Making A Info Graph Using An LLM

!pip arrange "mirascope[openai]" matplotlib networkx 

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

from pydantic import BaseModel, Topic

class Edge(BaseModel):
    provide: str
    aim: str
    relationship: str

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

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

patient_log = """
Mary known as for help at 3:45 AM, reporting that she had fallen whereas going to the bathroom. This marks the second fall incident inside per week. She complained of dizziness sooner than the autumn.

Earlier throughout the day, Mary was seen wandering the hallway and appeared confused when requested major questions. She was unable to recall the names of her medicine and requested the similar question quite a lot of events.

Mary skipped every lunch and dinner, stating she didn't actually really feel hungry. When the nurse checked her room throughout the night time, Mary was lying in mattress with light bruising on her left arm and complained of hip ache.

Vital indicators taken at 9:00 PM confirmed barely elevated blood stress and a low-grade fever (99.8°F). Nurse moreover well-known elevated forgetfulness and attainable indicators of dehydration.

This habits is rather like earlier episodes reported last month.
"""

from mirascope.core import openai, prompt_template

@openai.identify(model="gpt-4o-mini", response_model=KnowledgeGraph)
@prompt_template(
    """
    SYSTEM:
    Extract a knowledge graph from this affected particular person log.
    Use Nodes to characterize of us, indicators, events, and observations.
    Use Edges to characterize relationships like "has symptom", "reported", "well-known", and plenty of others.

    The log:
    {log_text}

    Occasion:
    Mary acknowledged help, I've fallen.
    Node(id="Mary", type="Affected particular person", properties={{}})
    Node(id="Fall Incident 1", type="Event", properties={{"time": "3:45 AM"}})
    Edge(provide="Mary", aim="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.identify(model="gpt-4o-mini")
@prompt_template(
    """
    SYSTEM:
    Use the information graph to answer the particular person's question.

    Graph:
    {knowledge_graph}

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

question = "What properly being risks or points does Mary exhibit based on her present habits 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.provide, edge.aim, label=edge.relationship)

    plt.decide(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="daring")
    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 Info Graph", fontsize=15)
    plt.current()

render_graph(kg)