Commit e3ef9b20 authored by myhloli's avatar myhloli

docs(readme): add link to FAQ for common issue resolution

parent 6d65855c
...@@ -168,7 +168,7 @@ magic-pdf --help ...@@ -168,7 +168,7 @@ magic-pdf --help
```python ```python
image_writer = DiskReaderWriter(local_image_dir) image_writer = DiskReaderWriter(local_image_dir)
image_dir = str(os.path.basename(local_image_dir)) image_dir = str(os.path.basename(local_image_dir))
jso_useful_key = {"_pdf_type": "", "model_list": []} jso_useful_key = {"_pdf_type": "", "model_list": model_json}
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer) pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
pipe.pipe_classify() pipe.pipe_classify()
pipe.pipe_parse() pipe.pipe_parse()
...@@ -181,7 +181,7 @@ s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint) ...@@ -181,7 +181,7 @@ s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
image_dir = "s3://img_bucket/" image_dir = "s3://img_bucket/"
s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir) s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN) pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
jso_useful_key = {"_pdf_type": "", "model_list": []} jso_useful_key = {"_pdf_type": "", "model_list": model_json}
pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli) pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
pipe.pipe_classify() pipe.pipe_classify()
pipe.pipe_parse() pipe.pipe_parse()
...@@ -191,6 +191,11 @@ md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none") ...@@ -191,6 +191,11 @@ md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
详细实现可参考 [demo.py](demo/demo.py) 详细实现可参考 [demo.py](demo/demo.py)
### 常见问题处理解答
参考 [FAQ](docs/FAQ_zh_cn.md)
# Magic-Doc # Magic-Doc
......
# 常见问题解答
##### 1.离线部署首次运行,报错urllib.error.URLError: <urlopen error [Errno 101] Network is unreachable>
首次运行需要在线下载一个小的语言检测模型,如果是离线部署需要手动下载该模型并放到指定目录。
参考:https://github.com/opendatalab/MinerU/issues/121
##### 2.在较新版本的mac上使用命令安装pip install magic-pdf[full-cpu] zsh: no matches found: magic-pdf[full-cpu]
在 macOS 上,默认的 shell 从 Bash 切换到了 Z shell,而 Z shell 对于某些类型的字符串匹配有特殊的处理逻辑,这可能导致no matches found错误。
可以通过在命令行禁用globbing特性,再尝试运行安装命令
```bash
setopt no_nomatch
pip install magic-pdf[full-cpu]
```
##### 3.在intel cpu 的mac上 安装最新版的 magic-pdf[full-cpu] (>=0.6.1) 不成功
完整功能包依赖的公式解析库unimernet限制了pytorch的最低版本为2.3.0,而pytorch官方没有为intel cpu的macOS 提供2.3.0版本的预编译包,所以会产生依赖不兼容的问题。
可以先尝试安装unimernet的老版本之后再尝试安装完整功能包的其他依赖。(为避免依赖冲突,请激活一个全新的虚拟环境)
```bash
pip install magic-pdf
pip install unimernet==0.1.0
pip install matplotlib ultralytics paddleocr==2.7.3 paddlepaddle
```
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