feat: optimize override app model config convert (#874)

api/core/agent/agent/structured_chat.py

@@ -112,7 +112,7 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
                                           "I don't know how to respond to that."}, "")
 
     def summarize_messages(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs):
-        if len(intermediate_steps) >= 2:
+        if len(intermediate_steps) >= 2 and self.summary_llm:
             should_summary_intermediate_steps = intermediate_steps[self.moving_summary_index:-1]
             should_summary_messages = [AIMessage(content=observation)
                                        for _, observation in should_summary_intermediate_steps]

api/core/agent/agent_executor.py

@@ -65,7 +65,8 @@ class AgentExecutor:
                 llm=self.configuration.model_instance.client,
                 tools=self.configuration.tools,
                 output_parser=StructuredChatOutputParser(),
-                summary_llm=self.configuration.summary_model_instance.client,
+                summary_llm=self.configuration.summary_model_instance.client
+                if self.configuration.summary_model_instance else None,
                 verbose=True
             )
         elif self.configuration.strategy == PlanningStrategy.FUNCTION_CALL:
@@ -74,7 +75,8 @@ class AgentExecutor:
                 llm=self.configuration.model_instance.client,
                 tools=self.configuration.tools,
                 extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,  # used for read chat histories memory
-                summary_llm=self.configuration.summary_model_instance.client,
+                summary_llm=self.configuration.summary_model_instance.client
+                if self.configuration.summary_model_instance else None,
                 verbose=True
             )
         elif self.configuration.strategy == PlanningStrategy.MULTI_FUNCTION_CALL:
@@ -83,7 +85,8 @@ class AgentExecutor:
                 llm=self.configuration.model_instance.client,
                 tools=self.configuration.tools,
                 extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,  # used for read chat histories memory
-                summary_llm=self.configuration.summary_model_instance.client,
+                summary_llm=self.configuration.summary_model_instance.client
+                if self.configuration.summary_model_instance else None,
                 verbose=True
             )
         elif self.configuration.strategy == PlanningStrategy.ROUTER:

api/core/conversation_message_task.py

@@ -60,17 +60,7 @@ class ConversationMessageTask:
     def init(self):
         override_model_configs = None
         if self.is_override:
-            override_model_configs = {
-                "model": self.app_model_config.model_dict,
-                "pre_prompt": self.app_model_config.pre_prompt,
-                "agent_mode": self.app_model_config.agent_mode_dict,
-                "opening_statement": self.app_model_config.opening_statement,
-                "suggested_questions": self.app_model_config.suggested_questions_list,
-                "suggested_questions_after_answer": self.app_model_config.suggested_questions_after_answer_dict,
-                "more_like_this": self.app_model_config.more_like_this_dict,
-                "sensitive_word_avoidance": self.app_model_config.sensitive_word_avoidance_dict,
-                "user_input_form": self.app_model_config.user_input_form_list,
-            }
+            override_model_configs = self.app_model_config.to_dict()
 
         introduction = ''
         system_instruction = ''

api/core/generator/llm_generator.py

@@ -2,7 +2,7 @@ import logging
 
 from langchain.schema import OutputParserException
 
-from core.model_providers.error import LLMError
+from core.model_providers.error import LLMError, ProviderTokenNotInitError
 from core.model_providers.model_factory import ModelFactory
 from core.model_providers.models.entity.message import PromptMessage, MessageType
 from core.model_providers.models.entity.model_params import ModelKwargs
@@ -108,13 +108,16 @@ class LLMGenerator:
 
         _input = prompt.format_prompt(histories=histories)
 
-        model_instance = ModelFactory.get_text_generation_model(
-            tenant_id=tenant_id,
-            model_kwargs=ModelKwargs(
-                max_tokens=256,
-                temperature=0
+        try:
+            model_instance = ModelFactory.get_text_generation_model(
+                tenant_id=tenant_id,
+                model_kwargs=ModelKwargs(
+                    max_tokens=256,
+                    temperature=0
+                )
             )
-        )
+        except ProviderTokenNotInitError:
+            return []
 
         prompts = [PromptMessage(content=_input.to_string())]
 

api/core/orchestrator_rule_parser.py

@@ -14,6 +14,7 @@ from core.callback_handler.main_chain_gather_callback_handler import MainChainGa
 from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
 from core.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceChain
 from core.conversation_message_task import ConversationMessageTask
+from core.model_providers.error import ProviderTokenNotInitError
 from core.model_providers.model_factory import ModelFactory
 from core.model_providers.models.entity.model_params import ModelKwargs, ModelMode
 from core.tool.dataset_retriever_tool import DatasetRetrieverTool
@@ -78,13 +79,16 @@ class OrchestratorRuleParser:
                 elif planning_strategy == PlanningStrategy.ROUTER:
                     planning_strategy = PlanningStrategy.REACT_ROUTER
 
-            summary_model_instance = ModelFactory.get_text_generation_model(
-                tenant_id=self.tenant_id,
-                model_kwargs=ModelKwargs(
-                    temperature=0,
-                    max_tokens=500
+            try:
+                summary_model_instance = ModelFactory.get_text_generation_model(
+                    tenant_id=self.tenant_id,
+                    model_kwargs=ModelKwargs(
+                        temperature=0,
+                        max_tokens=500
+                    )
                 )
-            )
+            except ProviderTokenNotInitError as e:
+                summary_model_instance = None
 
             tools = self.to_tools(
                 tool_configs=tool_configs,

api/core/prompt/output_parser/suggested_questions_after_answer.py

@@ -1,7 +1,10 @@
 import json
+import re
 from typing import Any
 
 from langchain.schema import BaseOutputParser
+
+from core.model_providers.error import LLMError
 from core.prompt.prompts import SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT
 
 
@@ -12,5 +15,10 @@ class SuggestedQuestionsAfterAnswerOutputParser(BaseOutputParser):
 
     def parse(self, text: str) -> Any:
         json_string = text.strip()
-        json_obj = json.loads(json_string)
+        action_match = re.search(r".*(\[\".+\"\]).*", json_string, re.DOTALL)
+        if action_match is not None:
+            json_obj = json.loads(action_match.group(1).strip(), strict=False)
+        else:
+            raise LLMError("Could not parse LLM output: {text}")
+
         return json_obj