ruff

src/langbot/pkg/workflow/nodes/llm_call.py

@@ -18,108 +18,120 @@ logger = logging.getLogger(__name__)
 class LLMCallNode(WorkflowNode):
     """LLM call node - invoke large language model"""
 
-    type_name = "llm_call"
-    category = "process"
-    icon = "🤖"
-    name = "llm_call"
-    name_zh = "LLM 调用"
-    name_en = "LLM Call"
-    description = "llm_call"
-    description_zh = "调用大语言模型生成响应"
-    description_en = "Call a large language model to generate responses"
+    type_name = 'llm_call'
+    category = 'process'
+    icon = '🤖'
+    name = 'llm_call'
+    name_zh = 'LLM 调用'
+    name_en = 'LLM Call'
+    description = 'llm_call'
+    description_zh = '调用大语言模型生成响应'
+    description_en = 'Call a large language model to generate responses'
 
     inputs: ClassVar[list[NodePort]] = [
-        NodePort(name="input", type="string", description="Input text to send to the model", required=False),
-        NodePort(name="context", type="object", description="Additional context data", required=False),
+        NodePort(name='input', type='string', description='Input text to send to the model', required=False),
+        NodePort(name='context', type='object', description='Additional context data', required=False),
     ]
     outputs: ClassVar[list[NodePort]] = [
-        NodePort(name="response", type="string", description="Model response text"),
-        NodePort(name="usage", type="object", description="Token usage information"),
+        NodePort(name='response', type='string', description='Model response text'),
+        NodePort(name='usage', type='object', description='Token usage information'),
     ]
     config_schema: ClassVar[list[NodeConfig]] = [
         NodeConfig(
-            name="model", type="llm-model-selector", required=True,
-            description="Select the LLM model to use",
-            label={"en_US": "Model", "zh_Hans": "模型"},
+            name='model',
+            type='llm-model-selector',
+            required=True,
+            description='Select the LLM model to use',
+            label={'en_US': 'Model', 'zh_Hans': '模型'},
         ),
         NodeConfig(
-            name="system_prompt", type="textarea", required=False, default="",
-            description="System prompt to set model behavior",
-            label={"en_US": "System Prompt", "zh_Hans": "系统提示词"},
+            name='system_prompt',
+            type='textarea',
+            required=False,
+            default='',
+            description='System prompt to set model behavior',
+            label={'en_US': 'System Prompt', 'zh_Hans': '系统提示词'},
         ),
         NodeConfig(
-            name="user_prompt_template", type="textarea", required=True, default="{{input}}",
-            description="User prompt template with variable placeholders",
-            label={"en_US": "User Prompt Template", "zh_Hans": "用户提示词模板"},
+            name='user_prompt_template',
+            type='textarea',
+            required=True,
+            default='{{input}}',
+            description='User prompt template with variable placeholders',
+            label={'en_US': 'User Prompt Template', 'zh_Hans': '用户提示词模板'},
         ),
         NodeConfig(
-            name="temperature", type="number", required=False, default=0.7,
-            description="Controls randomness (0.0-2.0)",
-            label={"en_US": "Temperature", "zh_Hans": "温度"},
-            min_value=0.0, max_value=2.0,
+            name='temperature',
+            type='number',
+            required=False,
+            default=0.7,
+            description='Controls randomness (0.0-2.0)',
+            label={'en_US': 'Temperature', 'zh_Hans': '温度'},
+            min_value=0.0,
+            max_value=2.0,
         ),
         NodeConfig(
-            name="max_tokens", type="integer", required=False, default=0,
-            description="Max tokens to generate (0 = model default)",
-            label={"en_US": "Max Tokens", "zh_Hans": "最大令牌数"},
+            name='max_tokens',
+            type='integer',
+            required=False,
+            default=0,
+            description='Max tokens to generate (0 = model default)',
+            label={'en_US': 'Max Tokens', 'zh_Hans': '最大令牌数'},
         ),
     ]
 
     def _resolve_template(self, template: str, inputs: dict[str, Any], context: ExecutionContext) -> str:
         """Resolve {{variable}} placeholders in a template string."""
+
         def replacer(match: re.Match) -> str:
             expr = match.group(1).strip()
             # Try inputs first
             if expr in inputs:
                 return str(inputs[expr])
             # Try context variables
-            if expr.startswith("variables."):
-                var_name = expr[len("variables."):]
-                return str(context.variables.get(var_name, ""))
+            if expr.startswith('variables.'):
+                var_name = expr[len('variables.') :]
+                return str(context.variables.get(var_name, ''))
             # Try message context
-            if expr.startswith("message.") and context.message_context:
-                attr = expr[len("message."):]
-                return str(getattr(context.message_context, attr, ""))
+            if expr.startswith('message.') and context.message_context:
+                attr = expr[len('message.') :]
+                return str(getattr(context.message_context, attr, ''))
             return match.group(0)  # leave unresolved
 
-        return re.sub(r"\{\{([^}]+)\}\}", replacer, template)
+        return re.sub(r'\{\{([^}]+)\}\}', replacer, template)
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
-        model_uuid = self.get_config("model", "")
+        model_uuid = self.get_config('model', '')
         if not model_uuid:
-            raise ValueError("No model configured for LLM call node")
+            raise ValueError('No model configured for LLM call node')
 
         if not self.ap:
-            raise RuntimeError("Application instance not available — cannot call LLM")
+            raise RuntimeError('Application instance not available — cannot call LLM')
 
         # Resolve prompts
-        system_prompt = self._resolve_template(
-            self.get_config("system_prompt", ""), inputs, context
-        )
-        user_prompt = self._resolve_template(
-            self.get_config("user_prompt_template", "{{input}}"), inputs, context
-        )
+        system_prompt = self._resolve_template(self.get_config('system_prompt', ''), inputs, context)
+        user_prompt = self._resolve_template(self.get_config('user_prompt_template', '{{input}}'), inputs, context)
 
         # Build messages
         messages: list[provider_message.Message] = []
         if system_prompt:
-            messages.append(provider_message.Message(role="system", content=system_prompt))
-        messages.append(provider_message.Message(role="user", content=user_prompt))
+            messages.append(provider_message.Message(role='system', content=system_prompt))
+        messages.append(provider_message.Message(role='user', content=user_prompt))
 
         # Get model
         runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
 
         # Build extra args from config
         extra_args: dict[str, Any] = {}
-        temperature = self.get_config("temperature")
+        temperature = self.get_config('temperature')
         if temperature is not None:
-            extra_args["temperature"] = float(temperature)
-        max_tokens = self.get_config("max_tokens", 0)
+            extra_args['temperature'] = float(temperature)
+        max_tokens = self.get_config('max_tokens', 0)
         if max_tokens and int(max_tokens) > 0:
-            extra_args["max_tokens"] = int(max_tokens)
+            extra_args['max_tokens'] = int(max_tokens)
 
         # Invoke LLM
-        logger.info(f"LLM call node {self.node_id}: invoking model {model_uuid}")
+        logger.info(f'LLM call node {self.node_id}: invoking model {model_uuid}')
         result_message = await runtime_model.provider.invoke_llm(
             query=None,
             model=runtime_model,
@@ -129,7 +141,7 @@ class LLMCallNode(WorkflowNode):
         )
 
         # Extract response text
-        response_text = ""
+        response_text = ''
         if isinstance(result_message.content, str):
             response_text = result_message.content
         elif isinstance(result_message.content, list):
@@ -141,23 +153,23 @@ class LLMCallNode(WorkflowNode):
                     response_text += elem
 
         # Extract usage info if available
-        usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
+        usage = {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
         if hasattr(result_message, 'usage') and result_message.usage:
             u = result_message.usage
             usage = {
-                "prompt_tokens": getattr(u, 'prompt_tokens', 0) or 0,
-                "completion_tokens": getattr(u, 'completion_tokens', 0) or 0,
-                "total_tokens": getattr(u, 'total_tokens', 0) or 0,
+                'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
+                'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
+                'total_tokens': getattr(u, 'total_tokens', 0) or 0,
             }
         elif hasattr(result_message, 'token_usage') and result_message.token_usage:
             u = result_message.token_usage
             usage = {
-                "prompt_tokens": getattr(u, 'prompt_tokens', 0) or 0,
-                "completion_tokens": getattr(u, 'completion_tokens', 0) or 0,
-                "total_tokens": getattr(u, 'total_tokens', 0) or 0,
+                'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
+                'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
+                'total_tokens': getattr(u, 'total_tokens', 0) or 0,
             }
 
         return {
-            "response": response_text,
-            "usage": usage,
+            'response': response_text,
+            'usage': usage,
         }