# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .._utils.cli import ( get_subcommand_args, str2bool, ) from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor from .utils import create_config_from_structure class PPChatOCRv4Doc(PaddleXPipelineWrapper): def __init__( self, layout_detection_model_name=None, layout_detection_model_dir=None, doc_orientation_classify_model_name=None, doc_orientation_classify_model_dir=None, doc_unwarping_model_name=None, doc_unwarping_model_dir=None, text_detection_model_name=None, text_detection_model_dir=None, textline_orientation_model_name=None, textline_orientation_model_dir=None, textline_orientation_batch_size=None, text_recognition_model_name=None, text_recognition_model_dir=None, text_recognition_batch_size=None, table_structure_recognition_model_name=None, table_structure_recognition_model_dir=None, seal_text_detection_model_name=None, seal_text_detection_model_dir=None, seal_text_recognition_model_name=None, seal_text_recognition_model_dir=None, seal_text_recognition_batch_size=None, use_doc_orientation_classify=None, use_doc_unwarping=None, use_textline_orientation=None, use_seal_recognition=None, use_table_recognition=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, text_rec_score_thresh=None, seal_det_limit_side_len=None, seal_det_limit_type=None, seal_det_thresh=None, seal_det_box_thresh=None, seal_det_unclip_ratio=None, seal_rec_score_thresh=None, retriever_config=None, mllm_chat_bot_config=None, chat_bot_config=None, **kwargs, ): params = locals().copy() params.pop("self") params.pop("kwargs") self._params = params super().__init__(**kwargs) @property def _paddlex_pipeline_name(self): return "PP-ChatOCRv4-doc" def save_vector(self, vector_info, save_path, retriever_config=None): return self.paddlex_pipeline.save_vector( vector_info=vector_info, save_path=save_path, retriever_config=retriever_config, ) def load_vector(self, data_path, retriever_config=None): return self.paddlex_pipeline.load_vector( data_path=data_path, retriever_config=retriever_config ) def load_visual_info_list(self, data_path): return self.paddlex_pipeline.load_visual_info_list(data_path=data_path) def save_visual_info_list(self, visual_info, save_path): return self.paddlex_pipeline.save_visual_info_list( visual_info=visual_info, save_path=save_path ) def visual_predict_iter( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_textline_orientation=None, use_seal_recognition=None, use_table_recognition=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, text_rec_score_thresh=None, seal_det_limit_side_len=None, seal_det_limit_type=None, seal_det_thresh=None, seal_det_box_thresh=None, seal_det_unclip_ratio=None, seal_rec_score_thresh=None, **kwargs, ): return self.paddlex_pipeline.visual_predict( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_textline_orientation=use_textline_orientation, use_seal_recognition=use_seal_recognition, use_table_recognition=use_table_recognition, layout_threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, text_det_limit_side_len=text_det_limit_side_len, text_det_limit_type=text_det_limit_type, text_det_thresh=text_det_thresh, text_det_box_thresh=text_det_box_thresh, text_det_unclip_ratio=text_det_unclip_ratio, text_rec_score_thresh=text_rec_score_thresh, seal_det_limit_side_len=seal_det_limit_side_len, seal_det_limit_type=seal_det_limit_type, seal_det_thresh=seal_det_thresh, seal_det_box_thresh=seal_det_box_thresh, seal_det_unclip_ratio=seal_det_unclip_ratio, seal_rec_score_thresh=seal_rec_score_thresh, **kwargs, ) def visual_predict( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_textline_orientation=None, use_seal_recognition=None, use_table_recognition=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, text_rec_score_thresh=None, seal_det_limit_side_len=None, seal_det_limit_type=None, seal_det_thresh=None, seal_det_box_thresh=None, seal_det_unclip_ratio=None, seal_rec_score_thresh=None, **kwargs, ): return list( self.visual_predict_iter( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_textline_orientation=use_textline_orientation, use_seal_recognition=use_seal_recognition, use_table_recognition=use_table_recognition, layout_threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, text_det_limit_side_len=text_det_limit_side_len, text_det_limit_type=text_det_limit_type, text_det_thresh=text_det_thresh, text_det_box_thresh=text_det_box_thresh, text_det_unclip_ratio=text_det_unclip_ratio, text_rec_score_thresh=text_rec_score_thresh, seal_det_limit_side_len=seal_det_limit_side_len, seal_det_limit_type=seal_det_limit_type, seal_det_thresh=seal_det_thresh, seal_det_box_thresh=seal_det_box_thresh, seal_det_unclip_ratio=seal_det_unclip_ratio, seal_rec_score_thresh=seal_rec_score_thresh, **kwargs, ) ) def build_vector( self, visual_info, *, min_characters=3500, block_size=300, flag_save_bytes_vector=False, retriever_config=None, ): return self.paddlex_pipeline.build_vector( visual_info, min_characters=min_characters, block_size=block_size, flag_save_bytes_vector=flag_save_bytes_vector, retriever_config=retriever_config, ) def mllm_pred(self, input, key_list, *, mllm_chat_bot_config=None): return self.paddlex_pipeline.mllm_pred( input, key_list, mllm_chat_bot_config=mllm_chat_bot_config, ) def chat( self, key_list, visual_info, *, use_vector_retrieval=True, vector_info=None, min_characters=3500, text_task_description=None, text_output_format=None, text_rules_str=None, text_few_shot_demo_text_content=None, text_few_shot_demo_key_value_list=None, table_task_description=None, table_output_format=None, table_rules_str=None, table_few_shot_demo_text_content=None, table_few_shot_demo_key_value_list=None, mllm_predict_info=None, mllm_integration_strategy="integration", chat_bot_config=None, retriever_config=None, ): return self.paddlex_pipeline.chat( key_list, visual_info, use_vector_retrieval=use_vector_retrieval, vector_info=vector_info, min_characters=min_characters, text_task_description=text_task_description, text_output_format=text_output_format, text_rules_str=text_rules_str, text_few_shot_demo_text_content=text_few_shot_demo_text_content, text_few_shot_demo_key_value_list=text_few_shot_demo_key_value_list, table_task_description=table_task_description, table_output_format=table_output_format, table_rules_str=table_rules_str, table_few_shot_demo_text_content=table_few_shot_demo_text_content, table_few_shot_demo_key_value_list=table_few_shot_demo_key_value_list, mllm_predict_info=mllm_predict_info, mllm_integration_strategy=mllm_integration_strategy, chat_bot_config=chat_bot_config, retriever_config=retriever_config, ) @classmethod def get_cli_subcommand_executor(cls): return PPChatOCRv4DocCLISubcommandExecutor() def _get_paddlex_config_overrides(self): STRUCTURE = { "SubPipelines.LayoutParser.SubModules.LayoutDetection.model_name": self._params[ "layout_detection_model_name" ], "SubPipelines.LayoutParser.SubModules.LayoutDetection.model_dir": self._params[ "layout_detection_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_name": self._params[ "doc_orientation_classify_model_name" ], "SubPipelines.LayoutParser.SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_dir": self._params[ "doc_orientation_classify_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_name": self._params[ "doc_unwarping_model_name" ], "SubPipelines.LayoutParser.SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_dir": self._params[ "doc_unwarping_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextDetection.model_name": self._params[ "text_detection_model_name" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextDetection.model_dir": self._params[ "text_detection_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_name": self._params[ "textline_orientation_model_name" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_dir": self._params[ "textline_orientation_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.batch_size": self._params[ "textline_orientation_batch_size" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextRecognition.model_name": self._params[ "text_recognition_model_name" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextRecognition.model_dir": self._params[ "text_recognition_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextRecognition.batch_size": self._params[ "text_recognition_batch_size" ], "SubPipelines.LayoutParser.SubPipelines.TableRecognition.SubModules.TableStructureRecognition.model_name": self._params[ "table_structure_recognition_model_name" ], "SubPipelines.LayoutParser.SubPipelines.TableRecognition.SubModules.TableStructureRecognition.model_dir": self._params[ "table_structure_recognition_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.model_name": self._params[ "seal_text_detection_model_name" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.model_dir": self._params[ "seal_text_detection_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.model_name": self._params[ "seal_text_recognition_model_name" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.model_dir": self._params[ "seal_text_recognition_model_dir" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.batch_size": self._params[ "seal_text_recognition_batch_size" ], "SubPipelines.LayoutParser.SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[ "use_doc_orientation_classify" ], "SubPipelines.LayoutParser.SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[ "use_doc_unwarping" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.use_textline_orientation": self._params[ "use_textline_orientation" ], "SubPipelines.LayoutParser.use_doc_preprocessor": self._params[ "use_doc_orientation_classify" ] or self._params["use_doc_unwarping"], "SubPipelines.LayoutParser.use_seal_recognition": self._params[ "use_seal_recognition" ], "SubPipelines.LayoutParser.use_table_recognition": self._params[ "use_table_recognition" ], "SubPipelines.LayoutParser.SubModules.LayoutDetection.threshold": self._params[ "layout_threshold" ], "SubPipelines.LayoutParser.SubModules.LayoutDetection.nms": self._params[ "layout_nms" ], "SubPipelines.LayoutParser.SubModules.LayoutDetection.unclip_ratio": self._params[ "layout_unclip_ratio" ], "SubPipelines.LayoutParser.SubModules.LayoutDetection.merge_bboxes_mode": self._params[ "layout_merge_bboxes_mode" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextDetection.limit_side_len": self._params[ "text_det_limit_side_len" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextDetection.limit_type": self._params[ "text_det_limit_type" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[ "text_det_thresh" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextDetection.box_thresh": self._params[ "text_det_box_thresh" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextDetection.unclip_ratio": self._params[ "text_det_unclip_ratio" ], "SubPipelines.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextRecognition.score_thresh": self._params[ "text_rec_score_thresh" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.limit_side_len": self._params[ "text_det_limit_side_len" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.limit_type": self._params[ "seal_det_limit_type" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.thresh": self._params[ "seal_det_thresh" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.box_thresh": self._params[ "seal_det_box_thresh" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.unclip_ratio": self._params[ "seal_det_unclip_ratio" ], "SubPipelines.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.score_thresh": self._params[ "seal_rec_score_thresh" ], "SubModules.LLM_Retriever": self._params["retriever_config"], "SubModules.MLLM_Chat": self._params["mllm_chat_bot_config"], "SubModules.LLM_Chat": self._params["chat_bot_config"], } return create_config_from_structure(STRUCTURE) class PPChatOCRv4DocCLISubcommandExecutor(PipelineCLISubcommandExecutor): @property def subparser_name(self): return "pp_chatocrv4_doc" def _update_subparser(self, subparser): subparser.add_argument( "-i", "--input", type=str, required=True, help="Input path or URL.", ) subparser.add_argument( "-k", "--keys", type=str, nargs="+", required=True, metavar="KEY", help="Keys use for information extraction.", ) subparser.add_argument( "--save_path", type=str, help="Path to the output directory.", ) subparser.add_argument( "--invoke_mllm", type=str2bool, default=False, help="Whether to invoke the multimodal large language model.", ) subparser.add_argument( "--layout_detection_model_name", type=str, help="Name of the layout detection model.", ) subparser.add_argument( "--layout_detection_model_dir", type=str, help="Path to the layout detection model directory.", ) subparser.add_argument( "--doc_orientation_classify_model_name", type=str, help="Name of the document image orientation classification model.", ) subparser.add_argument( "--doc_orientation_classify_model_dir", type=str, help="Path to the document image orientation classification model directory.", ) subparser.add_argument( "--doc_unwarping_model_name", type=str, help="Name of the text image unwarping model.", ) subparser.add_argument( "--doc_unwarping_model_dir", type=str, help="Path to the image unwarping model directory.", ) subparser.add_argument( "--text_detection_model_name", type=str, help="Name of the text detection model.", ) subparser.add_argument( "--text_detection_model_dir", type=str, help="Path to the text detection model directory.", ) subparser.add_argument( "--textline_orientation_model_name", type=str, help="Name of the text line orientation classification model.", ) subparser.add_argument( "--textline_orientation_model_dir", type=str, help="Path to the text line orientation classification model directory.", ) subparser.add_argument( "--textline_orientation_batch_size", type=int, help="Batch size for the text line orientation classification model.", ) subparser.add_argument( "--text_recognition_model_name", type=str, help="Name of the text recognition model.", ) subparser.add_argument( "--text_recognition_model_dir", type=str, help="Path to the text recognition model directory.", ) subparser.add_argument( "--text_recognition_batch_size", type=int, help="Batch size for the text recognition model.", ) subparser.add_argument( "--table_structure_recognition_model_name", type=str, help="Name of the table structure recognition model.", ) subparser.add_argument( "--table_structure_recognition_model_dir", type=str, help="Path to the table structure recognition model directory.", ) subparser.add_argument( "--seal_text_detection_model_name", type=str, help="Name of the seal text detection model.", ) subparser.add_argument( "--seal_text_detection_model_dir", type=str, help="Path to the seal text detection model directory.", ) subparser.add_argument( "--seal_text_recognition_model_name", type=str, help="Name of the seal text recognition model.", ) subparser.add_argument( "--seal_text_recognition_model_dir", type=str, help="Path to the seal text recognition model directory.", ) subparser.add_argument( "--seal_text_recognition_batch_size", type=int, help="Batch size for the seal text recognition model.", ) subparser.add_argument( "--use_doc_orientation_classify", type=str2bool, help="Whether to use document image orientation classification.", ) subparser.add_argument( "--use_doc_unwarping", type=str2bool, help="Whether to use text image unwarping.", ) subparser.add_argument( "--use_textline_orientation", type=str2bool, help="Whether to use text line orientation classification.", ) subparser.add_argument( "--use_seal_recognition", type=str2bool, help="Whether to use seal recognition.", ) subparser.add_argument( "--use_table_recognition", type=str2bool, help="Whether to use table recognition.", ) # TODO: Support dict and list types subparser.add_argument( "--layout_threshold", type=float, help="Score threshold for the layout detection model.", ) subparser.add_argument( "--layout_nms", type=str2bool, help="Whether to use NMS in layout detection.", ) subparser.add_argument( "--layout_unclip_ratio", type=float, help="Expansion coefficient for layout detection.", ) subparser.add_argument( "--layout_merge_bboxes_mode", type=str, help="Overlapping box filtering method.", ) subparser.add_argument( "--text_det_limit_side_len", type=int, help="This sets a limit on the side length of the input image for the text detection model.", ) subparser.add_argument( "--text_det_limit_type", type=str, help="This determines how the side length limit is applied to the input image before feeding it into the text deteciton model.", ) subparser.add_argument( "--text_det_thresh", type=float, help="Detection pixel threshold for the text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.", ) subparser.add_argument( "--text_det_box_thresh", type=float, help="Detection box threshold for the text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.", ) subparser.add_argument( "--text_det_unclip_ratio", type=float, help="Text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.", ) subparser.add_argument( "--text_rec_score_thresh", type=float, help="Text recognition threshold used in general OCR. Text results with scores greater than this threshold are retained.", ) subparser.add_argument( "--seal_det_limit_side_len", type=int, help="This sets a limit on the side length of the input image for the seal text detection model.", ) subparser.add_argument( "--seal_det_limit_type", type=str, help="This determines how the side length limit is applied to the input image before feeding it into the seal text deteciton model.", ) subparser.add_argument( "--seal_det_thresh", type=float, help="Detection pixel threshold for the seal text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.", ) subparser.add_argument( "--seal_det_box_thresh", type=float, help="Detection box threshold for the seal text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.", ) subparser.add_argument( "--seal_det_unclip_ratio", type=float, help="Seal text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.", ) subparser.add_argument( "--seal_rec_score_thresh", type=float, help="Seal text recognition threshold. Text results with scores greater than this threshold are retained.", ) # FIXME: Passing API key through CLI is not secure; consider using # environment variables. subparser.add_argument( "--qianfan_api_key", type=str, help="Configuration for the embedding model.", ) subparser.add_argument( "--pp_docbee_base_url", type=str, help="Configuration for the multimodal large language model.", ) def execute_with_args(self, args): params = get_subcommand_args(args) input = params.pop("input") keys = params.pop("keys") save_path = params.pop("save_path") invoke_mllm = params.pop("invoke_mllm") qianfan_api_key = params.pop("qianfan_api_key") if qianfan_api_key is not None: params["retriever_config"] = { "module_name": "retriever", "model_name": "embedding-v1", "base_url": "https://qianfan.baidubce.com/v2", "api_type": "qianfan", "api_key": qianfan_api_key, } params["chat_bot_config"] = { "module_name": "chat_bot", "model_name": "ernie-3.5-8k", "base_url": "https://qianfan.baidubce.com/v2", "api_type": "openai", "api_key": qianfan_api_key, } pp_docbee_base_url = params.pop("pp_docbee_base_url") if pp_docbee_base_url is not None: params["mllm_chat_bot_config"] = { "module_name": "chat_bot", "model_name": "PP-DocBee", # PaddleX requires endpoints such as ".../chat/completions", # which, as the parameter name suggests, are not base URLs. "base_url": pp_docbee_base_url, "api_type": "openai", "api_key": "fake_key", } chatocr = PPChatOCRv4Doc(**params) result_visual = chatocr.visual_predict_iter(input) visual_info_list = [] for res in result_visual: visual_info_list.append(res["visual_info"]) if save_path: res["layout_parsing_result"].save_all(save_path) vector_info = chatocr.build_vector(visual_info_list) if invoke_mllm: result_mllm = chatocr.mllm_pred(input, keys) mllm_predict_info = result_mllm["mllm_res"] else: mllm_predict_info = None result_chat = chatocr.chat( keys, visual_info_list, vector_info=vector_info, mllm_predict_info=mllm_predict_info, ) # Print the result to stdout for k, v in result_chat["chat_res"].items(): print(f"{k} {v}")