chore: 🧹 removing clutter

src/experts/ingestion_agent.py

@@ -1,22 +1,31 @@
 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.")
 
-class ingestionSignature(dspy.Signature):
-    """You are going to be given dungeon masters notes, on session plans, recaps, npcs, players.
-    You must summarize these document in one sentence
-    and extract as many relevant tags aspossible as a JSON list:
-    {{'synopsis': '...', 'tags': [...]}}\n\nDocument:\n{content}"
-    /no_think
+class IngestionSignature(dspy.Signature):
     """
-
-    note: str = dspy.InputField()
-    answer: str = dspy.OutputField()
-
+    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):
-    """The Ingestion Agent is responsible for Document tagging and summarising."""
-
     def __init__(self):
-        """Initialize the Oracle with available expert tools."""
-        # self.tools = []
-        self.ingest = dspy.Predict(signature=ingestionSignature)
+        super().__init__()
+        # We use TypedPredictor to enforce the Pydantic schema
+        # 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