Updates poetry.lock content hash for consistency

Changes the content hash in poetry.lock to ensure the lock
file's integrity aligns with the updated project dependencies.
No package versions changed in this update.

api/configs/app_config.py

@@ -27,7 +27,6 @@ class DifyConfig(
         # read from dotenv format config file
         env_file=".env",
         env_file_encoding="utf-8",
-        frozen=True,
         # ignore extra attributes
         extra="ignore",
     )

api/core/app/app_config/easy_ui_based_app/model_config/converter.py

@@ -11,7 +11,7 @@ from core.provider_manager import ProviderManager
 
 class ModelConfigConverter:
     @classmethod
-    def convert(cls, app_config: EasyUIBasedAppConfig, skip_check: bool = False) -> ModelConfigWithCredentialsEntity:
+    def convert(cls, app_config: EasyUIBasedAppConfig) -> ModelConfigWithCredentialsEntity:
         """
         Convert app model config dict to entity.
         :param app_config: app config
@@ -38,12 +38,8 @@ class ModelConfigConverter:
         )
 
         if model_credentials is None:
-            if not skip_check:
             raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
-            else:
-                model_credentials = {}
 
-        if not skip_check:
         # check model
         provider_model = provider_model_bundle.configuration.get_provider_model(
             model=model_config.model, model_type=ModelType.LLM
@@ -76,7 +72,7 @@ class ModelConfigConverter:
 
         model_schema = model_type_instance.get_model_schema(model_config.model, model_credentials)
 
-        if not skip_check and not model_schema:
+        if not model_schema:
             raise ValueError(f"Model {model_name} not exist.")
 
         return ModelConfigWithCredentialsEntity(

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -217,6 +217,7 @@ class WorkflowCycleManage:
             ).total_seconds()
             db.session.commit()
 
+        db.session.add(workflow_run)
         db.session.refresh(workflow_run)
         db.session.close()
 

api/core/file/file_manager.py

@@ -74,6 +74,8 @@ def to_prompt_message_content(
                 data = _to_url(f)
             else:
                 data = _to_base64_data_string(f)
+            if f.extension is None:
+                raise ValueError("Missing file extension")
             return VideoPromptMessageContent(data=data, format=f.extension.lstrip("."))
         case _:
             raise ValueError("file type f.type is not supported")

api/core/memory/token_buffer_memory.py

@@ -1,3 +1,4 @@
+from collections.abc import Sequence
 from typing import Optional
 
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
@@ -27,7 +28,7 @@ class TokenBufferMemory:
 
     def get_history_prompt_messages(
         self, max_token_limit: int = 2000, message_limit: Optional[int] = None
-    ) -> list[PromptMessage]:
+    ) -> Sequence[PromptMessage]:
         """
         Get history prompt messages.
         :param max_token_limit: max token limit

api/core/model_manager.py

@@ -100,10 +100,10 @@ class ModelInstance:
 
     def invoke_llm(
         self,
-        prompt_messages: list[PromptMessage],
+        prompt_messages: Sequence[PromptMessage],
         model_parameters: Optional[dict] = None,
         tools: Sequence[PromptMessageTool] | None = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,

api/core/model_runtime/callbacks/base_callback.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from collections.abc import Sequence
 from typing import Optional
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
@@ -31,7 +32,7 @@ class Callback(ABC):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> None:
@@ -60,7 +61,7 @@ class Callback(ABC):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ):
@@ -90,7 +91,7 @@ class Callback(ABC):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> None:
@@ -120,7 +121,7 @@ class Callback(ABC):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> None:

api/core/model_runtime/entities/message_entities.py

@@ -1,4 +1,5 @@
 from abc import ABC
+from collections.abc import Sequence
 from enum import Enum
 from typing import Optional
 
@@ -57,6 +58,7 @@ class PromptMessageContentType(Enum):
     IMAGE = "image"
     AUDIO = "audio"
     VIDEO = "video"
+    DOCUMENT = "document"
 
 
 class PromptMessageContent(BaseModel):
@@ -107,7 +109,7 @@ class PromptMessage(ABC, BaseModel):
     """
 
     role: PromptMessageRole
-    content: Optional[str | list[PromptMessageContent]] = None
+    content: Optional[str | Sequence[PromptMessageContent]] = None
     name: Optional[str] = None
 
     def is_empty(self) -> bool:

api/core/model_runtime/entities/model_entities.py

@@ -87,6 +87,9 @@ class ModelFeature(Enum):
     AGENT_THOUGHT = "agent-thought"
     VISION = "vision"
     STREAM_TOOL_CALL = "stream-tool-call"
+    DOCUMENT = "document"
+    VIDEO = "video"
+    AUDIO = "audio"
 
 
 class DefaultParameterName(str, Enum):

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -2,7 +2,7 @@ import logging
 import re
 import time
 from abc import abstractmethod
-from collections.abc import Generator, Mapping
+from collections.abc import Generator, Mapping, Sequence
 from typing import Optional, Union
 
 from pydantic import ConfigDict
@@ -48,7 +48,7 @@ class LargeLanguageModel(AIModel):
         prompt_messages: list[PromptMessage],
         model_parameters: Optional[dict] = None,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -169,7 +169,7 @@ class LargeLanguageModel(AIModel):
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -212,7 +212,7 @@ if you are not sure about the structure.
             )
 
         model_parameters.pop("response_format")
-        stop = stop or []
+        stop = list(stop) if stop is not None else []
         stop.extend(["\n```", "```\n"])
         block_prompts = block_prompts.replace("{{block}}", code_block)
 
@@ -408,7 +408,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -479,7 +479,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
@@ -601,7 +601,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -647,7 +647,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -694,7 +694,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,
@@ -742,7 +742,7 @@ if you are not sure about the structure.
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
         tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
         callbacks: Optional[list[Callback]] = None,

api/core/model_runtime/model_providers/openai/llm/gpt-4o-audio-preview.yaml

@@ -8,6 +8,7 @@ features:
   - agent-thought
   - stream-tool-call
   - vision
+  - audio
 model_properties:
   mode: chat
   context_size: 128000

api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/common.py

@@ -1,5 +1,6 @@
 import json
 import random
+from collections import UserDict
 from datetime import datetime
 
 
@@ -10,9 +11,9 @@ class ChatRole:
     FUNCTION = "function"
 
 
-class _Dict(dict):
-    __setattr__ = dict.__setitem__
-    __getattr__ = dict.__getitem__
+class _Dict(UserDict):
+    __setattr__ = UserDict.__setitem__
+    __getattr__ = UserDict.__getitem__
 
     def __missing__(self, key):
         return None

api/core/prompt/utils/prompt_message_util.py

@@ -1,3 +1,4 @@
+from collections.abc import Sequence
 from typing import cast
 
 from core.model_runtime.entities import (
@@ -14,7 +15,7 @@ from core.prompt.simple_prompt_transform import ModelMode
 
 class PromptMessageUtil:
     @staticmethod
-    def prompt_messages_to_prompt_for_saving(model_mode: str, prompt_messages: list[PromptMessage]) -> list[dict]:
+    def prompt_messages_to_prompt_for_saving(model_mode: str, prompt_messages: Sequence[PromptMessage]) -> list[dict]:
         """
         Prompt messages to prompt for saving.
         :param model_mode: model mode

api/core/tools/provider/builtin/fal/tools/wizper.py

@@ -0,0 +1,52 @@
+import io
+import os
+from typing import Any
+
+import fal_client
+
+from core.file.enums import FileAttribute, FileType
+from core.file.file_manager import download, get_attr
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class WizperTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        audio_file = tool_parameters.get("audio_file")
+        task = tool_parameters.get("task", "transcribe")
+        language = tool_parameters.get("language", "en")
+        chunk_level = tool_parameters.get("chunk_level", "segment")
+        version = tool_parameters.get("version", "3")
+
+        if audio_file.type != FileType.AUDIO:
+            return [self.create_text_message("Not a valid audio file.")]
+
+        api_key = self.runtime.credentials["fal_api_key"]
+
+        os.environ["FAL_KEY"] = api_key
+
+        audio_binary = io.BytesIO(download(audio_file))
+        mime_type = get_attr(file=audio_file, attr=FileAttribute.MIME_TYPE)
+        file_data = audio_binary.getvalue()
+
+        try:
+            audio_url = fal_client.upload(file_data, mime_type)
+
+        except Exception as e:
+            return [self.create_text_message(f"Error uploading audio file: {str(e)}")]
+
+        arguments = {
+            "audio_url": audio_url,
+            "task": task,
+            "language": language,
+            "chunk_level": chunk_level,
+            "version": version,
+        }
+
+        result = fal_client.subscribe(
+            "fal-ai/wizper",
+            arguments=arguments,
+            with_logs=False,
+        )
+
+        return self.create_json_message(result)

api/core/tools/provider/builtin/fal/tools/wizper.yaml

@@ -0,0 +1,489 @@
+identity:
+  name: wizper
+  author: Kalo Chin
+  label:
+    en_US: Wizper
+    zh_Hans: Wizper
+description:
+  human:
+    en_US: Transcribe an audio file using the Whisper model.
+    zh_Hans: 使用 Whisper 模型转录音频文件。
+  llm: Transcribe an audio file using the Whisper model.
+parameters:
+  - name: audio_file
+    type: file
+    required: true
+    label:
+      en_US: Audio File
+      zh_Hans: 音频文件
+    human_description:
+      en_US: "Upload an audio file to transcribe. Supports mp3, mp4, mpeg, mpga, m4a, wav, or webm formats."
+      zh_Hans: "上传要转录的音频文件。支持 mp3、mp4、mpeg、mpga、m4a、wav 或 webm 格式。"
+    llm_description: "Audio file to transcribe. Supported formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm."
+    form: llm
+  - name: task
+    type: select
+    required: true
+    label:
+      en_US: Task
+      zh_Hans: 任务
+    human_description:
+      en_US: "Choose whether to transcribe the audio in its original language or translate it to English"
+      zh_Hans: "选择是以原始语言转录音频还是将其翻译成英语"
+    llm_description: "Task to perform on the audio file. Either transcribe or translate. Default value: 'transcribe'. If 'translate' is selected as the task, the audio will be translated to English, regardless of the language selected."
+    form: form
+    default: transcribe
+    options:
+      - value: transcribe
+        label:
+          en_US: Transcribe
+          zh_Hans: 转录
+      - value: translate
+        label:
+          en_US: Translate
+          zh_Hans: 翻译
+  - name: language
+    type: select
+    required: true
+    label:
+      en_US: Language
+      zh_Hans: 语言
+    human_description:
+      en_US: "Select the primary language spoken in the audio file"
+      zh_Hans: "选择音频文件中使用的主要语言"
+    llm_description: "Language of the audio file."
+    form: form
+    default: en
+    options:
+      - value: af
+        label:
+          en_US: Afrikaans
+          zh_Hans: 南非语
+      - value: am
+        label:
+          en_US: Amharic
+          zh_Hans: 阿姆哈拉语
+      - value: ar
+        label:
+          en_US: Arabic
+          zh_Hans: 阿拉伯语
+      - value: as
+        label:
+          en_US: Assamese
+          zh_Hans: 阿萨姆语
+      - value: az
+        label:
+          en_US: Azerbaijani
+          zh_Hans: 阿塞拜疆语
+      - value: ba
+        label:
+          en_US: Bashkir
+          zh_Hans: 巴什基尔语
+      - value: be
+        label:
+          en_US: Belarusian
+          zh_Hans: 白俄罗斯语
+      - value: bg
+        label:
+          en_US: Bulgarian
+          zh_Hans: 保加利亚语
+      - value: bn
+        label:
+          en_US: Bengali
+          zh_Hans: 孟加拉语
+      - value: bo
+        label:
+          en_US: Tibetan
+          zh_Hans: 藏语
+      - value: br
+        label:
+          en_US: Breton
+          zh_Hans: 布列塔尼语
+      - value: bs
+        label:
+          en_US: Bosnian
+          zh_Hans: 波斯尼亚语
+      - value: ca
+        label:
+          en_US: Catalan
+          zh_Hans: 加泰罗尼亚语
+      - value: cs
+        label:
+          en_US: Czech
+          zh_Hans: 捷克语
+      - value: cy
+        label:
+          en_US: Welsh
+          zh_Hans: 威尔士语
+      - value: da
+        label:
+          en_US: Danish
+          zh_Hans: 丹麦语
+      - value: de
+        label:
+          en_US: German
+          zh_Hans: 德语
+      - value: el
+        label:
+          en_US: Greek
+          zh_Hans: 希腊语
+      - value: en
+        label:
+          en_US: English
+          zh_Hans: 英语
+      - value: es
+        label:
+          en_US: Spanish
+          zh_Hans: 西班牙语
+      - value: et
+        label:
+          en_US: Estonian
+          zh_Hans: 爱沙尼亚语
+      - value: eu
+        label:
+          en_US: Basque
+          zh_Hans: 巴斯克语
+      - value: fa
+        label:
+          en_US: Persian
+          zh_Hans: 波斯语
+      - value: fi
+        label:
+          en_US: Finnish
+          zh_Hans: 芬兰语
+      - value: fo
+        label:
+          en_US: Faroese
+          zh_Hans: 法罗语
+      - value: fr
+        label:
+          en_US: French
+          zh_Hans: 法语
+      - value: gl
+        label:
+          en_US: Galician
+          zh_Hans: 加利西亚语
+      - value: gu
+        label:
+          en_US: Gujarati
+          zh_Hans: 古吉拉特语
+      - value: ha
+        label:
+          en_US: Hausa
+          zh_Hans: 毫萨语
+      - value: haw
+        label:
+          en_US: Hawaiian
+          zh_Hans: 夏威夷语
+      - value: he
+        label:
+          en_US: Hebrew
+          zh_Hans: 希伯来语
+      - value: hi
+        label:
+          en_US: Hindi
+          zh_Hans: 印地语
+      - value: hr
+        label:
+          en_US: Croatian
+          zh_Hans: 克罗地亚语
+      - value: ht
+        label:
+          en_US: Haitian Creole
+          zh_Hans: 海地克里奥尔语
+      - value: hu
+        label:
+          en_US: Hungarian
+          zh_Hans: 匈牙利语
+      - value: hy
+        label:
+          en_US: Armenian
+          zh_Hans: 亚美尼亚语
+      - value: id
+        label:
+          en_US: Indonesian
+          zh_Hans: 印度尼西亚语
+      - value: is
+        label:
+          en_US: Icelandic
+          zh_Hans: 冰岛语
+      - value: it
+        label:
+          en_US: Italian
+          zh_Hans: 意大利语
+      - value: ja
+        label:
+          en_US: Japanese
+          zh_Hans: 日语
+      - value: jw
+        label:
+          en_US: Javanese
+          zh_Hans: 爪哇语
+      - value: ka
+        label:
+          en_US: Georgian
+          zh_Hans: 格鲁吉亚语
+      - value: kk
+        label:
+          en_US: Kazakh
+          zh_Hans: 哈萨克语
+      - value: km
+        label:
+          en_US: Khmer
+          zh_Hans: 高棉语
+      - value: kn
+        label:
+          en_US: Kannada
+          zh_Hans: 卡纳达语
+      - value: ko
+        label:
+          en_US: Korean
+          zh_Hans: 韩语
+      - value: la
+        label:
+          en_US: Latin
+          zh_Hans: 拉丁语
+      - value: lb
+        label:
+          en_US: Luxembourgish
+          zh_Hans: 卢森堡语
+      - value: ln
+        label:
+          en_US: Lingala
+          zh_Hans: 林加拉语
+      - value: lo
+        label:
+          en_US: Lao
+          zh_Hans: 老挝语
+      - value: lt
+        label:
+          en_US: Lithuanian
+          zh_Hans: 立陶宛语
+      - value: lv
+        label:
+          en_US: Latvian
+          zh_Hans: 拉脱维亚语
+      - value: mg
+        label:
+          en_US: Malagasy
+          zh_Hans: 马尔加什语
+      - value: mi
+        label:
+          en_US: Maori
+          zh_Hans: 毛利语
+      - value: mk
+        label:
+          en_US: Macedonian
+          zh_Hans: 马其顿语
+      - value: ml
+        label:
+          en_US: Malayalam
+          zh_Hans: 马拉雅拉姆语
+      - value: mn
+        label:
+          en_US: Mongolian
+          zh_Hans: 蒙古语
+      - value: mr
+        label:
+          en_US: Marathi
+          zh_Hans: 马拉地语
+      - value: ms
+        label:
+          en_US: Malay
+          zh_Hans: 马来语
+      - value: mt
+        label:
+          en_US: Maltese
+          zh_Hans: 马耳他语
+      - value: my
+        label:
+          en_US: Burmese
+          zh_Hans: 缅甸语
+      - value: ne
+        label:
+          en_US: Nepali
+          zh_Hans: 尼泊尔语
+      - value: nl
+        label:
+          en_US: Dutch
+          zh_Hans: 荷兰语
+      - value: nn
+        label:
+          en_US: Norwegian Nynorsk
+          zh_Hans: 新挪威语
+      - value: no
+        label:
+          en_US: Norwegian
+          zh_Hans: 挪威语
+      - value: oc
+        label:
+          en_US: Occitan
+          zh_Hans: 奥克语
+      - value: pa
+        label:
+          en_US: Punjabi
+          zh_Hans: 旁遮普语
+      - value: pl
+        label:
+          en_US: Polish
+          zh_Hans: 波兰语
+      - value: ps
+        label:
+          en_US: Pashto
+          zh_Hans: 普什图语
+      - value: pt
+        label:
+          en_US: Portuguese
+          zh_Hans: 葡萄牙语
+      - value: ro
+        label:
+          en_US: Romanian
+          zh_Hans: 罗马尼亚语
+      - value: ru
+        label:
+          en_US: Russian
+          zh_Hans: 俄语
+      - value: sa
+        label:
+          en_US: Sanskrit
+          zh_Hans: 梵语
+      - value: sd
+        label:
+          en_US: Sindhi
+          zh_Hans: 信德语
+      - value: si
+        label:
+          en_US: Sinhala
+          zh_Hans: 僧伽罗语
+      - value: sk
+        label:
+          en_US: Slovak
+          zh_Hans: 斯洛伐克语
+      - value: sl
+        label:
+          en_US: Slovenian
+          zh_Hans: 斯洛文尼亚语
+      - value: sn
+        label:
+          en_US: Shona
+          zh_Hans: 修纳语
+      - value: so
+        label:
+          en_US: Somali
+          zh_Hans: 索马里语
+      - value: sq
+        label:
+          en_US: Albanian
+          zh_Hans: 阿尔巴尼亚语
+      - value: sr
+        label:
+          en_US: Serbian
+          zh_Hans: 塞尔维亚语
+      - value: su
+        label:
+          en_US: Sundanese
+          zh_Hans: 巽他语
+      - value: sv
+        label:
+          en_US: Swedish
+          zh_Hans: 瑞典语
+      - value: sw
+        label:
+          en_US: Swahili
+          zh_Hans: 斯瓦希里语
+      - value: ta
+        label:
+          en_US: Tamil
+          zh_Hans: 泰米尔语
+      - value: te
+        label:
+          en_US: Telugu
+          zh_Hans: 泰卢固语
+      - value: tg
+        label:
+          en_US: Tajik
+          zh_Hans: 塔吉克语
+      - value: th
+        label:
+          en_US: Thai
+          zh_Hans: 泰语
+      - value: tk
+        label:
+          en_US: Turkmen
+          zh_Hans: 土库曼语
+      - value: tl
+        label:
+          en_US: Tagalog
+          zh_Hans: 他加禄语
+      - value: tr
+        label:
+          en_US: Turkish
+          zh_Hans: 土耳其语
+      - value: tt
+        label:
+          en_US: Tatar
+          zh_Hans: 鞑靼语
+      - value: uk
+        label:
+          en_US: Ukrainian
+          zh_Hans: 乌克兰语
+      - value: ur
+        label:
+          en_US: Urdu
+          zh_Hans: 乌尔都语
+      - value: uz
+        label:
+          en_US: Uzbek
+          zh_Hans: 乌兹别克语
+      - value: vi
+        label:
+          en_US: Vietnamese
+          zh_Hans: 越南语
+      - value: yi
+        label:
+          en_US: Yiddish
+          zh_Hans: 意第绪语
+      - value: yo
+        label:
+          en_US: Yoruba
+          zh_Hans: 约鲁巴语
+      - value: yue
+        label:
+          en_US: Cantonese
+          zh_Hans: 粤语
+      - value: zh
+        label:
+          en_US: Chinese
+          zh_Hans: 中文
+  - name: chunk_level
+    type: select
+    label:
+      en_US: Chunk Level
+      zh_Hans: 分块级别
+    human_description:
+      en_US: "Choose how the transcription should be divided into chunks"
+      zh_Hans: "选择如何将转录内容分成块"
+    llm_description: "Level of the chunks to return."
+    form: form
+    default: segment
+    options:
+      - value: segment
+        label:
+          en_US: Segment
+          zh_Hans: 段
+  - name: version
+    type: select
+    label:
+      en_US: Version
+      zh_Hans: 版本
+    human_description:
+      en_US: "Select which version of the Whisper large model to use"
+      zh_Hans: "选择要使用的 Whisper large 模型版本"
+    llm_description: "Version of the model to use. All of the models are the Whisper large variant."
+    form: form
+    default: "3"
+    options:
+      - value: "3"
+        label:
+          en_US: Version 3
+          zh_Hans: 版本 3

api/core/variables/segments.py

@@ -118,11 +118,11 @@ class FileSegment(Segment):
 
     @property
     def log(self) -> str:
-        return str(self.value)
+        return ""
 
     @property
     def text(self) -> str:
-        return str(self.value)
+        return ""
 
 
 class ArrayAnySegment(ArraySegment):
@@ -155,3 +155,11 @@ class ArrayFileSegment(ArraySegment):
         for item in self.value:
             items.append(item.markdown)
         return "\n".join(items)
+
+    @property
+    def log(self) -> str:
+        return ""
+
+    @property
+    def text(self) -> str:
+        return ""

api/core/workflow/nodes/llm/entities.py

@@ -39,7 +39,14 @@ class VisionConfig(BaseModel):
 
 
 class PromptConfig(BaseModel):
-    jinja2_variables: Optional[list[VariableSelector]] = None
+    jinja2_variables: Sequence[VariableSelector] = Field(default_factory=list)
+
+    @field_validator("jinja2_variables", mode="before")
+    @classmethod
+    def convert_none_jinja2_variables(cls, v: Any):
+        if v is None:
+            return []
+        return v
 
 
 class LLMNodeChatModelMessage(ChatModelMessage):
@@ -53,7 +60,14 @@ class LLMNodeCompletionModelPromptTemplate(CompletionModelPromptTemplate):
 class LLMNodeData(BaseNodeData):
     model: ModelConfig
     prompt_template: Sequence[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate
-    prompt_config: Optional[PromptConfig] = None
+    prompt_config: PromptConfig = Field(default_factory=PromptConfig)
     memory: Optional[MemoryConfig] = None
     context: ContextConfig
     vision: VisionConfig = Field(default_factory=VisionConfig)
+
+    @field_validator("prompt_config", mode="before")
+    @classmethod
+    def convert_none_prompt_config(cls, v: Any):
+        if v is None:
+            return PromptConfig()
+        return v

api/core/workflow/nodes/llm/exc.py

@@ -24,3 +24,11 @@ class LLMModeRequiredError(LLMNodeError):
 
 class NoPromptFoundError(LLMNodeError):
     """Raised when no prompt is found in the LLM configuration."""
+
+
+class NotSupportedPromptTypeError(LLMNodeError):
+    """Raised when the prompt type is not supported."""
+
+
+class MemoryRolePrefixRequiredError(LLMNodeError):
+    """Raised when memory role prefix is required for completion model."""

api/core/workflow/nodes/llm/node.py

@@ -1,4 +1,5 @@
 import json
+import logging
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Optional, cast
 
@@ -6,21 +7,26 @@ from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEnti
 from core.entities.model_entities import ModelStatus
 from core.entities.provider_entities import QuotaUnit
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.file import FileType, file_manager
+from core.helper.code_executor import CodeExecutor, CodeLanguage
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities import (
-    AudioPromptMessageContent,
     ImagePromptMessageContent,
     PromptMessage,
     PromptMessageContentType,
     TextPromptMessageContent,
-    VideoPromptMessageContent,
 )
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
-from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessageRole,
+    SystemPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey, ModelType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import CompletionModelPromptTemplate, MemoryConfig
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
 from core.variables import (
@@ -32,8 +38,9 @@ from core.variables import (
     ObjectSegment,
     StringSegment,
 )
-from core.workflow.constants import SYSTEM_VARIABLE_NODE_ID
 from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult
+from core.workflow.entities.variable_entities import VariableSelector
+from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.event import InNodeEvent
 from core.workflow.nodes.base import BaseNode
@@ -62,14 +69,18 @@ from .exc import (
     InvalidVariableTypeError,
     LLMModeRequiredError,
     LLMNodeError,
+    MemoryRolePrefixRequiredError,
     ModelNotExistError,
     NoPromptFoundError,
+    NotSupportedPromptTypeError,
     VariableNotFoundError,
 )
 
 if TYPE_CHECKING:
     from core.file.models import File
 
+logger = logging.getLogger(__name__)
+
 
 class LLMNode(BaseNode[LLMNodeData]):
     _node_data_cls = LLMNodeData
@@ -123,17 +134,13 @@ class LLMNode(BaseNode[LLMNodeData]):
 
             # fetch prompt messages
             if self.node_data.memory:
-                query = self.graph_runtime_state.variable_pool.get((SYSTEM_VARIABLE_NODE_ID, SystemVariableKey.QUERY))
-                if not query:
-                    raise VariableNotFoundError("Query not found")
-                query = query.text
+                query = self.node_data.memory.query_prompt_template
             else:
                 query = None
 
             prompt_messages, stop = self._fetch_prompt_messages(
-                system_query=query,
-                inputs=inputs,
-                files=files,
+                user_query=query,
+                user_files=files,
                 context=context,
                 memory=memory,
                 model_config=model_config,
@@ -141,6 +148,8 @@ class LLMNode(BaseNode[LLMNodeData]):
                 memory_config=self.node_data.memory,
                 vision_enabled=self.node_data.vision.enabled,
                 vision_detail=self.node_data.vision.configs.detail,
+                variable_pool=self.graph_runtime_state.variable_pool,
+                jinja2_variables=self.node_data.prompt_config.jinja2_variables,
             )
 
             process_data = {
@@ -181,6 +190,17 @@ class LLMNode(BaseNode[LLMNodeData]):
                 )
             )
             return
+        except Exception as e:
+            logger.exception(f"Node {self.node_id} failed to run: {e}")
+            yield RunCompletedEvent(
+                run_result=NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    error=str(e),
+                    inputs=node_inputs,
+                    process_data=process_data,
+                )
+            )
+            return
 
         outputs = {"text": result_text, "usage": jsonable_encoder(usage), "finish_reason": finish_reason}
 
@@ -203,8 +223,8 @@ class LLMNode(BaseNode[LLMNodeData]):
         self,
         node_data_model: ModelConfig,
         model_instance: ModelInstance,
-        prompt_messages: list[PromptMessage],
-        stop: Optional[list[str]] = None,
+        prompt_messages: Sequence[PromptMessage],
+        stop: Optional[Sequence[str]] = None,
     ) -> Generator[NodeEvent, None, None]:
         db.session.close()
 
@@ -519,9 +539,8 @@ class LLMNode(BaseNode[LLMNodeData]):
     def _fetch_prompt_messages(
         self,
         *,
-        system_query: str | None = None,
-        inputs: dict[str, str] | None = None,
-        files: Sequence["File"],
+        user_query: str | None = None,
+        user_files: Sequence["File"],
         context: str | None = None,
         memory: TokenBufferMemory | None = None,
         model_config: ModelConfigWithCredentialsEntity,
@@ -529,58 +548,146 @@ class LLMNode(BaseNode[LLMNodeData]):
         memory_config: MemoryConfig | None = None,
         vision_enabled: bool = False,
         vision_detail: ImagePromptMessageContent.DETAIL,
-    ) -> tuple[list[PromptMessage], Optional[list[str]]]:
-        inputs = inputs or {}
+        variable_pool: VariablePool,
+        jinja2_variables: Sequence[VariableSelector],
+    ) -> tuple[Sequence[PromptMessage], Optional[Sequence[str]]]:
+        prompt_messages = []
 
-        prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
-        prompt_messages = prompt_transform.get_prompt(
-            prompt_template=prompt_template,
-            inputs=inputs,
-            query=system_query or "",
-            files=files,
+        if isinstance(prompt_template, list):
+            # For chat model
+            prompt_messages.extend(
+                _handle_list_messages(
+                    messages=prompt_template,
                     context=context,
-            memory_config=memory_config,
+                    jinja2_variables=jinja2_variables,
+                    variable_pool=variable_pool,
+                    vision_detail_config=vision_detail,
+                )
+            )
+
+            # Get memory messages for chat mode
+            memory_messages = _handle_memory_chat_mode(
                 memory=memory,
+                memory_config=memory_config,
                 model_config=model_config,
             )
-        stop = model_config.stop
+            # Extend prompt_messages with memory messages
+            prompt_messages.extend(memory_messages)
+
+            # Add current query to the prompt messages
+            if user_query:
+                message = LLMNodeChatModelMessage(
+                    text=user_query,
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                )
+                prompt_messages.extend(
+                    _handle_list_messages(
+                        messages=[message],
+                        context="",
+                        jinja2_variables=[],
+                        variable_pool=variable_pool,
+                        vision_detail_config=vision_detail,
+                    )
+                )
+
+        elif isinstance(prompt_template, LLMNodeCompletionModelPromptTemplate):
+            # For completion model
+            prompt_messages.extend(
+                _handle_completion_template(
+                    template=prompt_template,
+                    context=context,
+                    jinja2_variables=jinja2_variables,
+                    variable_pool=variable_pool,
+                )
+            )
+
+            # Get memory text for completion model
+            memory_text = _handle_memory_completion_mode(
+                memory=memory,
+                memory_config=memory_config,
+                model_config=model_config,
+            )
+            # Insert histories into the prompt
+            prompt_content = prompt_messages[0].content
+            if "#histories#" in prompt_content:
+                prompt_content = prompt_content.replace("#histories#", memory_text)
+            else:
+                prompt_content = memory_text + "\n" + prompt_content
+            prompt_messages[0].content = prompt_content
+
+            # Add current query to the prompt message
+            if user_query:
+                prompt_content = prompt_messages[0].content.replace("#sys.query#", user_query)
+                prompt_messages[0].content = prompt_content
+        else:
+            errmsg = f"Prompt type {type(prompt_template)} is not supported"
+            logger.warning(errmsg)
+            raise NotSupportedPromptTypeError(errmsg)
+
+        if vision_enabled and user_files:
+            file_prompts = []
+            for file in user_files:
+                file_prompt = file_manager.to_prompt_message_content(file, image_detail_config=vision_detail)
+                file_prompts.append(file_prompt)
+            if (
+                len(prompt_messages) > 0
+                and isinstance(prompt_messages[-1], UserPromptMessage)
+                and isinstance(prompt_messages[-1].content, list)
+            ):
+                prompt_messages[-1] = UserPromptMessage(content=prompt_messages[-1].content + file_prompts)
+            else:
+                prompt_messages.append(UserPromptMessage(content=file_prompts))
+
+        # Filter prompt messages
         filtered_prompt_messages = []
         for prompt_message in prompt_messages:
+            if isinstance(prompt_message.content, list):
+                prompt_message_content = []
+                for content_item in prompt_message.content:
+                    # Skip content if features are not defined
+                    if not model_config.model_schema.features:
+                        if content_item.type != PromptMessageContentType.TEXT:
+                            continue
+                        prompt_message_content.append(content_item)
+                        continue
+
+                    # Skip content if corresponding feature is not supported
+                    if (
+                        (
+                            content_item.type == PromptMessageContentType.IMAGE
+                            and ModelFeature.VISION not in model_config.model_schema.features
+                        )
+                        or (
+                            content_item.type == PromptMessageContentType.DOCUMENT
+                            and ModelFeature.DOCUMENT not in model_config.model_schema.features
+                        )
+                        or (
+                            content_item.type == PromptMessageContentType.VIDEO
+                            and ModelFeature.VIDEO not in model_config.model_schema.features
+                        )
+                        or (
+                            content_item.type == PromptMessageContentType.AUDIO
+                            and ModelFeature.AUDIO not in model_config.model_schema.features
+                        )
+                    ):
+                        continue
+                    prompt_message_content.append(content_item)
+                if len(prompt_message_content) == 1 and prompt_message_content[0].type == PromptMessageContentType.TEXT:
+                    prompt_message.content = prompt_message_content[0].data
+                else:
+                    prompt_message.content = prompt_message_content
             if prompt_message.is_empty():
                 continue
-
-            if not isinstance(prompt_message.content, str):
-                prompt_message_content = []
-                for content_item in prompt_message.content or []:
-                    # Skip image if vision is disabled
-                    if not vision_enabled and content_item.type == PromptMessageContentType.IMAGE:
-                        continue
-
-                    if isinstance(content_item, ImagePromptMessageContent):
-                        # Override vision config if LLM node has vision config,
-                        # cuz vision detail is related to the configuration from FileUpload feature.
-                        content_item.detail = vision_detail
-                        prompt_message_content.append(content_item)
-                    elif isinstance(
-                        content_item, TextPromptMessageContent | AudioPromptMessageContent | VideoPromptMessageContent
-                    ):
-                        prompt_message_content.append(content_item)
-
-                if len(prompt_message_content) > 1:
-                    prompt_message.content = prompt_message_content
-                elif (
-                    len(prompt_message_content) == 1 and prompt_message_content[0].type == PromptMessageContentType.TEXT
-                ):
-                    prompt_message.content = prompt_message_content[0].data
-
             filtered_prompt_messages.append(prompt_message)
 
-        if not filtered_prompt_messages:
+        if len(filtered_prompt_messages) == 0:
             raise NoPromptFoundError(
                 "No prompt found in the LLM configuration. "
                 "Please ensure a prompt is properly configured before proceeding."
             )
 
+        stop = model_config.stop
         return filtered_prompt_messages, stop
 
     @classmethod
@@ -715,3 +822,198 @@ class LLMNode(BaseNode[LLMNodeData]):
                 }
             },
         }
+
+
+def _combine_text_message_with_role(*, text: str, role: PromptMessageRole):
+    match role:
+        case PromptMessageRole.USER:
+            return UserPromptMessage(content=[TextPromptMessageContent(data=text)])
+        case PromptMessageRole.ASSISTANT:
+            return AssistantPromptMessage(content=[TextPromptMessageContent(data=text)])
+        case PromptMessageRole.SYSTEM:
+            return SystemPromptMessage(content=[TextPromptMessageContent(data=text)])
+    raise NotImplementedError(f"Role {role} is not supported")
+
+
+def _render_jinja2_message(
+    *,
+    template: str,
+    jinjia2_variables: Sequence[VariableSelector],
+    variable_pool: VariablePool,
+):
+    if not template:
+        return ""
+
+    jinjia2_inputs = {}
+    for jinja2_variable in jinjia2_variables:
+        variable = variable_pool.get(jinja2_variable.value_selector)
+        jinjia2_inputs[jinja2_variable.variable] = variable.to_object() if variable else ""
+    code_execute_resp = CodeExecutor.execute_workflow_code_template(
+        language=CodeLanguage.JINJA2,
+        code=template,
+        inputs=jinjia2_inputs,
+    )
+    result_text = code_execute_resp["result"]
+    return result_text
+
+
+def _handle_list_messages(
+    *,
+    messages: Sequence[LLMNodeChatModelMessage],
+    context: Optional[str],
+    jinja2_variables: Sequence[VariableSelector],
+    variable_pool: VariablePool,
+    vision_detail_config: ImagePromptMessageContent.DETAIL,
+) -> Sequence[PromptMessage]:
+    prompt_messages = []
+    for message in messages:
+        if message.edition_type == "jinja2":
+            result_text = _render_jinja2_message(
+                template=message.jinja2_text or "",
+                jinjia2_variables=jinja2_variables,
+                variable_pool=variable_pool,
+            )
+            prompt_message = _combine_text_message_with_role(text=result_text, role=message.role)
+            prompt_messages.append(prompt_message)
+        else:
+            # Get segment group from basic message
+            if context:
+                template = message.text.replace("{#context#}", context)
+            else:
+                template = message.text
+            segment_group = variable_pool.convert_template(template)
+
+            # Process segments for images
+            file_contents = []
+            for segment in segment_group.value:
+                if isinstance(segment, ArrayFileSegment):
+                    for file in segment.value:
+                        if file.type in {FileType.IMAGE, FileType.VIDEO, FileType.AUDIO}:
+                            file_content = file_manager.to_prompt_message_content(
+                                file, image_detail_config=vision_detail_config
+                            )
+                            file_contents.append(file_content)
+                if isinstance(segment, FileSegment):
+                    file = segment.value
+                    if file.type in {FileType.IMAGE, FileType.VIDEO, FileType.AUDIO}:
+                        file_content = file_manager.to_prompt_message_content(
+                            file, image_detail_config=vision_detail_config
+                        )
+                        file_contents.append(file_content)
+
+            # Create message with text from all segments
+            plain_text = segment_group.text
+            if plain_text:
+                prompt_message = _combine_text_message_with_role(text=plain_text, role=message.role)
+                prompt_messages.append(prompt_message)
+
+            if file_contents:
+                # Create message with image contents
+                prompt_message = UserPromptMessage(content=file_contents)
+                prompt_messages.append(prompt_message)
+
+    return prompt_messages
+
+
+def _calculate_rest_token(
+    *, prompt_messages: list[PromptMessage], model_config: ModelConfigWithCredentialsEntity
+) -> int:
+    rest_tokens = 2000
+
+    model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
+    if model_context_tokens:
+        model_instance = ModelInstance(
+            provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
+        )
+
+        curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
+
+        max_tokens = 0
+        for parameter_rule in model_config.model_schema.parameter_rules:
+            if parameter_rule.name == "max_tokens" or (
+                parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+            ):
+                max_tokens = (
+                    model_config.parameters.get(parameter_rule.name)
+                    or model_config.parameters.get(str(parameter_rule.use_template))
+                    or 0
+                )
+
+        rest_tokens = model_context_tokens - max_tokens - curr_message_tokens
+        rest_tokens = max(rest_tokens, 0)
+
+    return rest_tokens
+
+
+def _handle_memory_chat_mode(
+    *,
+    memory: TokenBufferMemory | None,
+    memory_config: MemoryConfig | None,
+    model_config: ModelConfigWithCredentialsEntity,
+) -> Sequence[PromptMessage]:
+    memory_messages = []
+    # Get messages from memory for chat model
+    if memory and memory_config:
+        rest_tokens = _calculate_rest_token(prompt_messages=[], model_config=model_config)
+        memory_messages = memory.get_history_prompt_messages(
+            max_token_limit=rest_tokens,
+            message_limit=memory_config.window.size if memory_config.window.enabled else None,
+        )
+    return memory_messages
+
+
+def _handle_memory_completion_mode(
+    *,
+    memory: TokenBufferMemory | None,
+    memory_config: MemoryConfig | None,
+    model_config: ModelConfigWithCredentialsEntity,
+) -> str:
+    memory_text = ""
+    # Get history text from memory for completion model
+    if memory and memory_config:
+        rest_tokens = _calculate_rest_token(prompt_messages=[], model_config=model_config)
+        if not memory_config.role_prefix:
+            raise MemoryRolePrefixRequiredError("Memory role prefix is required for completion model.")
+        memory_text = memory.get_history_prompt_text(
+            max_token_limit=rest_tokens,
+            message_limit=memory_config.window.size if memory_config.window.enabled else None,
+            human_prefix=memory_config.role_prefix.user,
+            ai_prefix=memory_config.role_prefix.assistant,
+        )
+    return memory_text
+
+
+def _handle_completion_template(
+    *,
+    template: LLMNodeCompletionModelPromptTemplate,
+    context: Optional[str],
+    jinja2_variables: Sequence[VariableSelector],
+    variable_pool: VariablePool,
+) -> Sequence[PromptMessage]:
+    """Handle completion template processing outside of LLMNode class.
+
+    Args:
+        template: The completion model prompt template
+        context: Optional context string
+        jinja2_variables: Variables for jinja2 template rendering
+        variable_pool: Variable pool for template conversion
+
+    Returns:
+        Sequence of prompt messages
+    """
+    prompt_messages = []
+    if template.edition_type == "jinja2":
+        result_text = _render_jinja2_message(
+            template=template.jinja2_text or "",
+            jinjia2_variables=jinja2_variables,
+            variable_pool=variable_pool,
+        )
+    else:
+        if context:
+            template_text = template.text.replace("{#context#}", context)
+        else:
+            template_text = template.text
+        result_text = variable_pool.convert_template(template_text).text
+    prompt_message = _combine_text_message_with_role(text=result_text, role=PromptMessageRole.USER)
+    prompt_messages.append(prompt_message)
+    return prompt_messages

api/core/workflow/nodes/question_classifier/question_classifier_node.py

@@ -86,12 +86,14 @@ class QuestionClassifierNode(LLMNode):
         )
         prompt_messages, stop = self._fetch_prompt_messages(
             prompt_template=prompt_template,
-            system_query=query,
+            user_query=query,
             memory=memory,
             model_config=model_config,
-            files=files,
+            user_files=files,
             vision_enabled=node_data.vision.enabled,
             vision_detail=node_data.vision.configs.detail,
+            variable_pool=variable_pool,
+            jinja2_variables=[],
         )
 
         # handle invoke result

api/core/workflow/nodes/tool/tool_node.py

@@ -1,5 +1,4 @@
 from collections.abc import Mapping, Sequence
-from os import path
 from typing import Any
 
 from sqlalchemy import select
@@ -180,7 +179,6 @@ class ToolNode(BaseNode[ToolNodeData]):
         for response in tool_response:
             if response.type in {ToolInvokeMessage.MessageType.IMAGE_LINK, ToolInvokeMessage.MessageType.IMAGE}:
                 url = str(response.message) if response.message else None
-                ext = path.splitext(url)[1] if url else ".bin"
                 tool_file_id = str(url).split("/")[-1].split(".")[0]
                 transfer_method = response.meta.get("transfer_method", FileTransferMethod.TOOL_FILE)
 
@@ -202,7 +200,6 @@ class ToolNode(BaseNode[ToolNodeData]):
                 )
                 result.append(file)
             elif response.type == ToolInvokeMessage.MessageType.BLOB:
-                # get tool file id
                 tool_file_id = str(response.message).split("/")[-1].split(".")[0]
                 with Session(db.engine) as session:
                     stmt = select(ToolFile).where(ToolFile.id == tool_file_id)
@@ -211,7 +208,6 @@ class ToolNode(BaseNode[ToolNodeData]):
                         raise ValueError(f"tool file {tool_file_id} not exists")
                 mapping = {
                     "tool_file_id": tool_file_id,
-                    "type": FileType.IMAGE,
                     "transfer_method": FileTransferMethod.TOOL_FILE,
                 }
                 file = file_factory.build_from_mapping(
@@ -228,13 +224,8 @@ class ToolNode(BaseNode[ToolNodeData]):
                     tool_file = session.scalar(stmt)
                     if tool_file is None:
                         raise ToolFileError(f"Tool file {tool_file_id} does not exist")
-                if "." in url:
-                    extension = "." + url.split("/")[-1].split(".")[1]
-                else:
-                    extension = ".bin"
                 mapping = {
                     "tool_file_id": tool_file_id,
-                    "type": FileType.IMAGE,
                     "transfer_method": transfer_method,
                     "url": url,
                 }

api/factories/file_factory.py

@@ -180,6 +180,20 @@ def _get_remote_file_info(url: str):
     return mime_type, filename, file_size
 
 
+def _get_file_type_by_mimetype(mime_type: str) -> FileType:
+    if "image" in mime_type:
+        file_type = FileType.IMAGE
+    elif "video" in mime_type:
+        file_type = FileType.VIDEO
+    elif "audio" in mime_type:
+        file_type = FileType.AUDIO
+    elif "text" in mime_type or "pdf" in mime_type:
+        file_type = FileType.DOCUMENT
+    else:
+        file_type = FileType.CUSTOM
+    return file_type
+
+
 def _build_from_tool_file(
     *,
     mapping: Mapping[str, Any],
@@ -199,12 +213,13 @@ def _build_from_tool_file(
         raise ValueError(f"ToolFile {mapping.get('tool_file_id')} not found")
 
     extension = "." + tool_file.file_key.split(".")[-1] if "." in tool_file.file_key else ".bin"
+    file_type = mapping.get("type", _get_file_type_by_mimetype(tool_file.mimetype))
 
     return File(
         id=mapping.get("id"),
         tenant_id=tenant_id,
         filename=tool_file.name,
-        type=FileType.value_of(mapping.get("type")),
+        type=file_type,
         transfer_method=transfer_method,
         remote_url=tool_file.original_url,
         related_id=tool_file.id,

api/poetry.lock

@@ -2411,6 +2411,41 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "faker"
+version = "32.1.0"
+description = "Faker is a Python package that generates fake data for you."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "Faker-32.1.0-py3-none-any.whl", hash = "sha256:c77522577863c264bdc9dad3a2a750ad3f7ee43ff8185072e482992288898814"},
+    {file = "faker-32.1.0.tar.gz", hash = "sha256:aac536ba04e6b7beb2332c67df78485fc29c1880ff723beac6d1efd45e2f10f5"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.4"
+typing-extensions = "*"
+
+[[package]]
+name = "fal-client"
+version = "0.5.6"
+description = "Python client for fal.ai"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fal_client-0.5.6-py3-none-any.whl", hash = "sha256:631fd857a3c44753ee46a2eea1e7276471453aca58faac9c3702f744c7c84050"},
+    {file = "fal_client-0.5.6.tar.gz", hash = "sha256:d3afc4b6250023d0ee8437ec504558231d3b106d7aabc12cda8c39883faddecb"},
+]
+
+[package.dependencies]
+httpx = ">=0.21.0,<1"
+httpx-sse = ">=0.4.0,<0.5"
+
+[package.extras]
+dev = ["fal-client[docs,test]"]
+docs = ["sphinx", "sphinx-autodoc-typehints", "sphinx-rtd-theme"]
+test = ["pillow", "pytest", "pytest-asyncio"]
+
 [[package]]
 name = "fastapi"
 version = "0.115.4"
@@ -4049,6 +4084,17 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "httpx-sse"
+version = "0.4.0"
+description = "Consume Server-Sent Event (SSE) messages with HTTPX."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721"},
+    {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
+]
+
 [[package]]
 name = "huggingface-hub"
 version = "0.16.4"
@@ -8466,29 +8512,29 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.6.9"
+version = "0.7.3"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.6.9-py3-none-linux_armv6l.whl", hash = "sha256:064df58d84ccc0ac0fcd63bc3090b251d90e2a372558c0f057c3f75ed73e1ccd"},
-    {file = "ruff-0.6.9-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:140d4b5c9f5fc7a7b074908a78ab8d384dd7f6510402267bc76c37195c02a7ec"},
-    {file = "ruff-0.6.9-py3-none-macosx_11_0_arm64.whl", hash = "sha256:53fd8ca5e82bdee8da7f506d7b03a261f24cd43d090ea9db9a1dc59d9313914c"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:645d7d8761f915e48a00d4ecc3686969761df69fb561dd914a773c1a8266e14e"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:eae02b700763e3847595b9d2891488989cac00214da7f845f4bcf2989007d577"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d5ccc9e58112441de8ad4b29dcb7a86dc25c5f770e3c06a9d57e0e5eba48829"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:417b81aa1c9b60b2f8edc463c58363075412866ae4e2b9ab0f690dc1e87ac1b5"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3c866b631f5fbce896a74a6e4383407ba7507b815ccc52bcedabb6810fdb3ef7"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7b118afbb3202f5911486ad52da86d1d52305b59e7ef2031cea3425142b97d6f"},
-    {file = "ruff-0.6.9-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a67267654edc23c97335586774790cde402fb6bbdb3c2314f1fc087dee320bfa"},
-    {file = "ruff-0.6.9-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:3ef0cc774b00fec123f635ce5c547dac263f6ee9fb9cc83437c5904183b55ceb"},
-    {file = "ruff-0.6.9-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:12edd2af0c60fa61ff31cefb90aef4288ac4d372b4962c2864aeea3a1a2460c0"},
-    {file = "ruff-0.6.9-py3-none-musllinux_1_2_i686.whl", hash = "sha256:55bb01caeaf3a60b2b2bba07308a02fca6ab56233302406ed5245180a05c5625"},
-    {file = "ruff-0.6.9-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:925d26471fa24b0ce5a6cdfab1bb526fb4159952385f386bdcc643813d472039"},
-    {file = "ruff-0.6.9-py3-none-win32.whl", hash = "sha256:eb61ec9bdb2506cffd492e05ac40e5bc6284873aceb605503d8494180d6fc84d"},
-    {file = "ruff-0.6.9-py3-none-win_amd64.whl", hash = "sha256:785d31851c1ae91f45b3d8fe23b8ae4b5170089021fbb42402d811135f0b7117"},
-    {file = "ruff-0.6.9-py3-none-win_arm64.whl", hash = "sha256:a9641e31476d601f83cd602608739a0840e348bda93fec9f1ee816f8b6798b93"},
-    {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"},
+    {file = "ruff-0.7.3-py3-none-linux_armv6l.whl", hash = "sha256:34f2339dc22687ec7e7002792d1f50712bf84a13d5152e75712ac08be565d344"},
+    {file = "ruff-0.7.3-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:fb397332a1879b9764a3455a0bb1087bda876c2db8aca3a3cbb67b3dbce8cda0"},
+    {file = "ruff-0.7.3-py3-none-macosx_11_0_arm64.whl", hash = "sha256:37d0b619546103274e7f62643d14e1adcbccb242efda4e4bdb9544d7764782e9"},
+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5d59f0c3ee4d1a6787614e7135b72e21024875266101142a09a61439cb6e38a5"},
+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:44eb93c2499a169d49fafd07bc62ac89b1bc800b197e50ff4633aed212569299"},
+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6d0242ce53f3a576c35ee32d907475a8d569944c0407f91d207c8af5be5dae4e"},
+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:6b6224af8b5e09772c2ecb8dc9f3f344c1aa48201c7f07e7315367f6dd90ac29"},
+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c50f95a82b94421c964fae4c27c0242890a20fe67d203d127e84fbb8013855f5"},
+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7f3eff9961b5d2644bcf1616c606e93baa2d6b349e8aa8b035f654df252c8c67"},
+    {file = "ruff-0.7.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8963cab06d130c4df2fd52c84e9f10d297826d2e8169ae0c798b6221be1d1d2"},
+    {file = "ruff-0.7.3-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:61b46049d6edc0e4317fb14b33bd693245281a3007288b68a3f5b74a22a0746d"},
+    {file = "ruff-0.7.3-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:10ebce7696afe4644e8c1a23b3cf8c0f2193a310c18387c06e583ae9ef284de2"},
+    {file = "ruff-0.7.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:3f36d56326b3aef8eeee150b700e519880d1aab92f471eefdef656fd57492aa2"},
+    {file = "ruff-0.7.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:5d024301109a0007b78d57ab0ba190087b43dce852e552734ebf0b0b85e4fb16"},
+    {file = "ruff-0.7.3-py3-none-win32.whl", hash = "sha256:4ba81a5f0c5478aa61674c5a2194de8b02652f17addf8dfc40c8937e6e7d79fc"},
+    {file = "ruff-0.7.3-py3-none-win_amd64.whl", hash = "sha256:588a9ff2fecf01025ed065fe28809cd5a53b43505f48b69a1ac7707b1b7e4088"},
+    {file = "ruff-0.7.3-py3-none-win_arm64.whl", hash = "sha256:1713e2c5545863cdbfe2cbce21f69ffaf37b813bfd1fb3b90dc9a6f1963f5a8c"},
+    {file = "ruff-0.7.3.tar.gz", hash = "sha256:e1d1ba2e40b6e71a61b063354d04be669ab0d39c352461f3d789cac68b54a313"},
 ]
 
 [[package]]
@@ -11005,4 +11051,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "f20bd678044926913dbbc24bd0cf22503a75817aa55f59457ff7822032139b77"
+content-hash = "cf4e0467f622e58b51411ee1d784928962f52dbf877b8ee013c810909a1f07db"

api/pyproject.toml

@@ -122,6 +122,7 @@ celery = "~5.4.0"
 chardet = "~5.1.0"
 cohere = "~5.2.4"
 dashscope = { version = "~1.17.0", extras = ["tokenizer"] }
+fal-client = "0.5.6"
 flask = "~3.0.1"
 flask-compress = "~1.14"
 flask-cors = "~4.0.0"
@@ -265,6 +266,7 @@ weaviate-client = "~3.21.0"
 optional = true
 [tool.poetry.group.dev.dependencies]
 coverage = "~7.2.4"
+faker = "~32.1.0"
 pytest = "~8.3.2"
 pytest-benchmark = "~4.0.0"
 pytest-env = "~1.1.3"
@@ -278,4 +280,4 @@ pytest-mock = "~3.14.0"
 optional = true
 [tool.poetry.group.lint.dependencies]
 dotenv-linter = "~0.5.0"
-ruff = "~0.6.9"
+ruff = "~0.7.3"

api/tests/integration_tests/model_runtime/azure_ai_studio/test_llm.py

@@ -11,7 +11,6 @@ from core.model_runtime.entities.message_entities import (
 )
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.azure_ai_studio.llm.llm import AzureAIStudioLargeLanguageModel
-from tests.integration_tests.model_runtime.__mock.azure_ai_studio import setup_azure_ai_studio_mock
 
 
 @pytest.mark.parametrize("setup_azure_ai_studio_mock", [["chat"]], indirect=True)

api/tests/integration_tests/model_runtime/azure_ai_studio/test_rerank.py

@@ -4,29 +4,21 @@ import pytest
 
 from core.model_runtime.entities.rerank_entities import RerankResult
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.azure_ai_studio.rerank.rerank import AzureAIStudioRerankModel
+from core.model_runtime.model_providers.azure_ai_studio.rerank.rerank import AzureRerankModel
 
 
 def test_validate_credentials():
-    model = AzureAIStudioRerankModel()
+    model = AzureRerankModel()
 
     with pytest.raises(CredentialsValidateFailedError):
         model.validate_credentials(
             model="azure-ai-studio-rerank-v1",
             credentials={"api_key": "invalid_key", "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE")},
-            query="What is the capital of the United States?",
-            docs=[
-                "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
-                "Census, Carson City had a population of 55,274.",
-                "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
-                "are a political division controlled by the United States. Its capital is Saipan.",
-            ],
-            score_threshold=0.8,
         )
 
 
 def test_invoke_model():
-    model = AzureAIStudioRerankModel()
+    model = AzureRerankModel()
 
     result = model.invoke(
         model="azure-ai-studio-rerank-v1",

api/tests/integration_tests/vdb/__mock/baiduvectordb.py

@@ -1,4 +1,5 @@
 import os
+from collections import UserDict
 from unittest.mock import MagicMock
 
 import pytest
@@ -11,7 +12,7 @@ from pymochow.model.table import Table
 from requests.adapters import HTTPAdapter
 
 
-class AttrDict(dict):
+class AttrDict(UserDict):
     def __getattr__(self, item):
         return self.get(item)
 

api/tests/integration_tests/vdb/__mock/upstashvectordb.py

@@ -1,4 +1,5 @@
 import os
+from collections import UserDict
 from typing import Optional
 
 import pytest
@@ -50,7 +51,7 @@ class MockIndex:
         return AttrDict({"dimension": 1024})
 
 
-class AttrDict(dict):
+class AttrDict(UserDict):
     def __getattr__(self, item):
         return self.get(item)
 

api/tests/unit_tests/core/workflow/nodes/llm/test_node.py

@@ -1,22 +1,61 @@
+from collections.abc import Sequence
+from typing import Optional
+
 import pytest
 
-from core.app.entities.app_invoke_entities import InvokeFrom
+from configs import dify_config
+from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
+from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
+from core.entities.provider_entities import CustomConfiguration, SystemConfiguration
 from core.file import File, FileTransferMethod, FileType
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    ImagePromptMessageContent,
+    PromptMessage,
+    PromptMessageRole,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelFeature, ModelType, ProviderModel
+from core.model_runtime.entities.provider_entities import ConfigurateMethod, ProviderEntity
+from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
+from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.variables import ArrayAnySegment, ArrayFileSegment, NoneSegment
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.graph_engine import Graph, GraphInitParams, GraphRuntimeState
 from core.workflow.nodes.answer import AnswerStreamGenerateRoute
 from core.workflow.nodes.end import EndStreamParam
-from core.workflow.nodes.llm.entities import ContextConfig, LLMNodeData, ModelConfig, VisionConfig, VisionConfigOptions
+from core.workflow.nodes.llm.entities import (
+    ContextConfig,
+    LLMNodeChatModelMessage,
+    LLMNodeData,
+    ModelConfig,
+    VisionConfig,
+    VisionConfigOptions,
+)
 from core.workflow.nodes.llm.node import LLMNode
 from models.enums import UserFrom
+from models.provider import ProviderType
 from models.workflow import WorkflowType
+from tests.unit_tests.core.workflow.nodes.llm.test_scenarios import LLMNodeTestScenario
+
+
+class MockTokenBufferMemory:
+    def __init__(self, history_messages=None):
+        self.history_messages = history_messages or []
+
+    def get_history_prompt_messages(
+        self, max_token_limit: int = 2000, message_limit: Optional[int] = None
+    ) -> Sequence[PromptMessage]:
+        if message_limit is not None:
+            return self.history_messages[-message_limit * 2 :]
+        return self.history_messages
 
 
-class TestLLMNode:
 @pytest.fixture
-    def llm_node(self):
+def llm_node():
     data = LLMNodeData(
         title="Test LLM",
         model=ModelConfig(provider="openai", name="gpt-3.5-turbo", mode="chat", completion_params={}),
@@ -70,7 +109,48 @@ class TestLLMNode:
     )
     return node
 
-    def test_fetch_files_with_file_segment(self, llm_node):
+
+@pytest.fixture
+def model_config():
+    # Create actual provider and model type instances
+    model_provider_factory = ModelProviderFactory()
+    provider_instance = model_provider_factory.get_provider_instance("openai")
+    model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
+
+    # Create a ProviderModelBundle
+    provider_model_bundle = ProviderModelBundle(
+        configuration=ProviderConfiguration(
+            tenant_id="1",
+            provider=provider_instance.get_provider_schema(),
+            preferred_provider_type=ProviderType.CUSTOM,
+            using_provider_type=ProviderType.CUSTOM,
+            system_configuration=SystemConfiguration(enabled=False),
+            custom_configuration=CustomConfiguration(provider=None),
+            model_settings=[],
+        ),
+        provider_instance=provider_instance,
+        model_type_instance=model_type_instance,
+    )
+
+    # Create and return a ModelConfigWithCredentialsEntity
+    return ModelConfigWithCredentialsEntity(
+        provider="openai",
+        model="gpt-3.5-turbo",
+        model_schema=AIModelEntity(
+            model="gpt-3.5-turbo",
+            label=I18nObject(en_US="GPT-3.5 Turbo"),
+            model_type=ModelType.LLM,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={},
+        ),
+        mode="chat",
+        credentials={},
+        parameters={},
+        provider_model_bundle=provider_model_bundle,
+    )
+
+
+def test_fetch_files_with_file_segment(llm_node):
     file = File(
         id="1",
         tenant_id="test",
@@ -84,7 +164,8 @@ class TestLLMNode:
     result = llm_node._fetch_files(selector=["sys", "files"])
     assert result == [file]
 
-    def test_fetch_files_with_array_file_segment(self, llm_node):
+
+def test_fetch_files_with_array_file_segment(llm_node):
     files = [
         File(
             id="1",
@@ -108,18 +189,296 @@ class TestLLMNode:
     result = llm_node._fetch_files(selector=["sys", "files"])
     assert result == files
 
-    def test_fetch_files_with_none_segment(self, llm_node):
+
+def test_fetch_files_with_none_segment(llm_node):
     llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], NoneSegment())
 
     result = llm_node._fetch_files(selector=["sys", "files"])
     assert result == []
 
-    def test_fetch_files_with_array_any_segment(self, llm_node):
+
+def test_fetch_files_with_array_any_segment(llm_node):
     llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], ArrayAnySegment(value=[]))
 
     result = llm_node._fetch_files(selector=["sys", "files"])
     assert result == []
 
-    def test_fetch_files_with_non_existent_variable(self, llm_node):
+
+def test_fetch_files_with_non_existent_variable(llm_node):
     result = llm_node._fetch_files(selector=["sys", "files"])
     assert result == []
+
+
+def test_fetch_prompt_messages__vison_disabled(faker, llm_node, model_config):
+    prompt_template = []
+    llm_node.node_data.prompt_template = prompt_template
+
+    fake_vision_detail = faker.random_element(
+        [ImagePromptMessageContent.DETAIL.HIGH, ImagePromptMessageContent.DETAIL.LOW]
+    )
+    fake_remote_url = faker.url()
+    files = [
+        File(
+            id="1",
+            tenant_id="test",
+            type=FileType.IMAGE,
+            filename="test1.jpg",
+            transfer_method=FileTransferMethod.REMOTE_URL,
+            remote_url=fake_remote_url,
+        )
+    ]
+
+    fake_query = faker.sentence()
+
+    prompt_messages, _ = llm_node._fetch_prompt_messages(
+        user_query=fake_query,
+        user_files=files,
+        context=None,
+        memory=None,
+        model_config=model_config,
+        prompt_template=prompt_template,
+        memory_config=None,
+        vision_enabled=False,
+        vision_detail=fake_vision_detail,
+        variable_pool=llm_node.graph_runtime_state.variable_pool,
+        jinja2_variables=[],
+    )
+
+    assert prompt_messages == [UserPromptMessage(content=fake_query)]
+
+
+def test_fetch_prompt_messages__basic(faker, llm_node, model_config):
+    # Setup dify config
+    dify_config.MULTIMODAL_SEND_IMAGE_FORMAT = "url"
+    dify_config.MULTIMODAL_SEND_VIDEO_FORMAT = "url"
+
+    # Generate fake values for prompt template
+    fake_assistant_prompt = faker.sentence()
+    fake_query = faker.sentence()
+    fake_context = faker.sentence()
+    fake_window_size = faker.random_int(min=1, max=3)
+    fake_vision_detail = faker.random_element(
+        [ImagePromptMessageContent.DETAIL.HIGH, ImagePromptMessageContent.DETAIL.LOW]
+    )
+    fake_remote_url = faker.url()
+
+    # Setup mock memory with history messages
+    mock_history = [
+        UserPromptMessage(content=faker.sentence()),
+        AssistantPromptMessage(content=faker.sentence()),
+        UserPromptMessage(content=faker.sentence()),
+        AssistantPromptMessage(content=faker.sentence()),
+        UserPromptMessage(content=faker.sentence()),
+        AssistantPromptMessage(content=faker.sentence()),
+    ]
+
+    # Setup memory configuration
+    memory_config = MemoryConfig(
+        role_prefix=MemoryConfig.RolePrefix(user="Human", assistant="Assistant"),
+        window=MemoryConfig.WindowConfig(enabled=True, size=fake_window_size),
+        query_prompt_template=None,
+    )
+
+    memory = MockTokenBufferMemory(history_messages=mock_history)
+
+    # Test scenarios covering different file input combinations
+    test_scenarios = [
+        LLMNodeTestScenario(
+            description="No files",
+            user_query=fake_query,
+            user_files=[],
+            features=[],
+            vision_enabled=False,
+            vision_detail=None,
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text=fake_context,
+                    role=PromptMessageRole.SYSTEM,
+                    edition_type="basic",
+                ),
+                LLMNodeChatModelMessage(
+                    text="{#context#}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+                LLMNodeChatModelMessage(
+                    text=fake_assistant_prompt,
+                    role=PromptMessageRole.ASSISTANT,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=[
+                SystemPromptMessage(content=fake_context),
+                UserPromptMessage(content=fake_context),
+                AssistantPromptMessage(content=fake_assistant_prompt),
+            ]
+            + mock_history[fake_window_size * -2 :]
+            + [
+                UserPromptMessage(content=fake_query),
+            ],
+        ),
+        LLMNodeTestScenario(
+            description="User files",
+            user_query=fake_query,
+            user_files=[
+                File(
+                    tenant_id="test",
+                    type=FileType.IMAGE,
+                    filename="test1.jpg",
+                    transfer_method=FileTransferMethod.REMOTE_URL,
+                    remote_url=fake_remote_url,
+                )
+            ],
+            vision_enabled=True,
+            vision_detail=fake_vision_detail,
+            features=[ModelFeature.VISION],
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text=fake_context,
+                    role=PromptMessageRole.SYSTEM,
+                    edition_type="basic",
+                ),
+                LLMNodeChatModelMessage(
+                    text="{#context#}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+                LLMNodeChatModelMessage(
+                    text=fake_assistant_prompt,
+                    role=PromptMessageRole.ASSISTANT,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=[
+                SystemPromptMessage(content=fake_context),
+                UserPromptMessage(content=fake_context),
+                AssistantPromptMessage(content=fake_assistant_prompt),
+            ]
+            + mock_history[fake_window_size * -2 :]
+            + [
+                UserPromptMessage(
+                    content=[
+                        TextPromptMessageContent(data=fake_query),
+                        ImagePromptMessageContent(data=fake_remote_url, detail=fake_vision_detail),
+                    ]
+                ),
+            ],
+        ),
+        LLMNodeTestScenario(
+            description="Prompt template with variable selector of File",
+            user_query=fake_query,
+            user_files=[],
+            vision_enabled=False,
+            vision_detail=fake_vision_detail,
+            features=[ModelFeature.VISION],
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text="{{#input.image#}}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=[
+                UserPromptMessage(
+                    content=[
+                        ImagePromptMessageContent(data=fake_remote_url, detail=fake_vision_detail),
+                    ]
+                ),
+            ]
+            + mock_history[fake_window_size * -2 :]
+            + [UserPromptMessage(content=fake_query)],
+            file_variables={
+                "input.image": File(
+                    tenant_id="test",
+                    type=FileType.IMAGE,
+                    filename="test1.jpg",
+                    transfer_method=FileTransferMethod.REMOTE_URL,
+                    remote_url=fake_remote_url,
+                )
+            },
+        ),
+        LLMNodeTestScenario(
+            description="Prompt template with variable selector of File without vision feature",
+            user_query=fake_query,
+            user_files=[],
+            vision_enabled=True,
+            vision_detail=fake_vision_detail,
+            features=[],
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text="{{#input.image#}}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=mock_history[fake_window_size * -2 :] + [UserPromptMessage(content=fake_query)],
+            file_variables={
+                "input.image": File(
+                    tenant_id="test",
+                    type=FileType.IMAGE,
+                    filename="test1.jpg",
+                    transfer_method=FileTransferMethod.REMOTE_URL,
+                    remote_url=fake_remote_url,
+                )
+            },
+        ),
+        LLMNodeTestScenario(
+            description="Prompt template with variable selector of File with video file and vision feature",
+            user_query=fake_query,
+            user_files=[],
+            vision_enabled=True,
+            vision_detail=fake_vision_detail,
+            features=[ModelFeature.VISION],
+            window_size=fake_window_size,
+            prompt_template=[
+                LLMNodeChatModelMessage(
+                    text="{{#input.image#}}",
+                    role=PromptMessageRole.USER,
+                    edition_type="basic",
+                ),
+            ],
+            expected_messages=mock_history[fake_window_size * -2 :] + [UserPromptMessage(content=fake_query)],
+            file_variables={
+                "input.image": File(
+                    tenant_id="test",
+                    type=FileType.VIDEO,
+                    filename="test1.mp4",
+                    transfer_method=FileTransferMethod.REMOTE_URL,
+                    remote_url=fake_remote_url,
+                    extension="mp4",
+                )
+            },
+        ),
+    ]
+
+    for scenario in test_scenarios:
+        model_config.model_schema.features = scenario.features
+
+        for k, v in scenario.file_variables.items():
+            selector = k.split(".")
+            llm_node.graph_runtime_state.variable_pool.add(selector, v)
+
+        # Call the method under test
+        prompt_messages, _ = llm_node._fetch_prompt_messages(
+            user_query=scenario.user_query,
+            user_files=scenario.user_files,
+            context=fake_context,
+            memory=memory,
+            model_config=model_config,
+            prompt_template=scenario.prompt_template,
+            memory_config=memory_config,
+            vision_enabled=scenario.vision_enabled,
+            vision_detail=scenario.vision_detail,
+            variable_pool=llm_node.graph_runtime_state.variable_pool,
+            jinja2_variables=[],
+        )
+
+        # Verify the result
+        assert len(prompt_messages) == len(scenario.expected_messages), f"Scenario failed: {scenario.description}"
+        assert (
+            prompt_messages == scenario.expected_messages
+        ), f"Message content mismatch in scenario: {scenario.description}"

api/tests/unit_tests/core/workflow/nodes/llm/test_scenarios.py

@@ -0,0 +1,25 @@
+from collections.abc import Mapping, Sequence
+
+from pydantic import BaseModel, Field
+
+from core.file import File
+from core.model_runtime.entities.message_entities import PromptMessage
+from core.model_runtime.entities.model_entities import ModelFeature
+from core.workflow.nodes.llm.entities import LLMNodeChatModelMessage
+
+
+class LLMNodeTestScenario(BaseModel):
+    """Test scenario for LLM node testing."""
+
+    description: str = Field(..., description="Description of the test scenario")
+    user_query: str = Field(..., description="User query input")
+    user_files: Sequence[File] = Field(default_factory=list, description="List of user files")
+    vision_enabled: bool = Field(default=False, description="Whether vision is enabled")
+    vision_detail: str | None = Field(None, description="Vision detail level if vision is enabled")
+    features: Sequence[ModelFeature] = Field(default_factory=list, description="List of model features")
+    window_size: int = Field(..., description="Window size for memory")
+    prompt_template: Sequence[LLMNodeChatModelMessage] = Field(..., description="Template for prompt messages")
+    file_variables: Mapping[str, File | Sequence[File]] = Field(
+        default_factory=dict, description="List of file variables"
+    )
+    expected_messages: Sequence[PromptMessage] = Field(..., description="Expected messages after processing")

api/tests/unit_tests/oss/__mock/volcengine_tos.py

@@ -1,4 +1,5 @@
 import os
+from collections import UserDict
 from unittest.mock import MagicMock
 
 import pytest
@@ -14,7 +15,7 @@ from tests.unit_tests.oss.__mock.base import (
 )
 
 
-class AttrDict(dict):
+class AttrDict(UserDict):
     def __getattr__(self, item):
         return self.get(item)
 

web/app/components/base/file-uploader/utils.ts

@@ -44,12 +44,6 @@ export const fileUpload: FileUpload = ({
 }
 
 export const getFileExtension = (fileName: string, fileMimetype: string, isRemote?: boolean) => {
-  if (fileMimetype)
-    return mime.getExtension(fileMimetype) || ''
-
-  if (isRemote)
-    return ''
-
   if (fileName) {
     const fileNamePair = fileName.split('.')
     const fileNamePairLength = fileNamePair.length
@@ -58,6 +52,12 @@ export const getFileExtension = (fileName: string, fileMimetype: string, isRemot
       return fileNamePair[fileNamePairLength - 1]
   }
 
+  if (fileMimetype)
+    return mime.getExtension(fileMimetype) || ''
+
+  if (isRemote)
+    return ''
+
   return ''
 }
 

web/app/components/workflow/nodes/llm/components/config-prompt-item.tsx

@@ -144,6 +144,7 @@ const ConfigPromptItem: FC<Props> = ({
       onEditionTypeChange={onEditionTypeChange}
       varList={varList}
       handleAddVariable={handleAddVariable}
+      isSupportFileVar
     />
   )
 }

web/app/components/workflow/nodes/llm/panel.tsx

@@ -67,6 +67,7 @@ const Panel: FC<NodePanelProps<LLMNodeType>> = ({
     handleStop,
     varInputs,
     runResult,
+    filterJinjia2InputVar,
   } = useConfig(id, data)
 
   const model = inputs.model
@@ -194,7 +195,7 @@ const Panel: FC<NodePanelProps<LLMNodeType>> = ({
               list={inputs.prompt_config?.jinja2_variables || []}
               onChange={handleVarListChange}
               onVarNameChange={handleVarNameChange}
-              filterVar={filterVar}
+              filterVar={filterJinjia2InputVar}
             />
           </Field>
         )}
@@ -233,6 +234,7 @@ const Panel: FC<NodePanelProps<LLMNodeType>> = ({
                 hasSetBlockStatus={hasSetBlockStatus}
                 nodesOutputVars={availableVars}
                 availableNodes={availableNodesWithParent}
+                isSupportFileVar
               />
 
               {inputs.memory.query_prompt_template && !inputs.memory.query_prompt_template.includes('{{#sys.query#}}') && (

web/app/components/workflow/nodes/llm/use-config.ts

@@ -278,11 +278,15 @@ const useConfig = (id: string, payload: LLMNodeType) => {
   }, [inputs, setInputs])
 
   const filterInputVar = useCallback((varPayload: Var) => {
+    return [VarType.number, VarType.string, VarType.secret, VarType.arrayString, VarType.arrayNumber, VarType.arrayFile].includes(varPayload.type)
+  }, [])
+
+  const filterJinjia2InputVar = useCallback((varPayload: Var) => {
     return [VarType.number, VarType.string, VarType.secret, VarType.arrayString, VarType.arrayNumber].includes(varPayload.type)
   }, [])
 
   const filterMemoryPromptVar = useCallback((varPayload: Var) => {
-    return [VarType.arrayObject, VarType.array, VarType.number, VarType.string, VarType.secret, VarType.arrayString, VarType.arrayNumber].includes(varPayload.type)
+    return [VarType.arrayObject, VarType.array, VarType.number, VarType.string, VarType.secret, VarType.arrayString, VarType.arrayNumber, VarType.arrayFile].includes(varPayload.type)
   }, [])
 
   const {
@@ -406,6 +410,7 @@ const useConfig = (id: string, payload: LLMNodeType) => {
     handleRun,
     handleStop,
     runResult,
+    filterJinjia2InputVar,
   }
 }
 

web/i18n/pt-BR/app-log.ts

@@ -86,8 +86,8 @@ const translation = {
   agenteLogDetail: {
     agentMode: 'Modo Agente',
     toolUsed: 'Ferramenta usada',
-    iterações: 'Iterações',
-    iteração: 'Iteração',
+    iterations: 'Iterações',
+    iteration: 'Iteração',
     finalProcessing: 'Processamento Final',
   },
   agentLogDetail: {