Update app.py

app.py

@@ -443,7 +443,6 @@ class EnhancedCognitiveProcessor(CognitiveProcessor):
         content = response.get('choices', [{}])[0].get('message', {}).get('content', '')
         node_id = self.knowledge_graph.create_node({"content": content}, "analysis")
         if self.knowledge_graph.node_counter > 1:
-            # Create a relation between the previous node and the new node
             self.knowledge_graph.create_relation(node_id - 1, node_id, "evolution", strength=0.8)
 
 # ------------------------------
@@ -520,7 +519,6 @@ class MultiModalRetriever:
         inputs = self.clip_processor(text=query, return_tensors="pt")
         with torch.no_grad():
             _ = self.clip_model.get_text_features(**inputs)
-        # Placeholder for image retrieval results
         return ["image_result_1.png", "image_result_2.png"]
 
     def _retrieve_code(self, query: str) -> List[str]:
@@ -563,13 +561,9 @@ class ResearchWorkflow:
     def ingest_query(self, state: AgentState) -> Dict:
         try:
             query = state["messages"][-1].content
-            domain = state.get("domain", "Biomedical Research")
-            new_context = {
-                "raw_query": query,
-                "domain": domain,
-                "refine_count": 0,
-                "refinement_history": []
-            }
+            # Retrieve the domain from the state's context (defaulting to Biomedical Research)
+            domain = state.get("context", {}).get("domain", "Biomedical Research")
+            new_context = {"raw_query": query, "domain": domain, "refine_count": 0, "refinement_history": []}
             logger.info(f"Query ingested. Domain: {domain}")
             return {
                 "messages": [AIMessage(content="Query ingested successfully")],