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Refactor workflow nodes to use generic node_data (#28782)

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This commit is contained in:
-LAN-
2025-11-27 20:46:56 +08:00
committed by GitHub
parent 002d8769b0
commit 8b761319f6
28 changed files with 121 additions and 170 deletions
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46
api/core/workflow/nodes/llm/node.py
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@@ -102,8 +102,6 @@ logger = logging.getLogger(__name__)
class LLMNode(Node[LLMNodeData]):
node_type = NodeType.LLM
_node_data: LLMNodeData
# Compiled regex for extracting <think> blocks (with compatibility for attributes)
_THINK_PATTERN = re.compile(r"<think[^>]*>(.*?)</think>", re.IGNORECASE | re.DOTALL)
@@ -154,13 +152,13 @@ class LLMNode(Node[LLMNodeData]):
try:
# init messages template
self._node_data.prompt_template = self._transform_chat_messages(self._node_data.prompt_template)
self.node_data.prompt_template = self._transform_chat_messages(self.node_data.prompt_template)
# fetch variables and fetch values from variable pool
inputs = self._fetch_inputs(node_data=self._node_data)
inputs = self._fetch_inputs(node_data=self.node_data)
# fetch jinja2 inputs
jinja_inputs = self._fetch_jinja_inputs(node_data=self._node_data)
jinja_inputs = self._fetch_jinja_inputs(node_data=self.node_data)
# merge inputs
inputs.update(jinja_inputs)
@@ -169,9 +167,9 @@ class LLMNode(Node[LLMNodeData]):
files = (
llm_utils.fetch_files(
variable_pool=variable_pool,
selector=self._node_data.vision.configs.variable_selector,
selector=self.node_data.vision.configs.variable_selector,
)
if self._node_data.vision.enabled
if self.node_data.vision.enabled
else []
)
@@ -179,7 +177,7 @@ class LLMNode(Node[LLMNodeData]):
node_inputs["#files#"] = [file.to_dict() for file in files]
# fetch context value
generator = self._fetch_context(node_data=self._node_data)
generator = self._fetch_context(node_data=self.node_data)
context = None
for event in generator:
context = event.context
@@ -189,7 +187,7 @@ class LLMNode(Node[LLMNodeData]):
# fetch model config
model_instance, model_config = LLMNode._fetch_model_config(
node_data_model=self._node_data.model,
node_data_model=self.node_data.model,
tenant_id=self.tenant_id,
)
@@ -197,13 +195,13 @@ class LLMNode(Node[LLMNodeData]):
memory = llm_utils.fetch_memory(
variable_pool=variable_pool,
app_id=self.app_id,
node_data_memory=self._node_data.memory,
node_data_memory=self.node_data.memory,
model_instance=model_instance,
)
query: str | None = None
if self._node_data.memory:
query = self._node_data.memory.query_prompt_template
if self.node_data.memory:
query = self.node_data.memory.query_prompt_template
if not query and (
query_variable := variable_pool.get((SYSTEM_VARIABLE_NODE_ID, SystemVariableKey.QUERY))
):
@@ -215,29 +213,29 @@ class LLMNode(Node[LLMNodeData]):
context=context,
memory=memory,
model_config=model_config,
prompt_template=self._node_data.prompt_template,
memory_config=self._node_data.memory,
vision_enabled=self._node_data.vision.enabled,
vision_detail=self._node_data.vision.configs.detail,
prompt_template=self.node_data.prompt_template,
memory_config=self.node_data.memory,
vision_enabled=self.node_data.vision.enabled,
vision_detail=self.node_data.vision.configs.detail,
variable_pool=variable_pool,
jinja2_variables=self._node_data.prompt_config.jinja2_variables,
jinja2_variables=self.node_data.prompt_config.jinja2_variables,
tenant_id=self.tenant_id,
)
# handle invoke result
generator = LLMNode.invoke_llm(
node_data_model=self._node_data.model,
node_data_model=self.node_data.model,
model_instance=model_instance,
prompt_messages=prompt_messages,
stop=stop,
user_id=self.user_id,
structured_output_enabled=self._node_data.structured_output_enabled,
structured_output=self._node_data.structured_output,
structured_output_enabled=self.node_data.structured_output_enabled,
structured_output=self.node_data.structured_output,
file_saver=self._llm_file_saver,
file_outputs=self._file_outputs,
node_id=self._node_id,
node_type=self.node_type,
reasoning_format=self._node_data.reasoning_format,
reasoning_format=self.node_data.reasoning_format,
)
structured_output: LLMStructuredOutput | None = None
@@ -253,12 +251,12 @@ class LLMNode(Node[LLMNodeData]):
reasoning_content = event.reasoning_content or ""
# For downstream nodes, determine clean text based on reasoning_format
if self._node_data.reasoning_format == "tagged":
if self.node_data.reasoning_format == "tagged":
# Keep <think> tags for backward compatibility
clean_text = result_text
else:
# Extract clean text from <think> tags
clean_text, _ = LLMNode._split_reasoning(result_text, self._node_data.reasoning_format)
clean_text, _ = LLMNode._split_reasoning(result_text, self.node_data.reasoning_format)
# Process structured output if available from the event.
structured_output = (
@@ -1204,7 +1202,7 @@ class LLMNode(Node[LLMNodeData]):
@property
def retry(self) -> bool:
return self._node_data.retry_config.retry_enabled
return self.node_data.retry_config.retry_enabled
def _combine_message_content_with_role(