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

tests/integration/test_nodes.py

@@ -514,6 +514,7 @@ def mock_state_coordinator():
     return {
         "messages": [{"role": "user", "content": "test"}],
         "locale": "en-US",
+        "enable_clarification": False,
     }
 
 
@@ -1385,3 +1386,183 @@ async def test_researcher_node_without_resources(
     tools = args[3]
     assert patch_get_web_search_tool.return_value in tools
     assert result == "RESEARCHER_RESULT"
+
+
+# ============================================================================
+# Clarification Feature Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio
+async def test_clarification_workflow_integration():
+    """Test the complete clarification workflow integration."""
+    import inspect
+
+    from src.workflow import run_agent_workflow_async
+
+    # Verify that the function accepts clarification parameters
+    sig = inspect.signature(run_agent_workflow_async)
+    assert "max_clarification_rounds" in sig.parameters
+    assert "enable_clarification" in sig.parameters
+    assert "initial_state" in sig.parameters
+
+
+def test_clarification_parameters_combinations():
+    """Test various combinations of clarification parameters."""
+    from src.graph.nodes import needs_clarification
+
+    test_cases = [
+        # (enable_clarification, clarification_rounds, max_rounds, is_complete, expected)
+        (True, 0, 3, False, False),  # No rounds started
+        (True, 1, 3, False, True),  # In progress
+        (True, 2, 3, False, True),  # In progress
+        (True, 3, 3, False, True),  # At max - still waiting for last answer
+        (True, 4, 3, False, False),  # Exceeded max
+        (True, 1, 3, True, False),  # Completed
+        (False, 1, 3, False, False),  # Disabled
+    ]
+
+    for enable, rounds, max_rounds, complete, expected in test_cases:
+        state = {
+            "enable_clarification": enable,
+            "clarification_rounds": rounds,
+            "max_clarification_rounds": max_rounds,
+            "is_clarification_complete": complete,
+        }
+
+        result = needs_clarification(state)
+        assert result == expected, f"Failed for case: {state}"
+
+
+def test_handoff_tools():
+    """Test that handoff tools are properly defined."""
+    from src.graph.nodes import handoff_after_clarification, handoff_to_planner
+
+    # Test handoff_to_planner tool - use invoke() method
+    result = handoff_to_planner.invoke(
+        {"research_topic": "renewable energy", "locale": "en-US"}
+    )
+    assert result is None  # Tool should return None (no-op)
+
+    # Test handoff_after_clarification tool - use invoke() method
+    result = handoff_after_clarification.invoke({"locale": "en-US"})
+    assert result is None  # Tool should return None (no-op)
+
+
+@patch("src.graph.nodes.get_llm_by_type")
+def test_coordinator_tools_with_clarification_enabled(mock_get_llm):
+    """Test that coordinator binds correct tools when clarification is enabled."""
+    # Mock LLM response
+    mock_llm = MagicMock()
+    mock_response = MagicMock()
+    mock_response.content = "Let me clarify..."
+    mock_response.tool_calls = []
+    mock_llm.bind_tools.return_value.invoke.return_value = mock_response
+    mock_get_llm.return_value = mock_llm
+
+    # State with clarification enabled (in progress)
+    state = {
+        "messages": [{"role": "user", "content": "Tell me about something"}],
+        "enable_clarification": True,
+        "clarification_rounds": 2,
+        "max_clarification_rounds": 3,
+        "is_clarification_complete": False,
+        "clarification_history": ["response 1", "response 2"],
+        "locale": "en-US",
+        "research_topic": "",
+    }
+
+    # Mock config
+    config = {"configurable": {"resources": []}}
+
+    # Call coordinator_node
+    coordinator_node(state, config)
+
+    # Verify that LLM was called with bind_tools
+    assert mock_llm.bind_tools.called
+    bound_tools = mock_llm.bind_tools.call_args[0][0]
+
+    # Should bind 2 tools when clarification is enabled
+    assert len(bound_tools) == 2
+    tool_names = [tool.name for tool in bound_tools]
+    assert "handoff_to_planner" in tool_names
+    assert "handoff_after_clarification" in tool_names
+
+
+@patch("src.graph.nodes.get_llm_by_type")
+def test_coordinator_tools_with_clarification_disabled(mock_get_llm):
+    """Test that coordinator binds only one tool when clarification is disabled."""
+    # Mock LLM response with tool call
+    mock_llm = MagicMock()
+    mock_response = MagicMock()
+    mock_response.content = ""
+    mock_response.tool_calls = [
+        {
+            "name": "handoff_to_planner",
+            "args": {"research_topic": "test", "locale": "en-US"},
+        }
+    ]
+    mock_llm.bind_tools.return_value.invoke.return_value = mock_response
+    mock_get_llm.return_value = mock_llm
+
+    # State with clarification disabled
+    state = {
+        "messages": [{"role": "user", "content": "Tell me about something"}],
+        "enable_clarification": False,
+        "locale": "en-US",
+        "research_topic": "",
+    }
+
+    # Mock config
+    config = {"configurable": {"resources": []}}
+
+    # Call coordinator_node
+    coordinator_node(state, config)
+
+    # Verify that LLM was called with bind_tools
+    assert mock_llm.bind_tools.called
+    bound_tools = mock_llm.bind_tools.call_args[0][0]
+
+    # Should bind only 1 tool when clarification is disabled
+    assert len(bound_tools) == 1
+    assert bound_tools[0].name == "handoff_to_planner"
+
+
+@patch("src.graph.nodes.get_llm_by_type")
+def test_coordinator_empty_llm_response_corner_case(mock_get_llm):
+    """
+    Corner case test: LLM returns empty response when clarification is enabled.
+
+    This tests error handling when LLM fails to return any content or tool calls
+    in the initial state (clarification_rounds=0). The system should gracefully
+    handle this by going to __end__ instead of crashing.
+
+    Note: This is NOT a typical clarification workflow test, but rather tests
+    fault tolerance when LLM misbehaves.
+    """
+    # Mock LLM response - empty response (failure scenario)
+    mock_llm = MagicMock()
+    mock_response = MagicMock()
+    mock_response.content = ""
+    mock_response.tool_calls = []
+    mock_llm.bind_tools.return_value.invoke.return_value = mock_response
+    mock_get_llm.return_value = mock_llm
+
+    # State with clarification enabled but initial round
+    state = {
+        "messages": [{"role": "user", "content": "test"}],
+        "enable_clarification": True,
+        # clarification_rounds: 0 (default, not started)
+        "locale": "en-US",
+        "research_topic": "",
+    }
+
+    # Mock config
+    config = {"configurable": {"resources": []}}
+
+    # Call coordinator_node - should not crash
+    result = coordinator_node(state, config)
+
+    # Should gracefully handle empty response by going to __end__
+    assert result.goto == "__end__"
+    assert result.update["locale"] == "en-US"