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Sep 23Tue
  1. Dify Blog

    Dify has launched the Knowledge Pipeline visualization process, which transforms enterprise messy data into high-quality LLM context.

    Dify has launched the Knowledge Pipeline visualization process, which transforms enterprise PDF, PPT, Excel, and other messy data into high-quality LLM context. This process manages data source connections, document parsing, text/image/table processing, knowledge distillation, and embedding through visual nodes, supporting over 30 local file formats, cloud storage, online documents, web crawlers, and other data sources. It provides 7 preset templates and pluggable parsing, enhancement, chunking, embedding, and loading modules, featuring real-time variable debugging capabilities.

Dec 26Thu
  1. Dify Blog65

    Dify v0.15.0 Released: Introducing Parent-Child Retrieval to Enhance Knowledge Acquisition

    Dify v0.15.0 Launches Parent-Child Retrieval Technology, Achieving Precise Retrieval by Matching Fine-Grained Child Fragments, Combined with Coarse-Grained Parent Fragments to Supplement Context, Improving RAG System's Response Accuracy and Information Richness. This Feature Supports Two Parent Fragment Splitting Modes (Paragraph/Full Text), Allows Users to Edit Parent-Child Fragments Independently and Choose Whether to Regenerate Child Fragments, While Optimizing Fragment Preview Interface to Enhance Low-Code Platform Usability.

Dec 5Tue
Nov 21Tue
  1. Dify Blog

    Dify Blog: Introducing hybrid search and re-ranking to improve the retrieval accuracy of RAG systems.

    Hybrid search and re-ranking techniques significantly improve the document recall and accuracy of RAG systems. Hybrid search combines the advantages of semantic relevance and keyword matching, while re-ranking techniques optimize the ordering of candidate results, making them suitable for scenarios such as customer service robots and corporate knowledge bases.

  2. Dify Blog

    Dify.AI v0.3.31 released: RAG technology improved by 20% over OpenAI Assistants API.

    Dify.AI v0.3.31 update enhances RAG technology through hybrid search, semantic reordering models, and multi-path retrieval, improving question-answering efficiency by 20%, significantly outperforming the OpenAI Assistants API. Ragas evaluation shows an increase of 18.44% in Ragas score, 20% improvement in context accuracy, and 35.71% increase in faithfulness.