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Fix: clarification bugs - max rounds, locale passing, and over-clarification (#647)

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Fixes: Max rounds bug, locale passing bug, over-clarification issue

* reslove Copilot spelling comments

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
This commit is contained in:
jimmyuconn1982
2025-10-24 16:43:39 +08:00
committed by GitHub
parent 5eada04f50
commit 2001a7c223
6 changed files with 119 additions and 40 deletions
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73
src/graph/nodes.py
View File

@@ -201,17 +201,20 @@ def planner_node(
configurable = Configuration.from_runnable_config(config)
plan_iterations = state["plan_iterations"] if state.get("plan_iterations", 0) else 0
# For clarification feature: only send the final clarified question to planner
if state.get("enable_clarification", False) and state.get("clarified_question"):
# Create a clean state with only the clarified question
clean_state = {
"messages": [{"role": "user", "content": state["clarified_question"]}],
"locale": state.get("locale", "en-US"),
"research_topic": state["clarified_question"],
}
messages = apply_prompt_template("planner", clean_state, configurable, state.get("locale", "en-US"))
# For clarification feature: use the clarified research topic (complete history)
if state.get("enable_clarification", False) and state.get(
"clarified_research_topic"
):
# Modify state to use clarified research topic instead of full conversation
modified_state = state.copy()
modified_state["messages"] = [
{"role": "user", "content": state["clarified_research_topic"]}
]
modified_state["research_topic"] = state["clarified_research_topic"]
messages = apply_prompt_template("planner", modified_state, configurable, state.get("locale", "en-US"))
logger.info(
f"Clarification mode: Using clarified question: {state['clarified_question']}"
f"Clarification mode: Using clarified research topic: {state['clarified_research_topic']}"
)
else:
# Normal mode: use full conversation history
@@ -435,24 +438,38 @@ def coordinator_node(
}
)
current_response = latest_user_content or "No response"
logger.info(
"Clarification round %s/%s | topic: %s | latest user content: %s",
"Clarification round %s/%s | topic: %s | current user response: %s",
clarification_rounds,
max_clarification_rounds,
clarified_topic or initial_topic,
latest_user_content or "N/A",
current_response,
)
current_response = latest_user_content or "No response"
clarification_context = f"""Continuing clarification (round {clarification_rounds}/{max_clarification_rounds}):
User's latest response: {current_response}
Ask for remaining missing dimensions. Do NOT repeat questions or start new topics."""
messages.append({"role": "system", "content": clarification_context})
# Bind both clarification tools
# Bind both clarification tools - let LLM choose the appropriate one
tools = [handoff_to_planner, handoff_after_clarification]
# Check if we've already reached max rounds
if clarification_rounds >= max_clarification_rounds:
# Max rounds reached - force handoff by adding system instruction
logger.warning(
f"Max clarification rounds ({max_clarification_rounds}) reached. Forcing handoff to planner. Using prepared clarified topic: {clarified_topic}"
)
# Add system instruction to force handoff - let LLM choose the right tool
messages.append(
{
"role": "system",
"content": f"MAX ROUNDS REACHED. You MUST call handoff_after_clarification (not handoff_to_planner) with the appropriate locale based on the user's language and research_topic='{clarified_topic}'. Do not ask any more questions.",
}
)
response = (
get_llm_by_type(AGENT_LLM_MAP["coordinator"])
.bind_tools(tools)
@@ -474,7 +491,15 @@ def coordinator_node(
# --- Process LLM response ---
# No tool calls - LLM is asking a clarifying question
if not response.tool_calls and response.content:
if clarification_rounds < max_clarification_rounds:
# Check if we've reached max rounds - if so, force handoff to planner
if clarification_rounds >= max_clarification_rounds:
logger.warning(
f"Max clarification rounds ({max_clarification_rounds}) reached. "
"LLM didn't call handoff tool, forcing handoff to planner."
)
goto = "planner"
# Continue to final section instead of early return
else:
# Continue clarification process
clarification_rounds += 1
# Do NOT add LLM response to clarification_history - only user responses
@@ -499,20 +524,11 @@ def coordinator_node(
"clarification_history": clarification_history,
"clarified_research_topic": clarified_topic,
"is_clarification_complete": False,
"clarified_question": "",
"goto": goto,
"__interrupt__": [("coordinator", response.content)],
},
goto=goto,
)
else:
# Max rounds reached - no more questions allowed
logger.warning(
f"Max clarification rounds ({max_clarification_rounds}) reached. Handing off to planner. Using prepared clarified topic: {clarified_topic}"
)
goto = "planner"
if state.get("enable_background_investigation"):
goto = "background_investigator"
else:
# LLM called a tool (handoff) or has no content - clarification complete
if response.tool_calls:
@@ -583,11 +599,7 @@ def coordinator_node(
clarified_research_topic_value = clarified_topic or research_topic
if enable_clarification:
handoff_topic = clarified_topic or research_topic
else:
handoff_topic = research_topic
# clarified_research_topic: Complete clarified topic with all clarification rounds
return Command(
update={
"messages": messages,
@@ -598,7 +610,6 @@ def coordinator_node(
"clarification_rounds": clarification_rounds,
"clarification_history": clarification_history,
"is_clarification_complete": goto != "coordinator",
"clarified_question": handoff_topic if goto != "coordinator" else "",
"goto": goto,
},
goto=goto,

5
src/graph/types.py
View File

@@ -16,7 +16,9 @@ class State(MessagesState):
# Runtime Variables
locale: str = "en-US"
research_topic: str = ""
clarified_research_topic: str = ""
clarified_research_topic: str = (
"" # Complete/final clarified topic with all clarification rounds
)
observations: list[str] = []
resources: list[Resource] = []
plan_iterations: int = 0
@@ -33,7 +35,6 @@ class State(MessagesState):
clarification_rounds: int = 0
clarification_history: list[str] = field(default_factory=list)
is_clarification_complete: bool = False
clarified_question: str = ""
max_clarification_rounds: int = (
3 # Default: 3 rounds (only used when enable_clarification=True)
)