Expanded the supported LLM list (infiniflow#960)

### 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

docker/README.md

@@ -67,7 +67,7 @@ The serving IP and port inside the docker container. This is not updating until
 Newly signed-up users use LLM configured by this part. Otherwise, user need to configure his own LLM in *setting*.
 
 ### factory
-The LLM suppliers. 'Tongyi-Qianwen', "OpenAI"， "Moonshot" and "ZHIPU-AI" are supported.
+The LLM suppliers. "OpenAI"， "Tongyi-Qianwen", "ZHIPU-AI", "Moonshot", "DeepSeek", "Baichuan", and "VolcEngine" are supported.
 
 ### api_key
 The corresponding API key of your assigned LLM vendor.

docs/guides/llm_api_key_setup.md

@@ -10,10 +10,15 @@ You have two ways to input your LLM API key.
 ## Before Starting The System
 
 In **user_default_llm** of [service_conf.yaml](https://github.com/infiniflow/ragflow/blob/main/docker/service_conf.yaml), you need to specify LLM factory and your own _API_KEY_. 
-RagFlow supports the flowing LLM factory, and with more coming in the pipeline:
-
-> [OpenAI](https://platform.openai.com/login?launch), [Tongyi-Qianwen](https://dashscope.console.aliyun.com/model), 
-> [ZHIPU-AI](https://open.bigmodel.cn/), [Moonshot](https://platform.moonshot.cn/docs)
+RAGFlow supports the flowing LLMs, with more coming in the pipeline:
+
+- [OpenAI](https://platform.openai.com/login?launch)
+- [Tongyi-Qianwen](https://dashscope.console.aliyun.com/model), 
+- [ZHIPU-AI](https://open.bigmodel.cn/), 
+- [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)
 
 After sign in these LLM suppliers, create your own API-Key, they all have a certain amount of free quota.
 

docs/quickstart.md

@@ -101,8 +101,11 @@ RAGFlow is a RAG engine, and it needs to work with an LLM to offer grounded, hal
 
 - OpenAI
 - Tongyi-Qianwen
+- ZHIPU-AI
 - 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. 
 

docs/references/api.md

@@ -108,13 +108,13 @@ This method retrieves the history of a specified conversation session.
     - `chunks`
         - `content_with_weight`: Content of the chunk.
         - `doc_name`: Name of the *hit* document.
-        - `img_id`: The image ID of the chunk. It is an optional field only for PDF, PPTX, and images. Call ['GET' /document/get/\<id\>](#get-document-content-or-image) to retrieve the image.
+        - `img_id`: The image ID of the chunk. It is an optional field only for PDF, PPTX, and images. Call ['GET' /document/get/\<id\>](#get-document-content) to retrieve the image.
         - positions: [page_number, [upleft corner(x, y)], [right bottom(x, y)]], the chunk position, only for PDF.
         - similarity: The hybrid similarity.
         - term_similarity: The keyword simimlarity.
         - vector_similarity: The embedding similarity.
     - `doc_aggs`:
-        - `doc_id`: ID of the *hit* document. Call ['GET' /document/get/\<id\>](#get-document-content-or-image) to retrieve the document.
+        - `doc_id`: ID of the *hit* document. Call ['GET' /document/get/\<id\>](#get-document-content) to retrieve the document.
         - `doc_name`: Name of the *hit* document.
         - `count`: The number of *hit* chunks in this document.
 
@@ -235,13 +235,13 @@ This method retrieves from RAGFlow the answer to the user's latest question.
     - `chunks`: The retrieved chunks that contribute to the answer.  
         - `content_with_weight`: Content of the chunk.
         - `doc_name`: Name of the *hit* document.
-        - `img_id`: The image ID of the chunk. It is an optional field only for PDF, PPTX, and images. Call ['GET' /document/get/\<id\>](#get-document-content-or-image) to retrieve the image.
+        - `img_id`: The image ID of the chunk. It is an optional field only for PDF, PPTX, and images. Call ['GET' /document/get/\<id\>](#get-document-content) to retrieve the image.
         - `positions`: [page_number, [upleft corner(x, y)], [right bottom(x, y)]], the chunk position, only for PDF.
         - `similarity`: The hybrid similarity.
         - `term_similarity`: The keyword simimlarity.
         - `vector_similarity`: The embedding similarity.
     - `doc_aggs`:
-        - `doc_id`: ID of the *hit* document. Call ['GET' /document/get/\<id\>](#get-document-content-or-image) to retrieve the document.
+        - `doc_id`: ID of the *hit* document. Call ['GET' /document/get/\<id\>](#get-document-content) to retrieve the document.
         - `doc_name`: Name of the *hit* document. 
         - `count`: The number of *hit* chunks in this document.