Refa: implement unified lazy image loading for Docx parsers (qa/manual) (#13329)

## Summary
This PR is the direct successor to the previous `docx` lazy-loading
implementation. It addresses the technical debt intentionally left out
in the last PR by fully migrating the `qa` and `manual` parsing
strategies to the new lazy-loading model.

Additionally, this PR comprehensively refactors the underlying `docx`
parsing pipeline to eliminate significant code redundancy and introduces
robust fallback mechanisms to handle completely corrupted image streams
safely.


## What's Changed

* **Centralized Abstraction (`docx_parser.py`)**: Moved the
`get_picture` extraction logic up to the `RAGFlowDocxParser` base class.
Previously, `naive`, `qa`, and `manual` parsers maintained separate,
redundant copies of this method. All downstream strategies now natively
gather raw blobs and return `LazyDocxImage` objects automatically.
* **Robust Corrupted Image Fallback (`docx_parser.py`)**: Handled edge
cases where `python-docx` encounters critically malformed magic headers.
Implemented an explicit `try-except` structure that safely intercepts
`UnrecognizedImageError` (and similar exceptions) and seamlessly falls
back to retrieving the raw binary via `getattr(related_part, "blob",
None)`, preventing parser crashes on damaged documents.

* **Legacy Code & Redundancy Purge**:
* Removed the duplicate `get_picture` methods from `naive.py`, `qa.py`,
and `manual.py`.
* Removed the standalone, immediate-decoding `concat_img` method in
`manual.py`. It has been completely replaced by the globally unified,
lazy-loading-compatible `rag.nlp.concat_img`.
* Cleaned up unused legacy imports (e.g., `PIL.Image`, docx exception
packages) across all updated strategy files.

## Scope
To keep this PR focused, I have restricted these changes strictly to the
unification of `docx` extraction logic and the lazy-load migration of
`qa` and `manual`.

## Validation & Testing
I've tested this to ensure no regressions and validated the fallback
logic:

* **Output Consistency**: Compared identical `.docx` inputs using `qa`
and `manual` strategies before and after this branch: chunk counts,
extracted text, table HTML, and attached images match perfectly.
* **Memory Footprint Drop**: Confirmed a noticeable drop in peak memory
usage when processing image-dense documents through the `qa` and
`manual` pipelines, bringing them up to parity with the `naive`
strategy's performance gains.

## Breaking Changes
* None.

rag/app/qa.py

@@ -27,7 +27,6 @@ from rag.nlp import is_english, random_choices, qbullets_category, add_positions
 from rag.nlp import rag_tokenizer, tokenize_table, concat_img
 from deepdoc.parser import PdfParser, ExcelParser, DocxParser
 from docx import Document
-from PIL import Image
 from markdown import markdown
 
 from common.float_utils import get_float
@@ -192,17 +191,6 @@ class Docx(DocxParser):
     def __init__(self):
         pass
 
-    def get_picture(self, document, paragraph):
-        img = paragraph._element.xpath('.//pic:pic')
-        if not img:
-            return None
-        img = img[0]
-        embed = img.xpath('.//a:blip/@r:embed')[0]
-        related_part = document.part.related_parts[embed]
-        image = related_part.image
-        image = Image.open(BytesIO(image.blob)).convert('RGB')
-        return image
-
     def __call__(self, filename, binary=None, from_page=0, to_page=100000, callback=None):
         self.doc = Document(
             filename) if not binary else Document(BytesIO(binary))