Update agents/planning.py

agents/planning.py

@@ -23,7 +23,7 @@ def planning_agent(anthropic_client, state: Dict[str, Any]) -> Dict[str, Any]:
     messages = state.get("messages", [])
     user_intent = state.get("user_intent", {})
     
-    # Add agent-specific instructions
+    # Add agent-specific instructions as system parameter
     system_message = """
     You are an AI assistant specializing in pharmaceutical data pipeline planning.
     Your job is to create a comprehensive ETL pipeline plan based on the user's intent.
@@ -50,17 +50,29 @@ def planning_agent(anthropic_client, state: Dict[str, Any]) -> Dict[str, Any]:
     - Data products: DP_SALES_DASHBOARD, DP_HCP_TARGETING
     """
     
-    # Prepare prompt for Claude
-    prompt_messages = [
-        MessageParam(role="system", content=system_message),
-        *[MessageParam(role=m["role"], content=m["content"]) for m in messages],
-        MessageParam(role="user", content=f"Based on my request, create a data pipeline plan. Here is information about available data: {context}")
-    ]
+    # Convert state messages to the format expected by Anthropic API
+    # Excluding any messages with role "system"
+    anthropic_messages = []
+    for msg in messages:
+        if msg["role"] != "system":  # Skip system messages
+            anthropic_messages.append(MessageParam(
+                role=msg["role"],
+                content=msg["content"]
+            ))
     
-    # Call Claude API
+    # Add final user message with context
+    anthropic_messages.append(
+        MessageParam(
+            role="user", 
+            content=f"Based on my request, create a data pipeline plan. Here is information about available data: {context}"
+        )
+    )
+    
+    # Call Claude API with system message as separate parameter
     response = anthropic_client.messages.create(
         model="claude-3-7-sonnet-20250219",
-        messages=prompt_messages,
+        system=system_message,
+        messages=anthropic_messages,
         max_tokens=3000
     )