To enhance multi-hop question-answering, RAGFlow adds a knowledge graph construction step between data extraction and indexing, as illustrated below. This step creates additional chunks from existing ones generated by your specified chunking method.
From v0.16.0 onward, RAGFlow supports constructing a knowledge graph on a dataset, allowing you to construct a *unified* graph across multiple files within your dataset. When a newly uploaded file starts parsing, the generated graph will automatically update.
Constructing a knowledge graph requires significant memory, computational resources, and tokens.
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## Scenarios
Knowledge graphs are especially useful for multi-hop question-answering involving *nested* logic. They outperform traditional extraction approaches when you are performing question answering on books or works with complex entities and relationships.
RAPTOR (Recursive Abstractive Processing for Tree Organized Retrieval) can also be used for multi-hop question-answering tasks. See [Enable RAPTOR](./enable_raptor.md) for details. You may use either approach or both, but ensure you understand the memory, computational, and token costs involved.
The types of the entities to extract from your dataset. The default types are: **organization**, **person**, **event**, and **category**. Add or remove types to suit your specific dataset.
- **General**: Use prompts provided by [GraphRAG](https://github.com/microsoft/graphrag) to extract entities and relationships.
- **Light**: (Default) Use prompts provided by [LightRAG](https://github.com/HKUDS/LightRAG) to extract entities and relationships. This option consumes fewer tokens, less memory, and fewer computational resources.
Whether to enable entity resolution. You can think of this as an entity deduplication switch. When enabled, the LLM will combine similar entities - e.g., '2025' and 'the year of 2025', or 'IT' and 'Information Technology' - to construct a more effective graph.
In a knowledge graph, a community is a cluster of entities linked by relationships. You can have the LLM generate an abstract for each community, known as a community report. See [here](https://www.microsoft.com/en-us/research/blog/graphrag-improving-global-search-via-dynamic-community-selection/) for more information. This indicates whether to generate community reports:
1. Navigate to the **Configuration** page of your dataset and update:
- Entity types: *Required* - Specifies the entity types in the knowledge graph to generate. You don't have to stick with the default, but you need to customize them for your documents.
- Method: *Optional*
- Entity resolution: *Optional*
- Community reports: *Optional*
*The default knowledge graph configurations for your dataset are now set.*
2. Navigate to the **Files** page of your dataset, click the **Generate** button on the top right corner of the page, then select **Knowledge graph** from the dropdown to initiate the knowledge graph generation process.
*Once a knowledge graph is generated, the **Knowledge graph** field changes from `Not generated` to `Generated at a specific timestamp`. You can delete it by clicking the recycle bin button to the right of the field.*
All chunks of the created knowledge graph are stored in RAGFlow's document engine: either Elasticsearch or [Infinity](https://github.com/infiniflow/infinity).
### How to export a created knowledge graph?
Nope. Exporting a created knowledge graph is not supported. If you still consider this feature essential, please [raise an issue](https://github.com/infiniflow/ragflow/issues) explaining your use case and its importance.
