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

src/server/app.py

@@ -113,6 +113,8 @@ async def chat_stream(request: ChatRequest):
             request.enable_background_investigation,
             request.report_style,
             request.enable_deep_thinking,
+            request.enable_clarification,
+            request.max_clarification_rounds,
         ),
         media_type="text/event-stream",
     )
@@ -288,6 +290,8 @@ async def _astream_workflow_generator(
     enable_background_investigation: bool,
     report_style: ReportStyle,
     enable_deep_thinking: bool,
+    enable_clarification: bool,
+    max_clarification_rounds: int,
 ):
     # Process initial messages
     for message in messages:
@@ -304,6 +308,8 @@ async def _astream_workflow_generator(
         "auto_accepted_plan": auto_accepted_plan,
         "enable_background_investigation": enable_background_investigation,
         "research_topic": messages[-1]["content"] if messages else "",
+        "enable_clarification": enable_clarification,
+        "max_clarification_rounds": max_clarification_rounds,
     }
 
     if not auto_accepted_plan and interrupt_feedback: