Configure dataset
Most of RAGFlow's chat assistants and Agents are based on datasets. Each of RAGFlow's datasets serves as a knowledge source, parsing files uploaded from your local machine and file references generated in RAGFlow's File system into the real 'knowledge' for future AI chats. This guide demonstrates some basic usages of the dataset feature, covering the following topics:
Set context window size
Set context window size for images and tables to improve long-context RAG performances.
Set metadata
Manually add metadata to an uploaded file
Auto-extract metadata
Automatically extract metadata from uploaded files.
Manage metadata
Manage metadata for your dataset and for your individual documents.
Configure child chunking strategy
Set parent-child chunking strategy to improve retrieval.
Select PDF parser
Select a visual model for parsing your PDFs.
Set page rank
Create a step-retrieval strategy using page rank.
Auto-keyword Auto-question
Use a chat model to generate keywords or questions from each chunk in the dataset.
Enable Excel2HTML
Convert complex Excel spreadsheets into HTML tables.
Extract table of contents
Extract table of contents (TOC) from documents to provide long context RAG and improve retrieval.
Use tag set
Use a tag set to auto-tag chunks in your datasets.
Enable RAPTOR
A recursive abstractive method used in long-context knowledge retrieval and summarization, balancing broad semantic understanding with fine details.
Construct knowledge graph
Generate a knowledge graph for your dataset.
Run retrieval test
Conduct a retrieval test on your dataset to check whether the intended chunks can be retrieved.
Add data source
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Best practices
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