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

 
 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

 
 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: