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fix: nested node single step run

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This commit is contained in:
Novice
2026-01-28 10:18:10 +08:00
parent a571b3abb2
commit cd688a0d8f
4 changed files with 132 additions and 31 deletions
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26
api/core/workflow/nodes/llm/node.py
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@ -1402,20 +1402,19 @@ class LLMNode(Node[LLMNodeData]):
# Create typed NodeData from dict
typed_node_data = LLMNodeData.model_validate(node_data)
prompt_template: (Sequence[LLMNodeChatModelMessage | PromptMessageContext] |
LLMNodeCompletionModelPromptTemplate) = typed_node_data.prompt_template
prompt_template = typed_node_data.prompt_template
variable_selectors = []
prompt_context_selectors: list[Sequence[str]] = []
if isinstance(prompt_template, list):
for prompt in prompt_template:
if isinstance(prompt, LLMNodeChatModelMessage) and prompt.edition_type == "jinja2":
variable_template_parser = VariableTemplateParser(template=prompt.text)
for item in prompt_template:
# Check PromptMessageContext first (same order as _parse_prompt_template)
# This extracts value_selector which is used by variable_pool.get(ctx_ref.value_selector)
if isinstance(item, PromptMessageContext):
if len(item.value_selector) >= 2:
prompt_context_selectors.append(item.value_selector)
elif isinstance(item, LLMNodeChatModelMessage) and item.edition_type == "jinja2":
variable_template_parser = VariableTemplateParser(template=item.text)
variable_selectors.extend(variable_template_parser.extract_variable_selectors())
continue
if isinstance(prompt, PromptMessageContext):
if len(prompt.value_selector) < 2:
continue
prompt_context_selectors.append(prompt.value_selector)
elif isinstance(prompt_template, LLMNodeCompletionModelPromptTemplate):
if prompt_template.edition_type != "jinja2":
variable_template_parser = VariableTemplateParser(template=prompt_template.text)
@ -1452,14 +1451,11 @@ class LLMNode(Node[LLMNodeData]):
enable_jinja = False
if isinstance(prompt_template, list):
for prompt in prompt_template:
if isinstance(prompt, LLMNodeChatModelMessage) and prompt.edition_type == "jinja2":
for item in prompt_template:
if isinstance(item, LLMNodeChatModelMessage) and item.edition_type == "jinja2":
enable_jinja = True
break
if isinstance(prompt, PromptMessageContext):
prompt_context_selectors.append(prompt.value_selector)
else:
prompt_template: LLMNodeCompletionModelPromptTemplate
enable_jinja = True
if enable_jinja: