### What problem does this PR solve?
1. Break huge function into smaller pieces
2. Add unit test for the smaller pieces function
3. Layer-ed design
a. infra layer - task_context.py, recording_context.py,
write_operation_interceptor.py, ...
b. service layer - *_service.py
c. business layer - task_handler.py
4. Default behavior: use "refactor-ed version" - can switch to original
version by change env variable
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
- [x] Performance Improvement
---------
Co-authored-by: Liu An <asiro@qq.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
## Summary
- Fix `a image` → `an image` in README and log message
- Fix `colomn` → `column` in table structure recognizer comment
- Fix `formated` → `formatted` in confluence connector docstring
- Fix `tabel of content` → `table of contents` in TOC prompt
## Test plan
- [ ] Documentation and comment changes, no functional impact
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: yuj <yuj@ztjzsoft.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
**Adds a new feature that enables the LLM to extract a structured table
of contents (TOC) directly from plain text.**
_This implementation prioritizes efficiency over reasoning — the model
runs in a strictly deterministic mode (thinking disabled) to minimize
latency.
As a result, overall performance may be less optimal, but the extraction
speed and consistency are guaranteed._
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
