feat(Agent): change plugin path

change plugin path

dbgpt/agent/agents/agent.py

@@ -6,7 +6,7 @@
 from typing import Any, Dict, List, Optional, Union
 
 from ..memory.gpts_memory import GptsMemory
-from dbgpt.core import LLMClient
+from dbgpt.core.interface.llm import ModelMetadata
 
 class Agent:
     """
@@ -166,13 +166,13 @@ def to_dict(self) -> Dict[str, Any]:
 @dataclass
 class AgentContext:
     conv_id: str
-    llm_provider: Optional[LLMClient]
+    llm_provider: Optional['LLMClient']
 
     gpts_name: Optional[str] = None
     resource_db: Optional[AgentResource] = None
     resource_knowledge: Optional[AgentResource] = None
     resource_internet: Optional[AgentResource] = None
-    llm_models: Optional[List[str]] = None
+    llm_models: Optional[List[ModelMetadata]] = None
     model_priority: Optional[dict] = None
     agents: Optional[List[str]] = None
 

dbgpt/agent/agents/base_agent.py

@@ -637,29 +637,33 @@ def _get_model_priority(self):
             return None
 
 
+    def _filter_health_models(self, need_uses: Optional[list]):
+        all_modes = self.agent_context.llm_models
+        can_uses = []
+        for item in all_modes:
+            if item.model in need_uses:
+                can_uses.append(item)
+        return can_uses
+
+
     def _select_llm_model(self, old_model: str = None):
         """
         LLM model selector, currently only supports manual selection, more strategies will be opened in the future
         Returns:
-
         """
         all_modes = self.agent_context.llm_models
         model_priority = self._get_model_priority()
         if model_priority and len(model_priority) > 0:
-            for model in model_priority:
-                if old_model and model == old_model:
-                    continue
-                if model in all_modes:
-                    return model
+            can_uses = self._filter_health_models(model_priority)
+            if len(can_uses) > 0:
+                return can_uses[0].model
 
+        now_model = all_modes[0]
         if old_model:
-            filtered_list = [item for item in all_modes if item != old_model]
+            filtered_list = [item for item in all_modes if item.model != old_model]
             if filtered_list and len(filtered_list) >= 1:
-                return filtered_list[0]
-            else:
-                return all_modes[0]
-        else:
-            return all_modes[0]
+                now_model= filtered_list[0]
+        return now_model.model
 
     async def a_reasoning_reply(
         self, messages: Union[List[Dict]]

dbgpt/agent/agents/planner_agent.py

@@ -92,7 +92,6 @@ def __init__(
         super().__init__(
             name=self.NAME,
             memory=memory,
-            llm_operator=llm_operator,
             system_message=self.DEFAULT_SYSTEM_MESSAGE,
             is_termination_msg=is_termination_msg,
             max_consecutive_auto_reply=max_consecutive_auto_reply,

dbgpt/serve/agent/agents/controller.py

@@ -44,6 +44,7 @@
 from dbgpt._private.config import Config
 from dbgpt.model.cluster.controller.controller import BaseModelController
 from dbgpt.agent.memory.gpts_memory import GptsMessage
+from dbgpt.model.cluster import WorkerManager, WorkerManagerFactory
 
 from dbgpt.model.cluster.client import DefaultLLMClient
 
@@ -100,7 +101,10 @@ async def plan_chat(
         )
 
         ### init chat param
-        llm_task =  DefaultLLMClient()
+        worker_manager = CFG.SYSTEM_APP.get_component(
+            ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
+        ).create()
+        llm_task =  DefaultLLMClient(worker_manager)
         context: AgentContext = AgentContext(
             conv_id=conv_id, llm_provider=llm_task
         )
@@ -110,7 +114,7 @@ async def plan_chat(
         context.resource_knowledge = resource_knowledge
         context.agents = agents_names
 
-        context.llm_models =  llm_task.models()
+        context.llm_models =  await llm_task.models()
         context.model_priority = llm_models_priority
 
         agent_map = defaultdict()