Files
deer-flow/tests/unit/graph/test_builder.py
jimmyuconn1982 2510cc61de feat: Add intelligent clarification feature in coordinate step for research queries (#613)
* fix: support local models by making thought field optional in Plan model

- Make thought field optional in Plan model to fix Pydantic validation errors with local models
- Add Ollama configuration example to conf.yaml.example
- Update documentation to include local model support
- Improve planner prompt with better JSON format requirements

Fixes local model integration issues where models like qwen3:14b would fail
due to missing thought field in JSON output.

* feat: Add intelligent clarification feature for research queries

- Add multi-turn clarification process to refine vague research questions
- Implement three-dimension clarification standard (Tech/App, Focus, Scope)
- Add clarification state management in coordinator node
- Update coordinator prompt with detailed clarification guidelines
- Add UI settings to enable/disable clarification feature (disabled by default)
- Update workflow to handle clarification rounds recursively
- Add comprehensive test coverage for clarification functionality
- Update documentation with clarification feature usage guide

Key components:
- src/graph/nodes.py: Core clarification logic and state management
- src/prompts/coordinator.md: Detailed clarification guidelines
- src/workflow.py: Recursive clarification handling
- web/: UI settings integration
- tests/: Comprehensive test coverage
- docs/: Updated configuration guide

* fix: Improve clarification conversation continuity

- Add comprehensive conversation history to clarification context
- Include previous exchanges summary in system messages
- Add explicit guidelines for continuing rounds in coordinator prompt
- Prevent LLM from starting new topics during clarification
- Ensure topic continuity across clarification rounds

Fixes issue where LLM would restart clarification instead of building upon previous exchanges.

* fix: Add conversation history to clarification context

* fix: resolve clarification feature message to planer, prompt, test issues

- Optimize coordinator.md prompt template for better clarification flow
- Simplify final message sent to planner after clarification
- Fix API key assertion issues in test_search.py

* fix: Add configurable max_clarification_rounds and comprehensive tests

- Add max_clarification_rounds parameter for external configuration
- Add comprehensive test cases for clarification feature in test_app.py
- Fixes issues found during interactive mode testing where:
  - Recursive call failed due to missing initial_state parameter
  - Clarification exited prematurely at max rounds
  - Incorrect logging of max rounds reached

* Move clarification tests to test_nodes.py and add max_clarification_rounds to zh.json
2025-10-14 13:35:57 +08:00

135 lines
4.4 KiB
Python

# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# SPDX-License-Identifier: MIT
import importlib
import sys
from unittest.mock import MagicMock, patch
import pytest
import src.graph.builder as builder_mod
@pytest.fixture
def mock_state():
class Step:
def __init__(self, execution_res=None, step_type=None):
self.execution_res = execution_res
self.step_type = step_type
class Plan:
def __init__(self, steps):
self.steps = steps
return {
"Step": Step,
"Plan": Plan,
}
def test_continue_to_running_research_team_no_plan(mock_state):
state = {"current_plan": None}
assert builder_mod.continue_to_running_research_team(state) == "planner"
def test_continue_to_running_research_team_no_steps(mock_state):
state = {"current_plan": mock_state["Plan"](steps=[])}
assert builder_mod.continue_to_running_research_team(state) == "planner"
def test_continue_to_running_research_team_all_executed(mock_state):
Step = mock_state["Step"]
Plan = mock_state["Plan"]
steps = [Step(execution_res=True), Step(execution_res=True)]
state = {"current_plan": Plan(steps=steps)}
assert builder_mod.continue_to_running_research_team(state) == "planner"
def test_continue_to_running_research_team_next_researcher(mock_state):
Step = mock_state["Step"]
Plan = mock_state["Plan"]
steps = [
Step(execution_res=True),
Step(execution_res=None, step_type=builder_mod.StepType.RESEARCH),
]
state = {"current_plan": Plan(steps=steps)}
assert builder_mod.continue_to_running_research_team(state) == "researcher"
def test_continue_to_running_research_team_next_coder(mock_state):
Step = mock_state["Step"]
Plan = mock_state["Plan"]
steps = [
Step(execution_res=True),
Step(execution_res=None, step_type=builder_mod.StepType.PROCESSING),
]
state = {"current_plan": Plan(steps=steps)}
assert builder_mod.continue_to_running_research_team(state) == "coder"
def test_continue_to_running_research_team_next_coder_withresult(mock_state):
Step = mock_state["Step"]
Plan = mock_state["Plan"]
steps = [
Step(execution_res=True),
Step(execution_res=True, step_type=builder_mod.StepType.PROCESSING),
]
state = {"current_plan": Plan(steps=steps)}
assert builder_mod.continue_to_running_research_team(state) == "planner"
def test_continue_to_running_research_team_default_planner(mock_state):
Step = mock_state["Step"]
Plan = mock_state["Plan"]
steps = [Step(execution_res=True), Step(execution_res=None, step_type=None)]
state = {"current_plan": Plan(steps=steps)}
assert builder_mod.continue_to_running_research_team(state) == "planner"
@patch("src.graph.builder.StateGraph")
def test_build_base_graph_adds_nodes_and_edges(MockStateGraph):
mock_builder = MagicMock()
MockStateGraph.return_value = mock_builder
builder_mod._build_base_graph()
# Check that all nodes and edges are added
assert mock_builder.add_edge.call_count >= 2
assert mock_builder.add_node.call_count >= 8
# Now we have 2 conditional edges: research_team and coordinator
assert mock_builder.add_conditional_edges.call_count == 2
@patch("src.graph.builder._build_base_graph")
@patch("src.graph.builder.MemorySaver")
def test_build_graph_with_memory_uses_memory(MockMemorySaver, mock_build_base_graph):
mock_builder = MagicMock()
mock_build_base_graph.return_value = mock_builder
mock_memory = MagicMock()
MockMemorySaver.return_value = mock_memory
builder_mod.build_graph_with_memory()
mock_builder.compile.assert_called_once_with(checkpointer=mock_memory)
@patch("src.graph.builder._build_base_graph")
def test_build_graph_without_memory(mock_build_base_graph):
mock_builder = MagicMock()
mock_build_base_graph.return_value = mock_builder
builder_mod.build_graph()
mock_builder.compile.assert_called_once_with()
def test_graph_is_compiled():
# The graph object should be the result of build_graph()
with patch("src.graph.builder._build_base_graph") as mock_base:
mock_builder = MagicMock()
mock_base.return_value = mock_builder
mock_builder.compile.return_value = "compiled_graph"
# reload the module to re-run the graph assignment
importlib.reload(sys.modules["src.graph.builder"])
assert builder_mod.graph is not None