feat: integrate dots.ocr native prompt modes and 2-stage qa_pairs pipeline

Prompt Modes Integration:
- Create local_runtime.py with DOTS_PROMPT_MAP
- Map OutputMode to native dots.ocr prompt modes (prompt_layout_all_en, prompt_ocr, etc.)
- Support dict_promptmode_to_prompt from dots.ocr with fallback prompts
- Add layout_only and region modes to OutputMode enum

2-Stage Q&A Pipeline:
- Create qa_builder.py for 2-stage qa_pairs generation
- Stage 1: PARSER (dots.ocr) → raw JSON via prompt_layout_all_en
- Stage 2: LLM (DAGI Router) → Q&A pairs via mode=qa_build
- Update endpoints.py to use 2-stage pipeline for qa_pairs mode
- Add ROUTER_BASE_URL and ROUTER_TIMEOUT to config

Updates:
- Update inference.py to use local_runtime with native prompts
- Update ollama_client.py to use same prompt map
- Add PROMPT_MODES.md documentation

services/parser-service/app/schemas.py

@@ -115,7 +115,7 @@ class ParsedDocument(BaseModel):
 class ParseRequest(BaseModel):
     """Parse request"""
     doc_url: Optional[str] = Field(None, description="Document URL")
-    output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks"] = Field(
+    output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"] = Field(
         "raw_json", description="Output mode"
     )
     dao_id: Optional[str] = Field(None, description="DAO ID")