chore: 🧹 removing clutter

src/retrieve.py

@@ -1,67 +1,6 @@
 import sys
 import dspy
-from langchain_community.vectorstores import FAISS
-from embedding import LocalLMEmbeddings
-from pathlib import Path
-
-# --- DSPy Signature ---
-class DnDContextQA(dspy.Signature):
-    """Answer DnD campaign questions using provided snippets and full file context."""
-    context = dspy.InputField(desc="Relevant chunks and full file contents from the campaign notes.")
-    question = dspy.InputField()
-    answer = dspy.OutputField(desc="A detailed answer based on the notes, citing the source file.")
-
-# --- DSPy Module ---
-class DnDRAG(dspy.Module):
-    def __init__(self, db_path="./local_faiss_db", k=3):
-        super().__init__()
-        # 1. Setup Embeddings & Load FAISS
-        self.embeddings = LocalLMEmbeddings(
-            model="text-embedding-qwen3-embedding-8b", 
-            base_url="http://192.168.0.49:1234"
-        )
-        self.vectorstore = FAISS.load_local(
-            db_path, self.embeddings, allow_dangerous_deserialization=True
-        )
-        self.k = k
-        
-        # 2. Setup the Predictor (Chain of Thought for better reasoning)
-        self.generate_answer = dspy.ChainOfThought(DnDContextQA)
-
-    def get_full_file_content(self, file_path):
-        """Helper to read the full source file if it exists."""
-        try:
-            return Path(file_path).read_text(encoding='utf-8')
-        except Exception:
-            return ""
-
-    def forward(self, question):
-        # 1. Search for top-k chunks
-        results = self.vectorstore.similarity_search(question, k=self.k)
-        
-        # 2. Extract unique file paths to load "Full Context"
-        # This prevents the LLM from being 'blind' to the rest of a relevant session note
-        unique_paths = list(set([doc.metadata.get("full_path") for doc in results]))
-        
-        context_parts = []
-        for i, doc in enumerate(results):
-            source = doc.metadata.get("source", "Unknown")
-            context_parts.append(f"--- Chunk {i+1} from {source} ---\n{doc.page_content}")
-
-        # 3. Add the Full Content of the top match (optional, but requested!)
-        # We'll just take the top 1 file to avoid context window explosion
-        if unique_paths:
-            top_file_content = self.get_full_file_content(unique_paths[0])
-            context_parts.append(f"\n=== FULL SOURCE FILE: {Path(unique_paths[0]).name} ===\n{top_file_content[:10000]}")
-
-        # 4. Join everything into one context string
-        context_str = "\n\n".join(context_parts)
-        
-        # 5. Generate Response
-        prediction = self.generate_answer(context=context_str, question=question)
-        return dspy.Prediction(answer=prediction.answer, context=context_str)
-    
-
+from experts.dnd_agent import DnDRAG
 
 def main():
     # 1. Setup the LLM