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

README.md

@@ -223,6 +223,13 @@ DeerFlow support private knowledgebase such as ragflow and vikingdb, so that you
 
 ### Human Collaboration
 
+- 💬 **Intelligent Clarification Feature**
+  - Multi-turn dialogue to clarify vague research topics
+  - Improve research precision and report quality
+  - Reduce ineffective searches and token usage
+  - Configurable switch for flexible enable/disable control
+  - See [Configuration Guide - Clarification](./docs/configuration_guide.md#multi-turn-clarification-feature) for details
+
 - 🧠 **Human-in-the-loop**
   - Supports interactive modification of research plans using natural language
   - Supports auto-acceptance of research plans