feat(chat): Implement real-time SSE streaming with reasoning steps and improved UI indicators.

backend/nj_voter_counts_by_county.csv

@@ -0,0 +1,22 @@
+county,voters,pct_total,rank_by_size
+Bergen,637753,10.46,1
+Middlesex,531951,8.72,2
+Essex,499446,8.19,3
+Monmouth,472627,7.75,4
+Ocean,453981,7.45,5
+Hudson,374651,6.14,6
+Morris,368252,6.04,7
+Camden,359742,5.90,8
+Union,354205,5.81,9
+Burlington,340761,5.59,10
+Passaic,313061,5.13,11
+Somerset,241463,3.96,12
+Mercer,241236,3.96,13
+Gloucester,217083,3.56,14
+Atlantic,189627,3.11,15
+Sussex,110789,1.82,16
+Hunterdon,100606,1.65,17
+Cumberland,90934,1.49,18
+Warren,81642,1.34,19
+Cape May,72299,1.19,20
+Salem,45018,0.74,21

backend/src/ea_chatbot/api/routers/agent.py

@@ -11,7 +11,7 @@ from ea_chatbot.history.models import User as UserDB, Conversation
 from ea_chatbot.api.schemas import ChatRequest
 import io
 import base64
-from langchain_core.messages import BaseMessage
+from langchain_core.runnables.config import RunnableConfig
 
 router = APIRouter(prefix="/chat", tags=["agent"])
 
@@ -39,7 +39,7 @@ async def stream_agent_events(
         "dfs": {}
     }
     
-    config = {"configurable": {"thread_id": thread_id}}
+    config: RunnableConfig = {"configurable": {"thread_id": thread_id}}
     
     assistant_chunks: List[str] = []
     assistant_plots: List[bytes] = []
@@ -56,23 +56,26 @@ async def stream_agent_events(
             ):
                 kind = event.get("event")
                 name = event.get("name")
+                node_name = event.get("metadata", {}).get("langgraph_node", name)
                 data = event.get("data", {})
                 
                 # Standardize event for frontend
                 output_event = {
                     "type": kind,
                     "name": name,
+                    "node": node_name,
                     "data": data
                 }
                 
                 # Buffer assistant chunks (summarizer and researcher might stream)
-                if kind == "on_chat_model_stream" and name in ["summarizer", "researcher"]:
+                if kind == "on_chat_model_stream" and node_name in ["summarizer", "researcher", "clarification"]:
                     chunk = data.get("chunk", "")
                     # Use utility to safely extract text content from the chunk
                     chunk_data = convert_to_json_compatible(chunk)
                     if isinstance(chunk_data, dict) and "content" in chunk_data:
                         assistant_chunks.append(str(chunk_data["content"]))
                     else:
+                        # TODO: need better way to handle this
                         assistant_chunks.append(str(chunk_data))
                 
                 # Buffer and encode plots
@@ -80,7 +83,7 @@ async def stream_agent_events(
                     output = data.get("output", {})
                     if isinstance(output, dict) and "plots" in output:
                         plots = output["plots"]
-                        encoded_plots = []
+                        encoded_plots: list[str] = []
                         for fig in plots:
                             buf = io.BytesIO()
                             fig.savefig(buf, format="png")
@@ -131,7 +134,7 @@ async def stream_agent_events(
     except Exception as e:
         error_msg = f"Agent execution failed: {str(e)}"
         history_manager.add_message(thread_id, "assistant", error_msg)
-        yield f"data: {json.dumps({'type': 'error', 'message': error_msg})}\n\n"
+        yield f"data: {json.dumps({'type': 'error', 'data': {'message': error_msg}})}\n\n"
 
 @router.post("/stream")
 async def chat_stream(

backend/src/ea_chatbot/api/utils.py

@@ -52,6 +52,11 @@ def decode_access_token(token: str) -> Optional[dict]:
 
 def convert_to_json_compatible(obj: Any) -> Any:
     """Recursively convert LangChain objects, Pydantic models, and others to JSON compatible formats."""
+    # Handle known non-serializable types first to avoid recursion
+    type_name = type(obj).__name__
+    if type_name == "Figure" or type_name == "DataFrame":
+        return f"<{type_name} object>"
+
     if isinstance(obj, list):
         return [convert_to_json_compatible(item) for item in obj]
     elif isinstance(obj, dict):
@@ -91,4 +96,11 @@ def convert_to_json_compatible(obj: Any) -> Any:
         return str(obj.content)
     elif isinstance(obj, (datetime, timezone)):
         return obj.isoformat()
-    return obj
+    
+    # Final fallback for any other types that might not be JSON serializable
+    import json
+    try:
+        json.dumps(obj)
+        return obj
+    except (TypeError, OverflowError):
+        return str(obj)