Retrieval-Augmented Generation (RAG)
Retrieval Modes
When users build AI applications with multiple knowledge bases, Dify’s retrieval strategy will determine which content will be retrieved.
Since multi-path retrieval mode does not rely on the model’s inference capability or dataset descriptions, it can achieve higher-quality retrieval results when retrieving across multiple datasets. Additionally, incorporating a re-ranking step can effectively improve document retrieval effectiveness. Therefore, when creating knowledge base Q&A applications associated with multiple datasets, we recommend configuring the retrieval mode as multi-path retrieval.
Edit this page | Report an issue
Retrieval Setting
Matches all datasets based on user intent, querying related text fragments from multiple datasets simultaneously. After a re-ranking step, the best results matching the user query are selected from the multi-path query results, requiring a configured Rerank model API. In multi-path retrieval mode, the retriever searches for text content related to the user query across all datasets associated with the application merge the relevant document results from multi-path retrieval and re-ranks the retrieved documents semantically using the Rerank model. In multi-path retrieval mode, it’s recommended that the Rerank model be configured. Below is the technical flowchart for the multi-path retrieval mode:
Since multi-path retrieval mode does not rely on the model’s inference capability or dataset descriptions, it can achieve higher-quality retrieval results when retrieving across multiple datasets. Additionally, incorporating a re-ranking step can effectively improve document retrieval effectiveness. Therefore, when creating knowledge base Q&A applications associated with multiple datasets, we recommend configuring the retrieval mode as multi-path retrieval.
Edit this page | Report an issue