fix: respect resolution settings for vision for basic chatbot, text generator, and parameter extractor node (#16041)

api/core/app/apps/base_app_runner.py

 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
-from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    ImagePromptMessageContent,
+    PromptMessage,
+)
 from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.errors.invoke import InvokeBadRequestError
 from core.moderation.input_moderation import InputModeration
         query: Optional[str] = None,
         context: Optional[str] = None,
         memory: Optional[TokenBufferMemory] = None,
+        image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         """
         Organize prompt messages
                 context=context,
                 memory=memory,
                 model_config=model_config,
+                image_detail_config=image_detail_config,
             )
         else:
             memory_config = MemoryConfig(window=MemoryConfig.WindowConfig(enabled=False))
                 memory_config=memory_config,
                 memory=memory,
                 model_config=model_config,
+                image_detail_config=image_detail_config,
             )
             stop = model_config.stop
 

api/core/app/apps/chat/app_runner.py

 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent
 from core.moderation.base import ModerationError
 from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from extensions.ext_database import db
         query = application_generate_entity.query
         files = application_generate_entity.files
 
+        image_detail_config = (
+            application_generate_entity.file_upload_config.image_config.detail
+            if (
+                application_generate_entity.file_upload_config
+                and application_generate_entity.file_upload_config.image_config
+            )
+            else None
+        )
+        image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
         # Pre-calculate the number of tokens of the prompt messages,
         # and return the rest number of tokens by model context token size limit and max token size limit.
         # If the rest number of tokens is not enough, raise exception.
             files=files,
             query=query,
             memory=memory,
+            image_detail_config=image_detail_config,
         )
 
         # moderation
             query=query,
             context=context,
             memory=memory,
+            image_detail_config=image_detail_config,
         )
 
         # check hosting moderation

api/core/app/apps/completion/app_runner.py

 )
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.model_manager import ModelInstance
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent
 from core.moderation.base import ModerationError
 from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from extensions.ext_database import db
         query = application_generate_entity.query
         files = application_generate_entity.files
 
+        image_detail_config = (
+            application_generate_entity.file_upload_config.image_config.detail
+            if (
+                application_generate_entity.file_upload_config
+                and application_generate_entity.file_upload_config.image_config
+            )
+            else None
+        )
+        image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
         # Pre-calculate the number of tokens of the prompt messages,
         # and return the rest number of tokens by model context token size limit and max token size limit.
         # If the rest number of tokens is not enough, raise exception.
             inputs=inputs,
             files=files,
             query=query,
+            image_detail_config=image_detail_config,
         )
 
         # moderation
             files=files,
             query=query,
             context=context,
+            image_detail_config=image_detail_config,
         )
 
         # check hosting moderation

api/core/prompt/advanced_prompt_transform.py

         memory_config: Optional[MemoryConfig],
         memory: Optional[TokenBufferMemory],
         model_config: ModelConfigWithCredentialsEntity,
+        image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> list[PromptMessage]:
         prompt_messages = []
 
                 memory_config=memory_config,
                 memory=memory,
                 model_config=model_config,
+                image_detail_config=image_detail_config,
             )
         elif isinstance(prompt_template, list) and all(isinstance(item, ChatModelMessage) for item in prompt_template):
             prompt_messages = self._get_chat_model_prompt_messages(
                 memory_config=memory_config,
                 memory=memory,
                 model_config=model_config,
+                image_detail_config=image_detail_config,
             )
 
         return prompt_messages
         memory_config: Optional[MemoryConfig],
         memory: Optional[TokenBufferMemory],
         model_config: ModelConfigWithCredentialsEntity,
+        image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> list[PromptMessage]:
         """
         Get completion model prompt messages.
             prompt_message_contents: list[PromptMessageContent] = []
             prompt_message_contents.append(TextPromptMessageContent(data=prompt))
             for file in files:
-                prompt_message_contents.append(file_manager.to_prompt_message_content(file))
+                prompt_message_contents.append(
+                    file_manager.to_prompt_message_content(file, image_detail_config=image_detail_config)
+                )
 
             prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
         else:
         memory_config: Optional[MemoryConfig],
         memory: Optional[TokenBufferMemory],
         model_config: ModelConfigWithCredentialsEntity,
+        image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> list[PromptMessage]:
         """
         Get chat model prompt messages.
                 prompt_message_contents: list[PromptMessageContent] = []
                 prompt_message_contents.append(TextPromptMessageContent(data=query))
                 for file in files:
-                    prompt_message_contents.append(file_manager.to_prompt_message_content(file))
+                    prompt_message_contents.append(
+                        file_manager.to_prompt_message_content(file, image_detail_config=image_detail_config)
+                    )
                 prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
             else:
                 prompt_messages.append(UserPromptMessage(content=query))
                     # get last user message content and add files
                     prompt_message_contents = [TextPromptMessageContent(data=cast(str, last_message.content))]
                     for file in files:
-                        prompt_message_contents.append(file_manager.to_prompt_message_content(file))
+                        prompt_message_contents.append(
+                            file_manager.to_prompt_message_content(file, image_detail_config=image_detail_config)
+                        )
 
                     last_message.content = prompt_message_contents
                 else:
                     prompt_message_contents = [TextPromptMessageContent(data="")]  # not for query
                     for file in files:
-                        prompt_message_contents.append(file_manager.to_prompt_message_content(file))
+                        prompt_message_contents.append(
+                            file_manager.to_prompt_message_content(file, image_detail_config=image_detail_config)
+                        )
 
                     prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
             else:
                 prompt_message_contents = [TextPromptMessageContent(data=query)]
                 for file in files:
-                    prompt_message_contents.append(file_manager.to_prompt_message_content(file))
+                    prompt_message_contents.append(
+                        file_manager.to_prompt_message_content(file, image_detail_config=image_detail_config)
+                    )
 
                 prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
         elif query:

api/core/prompt/simple_prompt_transform.py

 from core.file import file_manager
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.entities.message_entities import (
+    ImagePromptMessageContent,
     PromptMessage,
     PromptMessageContent,
     SystemPromptMessage,
         context: Optional[str],
         memory: Optional[TokenBufferMemory],
         model_config: ModelConfigWithCredentialsEntity,
+        image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         inputs = {key: str(value) for key, value in inputs.items()}
 
                 context=context,
                 memory=memory,
                 model_config=model_config,
+                image_detail_config=image_detail_config,
             )
         else:
             prompt_messages, stops = self._get_completion_model_prompt_messages(
                 context=context,
                 memory=memory,
                 model_config=model_config,
+                image_detail_config=image_detail_config,
             )
 
         return prompt_messages, stops
         files: Sequence["File"],
         memory: Optional[TokenBufferMemory],
         model_config: ModelConfigWithCredentialsEntity,
+        image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         prompt_messages: list[PromptMessage] = []
 
             )
 
         if query:
-            prompt_messages.append(self.get_last_user_message(query, files))
+            prompt_messages.append(self.get_last_user_message(query, files, image_detail_config))
         else:
-            prompt_messages.append(self.get_last_user_message(prompt, files))
+            prompt_messages.append(self.get_last_user_message(prompt, files, image_detail_config))
 
         return prompt_messages, None
 
         files: Sequence["File"],
         memory: Optional[TokenBufferMemory],
         model_config: ModelConfigWithCredentialsEntity,
+        image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         # get prompt
         prompt, prompt_rules = self.get_prompt_str_and_rules(
         if stops is not None and len(stops) == 0:
             stops = None
 
-        return [self.get_last_user_message(prompt, files)], stops
+        return [self.get_last_user_message(prompt, files, image_detail_config)], stops
 
-    def get_last_user_message(self, prompt: str, files: Sequence["File"]) -> UserPromptMessage:
+    def get_last_user_message(
+        self,
+        prompt: str,
+        files: Sequence["File"],
+        image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
+    ) -> UserPromptMessage:
         if files:
             prompt_message_contents: list[PromptMessageContent] = []
             prompt_message_contents.append(TextPromptMessageContent(data=prompt))
             for file in files:
-                prompt_message_contents.append(file_manager.to_prompt_message_content(file))
+                prompt_message_contents.append(
+                    file_manager.to_prompt_message_content(file, image_detail_config=image_detail_config)
+                )
 
             prompt_message = UserPromptMessage(content=prompt_message_contents)
         else:

api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py

 from core.file import File
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
+from core.model_runtime.entities import ImagePromptMessageContent
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
                 model_config=model_config,
                 memory=memory,
                 files=files,
+                vision_detail=node_data.vision.configs.detail,
             )
         else:
             # use prompt engineering
                 model_config=model_config,
                 memory=memory,
                 files=files,
+                vision_detail=node_data.vision.configs.detail,
             )
 
             prompt_message_tools = []
         model_config: ModelConfigWithCredentialsEntity,
         memory: Optional[TokenBufferMemory],
         files: Sequence[File],
+        vision_detail: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> tuple[list[PromptMessage], list[PromptMessageTool]]:
         """
         Generate function call prompt.
             memory_config=node_data.memory,
             memory=None,
             model_config=model_config,
+            image_detail_config=vision_detail,
         )
 
         # find last user message
         model_config: ModelConfigWithCredentialsEntity,
         memory: Optional[TokenBufferMemory],
         files: Sequence[File],
+        vision_detail: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> list[PromptMessage]:
         """
         Generate prompt engineering prompt.
                 model_config=model_config,
                 memory=memory,
                 files=files,
+                vision_detail=vision_detail,
             )
         elif model_mode == ModelMode.CHAT:
             return self._generate_prompt_engineering_chat_prompt(
                 model_config=model_config,
                 memory=memory,
                 files=files,
+                vision_detail=vision_detail,
             )
         else:
             raise InvalidModelModeError(f"Invalid model mode: {model_mode}")
         model_config: ModelConfigWithCredentialsEntity,
         memory: Optional[TokenBufferMemory],
         files: Sequence[File],
+        vision_detail: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> list[PromptMessage]:
         """
         Generate completion prompt.
             memory_config=node_data.memory,
             memory=memory,
             model_config=model_config,
+            image_detail_config=vision_detail,
         )
 
         return prompt_messages
         model_config: ModelConfigWithCredentialsEntity,
         memory: Optional[TokenBufferMemory],
         files: Sequence[File],
+        vision_detail: Optional[ImagePromptMessageContent.DETAIL] = None,
     ) -> list[PromptMessage]:
         """
         Generate chat prompt.
             memory_config=node_data.memory,
             memory=None,
             model_config=model_config,
+            image_detail_config=vision_detail,
         )
 
         # find last user message