fix(node): handle empty text segments gracefully

Ensure that messages are only created from non-empty text segments, preventing potential issues with empty content.

test: add scenario for file variable handling

Introduce a test case for scenarios involving prompt templates with file variables, particularly images, to improve reliability and test coverage. Updated `LLMNodeTestScenario` to use `Sequence` and `Mapping` for more flexible configurations.

Closes #123, relates to #456.

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

@@ -868,8 +868,10 @@ class LLMNode(BaseNode[LLMNodeData]):
                             image_contents.append(image_content)
 
                 # Create message with text from all segments
-                prompt_message = _combine_text_message_with_role(text=segment_group.text, role=message.role)
-                prompt_messages.append(prompt_message)
+                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 image_contents:
                     # Create message with image contents

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

@@ -363,11 +363,49 @@ def test_fetch_prompt_messages__basic(faker, llm_node, model_config):
                 ),
             ],
         ),
+        LLMNodeTestScenario(
+            description="Prompt template with variable selector of File",
+            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=[
+                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,
+                )
+            },
+        ),
     ]
 
     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,

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

@@ -1,3 +1,5 @@
+from collections.abc import Mapping, Sequence
+
 from pydantic import BaseModel, Field
 
 from core.file import File
@@ -11,10 +13,13 @@ class LLMNodeTestScenario(BaseModel):
 
     description: str = Field(..., description="Description of the test scenario")
     user_query: str = Field(..., description="User query input")
-    user_files: list[File] = Field(default_factory=list, description="List of user files")
+    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: list[ModelFeature] = Field(default_factory=list, description="List of model features")
+    features: Sequence[ModelFeature] = Field(default_factory=list, description="List of model features")
     window_size: int = Field(..., description="Window size for memory")
-    prompt_template: list[LLMNodeChatModelMessage] = Field(..., description="Template for prompt messages")
-    expected_messages: list[PromptMessage] = Field(..., description="Expected messages after processing")
+    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")