mirror of
https://github.com/langgenius/dify.git
synced 2026-04-30 15:38:08 +08:00
refactor: streamline output building in LLMNode with new _build_outputs method
This commit is contained in:
Novice
@ -436,48 +436,19 @@ class LLMNode(Node[LLMNodeData]):
|
||||
if tool.enabled
|
||||
]
|
||||
|
||||
# Unified outputs building
|
||||
outputs = {
|
||||
"text": clean_text,
|
||||
"reasoning_content": reasoning_content,
|
||||
"usage": jsonable_encoder(usage),
|
||||
"finish_reason": finish_reason,
|
||||
"context": llm_utils.build_context(prompt_messages, clean_text, generation_data),
|
||||
}
|
||||
|
||||
# Build generation field
|
||||
if generation_data:
|
||||
# Use generation_data from tool invocation (supports multi-turn)
|
||||
generation = {
|
||||
"content": generation_data.text,
|
||||
"reasoning_content": generation_data.reasoning_contents, # [thought1, thought2, ...]
|
||||
"tool_calls": [self._serialize_tool_call(item) for item in generation_data.tool_calls],
|
||||
"sequence": generation_data.sequence,
|
||||
}
|
||||
files_to_output = generation_data.files
|
||||
else:
|
||||
# Traditional LLM invocation
|
||||
generation_reasoning = generation_reasoning_content or reasoning_content
|
||||
generation_content = generation_clean_content or clean_text
|
||||
sequence: list[dict[str, Any]] = []
|
||||
if generation_reasoning:
|
||||
sequence = [
|
||||
{"type": "reasoning", "index": 0},
|
||||
{"type": "content", "start": 0, "end": len(generation_content)},
|
||||
]
|
||||
generation = {
|
||||
"content": generation_content,
|
||||
"reasoning_content": [generation_reasoning] if generation_reasoning else [],
|
||||
"tool_calls": [],
|
||||
"sequence": sequence,
|
||||
}
|
||||
files_to_output = self._file_outputs
|
||||
|
||||
outputs["generation"] = generation
|
||||
if files_to_output:
|
||||
outputs["files"] = ArrayFileSegment(value=files_to_output)
|
||||
if structured_output:
|
||||
outputs["structured_output"] = structured_output.structured_output
|
||||
is_sandbox = self.graph_runtime_state.sandbox is not None
|
||||
outputs = self._build_outputs(
|
||||
is_sandbox=is_sandbox,
|
||||
clean_text=clean_text,
|
||||
reasoning_content=reasoning_content,
|
||||
generation_reasoning_content=generation_reasoning_content,
|
||||
generation_clean_content=generation_clean_content,
|
||||
usage=usage,
|
||||
finish_reason=finish_reason,
|
||||
prompt_messages=prompt_messages,
|
||||
generation_data=generation_data,
|
||||
structured_output=structured_output,
|
||||
)
|
||||
|
||||
# Send final chunk event to indicate streaming is complete
|
||||
# For tool calls and sandbox, final events are already sent in _process_tool_outputs
|
||||
@ -543,6 +514,95 @@ class LLMNode(Node[LLMNodeData]):
|
||||
)
|
||||
)
|
||||
|
||||
def _build_outputs(
|
||||
self,
|
||||
*,
|
||||
is_sandbox: bool,
|
||||
clean_text: str,
|
||||
reasoning_content: str,
|
||||
generation_reasoning_content: str,
|
||||
generation_clean_content: str,
|
||||
usage: LLMUsage,
|
||||
finish_reason: str | None,
|
||||
prompt_messages: Sequence[PromptMessage],
|
||||
generation_data: LLMGenerationData | None,
|
||||
structured_output: LLMStructuredOutput | None,
|
||||
) -> dict[str, Any]:
|
||||
"""Build the outputs dictionary for the LLM node.
|
||||
|
||||
Two runtime modes produce different output shapes:
|
||||
- **Classical** (is_sandbox=False): top-level ``text`` and ``reasoning_content``
|
||||
are preserved for backward compatibility with existing users.
|
||||
- **Sandbox** (is_sandbox=True): ``text`` and ``reasoning_content`` are omitted
|
||||
from the top level because they duplicate fields inside ``generation``.
|
||||
|
||||
The ``generation`` field always carries the full structured representation
|
||||
(content, reasoning, tool_calls, sequence) regardless of runtime mode.
|
||||
|
||||
Args:
|
||||
is_sandbox: Whether the current runtime is sandbox mode.
|
||||
clean_text: Processed text for outputs["text"]; may keep <think> tags for "tagged" format.
|
||||
reasoning_content: Native model reasoning from the API response.
|
||||
generation_reasoning_content: Reasoning for the generation field, extracted from <think>
|
||||
tags via _split_reasoning (always tag-free). Falls back to reasoning_content
|
||||
if empty (no <think> tags found).
|
||||
generation_clean_content: Clean text for the generation field (always tag-free).
|
||||
Differs from clean_text only when reasoning_format is "tagged".
|
||||
usage: LLM usage statistics.
|
||||
finish_reason: Finish reason from LLM.
|
||||
prompt_messages: Prompt messages sent to the LLM.
|
||||
generation_data: Multi-turn generation data from tool/sandbox invocation, or None.
|
||||
structured_output: Structured output if enabled.
|
||||
"""
|
||||
# Common outputs shared by both runtimes
|
||||
outputs: dict[str, Any] = {
|
||||
"usage": jsonable_encoder(usage),
|
||||
"finish_reason": finish_reason,
|
||||
"context": llm_utils.build_context(prompt_messages, clean_text, generation_data),
|
||||
}
|
||||
|
||||
# Classical runtime keeps top-level text/reasoning_content for backward compatibility
|
||||
if not is_sandbox:
|
||||
outputs["text"] = clean_text
|
||||
outputs["reasoning_content"] = reasoning_content
|
||||
|
||||
# Build generation field
|
||||
if generation_data:
|
||||
# Agent/sandbox runtime: generation_data captures multi-turn interactions
|
||||
generation = {
|
||||
"content": generation_data.text,
|
||||
"reasoning_content": generation_data.reasoning_contents, # [thought1, thought2, ...]
|
||||
"tool_calls": [self._serialize_tool_call(item) for item in generation_data.tool_calls],
|
||||
"sequence": generation_data.sequence,
|
||||
}
|
||||
files_to_output = generation_data.files
|
||||
else:
|
||||
# Classical runtime: use pre-computed generation-specific text pair,
|
||||
# falling back to native model reasoning if no <think> tags were found.
|
||||
generation_reasoning = generation_reasoning_content or reasoning_content
|
||||
generation_content = generation_clean_content or clean_text
|
||||
sequence: list[dict[str, Any]] = []
|
||||
if generation_reasoning:
|
||||
sequence = [
|
||||
{"type": "reasoning", "index": 0},
|
||||
{"type": "content", "start": 0, "end": len(generation_content)},
|
||||
]
|
||||
generation = {
|
||||
"content": generation_content,
|
||||
"reasoning_content": [generation_reasoning] if generation_reasoning else [],
|
||||
"tool_calls": [],
|
||||
"sequence": sequence,
|
||||
}
|
||||
files_to_output = self._file_outputs
|
||||
|
||||
outputs["generation"] = generation
|
||||
if files_to_output:
|
||||
outputs["files"] = ArrayFileSegment(value=files_to_output)
|
||||
if structured_output:
|
||||
outputs["structured_output"] = structured_output.structured_output
|
||||
|
||||
return outputs
|
||||
|
||||
@staticmethod
|
||||
def invoke_llm(
|
||||
*,
|
||||
@ -1865,19 +1925,25 @@ class LLMNode(Node[LLMNodeData]):
|
||||
NodeEventBase,
|
||||
None,
|
||||
tuple[
|
||||
str,
|
||||
str,
|
||||
str,
|
||||
str,
|
||||
str, # clean_text: processed text for outputs["text"]
|
||||
str, # reasoning_content: native model reasoning
|
||||
str, # generation_reasoning_content: reasoning for generation field (from <think> tags)
|
||||
str, # generation_clean_content: clean text for generation field (always tag-free)
|
||||
LLMUsage,
|
||||
str | None,
|
||||
LLMStructuredOutput | None,
|
||||
LLMGenerationData | None,
|
||||
],
|
||||
]:
|
||||
"""
|
||||
Stream events and capture generator return value in one place.
|
||||
"""Stream events and capture generator return value in one place.
|
||||
|
||||
Uses generator delegation so _run stays concise while still emitting events.
|
||||
|
||||
Returns two pairs of text fields because outputs["text"] and generation["content"]
|
||||
may differ when reasoning_format is "tagged":
|
||||
- clean_text / reasoning_content: for top-level outputs (may keep <think> tags)
|
||||
- generation_clean_content / generation_reasoning_content: for the generation field
|
||||
(always tag-free, extracted via _split_reasoning with "separated" mode)
|
||||
"""
|
||||
clean_text = ""
|
||||
reasoning_content = ""
|
||||
|
||||
Reference in New Issue
Block a user