1.0 Release

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