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Added an FAQ (#5092)

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### What problem does this PR solve?


### Type of change


- [x] Documentation Update
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2
docs/guides/accelerate_question_answering.mdx
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@ -16,7 +16,7 @@ Please note that some of your settings may consume a significant amount of time.
- Use GPU to reduce embedding time. - Use GPU to reduce embedding time.
- On the configuration page of your knowledge base, switch off **Use RAPTOR to enhance retrieval**. - On the configuration page of your knowledge base, switch off **Use RAPTOR to enhance retrieval**.
- The **Knowledge Graph** chunk method (GraphRAG) is time-consuming. - Extracting knowledge graph (GraphRAG) is time-consuming.
- Disable **Auto-keyword** and **Auto-question** on the configuration page of yor knowledge base, as both depend on the LLM. - Disable **Auto-keyword** and **Auto-question** on the configuration page of yor knowledge base, as both depend on the LLM.
## 2. Accelerate question answering ## 2. Accelerate question answering

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docs/guides/configure_knowledge_base/construct_knowledge_graph.md
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@ -44,23 +44,23 @@ The method to use to construct knowledge graph:
### Entity resolution ### Entity resolution
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 accurate graph. 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.
- (Default) Disable entity resolution. This option consumes fewer tokens. - (Default) Disable entity resolution.
- Enable entity resolution. - Enable entity resolution. This option consumes more tokens.
### Community report generation ### Community report generation
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: 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. - Generate community reports. This option consumes more tokens.
- (Default) Do not generate community reports. This options consumes fewer tokens. - (Default) Do not generate community reports.
## Procedure ## Procedure
1. On the **Configuration** page of your knowledge base, switch on **Extract knowledge graph** or adjust its settings as needed, and click **Save** to confirm your changes. 1. On the **Configuration** page of your knowledge base, switch on **Extract knowledge graph** or adjust its settings as needed, and click **Save** to confirm your changes.
- *The default GraphRAG configurations for your knowlege base are now set and files uploaded from this point onward will automatically use these settings during parsing.* - *The default knowledge graph configurations for your knowlege base 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.* - *Files parsed before this update will retain their original knowledge graph settings.*
2. The knowledge graph of your knowlege base does *not* automatically update *until* a newly uploaded file is parsed. 2. The knowledge graph of your knowlege base does *not* automatically update *until* a newly uploaded file is parsed.

29
docs/references/faq.md
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@ -22,6 +22,35 @@ The "garbage in garbage out" status quo remains unchanged despite the fact that
--- ---
### Where to find the version of RAGFlow? How to interprete it?
You can find the RAGFlow version number on the **System** page of the UI:
![Image](https://github.com/user-attachments/assets/20cf7213-2537-4e18-a88c-4dadf6228c6b)
If you build RAGFlow from source, the version number is also in the system log:
```
____ ___ ______ ______ __
/ __ \ / | / ____// ____// /____ _ __
/ /_/ // /| | / / __ / /_ / // __ \| | /| / /
/ _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ /
/_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/
2025-02-18 10:10:43,835 INFO 1445658 RAGFlow version: v0.16.0-50-g6daae7f2 full
```
Where:
- `v0.16.0`: The officially published release.
- `50`: The number of git commits since the official release.
- `g6daae7f2`: `g` is the prefix, and `6daae7f2` is the first seven characters of the current commit ID.
- `full`/`slim`: The RAGFlow edition.
- `full`: The full RAGFlow edition.
- `slim`: The RAGFlow edition without embedding models and Python packages.
---
### Why does it take longer for RAGFlow to parse a document than LangChain? ### Why does it take longer for RAGFlow to parse a document than LangChain?
We put painstaking effort into document pre-processing tasks like layout analysis, table structure recognition, and OCR (Optical Character Recognition) using our vision models. This contributes to the additional time required. We put painstaking effort into document pre-processing tasks like layout analysis, table structure recognition, and OCR (Optical Character Recognition) using our vision models. This contributes to the additional time required.

30
docs/references/http_api_reference.md
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@ -2178,10 +2178,12 @@ Creates a session with an agent.
- Body: - Body:
- the required parameters:`str` - the required parameters:`str`
- other parameters: - other parameters:
The parameters in the begin component. The parameters set in the **Begin** component.
##### Request example ##### Request example
If `begin` component in the agent doesn't have required parameters:
If the **Begin** component in your agent does not have required parameters:
```bash ```bash
curl --request POST \ curl --request POST \
--url http://{address}/api/v1/agents/{agent_id}/sessions \ --url http://{address}/api/v1/agents/{agent_id}/sessions \
@ -2190,7 +2192,9 @@ curl --request POST \
--data '{ --data '{
}' }'
``` ```
If `begin` component in the agent has required parameters:
If the **Begin** component in your agent has required parameters:
```bash ```bash
curl --request POST \ curl --request POST \
--url http://{address}/api/v1/agents/{agent_id}/sessions \ --url http://{address}/api/v1/agents/{agent_id}/sessions \
@ -2201,7 +2205,9 @@ curl --request POST \
"file":"Who are you" "file":"Who are you"
}' }'
``` ```
If `begin` component in the agent has required file parameters:
If the **Begin** component in your agent has required file parameters:
```bash ```bash
curl --request POST \ curl --request POST \
--url http://{address}/api/v1/agents/{agent_id}/sessions?user_id={user_id} \ --url http://{address}/api/v1/agents/{agent_id}/sessions?user_id={user_id} \
@ -2215,7 +2221,7 @@ curl --request POST \
- `agent_id`: (*Path parameter*) - `agent_id`: (*Path parameter*)
The ID of the associated agent. The ID of the associated agent.
- `user_id`: (*Filter parameter*), string - `user_id`: (*Filter parameter*), string
The optional user-defined ID for parsing docs(especially images) when creating session while uploading files. The optional user-defined ID for parsing docs (especially images) when creating a session while uploading files.
#### Response #### Response
@ -2367,7 +2373,7 @@ Asks a specified agent a question to start an AI-powered conversation.
- `"user_id"`: `string`(optional) - `"user_id"`: `string`(optional)
- other parameters: `string` - other parameters: `string`
##### Request example ##### Request example
If the `begin` component doesn't have parameters, the following code will create a session. Ifthe **Begin** component doesn't have parameters, the following code will create a session.
```bash ```bash
curl --request POST \ curl --request POST \
--url http://{address}/api/v1/agents/{agent_id}/completions \ --url http://{address}/api/v1/agents/{agent_id}/completions \
@ -2377,7 +2383,7 @@ curl --request POST \
{ {
}' }'
``` ```
If the `begin` component have parameters, the following code will create a session. Ifthe **Begin** component have parameters, the following code will create a session.
```bash ```bash
curl --request POST \ curl --request POST \
--url http://{address}/api/v1/agents/{agent_id}/completions \ --url http://{address}/api/v1/agents/{agent_id}/completions \
@ -2403,7 +2409,6 @@ curl --request POST \
}' }'
``` ```
##### Request Parameters ##### Request Parameters
- `agent_id`: (*Path parameter*), `string` - `agent_id`: (*Path parameter*), `string`
@ -2419,9 +2424,10 @@ curl --request POST \
- `"user_id"`: (*Body parameter*), `string` - `"user_id"`: (*Body parameter*), `string`
The optional user-defined ID. Valid *only* when no `session_id` is provided. The optional user-defined ID. Valid *only* when no `session_id` is provided.
- Other parameters: (*Body Parameter*) - Other parameters: (*Body Parameter*)
The parameters in the begin component. Parameters specified in the **Begin** component.
#### Response #### Response
success without `session_id` provided and with no parameters in the `begin` component: success without `session_id` provided and with no parameters inthe **Begin** component:
```json ```json
data:{ data:{
"code": 0, "code": 0,
@ -2439,7 +2445,7 @@ data:{
"data": true "data": true
} }
``` ```
Success without `session_id` provided and with parameters in the `begin` component: Success without `session_id` provided and with parameters inthe **Begin** component:
```json ```json
data:{ data:{
@ -2475,7 +2481,7 @@ data:{
} }
data: data:
``` ```
Success with parameters in the `begin` component: Success with parameters inthe **Begin** component:
```json ```json
data:{ data:{
"code": 0, "code": 0,

2
docs/references/python_api_reference.md
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@ -1461,7 +1461,7 @@ In streaming mode, not all responses include a reference, as this depends on the
##### question: `str` ##### question: `str`
The question to start an AI-powered conversation. If the `begin` component takes parameters, a question is not required. The question to start an AI-powered conversation. Ifthe **Begin** component takes parameters, a question is not required.
##### stream: `bool` ##### stream: `bool`

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docs/release_notes.md
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@ -14,7 +14,7 @@ Released on February 6, 2025.
### New features ### New features
- Supports DeepSeek R1 and DeepSeek V3. - Supports DeepSeek R1 and DeepSeek V3.
- GraphRAG refactor: Knowledge graph is dynamically built on an entire knowledge base (dataset) rather than on an individual file, and automatically updated when files are added or removed. - GraphRAG refactor: Knowledge graph is dynamically built on an entire knowledge base (dataset) rather than on an individual file, and automatically updated when files are added or removed. See [here](https://ragflow.io/docs/dev/construct_knowledge_graph).
- Adds an **Iteration** agent component and a **Research report generator** agent template. - Adds an **Iteration** agent component and a **Research report generator** agent template.
- New UI language: Portuguese. - New UI language: Portuguese.
- Allows setting metadata for a specific file in a knowledge base to support AI-powered chats. - Allows setting metadata for a specific file in a knowledge base to support AI-powered chats.

10
web/src/locales/en.ts
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@ -369,15 +369,15 @@ This procedure will improve precision of retrieval by adding more information to
addTag: 'Add tag', addTag: 'Add tag',
useGraphRag: 'Extract knowledge graph', useGraphRag: 'Extract knowledge graph',
useGraphRagTip: useGraphRagTip:
'After files being chunked, all the chunks will be used for knowlege graph generation which helps inference of multi-hop and complex problems a lot.', 'Construct a knowledge graph over extracted file chunks to enhance multi-hop question answering.',
graphRagMethod: 'Method', graphRagMethod: 'Method',
graphRagMethodTip: `Light: the entity and relation extraction prompt is from GitHub - HKUDS/LightRAG: "LightRAG: Simple and Fast Retrieval-Augmented Generation"</br> graphRagMethodTip: `Light: (Default) Use prompts provided by github.com/HKUDS/LightRAG to extract entities and relationships. This option consumes fewer tokens, less memory, and fewer computational resources.</br>
General: the entity and relation extraction prompt is from GitHub - microsoft/graphrag: A modular graph-based Retrieval-Augmented Generation (RAG) system`, General: Use prompts provided by github.com/microsoft/graphrag to extract entities and relationships`,
resolution: 'Entity resolution', resolution: 'Entity resolution',
resolutionTip: `The resolution procedure would merge entities with the same meaning together which allows the graph conciser and more accurate. Entities as following should be merged: President Trump, Donald Trump, Donald J. Trump, Donald John Trump`, resolutionTip: `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 accurate graph`,
community: 'Community reports generation', community: 'Community reports generation',
communityTip: communityTip:
'Chunks are clustered into hierarchical communities with entities and relationships connecting each segment up through higher levels of abstraction. We then use an LLM to generate a summary of each community, known as a community report. More: https://www.microsoft.com/en-us/research/blog/graphrag-improving-global-search-via-dynamic-community-selection/', '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 for more information: https://www.microsoft.com/en-us/research/blog/graphrag-improving-global-search-via-dynamic-community-selection/',
}, },
chunk: { chunk: {
chunk: 'Chunk', chunk: 'Chunk',