pengjunjie/ragflow
1
0
Fork 1
mirror of https://github.com/infiniflow/ragflow.git synced 2025-12-08 20:42:30 +08:00
Code Issues Packages Projects Releases Wiki Activity

Docs: Updated dataset configuration, KG building and RAPTOR building for v0.21.0 (#10584)

Browse Source 
### What problem does this PR solve?



### Type of change


- [x] Documentation Update
This commit is contained in:
writinwaters
2025-10-15 16:39:26 +08:00
committed by GitHub
parent 8844826208
commit 205a5eb9f5
5 changed files with 61 additions and 22 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
docs/guides/dataset/configure_knowledge_base.md
View File

@ -37,7 +37,7 @@ This section covers the following topics:
### Select chunking method
RAGFlow offers multiple chunking template to facilitate chunking files of different layouts and ensure semantic integrity. In **Chunking method**, you can choose the default template that suits the layouts and formats of your files. The following table shows the descriptions and the compatible file formats of each supported chunk template:
RAGFlow offers multiple built-in chunking template to facilitate chunking files of different layouts and ensure semantic integrity. From the **Built-in** chunking method dropdown under **Parse type**, you can choose the default template that suits the layouts and formats of your files. The following table shows the descriptions and the compatible file formats of each supported chunk template:
| **Template** | Description | File format |
|--------------|-----------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
@ -54,9 +54,26 @@ RAGFlow offers multiple chunking template to facilitate chunking files of differ
| One | Each document is chunked in its entirety (as one). | DOCX, XLSX, XLS (Excel 97-2003), PDF, TXT |
| Tag | The dataset functions as a tag set for the others. | XLSX, CSV/TXT |
You can also change a file's chunking method on the **Datasets** page.
You can also change a file's chunking method on the **Files** page.
![change chunking method](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/embedded_chat_app.jpg)
![change chunking method](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/change_chunking_method.jpg)
:::tip NOTE
From v0.21.0, RAGFlow supports ingestion pipeline to allow for customized
<details>
<summary>From v0.21.0 onward, RAGFlow supports ingestion pipeline to allow for customized data ingestion and cleansing workflows.</summary>
To use a customized data pipeline:
1. On the **Agent** page, click **+ Create agent** > **Create from blank**.
2. Select **Ingestion pipeline** and name your data pipeline in the popup, then click **Save** to show the data pipeline canvas.
3. After updating your data pipeline, click **Save** on the top right of the canvas.
4. Navigate to the **Configuration** page of your dataset, select **Choose pipeline** in **Ingestion pipeline**.
*Your saved data pipeline will appear in the dropdown menu below.*
</details>
### Select embedding model

30
docs/guides/dataset/construct_knowledge_graph.md
View File

@ -53,25 +53,31 @@ Whether to enable entity resolution. You can think of this as an entity deduplic
- (Default) Disable entity resolution.
- Enable entity resolution. This option consumes more tokens.
### Community report generation
### Community reports
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:
- Generate community reports. This option consumes more tokens.
- (Default) Do not generate community reports.
## Procedure
## Quickstart
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.
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.*
- *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.*
- *Files parsed before this update will retain their original knowledge graph settings.*
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.
2. The knowledge graph of your dataset does *not* automatically update *until* a newly uploaded file is parsed.
*You can click the pause button in the dropdown to halt the build process when necessary.*
_A **Knowledge graph** entry appears under **Configuration** once a knowledge graph is created._
3. Go back to the **Configuration** page:
*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.*
3. Click **Knowledge graph** to view the details of the generated graph.
4. To use the created knowledge graph, do either of the following:
- In the **Chat setting** panel of your chat app, switch on the **Use knowledge graph** toggle.
@ -79,17 +85,13 @@ In a knowledge graph, a community is a cluster of entities linked by relationshi
## Frequently asked questions
### Can I have different knowledge graph settings for different files in my dataset?
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.
### Does the knowledge graph automatically update when I remove a related file?
Nope. The knowledge graph does *not* automatically update *until* a newly uploaded document is parsed.
Nope. The knowledge graph does *not* update *until* you regenerate a knowledge graph for your dataset.
### How to remove a generated knowledge graph?
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.
On the **Configuration** page of your dataset, find the **Knoweledge graph** field and click the recycle bin button to the right of the field.
### Where is the created knowledge graph stored?

19
docs/guides/dataset/enable_raptor.md
View File

@ -72,3 +72,22 @@ The maximum number of clusters to create. Defaults to 64, with a maximum limit o
### Random seed
A random seed. Click **+** to change the seed value.
## Quickstart
1. Navigate to the **Configuration** page of your dataset and update:
- Prompt: *Optional* - We recommend that you keep it as-is until you understand the mechanism behind.
- Max token: *Optional*
- Threshold: *Optional*
- Max cluster: *Optional*
2. Navigate to the **Files** page of your dataset, click the **Generate** button on the top right corner of the page, then select **RAPTOR** from the dropdown to initiate the RAPTOR build process.
*You can click the pause button in the dropdown to halt the build process when necessary.*
3. Go back to the **Configuration** page:
*The **RAPTOR** field changes from `Not generated` to `Generated at a specific timestamp` when a RAPTOR hierarchical tree structure is generated. You can delete it by clicking the recycle bin button to the right of the field.*
4. Once a RAPTOR hierarchical tree structure is generated, your chat assistant and **Retrieval** agent component will use it for retrieval as a default.

9
docs/quickstart.mdx
View File

@ -343,19 +343,20 @@ You can add keywords or questions to a file chunk to improve its ranking for que
Conversations in RAGFlow are based on a particular dataset or multiple datasets. Once you have created your dataset and finished file parsing, you can go ahead and start an AI conversation.
1. Click the **Chat** tab in the middle top of the mage **>** **Create an assistant** to show the **Chat Configuration** dialogue *of your next dialogue*.
1. Click the **Chat** tab in the middle top of the page **>** **Create chat** to create a chat assistant.
2. Click the created chat app to enter its configuration page.
> RAGFlow offer the flexibility of choosing a different chat model for each dialogue, while allowing you to set the default models in **System Model Settings**.
2. Update **Assistant settings**:
2. Update **Chat setting** on the right of the configuration page:
- Name your assistant and specify your datasets.
- **Empty response**:
- If you wish to *confine* RAGFlow's answers to your datasets, leave a response here. Then when it doesn't retrieve an answer, it *uniformly* responds with what you set here.
- If you wish RAGFlow to *improvise* when it doesn't retrieve an answer from your datasets, leave it blank, which may give rise to hallucinations.
3. Update **Prompt engine** or leave it as is for the beginning.
3. Update **System prompt** or leave it as is for the beginning.
4. Update **Model settings**.
4. Select a chat model in the **Model** dropdown list.
5. Now, let's start the show:

2
docs/release_notes.md
View File

@ -45,7 +45,7 @@ Released on October 15, 2025.
- Claude Sonnet 4.5
- Meituan LongCat-Flash-Thinking
## New agent templates
### New agent templates
- Company Research Report Deep Dive Agent: Designed for financial institutions to help analysts quickly organize information, generate research reports, and make investment decisions.
- Orchestratable Ingestion Pipeline Template: Allows users to apply this template on the canvas to rapidly establish standardized data ingestion and cleansing processes.