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https://github.com/semantica-agi/semantica.git
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KaifAhmad1
c469f5455b
- Add new Graph_Store.ipynb introduction notebook - Update Advanced_Graph_Analytics.ipynb with graph store persistence - Update Fraud_Detection.ipynb with graph database storage - Update Transaction_Network_Analysis.ipynb with blockchain graph storage - Update Criminal_Network_Analysis.ipynb with criminal network persistence - Update Welcome_to_Semantica.ipynb with Graph Store module documentation - Update docs/cookbook.md, docs/examples.md, docs/CodeExamples.md - Sync all notebooks to docs/cookbook directory
370 lines
11 KiB
Plaintext
370 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Graph Store\n",
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"\n",
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"## Overview\n",
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"\n",
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"This notebook demonstrates how to store and query property graphs using Semantica's graph store modules. You'll learn to use `GraphStore` with multiple backends including Neo4j, KuzuDB, and FalkorDB.\n",
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"\n",
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"### Learning Objectives\n",
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"\n",
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"- Use `GraphStore` to store nodes and relationships\n",
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"- Execute Cypher queries for graph retrieval\n",
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"- Use graph analytics (shortest path, neighbors)\n",
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"- Compare different graph database backends\n",
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"\n",
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"---\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"Install the required graph database client:\n",
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"\n",
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"```bash\n",
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"# For Neo4j\n",
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"pip install neo4j\n",
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"\n",
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"# For KuzuDB (embedded - no server required)\n",
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"pip install kuzu\n",
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"\n",
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"# For FalkorDB\n",
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"pip install falkordb\n",
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"# And run: docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb\n",
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"```\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 1: Initialize Graph Store\n",
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"\n",
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"Create a graph store with your preferred backend.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from semantica.graph_store import GraphStore\n",
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"\n",
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"# Option 1: Neo4j (requires Neo4j server)\n",
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"# store = GraphStore(\n",
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"# backend=\"neo4j\",\n",
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"# uri=\"bolt://localhost:7687\",\n",
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"# user=\"neo4j\",\n",
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"# password=\"password\"\n",
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"# )\n",
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"\n",
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"# Option 2: KuzuDB (embedded - no server required)\n",
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"store = GraphStore(\n",
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" backend=\"kuzu\",\n",
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" database_path=\"./demo_graph_db\"\n",
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")\n",
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"\n",
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"# Option 3: FalkorDB (requires Redis/FalkorDB server)\n",
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"# store = GraphStore(\n",
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"# backend=\"falkordb\",\n",
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"# host=\"localhost\",\n",
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"# port=6379,\n",
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"# graph_name=\"demo_graph\"\n",
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"# )\n",
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"\n",
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"# Connect to the database\n",
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"store.connect()\n",
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"print(\"Connected to graph store!\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 2: Create Nodes\n",
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"\n",
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"Create nodes with labels and properties.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create individual nodes\n",
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"apple = store.create_node(\n",
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" labels=[\"Company\"],\n",
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" properties={\"name\": \"Apple Inc.\", \"founded\": 1976, \"industry\": \"Technology\"}\n",
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")\n",
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"print(f\"Created company node: {apple}\")\n",
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"\n",
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"tim_cook = store.create_node(\n",
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" labels=[\"Person\"],\n",
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" properties={\"name\": \"Tim Cook\", \"title\": \"CEO\", \"age\": 63}\n",
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")\n",
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"print(f\"Created person node: {tim_cook}\")\n",
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"\n",
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"cupertino = store.create_node(\n",
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" labels=[\"Location\"],\n",
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" properties={\"name\": \"Cupertino\", \"state\": \"California\", \"country\": \"USA\"}\n",
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")\n",
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"print(f\"Created location node: {cupertino}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create multiple nodes in batch\n",
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"other_companies = store.create_nodes([\n",
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" {\"labels\": [\"Company\"], \"properties\": {\"name\": \"Microsoft\", \"founded\": 1975}},\n",
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" {\"labels\": [\"Company\"], \"properties\": {\"name\": \"Google\", \"founded\": 1998}},\n",
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" {\"labels\": [\"Company\"], \"properties\": {\"name\": \"Amazon\", \"founded\": 1994}},\n",
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"])\n",
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"print(f\"Created {len(other_companies)} company nodes in batch\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 3: Create Relationships\n",
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"\n",
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"Create relationships between nodes.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create relationships\n",
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"ceo_rel = store.create_relationship(\n",
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" start_node_id=tim_cook[\"id\"],\n",
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" end_node_id=apple[\"id\"],\n",
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" rel_type=\"CEO_OF\",\n",
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" properties={\"since\": 2011}\n",
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")\n",
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"print(f\"Created CEO relationship: {ceo_rel}\")\n",
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"\n",
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"location_rel = store.create_relationship(\n",
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" start_node_id=apple[\"id\"],\n",
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" end_node_id=cupertino[\"id\"],\n",
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" rel_type=\"HEADQUARTERED_IN\",\n",
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" properties={\"since\": 1977}\n",
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")\n",
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"print(f\"Created location relationship: {location_rel}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 4: Query Nodes and Relationships\n",
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"\n",
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"Retrieve nodes and relationships from the graph.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get all Company nodes\n",
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"companies = store.get_nodes(labels=[\"Company\"], limit=10)\n",
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"print(f\"Found {len(companies)} companies:\")\n",
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"for company in companies:\n",
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" print(f\" - {company.get('properties', {}).get('name', 'Unknown')}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get relationships for a node\n",
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"relationships = store.get_relationships(node_id=apple[\"id\"], direction=\"both\")\n",
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"print(f\"Found {len(relationships)} relationships for Apple:\")\n",
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"for rel in relationships:\n",
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" print(f\" - Type: {rel.get('type')}, Properties: {rel.get('properties')}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 5: Execute Cypher Queries\n",
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"\n",
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"Use Cypher queries for complex graph operations.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Execute a Cypher query\n",
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"results = store.execute_query(\"\"\"\n",
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" MATCH (p:Person)-[r:CEO_OF]->(c:Company)\n",
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" RETURN p.name as person, c.name as company, r.since as since\n",
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"\"\"\")\n",
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"\n",
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"print(\"CEO relationships:\")\n",
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"for record in results.get(\"records\", []):\n",
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" print(f\" {record}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Parameterized query\n",
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"results = store.execute_query(\n",
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" \"MATCH (c:Company) WHERE c.founded > $year RETURN c.name, c.founded\",\n",
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" parameters={\"year\": 1990}\n",
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")\n",
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"\n",
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"print(\"Companies founded after 1990:\")\n",
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"for record in results.get(\"records\", []):\n",
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" print(f\" {record}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 6: Graph Analytics\n",
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"\n",
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"Use built-in graph analytics functions.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get neighbors of a node\n",
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"neighbors = store.get_neighbors(\n",
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" node_id=apple[\"id\"],\n",
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" direction=\"both\",\n",
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" depth=2\n",
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")\n",
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"\n",
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"print(f\"Found {len(neighbors)} neighbors (up to depth 2):\")\n",
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"for neighbor in neighbors:\n",
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" print(f\" - {neighbor.get('properties', {}).get('name', 'Unknown')}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Find shortest path (if nodes are connected)\n",
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"path = store.shortest_path(\n",
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" start_node_id=tim_cook[\"id\"],\n",
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" end_node_id=cupertino[\"id\"],\n",
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" max_depth=5\n",
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")\n",
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"\n",
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"if path:\n",
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" print(f\"Shortest path length: {path.get('length')}\")\n",
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" print(f\"Nodes in path: {len(path.get('nodes', []))}\")\n",
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"else:\n",
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" print(\"No path found\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 7: Get Graph Statistics\n",
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"\n",
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"Get statistics about the graph.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stats = store.get_stats()\n",
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"\n",
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"print(\"Graph Statistics:\")\n",
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"print(f\" Node count: {stats.get('node_count', 'N/A')}\")\n",
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"print(f\" Relationship count: {stats.get('relationship_count', 'N/A')}\")\n",
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"print(f\" Label counts: {stats.get('label_counts', {})}\")\n",
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"print(f\" Relationship types: {stats.get('relationship_type_counts', {})}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 8: Clean Up\n",
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"\n",
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"Close the connection when done.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Close the connection\n",
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"store.close()\n",
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"print(\"Connection closed.\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Summary\n",
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"\n",
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"You've learned how to use graph stores:\n",
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"\n",
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"- **GraphStore**: Unified interface for property graph databases\n",
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"- **Multiple Backends**: Neo4j, KuzuDB, FalkorDB support\n",
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"- **Node Operations**: Create, read, update, delete nodes\n",
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"- **Relationship Operations**: Create and query relationships\n",
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"- **Cypher Queries**: Execute powerful graph queries\n",
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"- **Graph Analytics**: Shortest path, neighbors, centrality\n",
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"\n",
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"### Backend Comparison\n",
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"\n",
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"| Backend | Best For | Deployment |\n",
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"|---------|----------|------------|\n",
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"| **Neo4j** | Enterprise, full features | Server/Cloud |\n",
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"| **KuzuDB** | Analytics, embedded | Embedded (no server) |\n",
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"| **FalkorDB** | LLM apps, real-time | Redis-based |\n",
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"\n",
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"Next: Learn how to visualize graphs in the Visualization notebook.\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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