1.0 Release

src/config.yaml

@@ -1,38 +0,0 @@
-# --- Connection Settings ---
-api:
-  base_url: "http://framework.tawny-bellatrix.ts.net:1234"
-  api_version: "/v1/"
-
-# --- Model Settings ---
-models:
-  enrich: "lm_studio/qwen-"
-  embedding: "text-embedding-qwen3-embedding-8b"
-  retrieval: "lm_studio/qwen/qwen3-30b-a3b-2507"
-
-# --- Ingestion Settings ---
-ingestion:
-  data_dir: "/home/cosmic/DnD"
-  db_path: "./data/dmv.db"
-  active_llms: 5
-  parallel_requests_per_llm: 2
-  chunk_size: 800
-  chunk_overlap: 100
-  embedding_batch_size: 32
-  time_file_location: "./data/time_file.txt"
-
-# ---- Agent Settings ----
-ingestion_agent: 
-  ingestion_signature: |
-    You are an expert Dungeon Master's assistant.
-    Analyze the provided notes and extract a concise synopsis and relevant metadata.
-    synopsis = A one-sentence summary of the document.
-    tags = Relevant tags (NPCs, Locations, Items, Plot Points).
-    entities = a list of Key names of people, places, or factions.
-    "note -> synopsis:str, tags: list[str], entities: list[str]"
-
-retrieval_agent:
-  retrieval_signature: |
-    You are an expert Dungeon Master's assistant.
-    Given the context and the question, answer the question.
-    Do not make things up, base all of your answers on the context.
-    Always site your sources

src/config_loader.py

@@ -1,7 +1,7 @@
 import yaml
 
 
-def load_config(config_path="src/config.yaml"):
+def load_config(config_path="config.yaml"):
     with open(config_path) as f:
         return yaml.safe_load(f)
 

src/embedding.py

@@ -6,10 +6,9 @@ CFG = load_config()
 API_BASE = CFG["api"]["base_url"]
 API_VERSION = CFG["api"]["api_version"]
 
+
 class LocalLMEmbeddings(Embeddings):
-    def __init__(
-        self, model: str, base_url: str = API_BASE, batch_size: int = 32
-    ):
+    def __init__(self, model: str, base_url: str = API_BASE, batch_size: int = 32):
         self.url = f"{base_url}/{API_VERSION}embeddings"
         self.model = model
         self.batch_size = batch_size

src/experts/ingestion_agent.py

@@ -5,11 +5,15 @@ from config_loader import load_config
 CFG = load_config()
 INGESTION_CONFIG = CFG["ingestion_agent"]
 
+
 class IngestionSignature(dspy.Signature):
-    f"{INGESTION_CONFIG["ingestion_signature"]}"
+    f"{INGESTION_CONFIG['ingestion_signature']}"
 
     note: str = dspy.InputField(desc="The DM notes or session recap content.")
-    answer: dict[str,str|List] = dspy.OutputField(desc="the metadata dictionary with the keys; synopsis, tags, entities")
+    answer: dict[str, str | List] = dspy.OutputField(
+        desc="the metadata dictionary with the keys; synopsis, tags, entities"
+    )
+
 
 class IngestionAgent(dspy.Module):
     def __init__(self):

src/experts/retrieval_agent.py

@@ -28,13 +28,12 @@ def retrieve_from_turso(embedded_question, k=5):
     rows = cur.fetchall()
     return rows
 
+
 # --- DSPy Signature ---
 class DnDContextQA(dspy.Signature):
-    f"{RETRIEVAL_CONFIG["retrieval_signature"]}"
+    f"{RETRIEVAL_CONFIG['retrieval_signature']}"
 
-    context = dspy.InputField(
-        desc="Relevant chunks and metadata from the campaign notes."
-    )
+    context = dspy.InputField(desc="Relevant chunks and metadata from the campaign notes.")
     question = dspy.InputField()
     answer = dspy.OutputField(desc="A detailed answer based on the notes, citing the source file.")
 
@@ -45,16 +44,14 @@ class DnDRAG(dspy.Module):
         self.embeddings_model = LocalLMEmbeddings(
             model=EMBEDDING_MODEL,
             base_url=API_BASE,
-            batch_size=1, # we only send 1 question at a time.
-            )
-                # Tools exposed to the ReAct loop
-        self.tools = [
-            self.load_file
-        ]
-        self.generate_answer = dspy.ReAct(signature=DnDContextQA,tools=self.tools)
+            batch_size=1,  # we only send 1 question at a time.
+        )
+        # Tools exposed to the ReAct loop
+        self.tools = [self.load_file]
+        self.generate_answer = dspy.ReAct(signature=DnDContextQA, tools=self.tools)
 
     def forward(self, question):
-        # TODO: Add step here to LLM Expand 
+        # TODO: Add step here to LLM Expand
         # given the current question, generate 3-5 distinct search queries.
         # embed all the questions
         embedded_question = self.embeddings_model._post_request(question)
@@ -66,13 +63,12 @@ class DnDRAG(dspy.Module):
         for i, row in enumerate(results):
             source = row[0]  # file_path
             synopsis = row[1]  # synopsis
-            tags = row[2]    # tags
+            tags = row[2]  # tags
             entities = row[3]  # entities
             content = row[4]  # chunk_data
 
-
             context_parts.append(f"""
---- Chunk {i+1} from {source} ---
+--- Chunk {i + 1} from {source} ---
 synopsis: {synopsis},
 tags: {tags},
 entities: {entities}
@@ -82,7 +78,7 @@ entities: {entities}
         # print('Closest embedding hits')
         # for part in context_parts:
         #     print(part)
-        
+
         context = "\n\n".join(context_parts)
 
         prediction = self.generate_answer(context=context, question=question)
@@ -97,4 +93,4 @@ entities: {entities}
             except Exception:
                 return None
         else:
-            return None
+            return None

src/retrieve.py

@@ -13,6 +13,7 @@ RETRIEVE_MODEL = CFG["models"]["retrieval"]
 API_BASE = CFG["api"]["base_url"]
 API_VERSION = CFG["api"]["api_version"]
 
+
 class CallbackHandler(BaseCallback):
     """Custom callback class for logging agent interactions."""
 
@@ -47,6 +48,7 @@ class CallbackHandler(BaseCallback):
     def _is_reasoning_output(self, outputs):
         return any(k.startswith("Thought") for k in outputs)
 
+
 def setup_logging():
     """Set up logging configuration for Merlin."""
     # Create a custom logger
@@ -60,15 +62,11 @@ def setup_logging():
     console_handler.setLevel(logging.INFO)
 
     # Create a file handler with rotation every 5MB
-    file_handler = RotatingFileHandler(
-        "dmv.log", maxBytes=5 * 1024 * 1024, backupCount=3
-    )
+    file_handler = RotatingFileHandler("data/dmv.log", maxBytes=5 * 1024 * 1024, backupCount=3)
     file_handler.setLevel(logging.DEBUG)
 
     # Create a formatter
-    formatter = logging.Formatter(
-        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    )
+    formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
 
     # Set the formatter for the handler
     console_handler.setFormatter(formatter)
@@ -129,5 +127,6 @@ def main():
         except Exception as e:
             print(f"\n⚠️ An error occurred: {e}")
 
+
 if __name__ == "__main__":
-    main()
+    main()