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/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._