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100 lines
5.4 KiB
Markdown
100 lines
5.4 KiB
Markdown
---
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sidebar_position: 8
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slug: /construct_knowledge_graph
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---
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# Construct knowledge graph
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Generate a knowledge graph for your dataset.
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---
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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.
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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.
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:::danger WARNING
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Constructing a knowledge graph requires significant memory, computational resources, and tokens.
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:::
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## Scenarios
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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.
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:::tip NOTE
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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.
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:::
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## Prerequisites
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The system's default chat model is used to generate knowledge graph. Before proceeding, ensure that you have a chat model properly configured:
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## Configurations
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### Entity types (*Required*)
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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.
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### Method
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The method to use to construct knowledge graph:
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- **General**: Use prompts provided by [GraphRAG](https://github.com/microsoft/graphrag) to extract entities and relationships.
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- **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.
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### Entity resolution
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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.
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- (Default) Disable entity resolution.
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- Enable entity resolution. This option consumes more tokens.
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### Community report generation
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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:
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- Generate community reports. This option consumes more tokens.
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- (Default) Do not generate community reports.
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## Procedure
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1. On the **Configuration** page of your dataset, switch on **Extract knowledge graph** or adjust its settings as needed, and click **Save** to confirm your changes.
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- *The default knowledge graph configurations for your dataset are now set and files uploaded from this point onward will automatically use these settings during parsing.*
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- *Files parsed before this update will retain their original knowledge graph settings.*
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2. The knowledge graph of your dataset does *not* automatically update *until* a newly uploaded file is parsed.
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_A **Knowledge graph** entry appears under **Configuration** once a knowledge graph is created._
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3. Click **Knowledge graph** to view the details of the generated graph.
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4. To use the created knowledge graph, do either of the following:
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- In the **Chat setting** panel of your chat app, switch on the **Use knowledge graph** toggle.
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- If you are using an agent, click the **Retrieval** agent component to specify the dataset(s) and switch on the **Use knowledge graph** toggle.
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## Frequently asked questions
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### Can I have different knowledge graph settings for different files in my dataset?
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Yes, you can. Just one graph is generated per dataset. The smaller graphs of your files will be *combined* into one big, unified graph at the end of the graph extraction process.
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### Does the knowledge graph automatically update when I remove a related file?
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Nope. The knowledge graph does *not* automatically update *until* a newly uploaded document is parsed.
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### How to remove a generated knowledge graph?
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To remove the generated knowledge graph, delete all related files in your dataset. Although the **Knowledge graph** entry will still be visible, the graph has actually been deleted.
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### Where is the created knowledge graph stored?
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All chunks of the created knowledge graph are stored in RAGFlow's document engine: either Elasticsearch or [Infinity](https://github.com/infiniflow/infinity).
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### How to export a created knowledge graph?
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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. |