fix: support dense_vector from ES fields response (ES 9.x compatibility) (#13972)

fix: support dense_vector from ES fields response (ES 9.x compatibility)

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Configuration Chore (non-breaking change which updates
configuration)


## Summary by CodeRabbit

* **Bug Fixes**
* More accurate handling and unwrapping of dense-vector fields so
returned values have correct shapes.
* Field selection reliably limits returned data and falls back to
alternate result locations when needed.
* Use of consistent result IDs and tolerant handling when score values
are missing.

* **Chores / Configuration**
* Increased build memory and adjusted build-time flags for the frontend
build.
* Simplified runtime model/GPU checks and removed an automated runtime
GPU-install attempt.

* **Build Fixes**
* `web/vite.config.ts`: make `build.minify` and `build.sourcemap`
respect `VITE_MINIFY` and `VITE_BUILD_SOURCEMAP` env vars from
Dockerfile instead of hardcoding `terser` and `true`.

* **Environment**
* Allow stack version override and default the runtime image tag to
"latest".

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **Bug Fixes**
* Correct unwrapping of dense-vector fields and reliable field selection
with fallback locations.
* Consistent use of hit-level IDs and tolerant handling when score
values are missing.

* **Chores / Configuration**
* Increased frontend build memory and added build-time minify/sourcemap
flags; build minification and sourcemap now configurable.
* Removed runtime GPU detection for model initialization; force CPU
initialization.

* **Environment**
* Allow stack version override and default runtime image tag to
"latest".

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

deepdoc/parser/pdf_parser.py

@@ -38,7 +38,6 @@ from sklearn.cluster import KMeans
 from sklearn.metrics import silhouette_score
 
 from common.file_utils import get_project_base_directory
-from common.misc_utils import pip_install_torch
 from deepdoc.vision import OCR, AscendLayoutRecognizer, LayoutRecognizer, Recognizer, TableStructureRecognizer
 from rag.nlp import rag_tokenizer
 from rag.prompts.generator import vision_llm_describe_prompt
@@ -91,14 +90,9 @@ class RAGFlowPdfParser:
         self.tbl_det = TableStructureRecognizer()
 
         self.updown_cnt_mdl = xgb.Booster()
-        try:
-            pip_install_torch()
-            import torch.cuda
-
-            if torch.cuda.is_available():
-                self.updown_cnt_mdl.set_param({"device": "cuda"})
-        except Exception:
-            logging.info("No torch found.")
+        # xgboost model is very small; using CPU explicitly
+        self.updown_cnt_mdl.set_param({"device": "cpu"})
+        logging.info("updown_cnt_mdl initialized on CPU")
         try:
             model_dir = os.path.join(get_project_base_directory(), "rag/res/deepdoc")
             self.updown_cnt_mdl.load_model(os.path.join(model_dir, "updown_concat_xgb.model"))