GitLab

- Update package sources to use Aliyun mirrors for faster downloads
- Upgrade pip and install Python packages in virtual environment
- Add python3.10-dev package to Huawei NPU Dockerfile
- Update requirements file URLs to master branch- Install specific version of torch_npu in Huawei NPU Dockerfile
- Update magic-pdf installation method
- Improve modelscope installation process
- Optimize model download and configuration update steps

- Update Dockerfiles in china, global, and huawei_npu directories
- Improve wget commands by specifying output file names
- Update READMEs to reflect new Dockerfile locations

- Add Dockerfile for global setup with Ubuntu base image
- Add Dockerfile for Huawei NPU setup with Ascend base image
- Update requirements file structure:  - Rename requirements-docker.txt to docker/china/requirements.txt - Add new requirements files for global and Huawei NPU setups
- Install necessary packages and dependencies in both Dockerfiles- Set up virtual environment and install Python packages
- Download models and configure magic-pdf for both setups

- Add section for using NPU acceleration in both English and Chinese README files
- Update system requirements to include CANN environment for NPU support
- Enhance the "Quick Start" guide with NPU-related information- Modify hardware requirements to specify "Ascend 910b" for NPU acceleration

- Add `draw_char_bbox` function to `draw_bbox.py` for drawing character bounding boxes
- Integrate `draw_char_bbox` into `common.py` for use in PDF processing pipeline
- Include option to draw character bounding boxes in debug mode

style(pdf_parse_union_core_v2): remove unnecessary spaces and improve code formatting- Remove extra space in conditional statement for character spacing logic
- Adjust spacing in trigonometric checks for line direction- Improve overall code readability and consistency

- Add missing 'else' statement in OCR model selection logic
- Ensure consistent formatting of 'if' statements for better readability
- Remove unnecessary empty line in the 'app.py' file

- Remove logger.info() call for additional_ocr_params to reduce log verbosity

- Implement ONNXModelSingleton to manage ONNX models
- Modify ModifiedPaddleOCR to use ONNX models on ARM CPUs without CUDA
- Update RapidTableModel to use RapidOCR with ONNXRuntime on CPU
- Add rapidocr_onnxruntime dependency in setup.py

fix(web_api): Modify the import path of InferenceResult

refactor(pdf_parse): improve character spacing handling in PDF text extraction

- Update the logic for inserting spaces between characters- Consider the next character's position instead of the previous one
- Adjust the spacing threshold to 25% of the average character width
- Ignore spaces at the end of lines to prevent double spaces

- Update the logic for inserting spaces between characters- Consider the next character's position instead of the previous one
- Adjust the spacing threshold to 25% of the average character width
- Ignore spaces at the end of lines to prevent double spaces

- Remove use_npu variable initialization
- Comment out device assignment and npu check
- Comment out use_npu parameter in ModifiedPaddleOCR constructor

- Update `clean_memory.py` to use `torch_npu.npu` instead of `torch.npu`
- Update `model_utils.py` to use `torch_npu.npu` instead of `torch.npu`
- Simplify NPU availability check and bfloat16 support in `pdf_parse_union_core_v2.py`

- Remove upper version limit for pydantic dependency
- This change allows for the use of the latest pydantic version

fix: s3 path join method

- Add openai package to requirements-docker.txt

- Update clean_memory function to support both CUDA and NPU devices
- Implement get_device function to centralize device selection logic
- Modify model initialization and memory cleaning to use the selected device
- Update RapidTableModel to support both RapidOCR and PaddleOCR engines

- Add NPU support for memory cleaning and model initialization
- Optimize table model initialization and prediction process
- Update memory utils to support NPU
- Add language parameter for table model

build(deps): upgrade unimernet to 0.2.3

- Update unimernet from 0.2.2 to 0.2.3 in requirements-docker.txt and setup.py
- Remove torchtext/eva-decord dependency

feat(llm_aided): add title optimization feature

- Comment out logging statements for title list, title completion, and length comparison
- Improve code readability and reduce clutter by removing unused debug information

- Implement llm_aided_title function to optimize document titles using LLM
- Update pdf_parse_union_core_v2.py to include title optimization
- Modify ocr_mkcontent.py to use optimized title levels- Add openai SDK dependency in setup.py

feat(llm): add LLM-aided formula and text correction

- Add LLM-aided formula and text correction functionality
- Update config reader to include LLM-aided settings
- Create new LLM-aided processing module
- Update main processing script to incorporate LLM-aided corrections
- Modify download scripts to check for new config version

refactor(pre_proc): improve character overlap handling in spans 

- Remove remove_overlaps_chars function
- Add check_chars_is_overlap_in_span function
- Update span processing logic to handle character overlaps- Improve efficiency and readability of overlap detection

docs: add more method description

feat(demo): add demo script for PDF processing

- Create demo.py script for PDF file processing
- Implement PDF reading, classification, and inference usingOpendatalab's magic_pdf library- Add pipelines for OCR and text modes
- Include result visualization and markdown export

feat(pre_proc): add function to remove overlapping characters in spans

- Implement remove_overlaps_chars function to detect and remove overlapping characters within spans
- Integrate remove_overlaps_chars function into the PDF parsing process
- Improve character-level processing and reduce redundancy in OCR results