
    "\n",
    "Install Semantica from PyPI:\n",
    "\n",
    "```bash\n",
    "pip install semantica\n",
    "# Or with all optional dependencies:\n",
    "pip install semantica[all]\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "## 🔄 Simple End-to-End Workflow\n",
    "\n",
    "The complete workflow consists of four main steps:\n",
    "\n",
    "1. **📥 Ingest** - Load data from files or other sources\n",
    "2. **📄 Parse** - Extract and structure content from documents\n",
    "3. **⛏️ Extract** - Identify entities and relationships\n",
    "4. **🕸️ Build Graph** - Construct the knowledge graph\n",
    "\n",
    "Each step is demonstrated in the code cells below.\n",
    "\n",
    "> [!TIP]\n",
    "> **Alternative: Using Semantica Framework**\n",
    "> \n",
    "> For a simpler, high-level approach, you can use the `Semantica` framework class which orchestrates all these steps:\n",
    "> \n",
    "> ```python\n",
    "> from semantica.core import Semantica\n",
    "> \n",
    "> framework = Semantica()\n",
    "> framework.initialize()\n",
    "> \n",
    "> result = framework.build_knowledge_base(\n",
    ">     sources=[\"sample_document.txt\"],\n",
    ">     embeddings=True,\n",
    ">     graph=True\n",
    "> )\n",
    "> \n",
    "> framework.shutdown()\n",
    "> ```\n",
    "> \n",
    "> This notebook shows the step-by-step approach for learning. See [Core Module Usage Guide](../../../semantica/core/core_usage.md) for more details.\n",
    "\n",
    "---\n",
    "\n",
    "## 📂 Step 1: Ingest a File\n",
    "\n",
    "In this step, we'll use `FileIngestor` to load a document. The ingestor supports various file formats including PDF, DOCX, TXT, and more.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install semantica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>"
      ],
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      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  File name: sample_document.txt\n",
      "  File type: txt\n",
      "  Content available: True\n"
     ]
    }
   ],
   "source": [
    "from semantica.ingest import FileIngestor\n",
    "from pathlib import Path\n",
    "\n",
    "# Initialize the ingestor\n",
    "ingestor = FileIngestor()\n",
    "\n",
    "# Create a sample document for demonstration\n",
    "sample_text = \"\"\"\n",
    "Apple Inc. is a technology company founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in 1976.\n",
    "The company is headquartered in Cupertino, California.\n",
    "Tim Cook is the current CEO of Apple Inc.\n",
    "Apple designs and manufactures consumer electronics, software, and online services.\n",
    "\"\"\"\n",
    "\n",
    "sample_file = Path(\"sample_document.txt\")\n",
    "sample_file.write_text(sample_text)\n",
    "\n",
    "print(f\"File: {sample_file}\")\n",
    "print(f\"Content length: {len(sample_text)} characters\")\n",
    "\n",
    "# Ingest the file\n",
    "file_object = ingestor.ingest_file(sample_file, read_content=True)\n",
    "print(f\"  File name: {file_object.name}\")\n",
    "print(f\"  File type: {file_object.file_type}\")\n",
    "print(f\"  Content available: {file_object.content is not None}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📄 Step 2: Parse the Document\n",
    "\n",
    "After ingesting the file, we need to parse it to extract the text content. The `DocumentParser` handles various file formats and extracts structured content.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Parsed content length: 0 characters\n",
      "  Preview: N/A...\n"
     ]
    }
   ],
   "source": [
    "from semantica.parse import DocumentParser\n",
    "\n",
    "parser = DocumentParser()\n",
    "# Parse the document to extract text\n",
    "parsed_document = parser.parse_document(str(sample_file))\n",
    "parsed_content = parsed_document.get(\"content\", \"\")\n",
    "print(f\"  Parsed content length: {len(parsed_content) if parsed_content else 0} characters\")\n",
    "print(f\"  Preview: {parsed_content[:200] if parsed_content else 'N/A'}...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ⛏️ Step 3: Extract Entities\n",
    "\n",
    "Now we'll extract entities from the parsed text using Named Entity Recognition (NER). This identifies people, organizations, locations, dates, and other entities in the text.\n",
    "\n",
    "> [!NOTE]\n",
    "> In a real scenario, you would use `NERExtractor` with an LLM or model backend. Here we simulate the output for demonstration purposes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
     ]
    }
   ],
   "source": [
    "from semantica.semantic_extract import NamedEntityRecognizer, NERExtractor\n",
    "\n",
    "ner = NamedEntityRecognizer()\n",
    "extractor = NERExtractor()\n",
    "\n",
    "print(f\"\\nText: {parsed_content[:100]}...\")\n",
    "\n",
    "# Simulated extraction results\n",
    "expected_entities = [\n",
    "    {\"text\": \"Apple Inc.\", \"type\": \"Organization\", \"start\": 0, \"end\": 10},\n",
    "    {\"text\": \"Steve Jobs\", \"type\": \"Person\", \"start\": 50, \"end\": 60},\n",
    "    {\"text\": \"Steve Wozniak\", \"type\": \"Person\", \"start\": 62, \"end\": 75},\n",
    "    {\"text\": \"Ronald Wayne\", \"type\": \"Person\", \"start\": 81, \"end\": 93},\n",
    "    {\"text\": \"1976\", \"type\": \"Date\", \"start\": 97, \"end\": 101},\n",
    "    {\"text\": \"Cupertino, California\", \"type\": \"Location\", \"start\": 130, \"end\": 151},\n",
    "    {\"text\": \"Tim Cook\", \"type\": \"Person\", \"start\": 153, \"end\": 161},\n",
    "]\n",
    "\n",
    "for entity in expected_entities:\n",
    "    print(f\"  - {entity['text']} ({entity['type']})\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🕸️ Step 4: Build the Knowledge Graph\n",
    "\n",
    "Using the extracted entities and relationships, we'll construct a knowledge graph. The graph represents entities as nodes and relationships as edges.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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"
     ]
    }
   ],
   "source": [
    "from semantica.kg import GraphBuilder\n",
    "import networkx as nx\n",
    "\n",
    "builder = GraphBuilder()\n",
    "\n",
    "# Prepare data for graph construction\n",
    "entities_data = [\n",
    "    {\"id\": f\"entity_{i}\", \"name\": entity[\"text\"], \"type\": entity[\"type\"]}\n",
    "    for i, entity in enumerate(expected_entities)\n",
    "]\n",
    "\n",
    "relationships_data = [\n",
    "    {\"source\": \"entity_0\", \"target\": \"entity_1\", \"type\": \"founded_by\"},\n",
    "    {\"source\": \"entity_0\", \"target\": \"entity_2\", \"type\": \"founded_by\"},\n",
    "    {\"source\": \"entity_0\", \"target\": \"entity_3\", \"type\": \"founded_by\"},\n",
    "    {\"source\": \"entity_0\", \"target\": \"entity_4\", \"type\": \"founded_in\"},\n",
    "    {\"source\": \"entity_0\", \"target\": \"entity_5\", \"type\": \"located_in\"},\n",
    "    {\"source\": \"entity_6\", \"target\": \"entity_0\", \"type\": \"ceo_of\"},\n",
    "]\n",
    "\n",
    "# Build the graph using NetworkX\n",
    "kg = nx.DiGraph()\n",
    "\n",
    "for entity in entities_data:\n",
    "    kg.add_node(entity[\"id\"], name=entity[\"name\"], type=entity[\"type\"])\n",
    "\n",
    "for rel in relationships_data:\n",
    "    source_name = entities_data[int(rel[\"source\"].split(\"_\")[1])][\"name\"]\n",
    "    target_name = entities_data[int(rel[\"target\"].split(\"_\")[1])][\"name\"]\n",
    "    kg.add_edge(rel[\"source\"], rel[\"target\"], type=rel[\"type\"])\n",
    "\n",
    "print(f\"  Nodes (entities): {len(kg.nodes)}\")\n",
    "print(f\"  Edges (relationships): {len(kg.edges)}\")\n",
    "\n",
    "for node_id in kg.nodes():\n",
    "    node_data = kg.nodes[node_id]\n",
    "    print(f\"  Node: {node_data['name']} ({node_data['type']})\")\n",
    "\n",
    "for source, target, data in kg.edges(data=True):\n",
    "    source_name = kg.nodes[source]['name']\n",
    "    target_name = kg.nodes[target]['name']\n",
    "    print(f\"  {source_name} --[{data['type']}]--> {target_name}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📊 Step 5: Visualize and Analyze\n",
    "\n",
    "Finally, we'll visualize the knowledge graph and analyze its structure. This helps you understand the relationships and entities in your data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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"
     ]
    }
   ],
   "source": [
    "from semantica.visualization import KGVisualizer\n",
    "\n",
    "visualizer = KGVisualizer()\n",
    "\n",
    "print(f\"  Total entities: {len(kg.nodes)}\")\n",
    "print(f\"  Total relationships: {len(kg.edges)}\")\n",
    "\n",
    "entity_types = {}\n",
    "for node_id in kg.nodes():\n",
    "    entity_type = kg.nodes[node_id]['type']\n",
    "    entity_types[entity_type] = entity_types.get(entity_type, 0) + 1\n",
    "\n",
    "for etype, count in entity_types.items():\n",
    "    print(f\"  - {etype}: {count}\")\n",
    "\n",
    "rel_types = {}\n",
    "for _, _, data in kg.edges(data=True):\n",
    "    rel_type = data.get('type', 'unknown')\n",
    "    rel_types[rel_type] = rel_types.get(rel_type, 0) + 1\n",
    "\n",
    "for rtype, count in rel_types.items():\n",
    "    print(f\"  - {rtype}: {count}\")\n",
    "\n",
    "# Cleanup\n",
    "if sample_file.exists():\n",
    "    sample_file.unlink()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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