✨ new implement : support Rerank models in model management and agent configurations

✨ new implement :  support Rerank models in model management and agent configurations

Author: WMC001

backend/agents/create_agent_info.py

@@ -14,6 +14,7 @@
     ElasticSearchService,
     get_vector_db_core,
     get_embedding_model,
+    get_rerank_model,
 )
 from services.remote_mcp_service import get_remote_mcp_server_list
 from services.memory_config_service import build_memory_context
@@ -350,11 +351,32 @@ async def create_tool_config_list(agent_id, tenant_id, user_id, version_no: int
                     tool_config.metadata = langchain_tool
                     break
 
-        # special logic for knowledge base search tool
+        # special logic for search tools that may use reranking models
         if tool_config.class_name == "KnowledgeBaseSearchTool":
-           tool_config.metadata = {
+            rerank = param_dict.get("rerank", False)
+            rerank_model_name = param_dict.get("rerank_model_name", "")
+            rerank_model = None
+            if rerank and rerank_model_name:
+                rerank_model = get_rerank_model(
+                    tenant_id=tenant_id, model_name=rerank_model_name
+                )
+
+            tool_config.metadata = {
                 "vdb_core": get_vector_db_core(),
                 "embedding_model": get_embedding_model(tenant_id=tenant_id),
+                "rerank_model": rerank_model,
+            }
+        elif tool_config.class_name in ["DifySearchTool", "DataMateSearchTool"]:
+            rerank = param_dict.get("rerank", False)
+            rerank_model_name = param_dict.get("rerank_model_name", "")
+            rerank_model = None
+            if rerank and rerank_model_name:
+                rerank_model = get_rerank_model(
+                    tenant_id=tenant_id, model_name=rerank_model_name
+                )
+
+            tool_config.metadata = {
+                "rerank_model": rerank_model,
             }
         elif tool_config.class_name == "AnalyzeTextFileTool":
             tool_config.metadata = {

backend/services/model_health_service.py

@@ -3,6 +3,7 @@
 from nexent.core import MessageObserver
 from nexent.core.models import OpenAIModel, OpenAIVLModel
 from nexent.core.models.embedding_model import JinaEmbedding, OpenAICompatibleEmbedding
+from nexent.core.models.rerank_model import OpenAICompatibleRerank
 
 from services.voice_service import get_voice_service
 from consts.const import LOCALHOST_IP, LOCALHOST_NAME, DOCKER_INTERNAL_HOST
@@ -102,7 +103,13 @@ async def _perform_connectivity_check(
             ssl_verify=ssl_verify
         ).check_connectivity()
     elif model_type == "rerank":
-        connectivity = False
+        rerank_model = OpenAICompatibleRerank(
+            model_name=model_name,
+            base_url=model_base_url,
+            api_key=model_api_key,
+            ssl_verify=ssl_verify,
+        )
+        connectivity = await rerank_model.connectivity_check()
     elif model_type == "vlm":
         observer = MessageObserver()
         connectivity = await OpenAIVLModel(

backend/services/model_provider_service.py

@@ -132,6 +132,11 @@ async def prepare_model_dict(provider: str, model: dict, model_url: str, model_a
             model_dict["base_url"] = f"{model_url.rstrip('/')}/{MODEL_ENGINE_NORTH_PREFIX}/embeddings"
         # The embedding dimension might differ from the provided max_tokens.
         model_dict["max_tokens"] = await embedding_dimension_check(model_dict)
+    elif model["model_type"] == "rerank":
+        if provider == ProviderEnum.DASHSCOPE.value:
+            model_dict["base_url"] = f"{model_url.replace('compatible-mode/v1','api/v1').rstrip('/')}/services/rerank/text-rerank/text-rerank"
+        else:
+            model_dict["base_url"] = f"{model_url.rstrip('/')}/rerank" 
     else:
         # For non-embedding models
         if provider == ProviderEnum.MODELENGINE.value:

backend/services/providers/dashscope_provider.py

@@ -58,7 +58,7 @@ async def get_models(self, provider_config: Dict) -> List[Dict]:
                 "chat": [],  # Maps to "llm"
                 "vlm": [],  # Maps to "vlm"
                 "embedding": [],  # Maps to "embedding" / "multi_embedding"
-                "reranker": [],  # Maps to "reranker"
+                "rerank": [],  # Maps to "rerank"
                 "tts": [],  # Maps to "tts"
                 "stt": []  # Maps to "stt"
             }
@@ -88,10 +88,10 @@ async def get_models(self, provider_config: Dict) -> List[Dict]:
                     categorized_models['embedding'].append(cleaned_model)
                     continue
 
-                # 2. Reranker
+                # 2. Rerank
                 if 'rerank' in m_id.lower() or '重排序' in desc:
-                    cleaned_model.update({"model_tag": "reranker", "model_type": "reranker"})
-                    categorized_models['reranker'].append(cleaned_model)
+                    cleaned_model.update({"model_tag": "rerank", "model_type": "rerank"})
+                    categorized_models['rerank'].append(cleaned_model)
                     continue
 
                 # 3. STT

backend/services/providers/silicon_provider.py

@@ -30,6 +30,8 @@ async def get_models(self, provider_config: Dict) -> List[Dict]:
                 silicon_url = f"{SILICON_GET_URL}?sub_type=chat"
             elif model_type in ("embedding", "multi_embedding"):
                 silicon_url = f"{SILICON_GET_URL}?sub_type=embedding"
+            elif model_type == "rerank":
+                silicon_url = f"{SILICON_GET_URL}?sub_type=reranker"
             else:
                 silicon_url = SILICON_GET_URL
 
@@ -48,6 +50,10 @@ async def get_models(self, provider_config: Dict) -> List[Dict]:
                 for item in model_list:
                     item["model_tag"] = "embedding"
                     item["model_type"] = model_type
+            elif model_type == "rerank":
+                for item in model_list:
+                    item["model_tag"] = "rerank"
+                    item["model_type"] = model_type
 
             # Return empty list to indicate successful API call but no models
             if not model_list:

backend/services/providers/tokenpony_provider.py

@@ -47,7 +47,7 @@ async def get_models(self, provider_config: Dict) -> List[Dict]:
                 "chat": [],       # Maps to "llm"
                 "vlm": [],        # Maps to "vlm"
                 "embedding": [],  # Maps to "embedding" / "multi_embedding"
-                "reranker": [],   # Maps to "reranker"
+                "rerank": [],   # Maps to "rerank"
                 "tts": [],        # Maps to "tts"
                 "stt": []         # Maps to "stt"
             }
@@ -66,10 +66,10 @@ async def get_models(self, provider_config: Dict) -> List[Dict]:
                     "model_type": "",
                     "max_tokens": DEFAULT_LLM_MAX_TOKENS
                 }
-                # 1. reranker
+                # 1. rerank
                 if 'rerank' in m_id:
-                    cleaned_model.update({"model_tag": "reranker", "model_type": "reranker"})
-                    categorized_models['reranker'].append(cleaned_model)
+                    cleaned_model.update({"model_tag": "rerank", "model_type": "rerank"})
+                    categorized_models['rerank'].append(cleaned_model)
                 #2. embedding
                 elif 'embedding' in m_id or m_id.startswith('bge-'):
                     cleaned_model.update({"model_tag": "embedding", "model_type": "embedding"})

backend/services/tool_configuration_service.py

@@ -28,7 +28,7 @@
     check_tool_list_initialized,
 )
 from services.file_management_service import get_llm_model
-from services.vectordatabase_service import get_embedding_model, get_vector_db_core
+from services.vectordatabase_service import get_embedding_model, get_rerank_model, get_vector_db_core
 from database.client import minio_client
 from services.image_service import get_vlm_model
 from utils.tool_utils import get_local_tools_classes, get_local_tools_description_zh
@@ -694,10 +694,32 @@ def _validate_local_tool(
         if tool_name == "knowledge_base_search":
             embedding_model = get_embedding_model(tenant_id=tenant_id)
             vdb_core = get_vector_db_core()
+
+            # Get rerank configuration
+            rerank = instantiation_params.get("rerank", False)
+            rerank_model_name = instantiation_params.get("rerank_model_name", "")
+            rerank_model = None
+            if rerank and rerank_model_name:
+                rerank_model = get_rerank_model(tenant_id=tenant_id, model_name=rerank_model_name)
+
             params = {
                 **instantiation_params,
                 'vdb_core': vdb_core,
                 'embedding_model': embedding_model,
+                'rerank_model': rerank_model,
+            }
+            tool_instance = tool_class(**params)
+        elif tool_name in ["dify_search", "datamate_search"]:
+            # Get rerank configuration for dify and datamate search tools
+            rerank = instantiation_params.get("rerank", False)
+            rerank_model_name = instantiation_params.get("rerank_model_name", "")
+            rerank_model = None
+            if rerank and rerank_model_name:
+                rerank_model = get_rerank_model(tenant_id=tenant_id, model_name=rerank_model_name)
+
+            params = {
+                **instantiation_params,
+                'rerank_model': rerank_model,
             }
             tool_instance = tool_class(**params)
         elif tool_name == "analyze_image":

backend/services/vectordatabase_service.py

@@ -21,6 +21,7 @@
 from fastapi import Body, Depends, Path, Query
 from fastapi.responses import StreamingResponse
 from nexent.core.models.embedding_model import OpenAICompatibleEmbedding, JinaEmbedding, BaseEmbedding
+from nexent.core.models.rerank_model import OpenAICompatibleRerank, BaseRerank
 from nexent.vector_database.base import VectorDatabaseCore
 from nexent.vector_database.elasticsearch_core import ElasticSearchCore
 from nexent.vector_database.datamate_core import DataMateCore
@@ -241,6 +242,52 @@ def get_embedding_model(tenant_id: str, model_name: Optional[str] = None):
         return None
 
 
+def get_rerank_model(tenant_id: str, model_name: Optional[str] = None):
+    """
+    Get the rerank model for the tenant, optionally using a specific model name.
+
+    Args:
+        tenant_id: Tenant ID
+        model_name: Optional specific model name to use (format: "model_repo/model_name" or just "model_name")
+                   If provided, will try to find the model in the tenant's model list.
+
+    Returns:
+        Rerank model instance or None
+    """
+    # If model_name is provided, try to find it in the tenant's models
+    if model_name:
+        try:
+            models = get_model_records({"model_type": "rerank"}, tenant_id)
+            for model in models:
+                model_display_name = model.get("model_repo") + "/" + model["model_name"] if model.get("model_repo") else model["model_name"]
+                if model_display_name == model_name:
+                    # Found the model, create rerank model instance
+                    return OpenAICompatibleRerank(
+                        model_name=get_model_name_from_config(model) or "",
+                        base_url=model.get("base_url", ""),
+                        api_key=model.get("api_key", ""),
+                        ssl_verify=model.get("ssl_verify", True),
+                    )
+        except Exception as e:
+            logger.warning(f"Failed to get rerank model by name {model_name}: {e}")
+
+    # Fall back to default rerank model
+    model_config = tenant_config_manager.get_model_config(
+        key="RERANK_ID", tenant_id=tenant_id)
+
+    model_type = model_config.get("model_type", "")
+
+    if model_type == "rerank":
+        return OpenAICompatibleRerank(
+            model_name=get_model_name_from_config(model_config) or "",
+            base_url=model_config.get("base_url", ""),
+            api_key=model_config.get("api_key", ""),
+            ssl_verify=model_config.get("ssl_verify", True),
+        )
+    else:
+        return None
+
+
 class ElasticSearchService:
     @staticmethod
     async def full_delete_knowledge_base(index_name: str, vdb_core: VectorDatabaseCore, user_id: str):

doc/docs/zh/user-guide/agent-development.md

@@ -130,10 +130,11 @@
      - 检索的模式 `search_mode`（默认为 `hybrid`）
      - 目标检索的知识库列表 `index_names`，如 `["医疗", "维生素知识大全"]`
      - 若不输入 `index_names`，则默认检索知识库页面所选中的全部知识库
+     - 是否启用重排模型（默认为 `false`），启用后配置重排模型，实现对检索结果的重排优化 
 6. 输入完成后点击"执行测试"开始测试，并在下方查看测试结果
 
 <div style="display: flex; justify-content: left;">
-  <img src="./assets/agent-development/tool-test-run.png" style="width: 80%; height: auto;" />
+  <img src="./assets/agent-development/tool-test-run-1.png" style="width: 80%; height: auto;" />
 </div>
 
 ## 📝 描述业务逻辑