fix: agent parse error

api/core/callback_handler/agent_loop_gather_callback_handler.py

@@ -6,7 +6,7 @@ from typing import Any, Dict, List, Union, Optional
 
 from langchain.agents import openai_functions_agent, openai_functions_multi_agent
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult
+from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration
 
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.conversation_message_task import ConversationMessageTask
@@ -66,12 +66,17 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         if self._current_loop and self._current_loop.status == 'llm_started':
             self._current_loop.status = 'llm_end'
             self._current_loop.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
-            completion_message = response.generations[0][0].message
+            completion_generation = response.generations[0][0]
+            if isinstance(completion_generation, ChatGeneration):
+                completion_message = completion_generation.message
                 if 'function_call' in completion_message.additional_kwargs:
                     self._current_loop.completion \
                         = json.dumps({'function_call': completion_message.additional_kwargs['function_call']})
                 else:
                     self._current_loop.completion = response.generations[0][0].text
+            else:
+                self._current_loop.completion = completion_generation.text
+
             self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens']
 
     def on_llm_error(

api/core/orchestrator_rule_parser.py

@@ -30,7 +30,6 @@ class OrchestratorRuleParser:
     def __init__(self, tenant_id: str, app_model_config: AppModelConfig):
         self.tenant_id = tenant_id
         self.app_model_config = app_model_config
-        self.agent_summary_model_name = "gpt-3.5-turbo-16k"
 
     def to_agent_executor(self, conversation_message_task: ConversationMessageTask, memory: Optional[BaseChatMemory],
                        rest_tokens: int, chain_callback: MainChainGatherCallbackHandler) \
@@ -71,7 +70,7 @@ class OrchestratorRuleParser:
 
             summary_llm = LLMBuilder.to_llm(
                 tenant_id=self.tenant_id,
-                model_name=self.agent_summary_model_name,
+                model_name=agent_model_name,
                 temperature=0,
                 max_tokens=500,
                 callbacks=[DifyStdOutCallbackHandler()]

api/services/app_model_config_service.py

@@ -316,7 +316,7 @@ class AppModelConfigService:
         if "strategy" not in config["agent_mode"] or not config["agent_mode"]["strategy"]:
             config["agent_mode"]["strategy"] = PlanningStrategy.ROUTER.value
 
-        if config["agent_mode"]["strategy"] not in PlanningStrategy.__members__:
+        if config["agent_mode"]["strategy"] not in [member.value for member in list(PlanningStrategy.__members__.values())]:
             raise ValueError("strategy in agent_mode must be in the specified strategy list")
 
         if "tools" not in config["agent_mode"] or not config["agent_mode"]["tools"]: