Creación de un gráfico de conocimiento utilizando un LLM

!pip install "mirascope[openai]" matplotlib networkx 

import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass('Enter OpenAI API Key: ')

from pydantic import BaseModel, Field

class Edge(BaseModel):
    source: str
    target: str
    relationship: str

class Node(BaseModel):
    id: str
    type: str
    properties: dict | None = None

class KnowledgeGraph(BaseModel):
    nodes: list[Node]
    edges: list[Edge]

patient_log = """
Mary called for help at 3:45 AM, reporting that she had fallen while going to the bathroom. This marks the second fall incident within a week. She complained of dizziness before the fall.

Earlier in the day, Mary was observed wandering the hallway and appeared confused when asked basic questions. She was unable to recall the names of her medications and asked the same question multiple times.

Mary skipped both lunch and dinner, stating she didn't feel hungry. When the nurse checked her room in the evening, Mary was lying in bed with mild bruising on her left arm and complained of hip pain.

Vital signs taken at 9:00 PM showed slightly elevated blood pressure and a low-grade fever (99.8°F). Nurse also noted increased forgetfulness and possible signs of dehydration.

This behavior is similar to previous episodes reported last month.
"""

from mirascope.core import openai, prompt_template

@openai.call(model="gpt-4o-mini", response_model=KnowledgeGraph)
@prompt_template(
    """
    SYSTEM:
    Extract a knowledge graph from this patient log.
    Use Nodes to represent people, symptoms, events, and observations.
    Use Edges to represent relationships like "has symptom", "reported", "noted", etc.

    The log:
    {log_text}

    Example:
    Mary said help, I've fallen.
    Node(id="Mary", type="Patient", properties={{}})
    Node(id="Fall Incident 1", type="Event", properties={{"time": "3:45 AM"}})
    Edge(source="Mary", target="Fall Incident 1", relationship="reported")
    """
)
def generate_kg(log_text: str) -> openai.OpenAIDynamicConfig:
    return {"log_text": log_text}
kg = generate_kg(patient_log)
print(kg)

@openai.call(model="gpt-4o-mini")
@prompt_template(
    """
    SYSTEM:
    Use the knowledge graph to answer the user's question.

    Graph:
    {knowledge_graph}

    USER:
    {question}
    """
)
def run(question: str, knowledge_graph: KnowledgeGraph): ...

question = "What health risks or concerns does Mary exhibit based on her recent behavior and vitals?"
print(run(question, kg))

import matplotlib.pyplot as plt
import networkx as nx

def render_graph(kg: KnowledgeGraph):
    G = nx.DiGraph()

    for node in kg.nodes:
        G.add_node(node.id, label=node.type, **(node.properties or {}))

    for edge in kg.edges:
        G.add_edge(edge.source, edge.target, label=edge.relationship)

    plt.figure(figsize=(15, 10))
    pos = nx.spring_layout(G)
    nx.draw_networkx_nodes(G, pos, node_size=2000, node_color="lightgreen")
    nx.draw_networkx_edges(G, pos, arrowstyle="->", arrowsize=20)
    nx.draw_networkx_labels(G, pos, font_size=12, font_weight="bold")
    edge_labels = nx.get_edge_attributes(G, "label")
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color="blue")
    plt.title("Healthcare Knowledge Graph", fontsize=15)
    plt.show()

render_graph(kg)