#html #data_pipelines #deep_learning #document_ai #document_image_analysis #document_image_processing #document_parser #document_parsing #docx #donut #information_retrieval #langchain #machine_learning #ml #natural_language_processing #nlp #ocr #pdf #pdf_to_json #pdf_to_text #preprocessing

https://github.com/Unstructured-IO/unstructured

#python #document_ai #document_image_analysis #document_layout_analysis #document_parser #document_understanding #layoutlm #nlp #ocr #publaynet #pubtabnet #pytorch #table_detection #table_recognition #tensorflow

https://github.com/deepdoctection/deepdoctection

#python #agent #agents #ai_search #chatbot #chatgpt #data_pipelines #deep_learning #document_parser #document_understanding #genai #graph #graphrag #llm #nlp #pdf_to_text #preprocessing #rag #retrieval_augmented_generation #table_structure_recognition #text2sql

RAGFlow is an open-source tool that helps businesses answer questions accurately using large language models and deep document understanding. It extracts information from various complex data formats, such as Word documents, Excel files, and web pages, and provides grounded citations to support its answers. You can try a demo online or set it up on your own server using Docker. The setup is relatively straightforward, requiring a few steps like cloning the repository, building the Docker image, and configuring the system settings. RAGFlow offers key features like template-based chunking, reduced hallucinations, and compatibility with multiple data sources, making it a powerful tool for truthful question-answering capabilities. This benefits users by providing reliable and explainable answers, streamlining their workflow, and supporting integration with their business systems.

https://github.com/infiniflow/ragflow