{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Cell 1: Markdown**\n\n# Active Graph Networks (AGN) Comprehensive Notebook\n\n## Table of Contents\n1. [Framework Overview](#Framework-Overview)\n2. [Imports and Setup](#Imports-and-Setup)\n3. [Domain and Subdomain Initialization](#Domain-and-Subdomain-Initialization)\n4. [Dynamic Relational Entities (DRE) Enhancements](#Dynamic-Relational-Entities-DRE-Enhancements)\n5. [Context Management](#Context-Management)\n6. [Query Functions with Inheritance-Based and Contextual Inference](#Query-Functions-with-Inheritance-Based-and-Contextual-Inference)\n7. [Parsing Scenarios with Enhanced NLP Integration](#Parsing-Scenarios-with-Enhanced-NLP-Integration)\n8. [Dynamic Expansion of Entities and Relationships](#Dynamic-Expansion-of-Entities-and-Relationships)\n9. [Visualization of the Dynamic Knowledge Graph](#Visualization-of-the-Dynamic-Knowledge-Graph)\n10. [Advanced Queries and AGI Support](#Advanced-Queries-and-AGI-Support)\n11. [Case Studies and Scenarios](#Case-Studies-and-Scenarios)\n12. [Integration with Artificial General Intelligence (AGI)](#Integration-with-Artificial-General-Intelligence-AGI)\n13. [Conclusion and Next Steps](#Conclusion-and-Next-Steps)\n\n---\n\n**Cell 2: Markdown**\n\n## Framework Overview\n\n**Objective:**\n\nProvide an overview of the Active Graph Networks (AGNs), their structure, purpose, and how they are enhanced with Dynamic Relational Entities (DRE) to support AGI functionalities.\n\n### Active Graph Networks (AGNs)\n\nActive Graph Networks are designed to model complex, interrelated data across multiple domains, such as Finance, Healthcare, Education, Transportation, and Public Safety. They incorporate entities, relationships, and dynamic inference capabilities, allowing for context-based responses and the ability to handle dynamic, evolving data.\n\n### Dynamic Relational Entities (DRE)\n\nDRE introduces dynamic properties to entities and relationships within the AGN, such as timestamps, statuses, and context-specific attributes. This allows the AGN to handle real-world scenarios where data changes over time or context, making it more robust and adaptable.\n\n---\n\n**Cell 3: Markdown**\n\n## Imports and Setup\n\n**Objective:**\n\nImport necessary libraries and set up the initial environment for the AGN framework.\n\n---\n\n**Cell 4: Code**\n","metadata":{}},{"cell_type":"code","source":"# Imports and Setup\n\nimport networkx as nx\nimport spacy\nimport re\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\n\n# Load spaCy English model\nnlp = spacy.load(\"en_core_web_sm\")\n\n# Initialize the main AGN graph\nagn_graph = nx.DiGraph()\n\n# Initialize context manager (will be defined later)\ncontext_manager = None","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:42.575560Z","iopub.execute_input":"2024-10-27T21:57:42.576129Z","iopub.status.idle":"2024-10-27T21:57:43.784473Z","shell.execute_reply.started":"2024-10-27T21:57:42.576082Z","shell.execute_reply":"2024-10-27T21:57:43.783401Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"markdown","source":"\n**Cell 5: Markdown**\n\n## Dynamic Relational Entities (DRE) Enhancements\n\n**Objective:**\n\nIntroduce DRE into the AGN framework by modifying node and edge definitions to include dynamic properties such as timestamps, statuses, and context-specific attributes.\n\n---\n\n**Cell 6: Code**\n","metadata":{}},{"cell_type":"code","source":"# Function to add dynamic properties to nodes\ndef add_dynamic_node(graph, node_name, **attributes):\n    dynamic_attributes = {\n        'created_at': datetime.now(),\n        'updated_at': datetime.now(),\n        'status': 'active'  # default status\n    }\n    dynamic_attributes.update(attributes)\n    graph.add_node(node_name, **dynamic_attributes)\n\n# Function to add dynamic properties to edges\ndef add_dynamic_edge(graph, source, target, **attributes):\n    dynamic_attributes = {\n        'created_at': datetime.now(),\n        'updated_at': datetime.now(),\n        'status': 'active'  # default status\n    }\n    dynamic_attributes.update(attributes)\n    graph.add_edge(source, target, **dynamic_attributes)","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:43.786884Z","iopub.execute_input":"2024-10-27T21:57:43.787267Z","iopub.status.idle":"2024-10-27T21:57:43.794966Z","shell.execute_reply.started":"2024-10-27T21:57:43.787233Z","shell.execute_reply":"2024-10-27T21:57:43.793708Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"markdown","source":"---\n\n**Cell 7: Markdown**\n\n## Domain and Subdomain Initialization\n\n**Objective:**\n\nInitialize domains and subdomains within the AGN, incorporating DRE enhancements.\n\n---\n","metadata":{}},{"cell_type":"code","source":"# Define core domains with subdomains and inheritance\ndomains = {\n    \"Finance\": {\n        \"Banking\": {\"type\": \"Service\", \"inherits_from\": \"Finance\"},\n        \"Investments\": {\"type\": \"Service\", \"inherits_from\": \"Finance\"},\n        \"Taxation\": {\"type\": \"Service\", \"inherits_from\": \"Finance\"},\n        \"Insurance\": {\"type\": \"Service\", \"inherits_from\": \"Finance\"}\n    },\n    \"Healthcare\": {\n        \"Hospitals\": {\"type\": \"Facility\", \"inherits_from\": \"Healthcare\"},\n        \"Public Health\": {\"type\": \"Service\", \"inherits_from\": \"Healthcare\"}\n    },\n    \"Education\": {\n        \"Schools\": {\"type\": \"Facility\", \"inherits_from\": \"Education\"},\n        \"Universities\": {\"type\": \"Facility\", \"inherits_from\": \"Education\"}\n    },\n    \"Transport\": {\n        \"Public Transport\": {\"type\": \"Service\", \"inherits_from\": \"Transport\"},\n        \"Logistics\": {\"type\": \"Service\", \"inherits_from\": \"Transport\"}\n    },\n    \"Public Safety\": {\n        \"Police\": {\"type\": \"Service\", \"inherits_from\": \"Public Safety\"},\n        \"Fire Department\": {\"type\": \"Service\", \"inherits_from\": \"Public Safety\"}\n    }\n}\n\n# Initialize domains and subdomains in the AGN graph with dynamic properties\nfor main_domain, subdomains in domains.items():\n    add_dynamic_node(agn_graph, main_domain, domain=True, label=main_domain)\n    for subdomain, attributes in subdomains.items():\n        add_dynamic_node(agn_graph, subdomain, **attributes)\n        add_dynamic_edge(agn_graph, subdomain, main_domain, relationship=\"inherits_from\")\n\nprint(\"Initialized Domains with Dynamic Properties:\")\nfor node, data in agn_graph.nodes(data=True):\n    print(f\"{node}: {data}\")","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:43.796473Z","iopub.execute_input":"2024-10-27T21:57:43.796903Z","iopub.status.idle":"2024-10-27T21:57:43.814283Z","shell.execute_reply.started":"2024-10-27T21:57:43.796850Z","shell.execute_reply":"2024-10-27T21:57:43.813170Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"Initialized Domains with Dynamic Properties:\nFinance: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809720), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809723), 'status': 'active', 'domain': True, 'label': 'Finance'}\nBanking: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809741), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809742), 'status': 'active', 'type': 'Service', 'inherits_from': 'Finance'}\nInvestments: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809758), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809758), 'status': 'active', 'type': 'Service', 'inherits_from': 'Finance'}\nTaxation: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809769), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809770), 'status': 'active', 'type': 'Service', 'inherits_from': 'Finance'}\nInsurance: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809780), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809780), 'status': 'active', 'type': 'Service', 'inherits_from': 'Finance'}\nHealthcare: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809791), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809792), 'status': 'active', 'domain': True, 'label': 'Healthcare'}\nHospitals: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809797), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809798), 'status': 'active', 'type': 'Facility', 'inherits_from': 'Healthcare'}\nPublic Health: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809807), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809808), 'status': 'active', 'type': 'Service', 'inherits_from': 'Healthcare'}\nEducation: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809818), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809818), 'status': 'active', 'domain': True, 'label': 'Education'}\nSchools: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809823), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809824), 'status': 'active', 'type': 'Facility', 'inherits_from': 'Education'}\nUniversities: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809833), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809834), 'status': 'active', 'type': 'Facility', 'inherits_from': 'Education'}\nTransport: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809845), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809845), 'status': 'active', 'domain': True, 'label': 'Transport'}\nPublic Transport: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809850), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809851), 'status': 'active', 'type': 'Service', 'inherits_from': 'Transport'}\nLogistics: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809860), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809861), 'status': 'active', 'type': 'Service', 'inherits_from': 'Transport'}\nPublic Safety: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809870), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809871), 'status': 'active', 'domain': True, 'label': 'Public Safety'}\nPolice: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809876), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809877), 'status': 'active', 'type': 'Service', 'inherits_from': 'Public Safety'}\nFire Department: {'created_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809886), 'updated_at': datetime.datetime(2024, 10, 27, 21, 57, 43, 809887), 'status': 'active', 'type': 'Service', 'inherits_from': 'Public Safety'}\n","output_type":"stream"}]},{"cell_type":"markdown","source":"---\n\n**Cell 9: Markdown**\n\n## Context Management\n\n**Objective:**\n\nIntroduce a context manager to handle dynamic querying based on time, location, or user roles.\n\n---","metadata":{}},{"cell_type":"code","source":"# Context Manager Class\nclass ContextManager:\n    def __init__(self):\n        self.current_time = datetime.now()\n        self.user_role = 'guest'  # default role\n    \n    def update_context(self, **kwargs):\n        for key, value in kwargs.items():\n            setattr(self, key, value)\n\n# Initialize context manager\ncontext_manager = ContextManager()\n\n# Update context as needed\ncontext_manager.update_context(user_role='admin', current_time=datetime(2023, 1, 1))","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:43.815971Z","iopub.execute_input":"2024-10-27T21:57:43.816358Z","iopub.status.idle":"2024-10-27T21:57:43.830672Z","shell.execute_reply.started":"2024-10-27T21:57:43.816320Z","shell.execute_reply":"2024-10-27T21:57:43.829642Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"markdown","source":"\n**Cell 11: Markdown**\n\n## Query Functions with Inheritance-Based and Contextual Inference\n\n**Objective:**\n\nAllow for both domain-specific and cross-domain queries, utilizing context and handling dynamic properties.\n\n---\n\n**Cell 12: Code**\n","metadata":{}},{"cell_type":"code","source":"def dynamic_query(graph, context_manager, node_type=None, **filters):\n    \"\"\"\n    Retrieves nodes based on dynamic properties and context.\n    \"\"\"\n    results = []\n    for node, data in graph.nodes(data=True):\n        match = True\n        if node_type and data.get('type') != node_type:\n            match = False\n        for key, value in filters.items():\n            if data.get(key) != value:\n                match = False\n                break\n        if match:\n            # Check if node is active within the context's time frame\n            if data.get('status') == 'active' and data.get('updated_at') <= context_manager.current_time:\n                results.append(node)\n    return results\n\n# Example usage\nfinance_services = dynamic_query(agn_graph, context_manager, node_type='Service', inherits_from='Finance')\nprint(\"\\nActive Finance Services as of Context Time:\")\nprint(finance_services)","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:43.833328Z","iopub.execute_input":"2024-10-27T21:57:43.833681Z","iopub.status.idle":"2024-10-27T21:57:43.847452Z","shell.execute_reply.started":"2024-10-27T21:57:43.833644Z","shell.execute_reply":"2024-10-27T21:57:43.846061Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stdout","text":"\nActive Finance Services as of Context Time:\n[]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"\n---\n\n**Cell 13: Markdown**\n\n## Parsing Scenarios with Enhanced NLP Integration\n\n**Objective:**\n\nImplement a more sophisticated NLP processor to extract dynamic entities, temporal expressions, and relationships.\n\n---\n\n**Cell 14: Code**","metadata":{}},{"cell_type":"code","source":"def parse_dynamic_scenario(scenario_text):\n    \"\"\"\n    Parses input scenario text to extract entities, actions, times, and relationships.\n    \"\"\"\n    doc = nlp(scenario_text)\n    entities = []\n    times = []\n    relationships = []\n\n    for ent in doc.ents:\n        if ent.label_ in ['PERSON', 'ORG', 'GPE']:\n            entities.append((ent.text, ent.label_))\n        elif ent.label_ in ['DATE', 'TIME']:\n            times.append(ent.text)\n\n    for token in doc:\n        if token.pos_ == \"VERB\":\n            subject = [child for child in token.children if child.dep_ == \"nsubj\"]\n            obj = [child for child in token.children if child.dep_ == \"dobj\"]\n            if subject and obj:\n                relationships.append((subject[0].text, token.lemma_, obj[0].text))\n\n    return entities, times, relationships\n\n# Example Scenario\nscenario = \"On January 5th, 2023, Patient A consulted Doctor B.\"\nentities, times, relationships = parse_dynamic_scenario(scenario)\nprint(\"\\nParsed Scenario:\")\nprint(\"Entities:\", entities)\nprint(\"Times:\", times)\nprint(\"Relationships:\", relationships)","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:43.848902Z","iopub.execute_input":"2024-10-27T21:57:43.849286Z","iopub.status.idle":"2024-10-27T21:57:43.871668Z","shell.execute_reply.started":"2024-10-27T21:57:43.849249Z","shell.execute_reply":"2024-10-27T21:57:43.870651Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"\nParsed Scenario:\nEntities: []\nTimes: ['January 5th, 2023']\nRelationships: []\n","output_type":"stream"}]},{"cell_type":"markdown","source":"\n\n---\n\n**Cell 15: Markdown**\n\n## Dynamic Expansion of Entities and Relationships\n\n**Objective:**\n\nEnsure AGNs can dynamically grow by adding new entities and relationships with dynamic properties as scenarios are processed.\n\n---\n\n**Cell 16: Code**\n","metadata":{}},{"cell_type":"code","source":"def integrate_dynamic_scenario(graph, scenario_text):\n    \"\"\"\n    Integrates a scenario into the AGN by parsing and adding entities and relationships with dynamic properties.\n    \"\"\"\n    entities, times, relationships = parse_dynamic_scenario(scenario_text)\n    for entity, label in entities:\n        add_dynamic_node(graph, entity, type=label, label=entity)\n    for subj, action, obj in relationships:\n        # Assuming action as a node\n        add_dynamic_node(graph, action, type='Action', label=action)\n        add_dynamic_edge(graph, subj, action, relationship='performs')\n        add_dynamic_edge(graph, action, obj, relationship='on')\n    print(\"\\nIntegrated Scenario into AGN with Dynamic Properties.\")\n\n# Integrate Example Scenario\nintegrate_dynamic_scenario(agn_graph, scenario)","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:43.873471Z","iopub.execute_input":"2024-10-27T21:57:43.873896Z","iopub.status.idle":"2024-10-27T21:57:43.891395Z","shell.execute_reply.started":"2024-10-27T21:57:43.873858Z","shell.execute_reply":"2024-10-27T21:57:43.890381Z"},"trusted":true},"execution_count":51,"outputs":[{"name":"stdout","text":"\nIntegrated Scenario into AGN with Dynamic Properties.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"---\n\n**Cell 17: Markdown**\n\n## Visualization of the Dynamic Knowledge Graph\n\n**Objective:**\n\nEnable the visual inspection of the graph, showing dynamic properties and context-based statuses.\n\n---\n\n**Cell 18: Code**\n","metadata":{}},{"cell_type":"code","source":"def visualize_dynamic_graph(graph, context_manager, title=\"Dynamic AGN Graph\"):\n    plt.figure(figsize=(12, 8))\n    pos = nx.spring_layout(graph, k=0.5, iterations=50)\n    \n    # Node colors based on status and context\n    node_colors = []\n    for node, data in graph.nodes(data=True):\n        if data.get('status') == 'active' and data.get('updated_at') <= context_manager.current_time:\n            node_colors.append(\"lightgreen\")\n        else:\n            node_colors.append(\"lightgrey\")\n    \n    nx.draw_networkx_nodes(graph, pos, node_color=node_colors, node_size=1500)\n    nx.draw_networkx_edges(graph, pos, arrowstyle='->', arrowsize=20)\n    nx.draw_networkx_labels(graph, pos, font_size=10, font_weight='bold')\n    \n    # Edge labels\n    edge_labels = {(u, v): d.get(\"relationship\", \"\") for u, v, d in graph.edges(data=True)}\n    nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels, font_color='red')\n    \n    plt.title(title)\n    plt.axis('off')\n    plt.show()\n\n# Visualize the graph\nvisualize_dynamic_graph(agn_graph, context_manager)","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:43.892647Z","iopub.execute_input":"2024-10-27T21:57:43.893052Z","iopub.status.idle":"2024-10-27T21:57:44.522225Z","shell.execute_reply.started":"2024-10-27T21:57:43.893007Z","shell.execute_reply":"2024-10-27T21:57:44.521132Z"},"trusted":true},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"---\n\n**Cell 19: Markdown**\n\n## Advanced Queries and AGI Support\n\n**Objective:**\n\nAllow queries to utilize AGI concepts, identify missing context, or additional needed details, and support dynamic inference.\n\n---\n\n**Cell 20: Code**","metadata":{}},{"cell_type":"code","source":"def advanced_dynamic_query(graph, context_manager, query_terms):\n    \"\"\"\n    Performs an advanced query by searching for multiple terms within the graph,\n    utilizing dynamic properties and AGI concepts.\n    \"\"\"\n    result_nodes = []\n    for term in query_terms:\n        for node, data in graph.nodes(data=True):\n            if term.lower() in node.lower() or term.lower() in data.get(\"label\", \"\").lower():\n                # Check if node is active within the context's time frame\n                if data.get('status') == 'active' and data.get('updated_at') <= context_manager.current_time:\n                    result_nodes.append(node)\n    return result_nodes\n\n# Example Advanced Query\nquery_terms = [\"Patient A\", \"Doctor B\", \"consulted\"]\nresults = advanced_dynamic_query(agn_graph, context_manager, query_terms)\nprint(\"\\nAdvanced Query Results:\")\nprint(results)","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:44.523711Z","iopub.execute_input":"2024-10-27T21:57:44.524110Z","iopub.status.idle":"2024-10-27T21:57:44.533479Z","shell.execute_reply.started":"2024-10-27T21:57:44.524070Z","shell.execute_reply":"2024-10-27T21:57:44.532166Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"\nAdvanced Query Results:\n[]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"\n---\n\n**Cell 21: Markdown**\n\n## Case Studies and Scenarios\n\n**Objective:**\n\nIntegrate real-world problems to simulate the framework’s application and demonstrate AGI capabilities.\n\n### Example Scenario\n\n*Scenario:*\n\n\"On March 1st, 2023, Patient A was admitted to the hospital and assigned to Doctor B. She was discharged on March 10th, 2023.\"\n\n**Implementation:**\n\n- Parse the scenario to extract entities, times, and relationships.\n- Integrate these into the AGN with dynamic properties.\n- Perform queries to retrieve information based on context.\n\n---\n\n**Cell 22: Code**","metadata":{}},{"cell_type":"code","source":"\n# New scenario\nscenario2 = \"On March 1st, 2023, Patient A was admitted to the hospital and assigned to Doctor B. She was discharged on March 10th, 2023.\"\n\n# Integrate the new scenario\nintegrate_dynamic_scenario(agn_graph, scenario2)\n\n# Update context to current date to see the current status\ncontext_manager.update_context(current_time=datetime.now())\n\n# Visualize the graph after new scenario integration\nvisualize_dynamic_graph(agn_graph, context_manager, title=\"AGN Graph After Scenario 2 Integration\")\n\n# Perform a query to find current patients\ncurrent_patients = dynamic_query(agn_graph, context_manager, type='PERSON')\nprint(\"\\nCurrent Patients:\")\nprint(current_patients)","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:44.535133Z","iopub.execute_input":"2024-10-27T21:57:44.535621Z","iopub.status.idle":"2024-10-27T21:57:45.142846Z","shell.execute_reply.started":"2024-10-27T21:57:44.535568Z","shell.execute_reply":"2024-10-27T21:57:45.141707Z"},"trusted":true},"execution_count":54,"outputs":[{"name":"stdout","text":"\nIntegrated Scenario into AGN with Dynamic Properties.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"\nCurrent Patients:\n['Doctor B.']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"\n---\n\n**Cell 23: Markdown**\n\n## Integration with Artificial General Intelligence (AGI)\n\n**Objective:**\n\nIncorporate AGI concepts such as self-evolving knowledge graphs, pattern recognition, and cross-domain learning to enhance the AGN framework.\n\n---\n\n**Cell 24: Code**","metadata":{}},{"cell_type":"code","source":"# Placeholder for AGI integration functions\ndef agi_pattern_recognition(graph, pattern):\n    \"\"\"\n    Recognize patterns within the graph to infer new relationships or entities.\n    \"\"\"\n    # Implement pattern recognition logic\n    pass\n\ndef agi_self_evolve(graph):\n    \"\"\"\n    Allow the graph to self-evolve by learning from new data and scenarios.\n    \"\"\"\n    # Implement self-evolution logic\n    pass\n\n# Example usage (functions to be implemented)\nagi_pattern_recognition(agn_graph, pattern=\"admission-discharge\")\nagi_self_evolve(agn_graph)","metadata":{"execution":{"iopub.status.busy":"2024-10-27T21:57:45.144318Z","iopub.execute_input":"2024-10-27T21:57:45.144693Z","iopub.status.idle":"2024-10-27T21:57:45.151281Z","shell.execute_reply.started":"2024-10-27T21:57:45.144655Z","shell.execute_reply":"2024-10-27T21:57:45.150064Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"markdown","source":"\n---\n\n**Cell 25: Markdown**\n\n## Conclusion and Next Steps\n\nBy integrating Dynamic Relational Entities (DRE) and incorporating AGI concepts, the AGN framework becomes more robust and capable of handling dynamic, real-world data. The enhancements allow for context-based querying, dynamic expansion, and the potential for self-evolving knowledge graphs.\n\n**Next Steps:**\n\n1. Implement AGI functions such as pattern recognition and self-evolution.\n2. Expand NLP capabilities for more complex scenario parsing.\n3. Integrate machine learning models to enhance inference mechanisms.\n4. Test the framework with more real-world scenarios and refine as needed.\n\n---\n\n**Cell 26: Markdown**\n\n## Instructions to Continue\n\n1. **Copy the above content** into a new Jupyter Notebook file (`AGN_Comprehensive_Notebook.ipynb`).\n2. **Ensure all dependencies** are installed. You can install them using:\n\n   ```bash\n   pip install networkx spacy matplotlib\n   python -m spacy download en_core_web_sm\n   ```\n\n3. **Run each cell sequentially**, verifying outputs and visualizations.\n4. **Expand the notebook** by adding more domains, nodes, relationships, and complex queries as needed.\n5. **Integrate additional NLP capabilities** or machine learning models to enhance scenario parsing and inference.\n\nFeel free to reach out if you need further assistance or refinements. Happy coding!\n\n---\n\n**Cell 27: Markdown**\n\n# References\n\n- [NetworkX Documentation](https://networkx.org/documentation/stable/)\n- [spaCy Documentation](https://spacy.io/usage)\n- [Matplotlib Documentation](https://matplotlib.org/stable/contents.html)\n\n---\n\nThis completes the comprehensive notebook with all the additions and enhancements to support AGNs and AGI. You can now copy each cell into your Jupyter Notebook and run them sequentially to build and test the framework.\n\nIf you have any questions or need further assistance with specific implementations or additional features, feel free to ask!","metadata":{}}]}