Merge branch 'langgenius:main' into main

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -394,28 +394,6 @@ def _process_stream_response(
                 # save workflow app log
                 self._save_workflow_app_log(workflow_run)
 
-                yield self._workflow_finish_to_stream_response(
-                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
-                )
-            elif isinstance(event, QueueWorkflowPartialSuccessEvent):
-                if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
-
-                if not graph_runtime_state:
-                    raise Exception("Graph runtime state not initialized.")
-                workflow_run = self._handle_workflow_run_partial_success(
-                    workflow_run=workflow_run,
-                    start_at=graph_runtime_state.start_at,
-                    total_tokens=graph_runtime_state.total_tokens,
-                    total_steps=graph_runtime_state.node_run_steps,
-                    outputs=event.outputs,
-                    exceptions_count=event.exceptions_count,
-                    conversation_id=None,
-                    trace_manager=trace_manager,
-                )
-                # save workflow app log
-                self._save_workflow_app_log(workflow_run)
-
                 yield self._workflow_finish_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                 )

api/core/model_runtime/model_providers/baichuan/llm/llm.py

@@ -10,6 +10,7 @@
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
     PromptMessage,
+    PromptMessageContentType,
     PromptMessageTool,
     SystemPromptMessage,
     ToolPromptMessage,
@@ -105,7 +106,11 @@ def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
             if isinstance(message.content, str):
                 message_dict = {"role": "user", "content": message.content}
             else:
-                raise ValueError("User message content must be str")
+                for message_content in message.content:
+                    if message_content.type == PromptMessageContentType.TEXT:
+                        message_dict = {"role": "user", "content": message_content.data}
+                    elif message_content.type == PromptMessageContentType.IMAGE:
+                        raise ValueError("Content object type not support image_url")
         elif isinstance(message, AssistantPromptMessage):
             message = cast(AssistantPromptMessage, message)
             message_dict = {"role": "assistant", "content": message.content}

api/core/model_runtime/model_providers/deepseek/llm/llm.py

@@ -24,6 +24,9 @@ def _invoke(
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
+        # {"response_format": "xx"} need convert to {"response_format": {"type": "xx"}}
+        if "response_format" in model_parameters:
+            model_parameters["response_format"] = {"type": model_parameters.get("response_format")}
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:

api/core/model_runtime/model_providers/volcengine_maas/legacy/client.py

@@ -68,7 +68,12 @@ def convert_prompt_message_to_maas_message(message: PromptMessage) -> dict:
                 content = []
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
-                        raise ValueError("Content object type only support image_url")
+                        content.append(
+                            {
+                                "type": "text",
+                                "text": message_content.data,
+                            }
+                        )
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
                         image_data = re.sub(r"^data:image\/[a-zA-Z]+;base64,", "", message_content.data)

api/core/model_runtime/model_providers/x/llm/grok-2-1212.yaml

@@ -0,0 +1,66 @@
+model: grok-2-1212
+label:
+  en_US: grok-2-1212
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 2.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: 0
+    max: 2.0
+    precision: 1
+    required: false
+    help:
+      en_US: "Number between 0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
+      zh_Hans: "介于0和2.0之间的数字。正值会根据新标记在文本中迄今为止的现有频率来惩罚它们，从而降低模型一字不差地重复同一句话的可能性。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/x/llm/grok-2-vision-1212.yaml

@@ -0,0 +1,64 @@
+model: grok-2-vision-1212
+label:
+  en_US: grok-2-vision-1212
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 2.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: 0
+    max: 2.0
+    precision: 1
+    required: false
+    help:
+      en_US: "Number between 0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
+      zh_Hans: "介于0和2.0之间的数字。正值会根据新标记在文本中迄今为止的现有频率来惩罚它们，从而降低模型一字不差地重复同一句话的可能性。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/x/llm/grok-beta.yaml

@@ -1,6 +1,6 @@
 model: grok-beta
 label:
-  en_US: Grok Beta
+  en_US: grok-beta
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/x/llm/grok-vision-beta.yaml

@@ -1,6 +1,6 @@
 model: grok-vision-beta
 label:
-  en_US: Grok Vision Beta
+  en_US: grok-vision-beta
 model_type: llm
 features:
   - agent-thought

api/core/workflow/nodes/http_request/executor.py

@@ -37,7 +37,7 @@
 class Executor:
     method: Literal["get", "head", "post", "put", "delete", "patch"]
     url: str
-    params: Mapping[str, str] | None
+    params: list[tuple[str, str]] | None
     content: str | bytes | None
     data: Mapping[str, Any] | None
     files: Mapping[str, tuple[str | None, bytes, str]] | None
@@ -67,7 +67,7 @@ def __init__(
         self.method = node_data.method
         self.auth = node_data.authorization
         self.timeout = timeout
-        self.params = {}
+        self.params = []
         self.headers = {}
         self.content = None
         self.files = None
@@ -89,14 +89,48 @@ def _init_url(self):
         self.url = self.variable_pool.convert_template(self.node_data.url).text
 
     def _init_params(self):
-        params = _plain_text_to_dict(self.node_data.params)
-        for key in params:
-            params[key] = self.variable_pool.convert_template(params[key]).text
-        self.params = params
+        """
+        Almost same as _init_headers(), difference:
+        1. response a list tuple to support same key, like 'aa=1&aa=2'
+        2. param value may have '\n', we need to splitlines then extract the variable value.
+        """
+        result = []
+        for line in self.node_data.params.splitlines():
+            if not (line := line.strip()):
+                continue
+
+            key, *value = line.split(":", 1)
+            if not (key := key.strip()):
+                continue
+
+            value = value[0].strip() if value else ""
+            result.append(
+                (self.variable_pool.convert_template(key).text, self.variable_pool.convert_template(value).text)
+            )
+
+        self.params = result
 
     def _init_headers(self):
+        """
+        Convert the header string of frontend to a dictionary.
+
+        Each line in the header string represents a key-value pair.
+        Keys and values are separated by ':'.
+        Empty values are allowed.
+
+        Examples:
+            'aa:bb\n cc:dd'  -> {'aa': 'bb', 'cc': 'dd'}
+            'aa:\n cc:dd\n'  -> {'aa': '', 'cc': 'dd'}
+            'aa\n cc : dd'   -> {'aa': '', 'cc': 'dd'}
+
+        """
         headers = self.variable_pool.convert_template(self.node_data.headers).text
-        self.headers = _plain_text_to_dict(headers)
+        self.headers = {
+            key.strip(): (value[0].strip() if value else "")
+            for line in headers.splitlines()
+            if line.strip()
+            for key, *value in [line.split(":", 1)]
+        }
 
     def _init_body(self):
         body = self.node_data.body
@@ -288,33 +322,6 @@ def to_log(self):
         return raw
 
 
-def _plain_text_to_dict(text: str, /) -> dict[str, str]:
-    """
-    Convert a string of key-value pairs to a dictionary.
-
-    Each line in the input string represents a key-value pair.
-    Keys and values are separated by ':'.
-    Empty values are allowed.
-
-    Examples:
-        'aa:bb\n cc:dd'  -> {'aa': 'bb', 'cc': 'dd'}
-        'aa:\n cc:dd\n'  -> {'aa': '', 'cc': 'dd'}
-        'aa\n cc : dd'   -> {'aa': '', 'cc': 'dd'}
-
-    Args:
-        convert_text (str): The input string to convert.
-
-    Returns:
-        dict[str, str]: A dictionary of key-value pairs.
-    """
-    return {
-        key.strip(): (value[0].strip() if value else "")
-        for line in text.splitlines()
-        if line.strip()
-        for key, *value in [line.split(":", 1)]
-    }
-
-
 def _generate_random_string(n: int) -> str:
     """
     Generate a random string of lowercase ASCII letters.

api/models/workflow.py

@@ -225,8 +225,10 @@ def tool_published(self) -> bool:
         from models.tools import WorkflowToolProvider
 
         return (
-            db.session.query(WorkflowToolProvider).filter(WorkflowToolProvider.app_id == self.app_id).first()
-            is not None
+            db.session.query(WorkflowToolProvider)
+            .filter(WorkflowToolProvider.tenant_id == self.tenant_id, WorkflowToolProvider.app_id == self.app_id)
+            .count()
+            > 0
         )
 
     @property