Update docs

Signed-off-by: Jin Hai <haijin.chn@gmail.com>

Author: JinHai-CN

docs/faq.mdx

@@ -151,7 +151,7 @@ See [Build a RAGFlow Docker image](./develop/build_docker_image.mdx).
 
 ### Cannot access https://huggingface.co
 
-A locally deployed RAGflow downloads OCR and embedding modules from [Huggingface website](https://huggingface.co) by default. If your machine is unable to access this site, the following error occurs and PDF parsing fails:
+A locally deployed RAGflow downloads OCR models from [Huggingface website](https://huggingface.co) by default. If your machine is unable to access this site, the following error occurs and PDF parsing fails:
 
 ```
 FileNotFoundError: [Errno 2] No such file or directory: '/root/.cache/huggingface/hub/models--InfiniFlow--deepdoc/snapshots/be0c1e50eef6047b412d1800aa89aba4d275f997/ocr.res'

docs/guides/agent/agent_component_reference/retrieval.mdx

@@ -38,7 +38,7 @@ By default, you can use `sys.query`, which is the user query and the default out
 
 ### 3. Select dataset(s) to query
 
-You can specify one or multiple datasets to retrieve data from. If selecting mutiple, ensure they use the same embedding model.
+You can specify one or multiple datasets to retrieve data from. If selecting multiple, ensure they use the same embedding model.
 
 ### 4. Expand **Advanced Settings** to configure the retrieval method
 

docs/guides/dataset/best_practices/accelerate_doc_indexing.mdx

@@ -12,7 +12,6 @@ A checklist to speed up document parsing and indexing.
 
 Please note that some of your settings may consume a significant amount of time. If you often find that document parsing is time-consuming, here is a checklist to consider:
 
-- Use GPU to reduce embedding time.
 - On the configuration page of your dataset, switch off **Use RAPTOR to enhance retrieval**.
 - Extracting knowledge graph (GraphRAG) is time-consuming.
 - Disable **Auto-keyword** and **Auto-question** on the configuration page of your dataset, as both depend on the LLM.

docs/guides/models/deploy_local_llm.mdx

@@ -107,7 +107,6 @@ Max retries exceeded with url: /api/chat (Caused by NewConnectionError('<urllib3
 Click on your logo **>** **Model providers** **>** **System Model Settings** to update your model:
 
 - *You should now be able to find **llama3.2** from the dropdown list under **Chat model**, and **bge-m3** from the dropdown list under **Embedding model**.*
-- _If your local model is an embedding model, you should find it under **Embedding model**._
 
 ### 6. Update Chat Configuration
 
@@ -158,14 +157,10 @@ Click on your logo **>** **Model providers** **>** **System Model Settings** to
 
 *You should now be able to find **mistral** from the dropdown list under **Chat model**.*
 
-> If your local model is an embedding model, you should find your local model under **Embedding model**.
-
 ### 7. Update Chat Configuration
 
 Update your chat model accordingly in **Chat Configuration**:
 
-> If your local model is an embedding model, update it on the configuration page of your dataset.
-
 ## Deploy a local model using IPEX-LLM
 
 [IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch library for running LLMs on local Intel CPUs or GPUs (including iGPU or discrete GPUs like Arc, Flex, and Max) with low latency. It supports Ollama on Linux and Windows systems.

docs/quickstart.mdx

@@ -190,7 +190,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 3. Use the pre-built Docker images and start up the server:
 
    ```bash
-   # Use CPU for embedding and DeepDoc tasks:
+   # Use CPU for DeepDoc tasks:
    $ docker compose -f docker-compose.yml up -d
    ```
 
@@ -207,15 +207,6 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 </APITable>
 ```
 
-:::danger IMPORTANT
-The embedding models included in `v0.21.1` and `nightly` are:
-
-- BAAI/bge-large-zh-v1.5
-- maidalun1020/bce-embedding-base_v1
-
-These two embedding models are optimized specifically for English and Chinese, so performance will be compromised if you use them to embed documents in other languages.
-:::
-
 :::tip NOTE
 The image size shown refers to the size of the *downloaded* Docker image, which is compressed. When Docker runs the image, it unpacks it, resulting in significantly greater disk usage. A Docker image will expand to around 7 GB once unpacked.
 :::