Refa: remove default build-in rerank model. (#6682)

### What problem does this PR solve?

### Type of change

- [x] Refactoring
- [x] Performance Improvement

Dockerfile

     if [ "$LIGHTEN" != "1" ]; then \
         (tar -cf - \
             /huggingface.co/BAAI/bge-large-zh-v1.5 \
-            /huggingface.co/BAAI/bge-reranker-v2-m3 \
             /huggingface.co/maidalun1020/bce-embedding-base_v1 \
-            /huggingface.co/maidalun1020/bce-reranker-base_v1 \
             | tar -xf - --strip-components=2 -C /root/.ragflow) \
     fi
 

api/apps/sdk/dataset.py

     else:
         valid_embedding_models = [
             "BAAI/bge-large-zh-v1.5",
-            "BAAI/bge-base-en-v1.5",
-            "BAAI/bge-large-en-v1.5",
-            "BAAI/bge-small-en-v1.5",
-            "BAAI/bge-small-zh-v1.5",
-            "jinaai/jina-embeddings-v2-base-en",
-            "jinaai/jina-embeddings-v2-small-en",
-            "nomic-ai/nomic-embed-text-v1.5",
-            "sentence-transformers/all-MiniLM-L6-v2",
-            "text-embedding-v2",
-            "text-embedding-v3",
             "maidalun1020/bce-embedding-base_v1",
         ]
         embd_model = LLMService.query(

conf/llm_factories.json

             "logo": "",
             "tags": "TEXT EMBEDDING",
             "status": "1",
-            "llm": [
-                {
-                    "llm_name": "BAAI/bge-small-en-v1.5",
-                    "tags": "TEXT EMBEDDING,",
-                    "max_tokens": 512,
-                    "model_type": "embedding"
-                },
-                {
-                    "llm_name": "BAAI/bge-small-zh-v1.5",
-                    "tags": "TEXT EMBEDDING,",
-                    "max_tokens": 512,
-                    "model_type": "embedding"
-                },
-                {
-                    "llm_name": "BAAI/bge-base-en-v1.5",
-                    "tags": "TEXT EMBEDDING,",
-                    "max_tokens": 512,
-                    "model_type": "embedding"
-                },
-                {
-                    "llm_name": "BAAI/bge-large-en-v1.5",
-                    "tags": "TEXT EMBEDDING,",
-                    "max_tokens": 512,
-                    "model_type": "embedding"
-                },
-                {
-                    "llm_name": "sentence-transformers/all-MiniLM-L6-v2",
-                    "tags": "TEXT EMBEDDING,",
-                    "max_tokens": 512,
-                    "model_type": "embedding"
-                },
-                {
-                    "llm_name": "nomic-ai/nomic-embed-text-v1.5",
-                    "tags": "TEXT EMBEDDING,",
-                    "max_tokens": 8192,
-                    "model_type": "embedding"
-                },
-                {
-                    "llm_name": "jinaai/jina-embeddings-v2-small-en",
-                    "tags": "TEXT EMBEDDING,",
-                    "max_tokens": 2147483648,
-                    "model_type": "embedding"
-                },
-                {
-                    "llm_name": "jinaai/jina-embeddings-v2-base-en",
-                    "tags": "TEXT EMBEDDING,",
-                    "max_tokens": 2147483648,
-                    "model_type": "embedding"
-                }
-            ]
+            "llm": []
         },
         {
             "name": "Xinference",
                     "tags": "TEXT EMBEDDING,",
                     "max_tokens": 512,
                     "model_type": "embedding"
-                },
-                {
-                    "llm_name": "maidalun1020/bce-reranker-base_v1",
-                    "tags": "RE-RANK, 512",
-                    "max_tokens": 512,
-                    "model_type": "rerank"
                 }
             ]
         },
                     "tags": "TEXT EMBEDDING,",
                     "max_tokens": 1024,
                     "model_type": "embedding"
-                },
-                {
-                    "llm_name": "BAAI/bge-reranker-v2-m3",
-                    "tags": "RE-RANK,2k",
-                    "max_tokens": 2048,
-                    "model_type": "rerank"
                 }
             ]
         },

docker/README.md

   - `infiniflow/ragflow:v0.17.2`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
-      - `BAAI/bge-reranker-v2-m3`
       - `maidalun1020/bce-embedding-base_v1`
-      - `maidalun1020/bce-reranker-base_v1`
-    - Embedding models that will be downloaded once you select them in the RAGFlow UI:
-      - `BAAI/bge-base-en-v1.5`
-      - `BAAI/bge-large-en-v1.5`
-      - `BAAI/bge-small-en-v1.5`
-      - `BAAI/bge-small-zh-v1.5`
-      - `jinaai/jina-embeddings-v2-base-en`
-      - `jinaai/jina-embeddings-v2-small-en`
-      - `nomic-ai/nomic-embed-text-v1.5`
-      - `sentence-transformers/all-MiniLM-L6-v2`
+
   
 > [!TIP]  
 > If you cannot download the RAGFlow Docker image, try the following mirrors.  

docs/configurations.md

   - `infiniflow/ragflow:v0.17.2`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
-      - `BAAI/bge-reranker-v2-m3`
       - `maidalun1020/bce-embedding-base_v1`
-      - `maidalun1020/bce-reranker-base_v1`
-    - Embedding models that will be downloaded once you select them in the RAGFlow UI:
-      - `BAAI/bge-base-en-v1.5`
-      - `BAAI/bge-large-en-v1.5`
-      - `BAAI/bge-small-en-v1.5`
-      - `BAAI/bge-small-zh-v1.5`
-      - `jinaai/jina-embeddings-v2-base-en`
-      - `jinaai/jina-embeddings-v2-small-en`
-      - `nomic-ai/nomic-embed-text-v1.5`
-      - `sentence-transformers/all-MiniLM-L6-v2`
-  
+
+
 :::tip NOTE  
 If you cannot download the RAGFlow Docker image, try the following mirrors.  
 

download_deps.py

     "InfiniFlow/deepdoc",
     "InfiniFlow/huqie",
     "BAAI/bge-large-zh-v1.5",
-    "BAAI/bge-reranker-v2-m3",
     "maidalun1020/bce-embedding-base_v1",
-    "maidalun1020/bce-reranker-base_v1",
 ]
 
 def download_model(repo_id):

sdk/python/test/test_http_api/test_chunk_management_within_dataset/test_retrieval_chunks.py

         else:
             assert expected_message in res["message"]
 
+    @pytest.mark.skip
     @pytest.mark.parametrize(
         "payload, expected_code, expected_message",
         [

sdk/python/test/test_http_api/test_dataset_mangement/test_create_dataset.py

         "name, embedding_model, expected_code",
         [
             ("BAAI/bge-large-zh-v1.5", "BAAI/bge-large-zh-v1.5", 0),
-            ("BAAI/bge-base-en-v1.5", "BAAI/bge-base-en-v1.5", 0),
-            ("BAAI/bge-large-en-v1.5", "BAAI/bge-large-en-v1.5", 0),
-            ("BAAI/bge-small-en-v1.5", "BAAI/bge-small-en-v1.5", 0),
-            ("BAAI/bge-small-zh-v1.5", "BAAI/bge-small-zh-v1.5", 0),
-            (
-                "jinaai/jina-embeddings-v2-base-en",
-                "jinaai/jina-embeddings-v2-base-en",
-                0,
-            ),
-            (
-                "jinaai/jina-embeddings-v2-small-en",
-                "jinaai/jina-embeddings-v2-small-en",
-                0,
-            ),
-            ("nomic-ai/nomic-embed-text-v1.5", "nomic-ai/nomic-embed-text-v1.5", 0),
-            (
-                "sentence-transformers/all-MiniLM-L6-v2",
-                "sentence-transformers/all-MiniLM-L6-v2",
-                0,
-            ),
-            ("text-embedding-v2", "text-embedding-v2", 0),
-            ("text-embedding-v3", "text-embedding-v3", 0),
             (
                 "maidalun1020/bce-embedding-base_v1",
                 "maidalun1020/bce-embedding-base_v1",

sdk/python/test/test_http_api/test_dataset_mangement/test_update_dataset.py

         "embedding_model, expected_code, expected_message",
         [
             ("BAAI/bge-large-zh-v1.5", 0, ""),
-            ("BAAI/bge-base-en-v1.5", 0, ""),
-            ("BAAI/bge-large-en-v1.5", 0, ""),
-            ("BAAI/bge-small-en-v1.5", 0, ""),
-            ("BAAI/bge-small-zh-v1.5", 0, ""),
-            ("jinaai/jina-embeddings-v2-base-en", 0, ""),
-            ("jinaai/jina-embeddings-v2-small-en", 0, ""),
-            ("nomic-ai/nomic-embed-text-v1.5", 0, ""),
-            ("sentence-transformers/all-MiniLM-L6-v2", 0, ""),
-            ("text-embedding-v2", 0, ""),
-            ("text-embedding-v3", 0, ""),
             ("maidalun1020/bce-embedding-base_v1", 0, ""),
             (
                 "other_embedding_model",