Added supported LLMs (#1517)

### What problem does this PR solve?

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._

### Type of change

- [x] Documentation Update

docs/guides/configure_knowledge_base.md

@@ -107,6 +107,10 @@ RAGFlow features visibility and explainability, allowing you to view the chunkin
 
    ![update chunk](https://github.com/infiniflow/ragflow/assets/93570324/1d84b408-4e9f-46fd-9413-8c1059bf9c76)
 
+:::caution NOTE 
+You can add keywords to a file chunk to increase its relevance. This action increases its keyword weight and can improve its position in search list.  
+:::
+
 4. In Retrieval testing, ask a quick question in **Test text** to double check if your configurations work:
 
    _As you can tell from the following, RAGFlow responds with truthful citations._

docs/guides/llm_api_key_setup.md

@@ -12,8 +12,14 @@ An API key is required for RAGFlow to interact with an online AI model. This gui
 For now, RAGFlow supports the following online LLMs. Click the corresponding link to apply for your API key. Most LLM providers grant newly-created accounts trial credit, which will expire in a couple of months, or a promotional amount of free quota.
 
 - [OpenAI](https://platform.openai.com/login?launch),
+- Azure-OpenAI,
+- Gemini,
+- Groq,
+- Mistral,
+- Bedrock,
 - [Tongyi-Qianwen](https://dashscope.console.aliyun.com/model),
 - [ZHIPU-AI](https://open.bigmodel.cn/),
+- MiniMax
 - [Moonshot](https://platform.moonshot.cn/docs),
 - [DeepSeek](https://platform.deepseek.com/api-docs/),
 - [Baichuan](https://www.baichuan-ai.com/home),

docs/quickstart.mdx

@@ -177,14 +177,22 @@ With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (*
 RAGFlow is a RAG engine, and it needs to work with an LLM to offer grounded, hallucination-free question-answering capabilities. For now, RAGFlow supports the following LLMs, and the list is expanding:
 
 - OpenAI
+- Azure-OpenAI
+- Gemini
+- Groq
+- Mistral
+- Bedrock
 - Tongyi-Qianwen
 - ZHIPU-AI
+- MiniMax
 - Moonshot
 - DeepSeek-V2
 - Baichuan
 - VolcEngine
 
->  RAGFlow also supports deploying LLMs locally using Ollama or Xinference, but this part is not covered in this quick start guide. 
+:::note 
+RAGFlow also supports deploying LLMs locally using Ollama or Xinference, but this part is not covered in this quick start guide. 
+:::
 
 To add and configure an LLM: 
 
@@ -192,7 +200,7 @@ To add and configure an LLM:
 
    ![add llm](https://github.com/infiniflow/ragflow/assets/93570324/10635088-028b-4b3d-add9-5c5a6e626814)
 
-   > Each RAGFlow account is able to use **text-embedding-v2** for free, a embedding model of Tongyi-Qianwen. This is why you can see Tongyi-Qianwen in the **Added models** list. And you may need to update your Tongyi-Qianwen API key at a later point.
+   > Each RAGFlow account is able to use **text-embedding-v2** for free, an embedding model of Tongyi-Qianwen. This is why you can see Tongyi-Qianwen in the **Added models** list. And you may need to update your Tongyi-Qianwen API key at a later point.
 
 2. Click on the desired LLM and update the API key accordingly (DeepSeek-V2 in this case):
 
@@ -228,7 +236,9 @@ To create your first knowledge base:
 
 3. RAGFlow offers multiple chunk templates that cater to different document layouts and file formats. Select the embedding model and chunk method (template) for your knowledge base. 
 
-   > IMPORTANT: Once you have selected an embedding model and used it to parse a file, you are no longer allowed to change it. The obvious reason is that we must ensure that all files in a specific knowledge base are parsed using the *same* embedding model (ensure that they are being compared in the same embedding space). 
+:::danger IMPORTANT 
+Once you have selected an embedding model and used it to parse a file, you are no longer allowed to change it. The obvious reason is that we must ensure that all files in a specific knowledge base are parsed using the *same* embedding model (ensure that they are being compared in the same embedding space). 
+:::
 
    _You are taken to the **Dataset** page of your knowledge base._
 
@@ -240,6 +250,11 @@ To create your first knowledge base:
 
    _When the file parsing completes, its parsing status changes to **SUCCESS**._
 
+:::alert NOTE 
+- If your file parsing gets stuck at below 1%, see [FAQ 4.3](https://ragflow.io/docs/dev/faq#43-why-does-my-document-parsing-stall-at-under-one-percent). 
+- If your file parsing gets stuck at near completion, see [FAQ 4.4](https://ragflow.io/docs/dev/faq#44-why-does-my-pdf-parsing-stall-near-completion-while-the-log-does-not-show-any-error)
+:::
+
 ## Intervene with file parsing 
 
 RAGFlow features visibility and explainability, allowing you to view the chunking results and intervene where necessary. To do so: 
@@ -256,6 +271,10 @@ RAGFlow features visibility and explainability, allowing you to view the chunkin
 
    ![update chunk](https://github.com/infiniflow/ragflow/assets/93570324/1d84b408-4e9f-46fd-9413-8c1059bf9c76)
 
+:::caution NOTE 
+You can add keywords to a file chunk to increase its relevance. This action increases its keyword weight and can improve its position in search list.  
+:::
+
 4. In Retrieval testing, ask a quick question in **Test text** to double check if your configurations work:
 
    _As you can tell from the following, RAGFlow responds with truthful citations._

docs/references/faq.md

@@ -198,7 +198,7 @@ Ignore this warning and continue. All system warnings can be ignored.
 
 ![stall](https://github.com/infiniflow/ragflow/assets/93570324/3589cc25-c733-47d5-bbfc-fedb74a3da50)
 
-If your RAGFlow is deployed *locally*, try the following: 
+Click the red cross beside the 'parsing status' bar, then restart the parsing process to see if the issue remains. If the issue persists and your RAGFlow is deployed locally, try the following: 
 
 1. Check the log of your RAGFlow server to see if it is running properly:
 ```bash
@@ -209,15 +209,17 @@ docker logs -f ragflow-server
 
 #### 4.4 Why does my pdf parsing stall near completion, while the log does not show any error?
 
-If your RAGFlow is deployed *locally*, the parsing process is likely killed due to insufficient RAM. Try increasing your memory allocation by increasing the `MEM_LIMIT` value in **docker/.env**.
+Click the red cross beside the 'parsing status' bar, then restart the parsing process to see if the issue remains. If the issue persists and your RAGFlow is deployed locally, the parsing process is likely killed due to insufficient RAM. Try increasing your memory allocation by increasing the `MEM_LIMIT` value in **docker/.env**.
 
-> Ensure that you restart up your RAGFlow server for your changes to take effect!
-> ```bash
-> docker compose stop
-> ```
-> ```bash
-> docker compose up -d
-> ```
+:::note
+Ensure that you restart up your RAGFlow server for your changes to take effect!
+```bash
+docker compose stop
+```
+```bash
+docker compose up -d
+```
+:::
 
 ![nearcompletion](https://github.com/infiniflow/ragflow/assets/93570324/563974c3-f8bb-4ec8-b241-adcda8929cbb)