ruff check and format

src/experts/ingestion_agent.py

@@ -1,31 +1,33 @@
 import dspy
 from pydantic import BaseModel, Field
-from typing import List
+
 
 # 1. Define the structure of your metadata
 class DocMetadata(BaseModel):
     synopsis: str = Field(description="A one-sentence summary of the document.")
-    tags: List[str] = Field(description="Relevant tags (NPCs, Locations, Items, Plot Points).")
-    entities: List[str] = Field(description="Key names of people, places, or factions.")
+    tags: list[str] = Field(description="Relevant tags (NPCs, Locations, Items, Plot Points).")
+    entities: list[str] = Field(description="Key names of people, places, or factions.")
+
 
 class IngestionSignature(dspy.Signature):
-    """
-    You are an expert Dungeon Master's assistant.
+    """You are an expert Dungeon Master's assistant.
     Analyze the provided notes and extract a concise synopsis and relevant metadata.
     """
+
     note: str = dspy.InputField(desc="The DM notes or session recap content.")
     # By using the Pydantic model as the type, DSPy handles the JSON formatting for you
     answer: DocMetadata = dspy.OutputField()
 
+
 class IngestionAgent(dspy.Module):
     def __init__(self):
         super().__init__()
         # We use TypedPredictor to enforce the Pydantic schema
-        # We use ChainOfThought because it helps 8B models "reason" through the tags 
+        # We use ChainOfThought because it helps 8B models "reason" through the tags
         # before committing to the final JSON structure.
         self.process = dspy.TypedPredictor(IngestionSignature)
 
     def forward(self, note: str):
         # The .answer will now be a DocMetadata object, not a string!
         prediction = self.process(note=note)
-        return prediction
+        return prediction