routing support streaming

lang_agent/graphs/routing.py

@@ -17,6 +17,7 @@ from lang_agent.base import GraphBase
 
 from langchain.chat_models import init_chat_model
 from langchain_core.messages import SystemMessage, HumanMessage, BaseMessage
+from langchain_core.messages.base import BaseMessageChunk
 from langchain.agents import create_agent
 
 from langgraph.graph import StateGraph, START, END
@@ -80,19 +81,19 @@ class RoutingGraph(GraphBase):
         self.workflow = self._build_graph()
     
 
-    def invoke(self, *nargs, as_stream:bool=False, as_raw:bool=False, **kwargs)->str:
+    def invoke(self, *nargs, as_stream:bool=False, as_raw:bool=False, **kwargs):
         self._validate_input(*nargs, **kwargs)
 
         if as_stream:
-            # TODO： this doesn't stream the entire process, we are blind
-            for step in self.workflow.stream({"inp": nargs}, stream_mode="updates", **kwargs):
-                last_el = jax.tree.leaves(step)[-1]
-                if isinstance(last_el, str):
-                    logger.info(last_el)
-                elif isinstance(last_el, BaseMessage):
-                    last_el.pretty_print()
+            # Stream messages from the workflow
+            for chunk, metadata in self.workflow.stream({"inp": nargs}, stream_mode="messages", **kwargs):
+                node = metadata.get("langgraph_node")
+                if node != "model":
+                    continue  # skip router or other intermediate nodes
 
-            state = step
+                # Yield only the final message content chunks
+                if isinstance(chunk, (BaseMessageChunk, BaseMessage)) and getattr(chunk, "content", None):
+                    yield chunk.content
         else:
             state = self.workflow.invoke({"inp": nargs})
             
@@ -256,5 +257,25 @@ class RoutingGraph(GraphBase):
         plt.show()
 
 if __name__ == "__main__":
-    route = RoutingConfig().setup()
-    route.show_graph()
+    from dotenv import load_dotenv
+    from langchain.messages import SystemMessage, HumanMessage
+    from langchain_core.messages.base import BaseMessageChunk
+    load_dotenv()
+
+    route:RoutingGraph = RoutingConfig().setup()
+    graph = route.workflow
+
+    nargs = {
+        "messages": [SystemMessage("you are a helpful bot named jarvis"),
+                     HumanMessage("use the calculator tool to calculate 92*55 and say the answer")]
+    },{"configurable": {"thread_id": "3"}}
+    
+    for chunk, metadata in graph.stream({"inp": nargs}, stream_mode="messages"):
+        node = metadata.get("langgraph_node")
+        if node not in ("model"):
+            continue  # skip router or other intermediate nodes
+
+        # Print only the final message content
+        if isinstance(chunk, (BaseMessageChunk, BaseMessage)) and getattr(chunk, "content", None):
+            print(chunk.content, end="", flush=True)
+