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### What problem does this PR solve? - Running DeepDoc OCR on large PDFs inside the GPU docker-compose setup would intermittently fail with [ONNXRuntimeError] ... p2o.Clip.6 ... Available memory of 0 is smaller than requested bytes ... - Root cause: load_model() in deepdoc/vision/ocr.py treated device_id=None as-is. torch.cuda.device_count() > device_id then raised a TypeError, the helper returned False, and ONNXRuntime quietly fell back to CPUExecutionProvider with the hard-coded 512 MB limit, which then triggered the allocator failure. - Environment where this reproduces: Windows 11, AMD 5900x, 64 GB RAM, RTX 3090 (24 GB), docker-compose-gpu.yml from upstream, default DeepDoc + GraphRAG parser settings, ingesting heavy PDF such as 《内科学》（第10版）.pdf (~180 MB). Fixes: - Normalize device_id to 0 when it is None before calling any CUDA APIs, so the GPU path is considered available. - Allow configuring the CUDA provider’s memory cap via OCR_GPU_MEM_LIMIT_MB (default 2048 MB) and expose OCR_ARENA_EXTEND_STRATEGY; the calculated byte limit is logged to confirm the effective settings. After the change, ragflow_server.log shows for example load_model ... uses GPU (device 0, gpu_mem_limit=21474836480, arena_strategy=kNextPowerOfTwo) and the same document finishes OCR without allocator errors. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)