feat: add qa_build mode, tests, and region mode support

Router Configuration:
- Add mode='qa_build' routing rule in router-config.yml
- Priority 8, uses local_qwen3_8b for Q&A generation

2-Stage Q&A Pipeline Tests:
- Create test_qa_pipeline.py with comprehensive tests
- Test prompt building, JSON parsing, router integration
- Mock DAGI Router responses for testing

Region Mode (Grounding OCR):
- Add region_bbox and region_page parameters to ParseRequest
- Support region mode in local_runtime with bbox in prompt
- Update endpoints to accept region parameters (x, y, width, height, page)
- Validate region parameters and filter pages for region mode
- Pass region_bbox through inference pipeline

Updates:
- Update local_runtime to support region_bbox in prompts
- Update inference.py to pass region_bbox to local_runtime
- Update endpoints.py to handle region mode parameters

services/parser-service/app/runtime/local_runtime.py

@@ -6,7 +6,7 @@ Maps OutputMode to dots.ocr prompt modes using dict_promptmode_to_prompt
 import os
 import tempfile
 import logging
-from typing import Literal, Optional
+from typing import Literal, Optional, Dict, Any
 
 import torch
 from transformers import AutoModelForVision2Seq, AutoProcessor
@@ -124,18 +124,29 @@ def get_model():
     return _model, _processor
 
 
-def _build_prompt(output_mode: str) -> str:
+def _build_prompt(output_mode: str, region_bbox: Optional[Dict[str, Any]] = None) -> str:
     """
     Build prompt for dots.ocr based on OutputMode
     
     Args:
         output_mode: One of "raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"
+        region_bbox: Optional bounding box for region mode {"x": float, "y": float, "width": float, "height": float}
     
     Returns:
         Prompt string for dots.ocr
     """
     prompt_key = DOTS_PROMPT_MAP.get(output_mode, "prompt_layout_all_en")
     
+    # For region mode, add bbox information to prompt
+    if output_mode == "region" and region_bbox:
+        base_prompt = FALLBACK_PROMPTS.get("prompt_grounding_ocr", "")
+        region_info = (
+            f"\n\nExtract content from the specified region:\n"
+            f"Bounding box: x={region_bbox.get('x', 0)}, y={region_bbox.get('y', 0)}, "
+            f"width={region_bbox.get('width', 0)}, height={region_bbox.get('height', 0)}"
+        )
+        return base_prompt + region_info
+    
     # Try to use native dots.ocr prompts
     if DOTS_PROMPTS_AVAILABLE and prompt_key in dict_promptmode_to_prompt:
         prompt = dict_promptmode_to_prompt[prompt_key]
@@ -167,7 +178,8 @@ def _build_messages(image_path: str, prompt: str) -> list:
 
 def _generate_from_path(
     image_path: str,
-    output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"]
+    output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"],
+    region_bbox: Optional[Dict[str, Any]] = None
 ) -> str:
     """
     Generate output from image path using dots.ocr model
@@ -180,7 +192,7 @@ def _generate_from_path(
         Generated text from model
     """
     model, processor = get_model()
-    prompt = _build_prompt(output_mode)
+    prompt = _build_prompt(output_mode, region_bbox)
     messages = _build_messages(image_path, prompt)
     
     # Apply chat template
@@ -236,7 +248,8 @@ def _generate_from_path(
 
 def parse_document_with_local(
     image_bytes: bytes,
-    output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"] = "raw_json"
+    output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"] = "raw_json",
+    region_bbox: Optional[Dict[str, Any]] = None
 ) -> str:
     """
     Parse document from image bytes using local dots.ocr model
@@ -264,7 +277,7 @@ def parse_document_with_local(
         f.write(image_bytes)
     
     try:
-        return _generate_from_path(tmp_path, output_mode)
+        return _generate_from_path(tmp_path, output_mode, region_bbox)
     finally:
         try:
             os.remove(tmp_path)