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fix: repair missing step_type fields in Plan validation (#653)

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* fix: resolve issue #650 - repair missing step_type fields in Plan validation

- Add step_type repair logic to validate_and_fix_plan() to auto-infer missing step_type
- Infer as 'research' when need_search=true, 'processing' when need_search=false
- Add explicit CRITICAL REQUIREMENT section to planner.md emphasizing step_type mandatory for every step
- Include validation checklist and examples showing both research and processing steps
- Add 23 comprehensive unit tests for validate_and_fix_plan() covering all scenarios
- Add 4 integration tests specifically for Issue #650 with actual Plan validation
- Prevents Pydantic ValidationError: 'Field required' for missing step_type

* Update tests/unit/graph/test_plan_validation.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update tests/unit/graph/test_plan_validation.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* update the planner.zh_CN.md with recent changes of planner.md

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Willem Jiang
2025-10-24 21:26:48 +08:00
committed by GitHub
parent 975b344ca7
commit 36bf5c9ccd
5 changed files with 794 additions and 6 deletions
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208
tests/integration/test_nodes.py
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@@ -1864,6 +1864,214 @@ def test_clarification_no_history_defaults_to_topic():
assert result.update["clarified_research_topic"] == "What is quantum computing?"
# ============================================================================
# Issue #650: Pydantic validation errors (missing step_type field)
# ============================================================================
def test_planner_node_issue_650_missing_step_type_basic():
"""Test planner_node with missing step_type fields (Issue #650)."""
from src.graph.nodes import validate_and_fix_plan
# Simulate LLM response with missing step_type (Issue #650 scenario)
llm_response = {
"locale": "en-US",
"has_enough_context": False,
"thought": "Need to gather data",
"title": "Test Plan",
"steps": [
{
"need_search": True,
"title": "Research Step",
"description": "Gather info",
# step_type MISSING - this is the issue
},
{
"need_search": False,
"title": "Processing Step",
"description": "Analyze",
# step_type MISSING
},
],
}
# Apply the fix
fixed_plan = validate_and_fix_plan(llm_response)
# Verify all steps have step_type after fix
assert isinstance(fixed_plan, dict)
assert fixed_plan["steps"][0]["step_type"] == "research"
assert fixed_plan["steps"][1]["step_type"] == "processing"
assert all("step_type" in step for step in fixed_plan["steps"])
def test_planner_node_issue_650_water_footprint_scenario():
"""Test the exact water footprint query scenario from Issue #650."""
from src.graph.nodes import validate_and_fix_plan
# Approximate the exact plan structure that caused Issue #650
# "How many liters of water are required to produce 1 kg of beef?"
llm_response = {
"locale": "en-US",
"has_enough_context": False,
"thought": "You asked about water footprint of beef - need comprehensive data gathering",
"title": "Research Plan — Water Footprint of 1 kg of Beef",
"steps": [
{
"need_search": True,
"title": "Authoritative global estimates",
"description": "Collect peer-reviewed estimates",
# MISSING step_type
},
{
"need_search": True,
"title": "System-specific data",
"description": "Gather system-level variation data",
# MISSING step_type
},
{
"need_search": False,
"title": "Synthesize estimates",
"description": "Calculate scenario-based estimates",
# MISSING step_type
},
],
}
# Apply the fix
fixed_plan = validate_and_fix_plan(llm_response)
# Verify structure - all steps should have step_type filled in
assert len(fixed_plan["steps"]) == 3
assert fixed_plan["steps"][0]["step_type"] == "research"
assert fixed_plan["steps"][1]["step_type"] == "research"
assert fixed_plan["steps"][2]["step_type"] == "processing"
assert all("step_type" in step for step in fixed_plan["steps"])
def test_planner_node_issue_650_validation_error_fixed():
"""Test that the validation error from Issue #650 is now prevented."""
from src.graph.nodes import validate_and_fix_plan
# This is the exact type of response that caused the error in Issue #650
malformed_response = {
"locale": "en-US",
"has_enough_context": False,
"title": "Test",
"thought": "Test",
"steps": [
{
"need_search": True,
"title": "Step 1",
"description": "Test description",
# Missing step_type - caused "Field required" error
},
],
}
# Before fix would raise:
# ValidationError: 1 validation error for Plan
# steps.0.step_type Field required [type=missing, ...]
# After fix should succeed without raising exception
fixed = validate_and_fix_plan(malformed_response)
# Verify the fix was applied
assert fixed["steps"][0]["step_type"] in ["research", "processing"]
assert "step_type" in fixed["steps"][0]
def test_human_feedback_node_issue_650_plan_parsing():
"""Test human_feedback_node with Issue #650 plan that has missing step_type."""
from src.graph.nodes import human_feedback_node
# Plan with missing step_type fields
state = {
"current_plan": json.dumps(
{
"locale": "en-US",
"has_enough_context": False,
"title": "Test Plan",
"thought": "Test",
"steps": [
{
"need_search": True,
"title": "Step 1",
"description": "Gather",
# MISSING step_type
},
],
}
),
"plan_iterations": 0,
"auto_accepted_plan": True,
}
config = MagicMock()
with patch(
"src.graph.nodes.Configuration.from_runnable_config",
return_value=MagicMock(enforce_web_search=False),
):
with patch("src.graph.nodes.Plan.model_validate", side_effect=lambda x: x):
with patch("src.graph.nodes.repair_json_output", side_effect=lambda x: x):
result = human_feedback_node(state, config)
# Should succeed without validation error
assert isinstance(result, Command)
assert result.goto == "research_team"
def test_plan_validation_with_all_issue_650_error_scenarios():
"""Test all variations of Issue #650 error scenarios."""
from src.graph.nodes import validate_and_fix_plan
test_scenarios = [
# Missing step_type with need_search=true
{
"steps": [
{"need_search": True, "title": "R", "description": "D"},
]
},
# Missing step_type with need_search=false
{
"steps": [
{"need_search": False, "title": "P", "description": "D"},
]
},
# Multiple missing step_types
{
"steps": [
{"need_search": True, "title": "R1", "description": "D"},
{"need_search": True, "title": "R2", "description": "D"},
{"need_search": False, "title": "P", "description": "D"},
]
},
# Mix of missing and present step_type
{
"steps": [
{"need_search": True, "title": "R", "description": "D", "step_type": "research"},
{"need_search": False, "title": "P", "description": "D"},
]
},
]
for scenario in test_scenarios:
plan = {
"locale": "en-US",
"has_enough_context": False,
"title": "Test",
"thought": "Test",
**scenario,
}
# Should not raise exception
fixed = validate_and_fix_plan(plan)
# All steps should have step_type after fix
for step in fixed["steps"]:
assert "step_type" in step
assert step["step_type"] in ["research", "processing"]
def test_clarification_skips_specific_topics():
"""Coordinator should skip clarification for already specific topics."""
from langchain_core.messages import AIMessage