所谓 Parent Document Retriever 其实就是进一步在 Document 和 Chunk 的大小之间做 trade-off,众所周知,chunk 的大小对最终交付的 RAG 效果至关重要,与其不停的在各种 size 的 chunk 大小之间上窜下跳的找最佳实践,不如路子玩儿的再野一点,在 Document 和 Chunk 之间加一层,这样一来增加了不同大小对最终效果的...
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from...
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from...
Markdown is a structured and formatted markup language and a popular input for enabling semantic chunking in RAG (Retrieval-Augmented Generation). You can use the Markdown content from the Layout model to split documents based on paragraph boundaries, create specific chunks for tables, and...
However, most chunking strategy in RAG today is still based on text length without much consideration on document structure. There’s a high demand for semantic chunking – so how do you divide a large body of texts or documents into smaller, meaningful chunks based o...
Markdown is a structured and formatted markup language and a popular input for enabling semantic chunking in RAG (Retrieval-Augmented Generation). You can use the Markdown content from the Layout model to split documents based on paragraph boundaries, create specific chunks for tables, and ...
information, context, or semantic integrity. The text's inherent meaning guides the chunking process. In any document extraction process, the chunking strategy requires careful consideration and planning, as it significantly impacts the relevance and accuracy of query responses in ...
For this post, we use a RAG-based approach to perform in-context Q&A with documents. In the following code sample, we extract text from a document and then split the document into smaller chunks of text. Chunking is required because we may have large multi-page ...
For this post, we use a RAG-based approach to perform in-context Q&A with documents. In the following code sample, we extract text from a document and then split the document into smaller chunks of text. Chunking is required because we may have large multi-page ...
Can Document Intelligence help with semantic chunking within documents for retrieval-augmented generation? Yes. Document Intelligence can provide the building blocks to enable semantic chunking. Semantic chunking is a key step in retrieval-augmented generation (RAG) to ensure context dense chunks and re...