feat: support human in the loop

src/graph/nodes.py

@@ -2,10 +2,10 @@ import logging
 import json
 from typing import Literal, Annotated
 
-from langchain_core.messages import HumanMessage
+from langchain_core.messages import HumanMessage, AIMessage
 from langchain_core.tools import tool
 from langchain_core.runnables import RunnableConfig
-from langgraph.types import Command
+from langgraph.types import Command, interrupt
 
 from src.llms.llm import get_llm_by_type
 from src.config.agents import AGENT_LLM_MAP
@@ -31,7 +31,7 @@ def handoff_to_planner(
 
 def planner_node(
     state: State, config: RunnableConfig
-) -> Command[Literal["research_team", "reporter", "__end__"]]:
+) -> Command[Literal["human_feedback", "reporter"]]:
     """Planner node that generate the full plan."""
     logger.info("Planner generating full plan")
     configurable = Configuration.from_runnable_config(config)
@@ -42,7 +42,6 @@ def planner_node(
         )
     else:
         llm = get_llm_by_type(AGENT_LLM_MAP["planner"])
-    current_plan = state.get("current_plan", None)
     plan_iterations = state["plan_iterations"] if state.get("plan_iterations", 0) else 0
 
     # if the plan iterations is greater than the max plan iterations, return the reporter node
@@ -60,13 +59,48 @@ def planner_node(
     logger.debug(f"Current state messages: {state['messages']}")
     logger.debug(f"Planner response: {full_response}")
 
+    return Command(
+        update={
+            "messages": [AIMessage(content=full_response, name="planner")],
+            "current_plan": full_response,
+        },
+        goto="human_feedback",
+    )
+
+
+def human_feedback_node(
+    state,
+) -> Command[Literal["planner", "research_team", "reporter", "__end__"]]:
+    current_plan = state.get("current_plan", "")
+    # check if the plan is auto accepted
+    auto_accepted_plan = state.get("auto_accepted_plan", False)
+    if not auto_accepted_plan:
+        feedback = interrupt(current_plan)
+
+        # if the feedback is not accepted, return the planner node
+        if feedback and str(feedback).upper() != "[ACCEPTED]":
+            return Command(
+                update={
+                    "messages": [
+                        HumanMessage(content=feedback, name="feedback"),
+                    ],
+                },
+                goto="planner",
+            )
+        elif feedback and str(feedback).upper() == "[ACCEPTED]":
+            logger.info("Plan is accepted by user.")
+        else:
+            raise TypeError(f"Interrupt value of {feedback} is not supported.")
+
+    # if the plan is accepted, run the following node
+    plan_iterations = state["plan_iterations"] if state.get("plan_iterations", 0) else 0
     goto = "research_team"
     try:
-        full_response = repair_json_output(full_response)
+        current_plan = repair_json_output(current_plan)
         # increment the plan iterations
         plan_iterations += 1
         # parse the plan
-        new_plan = json.loads(full_response)
+        new_plan = json.loads(current_plan)
         if new_plan["has_enough_context"]:
             goto = "reporter"
     except json.JSONDecodeError:
@@ -78,8 +112,6 @@ def planner_node(
 
     return Command(
         update={
-            "messages": [HumanMessage(content=full_response, name="planner")],
-            "last_plan": current_plan,
             "current_plan": Plan.model_validate(new_plan),
             "plan_iterations": plan_iterations,
         },