update quickstart and llm_api_key_setup document (#1615)

### What problem does this PR solve?

update quickstart and llm_api_key_setup document

### Type of change

- [x] Documentation Update

---------

Co-authored-by: Zhedong Cen <cenzhedong2@126.com>

docs/guides/llm_api_key_setup.md

@@ -11,22 +11,25 @@ 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),
-- [VolcEngine](https://www.volcengine.com/docs/82379).
+- [OpenAI](https://platform.openai.com/login?launch)
+- [Azure-OpenAI](https://ai.azure.com/)
+- [Gemini](https://aistudio.google.com/)
+- [Groq](https://console.groq.com/)
+- [Mistral](https://mistral.ai/)
+- [Bedrock](https://aws.amazon.com/cn/bedrock/)
+- [Tongyi-Qianwen](https://dashscope.console.aliyun.com/model)
+- [ZHIPU-AI](https://open.bigmodel.cn/)
+- [MiniMax](https://platform.minimaxi.com/)
+- [Moonshot](https://platform.moonshot.cn/docs)
+- [DeepSeek](https://platform.deepseek.com/api-docs/)
+- [Baichuan](https://www.baichuan-ai.com/home)
+- [VolcEngine](https://www.volcengine.com/docs/82379)
+- [Jina](https://jina.ai/reader/)
+- [OpenRouter](https://openrouter.ai/)
+- [StepFun](https://platform.stepfun.com/)
 
 :::note
-If you find your online LLM is not on the list, don't feel disheartened. The list is expanding, and you can [file a feature request](https://github.com/infiniflow/ragflow/issues/new?assignees=&labels=feature+request&projects=&template=feature_request.yml&title=%5BFeature+Request%5D%3A+) with us! Alternatively, if you have customized or locally-deployed models, you can [bind them to RAGFlow using Ollama or Xinference](./deploy_local_llm.md).
+If you find your online LLM is not on the list, don't feel disheartened. The list is expanding, and you can [file a feature request](https://github.com/infiniflow/ragflow/issues/new?assignees=&labels=feature+request&projects=&template=feature_request.yml&title=%5BFeature+Request%5D%3A+) with us! Alternatively, if you have customized or locally-deployed models, you can [bind them to RAGFlow using Ollama, Xinferenc, or LocalAI](./deploy_local_llm.md).
 :::
 
 ## Configure your API key

docs/quickstart.mdx

@@ -176,22 +176,25 @@ 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
+- [OpenAI](https://platform.openai.com/login?launch)
+- [Azure-OpenAI](https://ai.azure.com/)
+- [Gemini](https://aistudio.google.com/)
+- [Groq](https://console.groq.com/)
+- [Mistral](https://mistral.ai/)
+- [Bedrock](https://aws.amazon.com/cn/bedrock/)
+- [Tongyi-Qianwen](https://dashscope.console.aliyun.com/model)
+- [ZHIPU-AI](https://open.bigmodel.cn/)
+- [MiniMax](https://platform.minimaxi.com/)
+- [Moonshot](https://platform.moonshot.cn/docs)
+- [DeepSeek](https://platform.deepseek.com/api-docs/)
+- [Baichuan](https://www.baichuan-ai.com/home)
+- [VolcEngine](https://www.volcengine.com/docs/82379)
+- [Jina](https://jina.ai/reader/)
+- [OpenRouter](https://openrouter.ai/)
+- [StepFun](https://platform.stepfun.com/)
 
 :::note 
-RAGFlow also supports deploying LLMs locally using Ollama or Xinference, but this part is not covered in this quick start guide. 
+RAGFlow also supports deploying LLMs locally using Ollama, Xinference, or LocalAI, but this part is not covered in this quick start guide. 
 :::
 
 To add and configure an LLM: