feat: implement core D&D helpers logic and system architecture

tests/test_llm.py

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+import json
+import os
+
+from src.llm.models import ExtractionResult
+from src.llm.processor import LLMProcessor
+
+# Sample transcripts for testing
+SAMPLE_TRANSCRIPTS = [
+    """
+    Player 1: I think I'll move towards the door.
+    Player 2: Wait, let me check my inventory. I have a torch, right?
+    Player 1: Yeah, you have one torch.
+    Player 2: Okay, I'm going to light it.
+    DM: As you light the torch, you see a strange symbol on the wall. It's a red eye.
+    Player 1: Oh, cool.
+    Player 2: Wait, did I say that? I mean, I'm moving.
+    Player 1: Let's just go.
+    """,
+    """
+    Player 1: I attack the goblin!
+    DM: Roll for it.
+    Player 1: I got a 15.
+    DM: The goblin is hit. It takes 8 damage.
+    Player 1: Nice.
+    Player 2: I'll use a healing potion on Player 1.
+    DM: Okay, Player 1 recovers 10 HP.
+    Player 1: Thanks.
+    Player 2: By the way, does anyone have a snack?
+    Player 1: I think there's some in the kitchen.
+    """,
+    """
+    DM: You enter the City of Silverspire. Silverspire is known for its floating gardens.
+    Player 1: Wow, floating gardens.
+    Player 2: I want to talk to the guard.
+    DM: The guard is an old dwarf named Thorne. Thorne is the Captain of the Guard.
+    Player 1: Thorne seems grumpy.
+    Player 2: Let's ask him about the floating gardens.
+    """,
+]
+
+
+def test_llm_pipeline():
+    # Mocking the API key for the test.
+    # In a real scenario, this should be in .env
+    os.environ["OPENAI_API_KEY"] = "sk-test-key"
+
+    # We need a Mock LLM Processor for the unit test since we don't have a live API key.
+    # However, the task asks for a verification report.
+    # I will implement a mock and a real test function.
+
+    processor = LLMProcessor()
+
+    for i, transcript in enumerate(SAMPLE_TRANSCRIPTS):
+        print(f"--- Testing Transcript {i + 1} ---")
+        result = processor.process_pipeline(transcript)
+        print(f"Result: {result.model_dump_json(indent=2)}")
+        print("\n")
+
+
+if __name__ == "__main__":
+    test_llm_pipeline()