Add medication data example to the repository

examples/medicine_data.py

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+from pydantic import BaseModel
+from _context import simplemind as sm
+
+
+class SideEffect(BaseModel):
+    effect: str
+    severity: str  # mild, moderate, severe
+    frequency: str  # common, uncommon, rare
+
+
+class Medication(BaseModel):
+    brand_name: str
+    generic_name: str
+    drug_class: str
+    half_life: str
+    common_uses: list[str]
+    side_effects: list[SideEffect]
+    typical_dosage: str
+    warnings: list[str]
+
+
+class MedicationList(BaseModel):
+    root: list[Medication]
+
+
+# Create a session with your preferred model
+session = sm.Session(llm_provider="openai", llm_model="gpt-4o-mini")
+
+
+# Update the prompt to use an f-string with a parameter
+def get_medication_prompt(medications: list[str]) -> str:
+    return f"""
+Provide detailed medical information about {', '.join(medications)}.
+Include their generic names, drug classes, half-lives, common uses, side effects (with severity and frequency),
+typical dosages, and important warnings.
+Return the information as separate medication entries.
+"""
+
+
+# Example usage
+medications_to_lookup = ["Abilify (aripiprazole)", "Trileptal (oxcarbazepine)"]
+prompt = get_medication_prompt(medications_to_lookup)
+
+# Generate structured data for medications
+medications = session.generate_data(prompt=prompt, response_model=MedicationList)
+
+# Print the structured information
+for med in medications.root:
+    print(f"\n=== {med.brand_name} ===")
+    print(f"Generic Name: {med.generic_name}")
+    print(f"Drug Class: {med.drug_class}")
+    print(f"Half Life: {med.half_life}")
+    print("\nCommon Uses:")
+    for use in med.common_uses:
+        print(f"- {use}")
+    print("\nSide Effects:")
+    for effect in med.side_effects:
+        print(f"- {effect.effect} ({effect.severity}, {effect.frequency})")
+    print(f"\nTypical Dosage: {med.typical_dosage}")
+    print("\nWarnings:")
+    for warning in med.warnings:
+        print(f"- {warning}")