Added an FAQ (#5092)

### What problem does this PR solve?


### Type of change


- [x] Documentation Update

docs/guides/accelerate_question_answering.mdx

 
 - Use GPU to reduce embedding time.
 - 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.
 
 ## 2. Accelerate question answering

docs/guides/configure_knowledge_base/construct_knowledge_graph.md

 
 ### 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.
-- Enable entity resolution.
+- (Default) Disable entity resolution.
+- Enable entity resolution. This option consumes more tokens.
 
 ### 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:
 
-- Generate community reports.
-- (Default) Do not generate community reports. This options consumes fewer tokens.
+- Generate community reports. This option consumes more tokens.
+- (Default) Do not generate community reports.
 
 ## 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.
 
-   - *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.*
 
 2. The knowledge graph of your knowlege base does *not* automatically update *until* a newly uploaded file is parsed.

docs/references/faq.md

 
 ---
 
+### 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?
 
 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.

docs/references/http_api_reference.md

 - Body:
   - the required parameters:`str`
   - other parameters:
-    The parameters in the begin component.
+    The parameters set in the **Begin** component.
 
 ##### 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
 curl --request POST \
      --url http://{address}/api/v1/agents/{agent_id}/sessions \
      --data '{
      }'
 ```
-If `begin` component in the agent has required parameters:
+
+If the **Begin** component in your agent has required parameters:
+
 ```bash
 curl --request POST \
      --url http://{address}/api/v1/agents/{agent_id}/sessions \
             "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
 curl --request POST \
      --url http://{address}/api/v1/agents/{agent_id}/sessions?user_id={user_id} \
 - `agent_id`: (*Path parameter*)  
   The ID of the associated agent.
 - `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
 
   - `"user_id"`: `string`(optional)
   - other parameters: `string`
 ##### 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 
 curl --request POST \
      --url http://{address}/api/v1/agents/{agent_id}/completions \
      {
      }'
 ```
-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
 curl --request POST \
      --url http://{address}/api/v1/agents/{agent_id}/completions \
      }'
 ```
 
-
 ##### Request Parameters
 
 - `agent_id`: (*Path parameter*), `string`  
 - `"user_id"`: (*Body parameter*), `string`  
   The optional user-defined ID. Valid *only* when no `session_id` is provided.
 - Other parameters: (*Body Parameter*)  
-  The parameters in the begin component.
+  Parameters specified in the **Begin** component.
+
 #### 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
 data:{
     "code": 0,
     "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
 data:{
 }
 data:
 ```
-Success with parameters in the `begin` component:
+Success with parameters inthe **Begin** component:
 ```json
 data:{
     "code": 0,

docs/references/python_api_reference.md

 
 ##### 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`
 

docs/release_notes.md

 ### New features
 
 - 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.
 - New UI language: Portuguese.
 - Allows setting metadata for a specific file in a knowledge base to support AI-powered chats.

web/src/locales/en.ts

       addTag: 'Add tag',
       useGraphRag: 'Extract knowledge graph',
       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',
-      graphRagMethodTip: `Light: the entity and relation extraction prompt is from GitHub - HKUDS/LightRAG: "LightRAG: Simple and Fast Retrieval-Augmented Generation"</br>
-        General: the entity and relation extraction prompt is from GitHub - microsoft/graphrag: A modular graph-based Retrieval-Augmented Generation (RAG) system`,
+      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: Use prompts provided by github.com/microsoft/graphrag to extract entities and relationships`,
       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',
       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',