mirror of https://github.com/langgenius/dify.git
feat: add tool call based structured output
This commit is contained in:
Novice
parent
39799b9db7
commit
87bcd70f59
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@ -26,7 +26,7 @@ from core.model_runtime.entities.message_entities import (
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SystemPromptMessage,
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TextPromptMessageContent,
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)
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from core.model_runtime.entities.model_entities import AIModelEntity, ParameterRule
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from core.model_runtime.entities.model_entities import AIModelEntity, ModelFeature, ParameterRule
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class ResponseFormat(StrEnum):
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@ -44,6 +44,12 @@ class SpecialModelType(StrEnum):
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OLLAMA = "ollama"
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# Tool name for structured output via tool call
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STRUCTURED_OUTPUT_TOOL_NAME = "structured_output"
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# Features that indicate tool call support
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TOOL_CALL_FEATURES = {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL}
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T = TypeVar("T", bound=BaseModel)
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@ -132,20 +138,24 @@ def invoke_llm_with_structured_output(
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file IDs in the output will be automatically converted to File objects.
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:return: full response or stream response chunk generator result
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"""
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# handle native json schema
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model_parameters_with_json_schema: dict[str, Any] = {
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**(model_parameters or {}),
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}
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# Determine structured output strategy
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if model_schema.support_structure_output:
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model_parameters = _handle_native_json_schema(
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# Priority 1: Native JSON schema support
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model_parameters_with_json_schema = _handle_native_json_schema(
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provider, model_schema, json_schema, model_parameters_with_json_schema, model_schema.parameter_rules
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)
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elif _supports_tool_call(model_schema):
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# Priority 2: Tool call based structured output
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structured_output_tool = _create_structured_output_tool(json_schema)
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tools = [structured_output_tool]
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else:
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# Set appropriate response format based on model capabilities
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# Priority 3: Prompt-based fallback
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_set_response_format(model_parameters_with_json_schema, model_schema.parameter_rules)
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# handle prompt based schema
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prompt_messages = _handle_prompt_based_schema(
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prompt_messages=prompt_messages,
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structured_output_schema=json_schema,
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@ -162,12 +172,11 @@ def invoke_llm_with_structured_output(
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)
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if isinstance(llm_result, LLMResult):
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if not isinstance(llm_result.message.content, str):
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raise OutputParserError(
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f"Failed to parse structured output, LLM result is not a string: {llm_result.message.content}"
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)
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# Non-streaming result
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structured_output = _extract_structured_output(llm_result)
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structured_output = _parse_structured_output(llm_result.message.content)
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# Fill missing fields with default values
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structured_output = fill_defaults_from_schema(structured_output, json_schema)
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# Convert file references if tenant_id is provided
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if tenant_id is not None:
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@ -189,13 +198,16 @@ def invoke_llm_with_structured_output(
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def generator() -> Generator[LLMResultChunkWithStructuredOutput, None, None]:
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result_text: str = ""
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tool_call_args: dict[str, str] = {} # tool_call_id -> arguments
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prompt_messages: Sequence[PromptMessage] = []
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system_fingerprint: str | None = None
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for event in llm_result:
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if isinstance(event, LLMResultChunk):
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prompt_messages = event.prompt_messages
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system_fingerprint = event.system_fingerprint
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# Collect text content
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if isinstance(event.delta.message.content, str):
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result_text += event.delta.message.content
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elif isinstance(event.delta.message.content, list):
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@ -203,6 +215,13 @@ def invoke_llm_with_structured_output(
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if isinstance(item, TextPromptMessageContent):
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result_text += item.data
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# Collect tool call arguments
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if event.delta.message.tool_calls:
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for tool_call in event.delta.message.tool_calls:
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call_id = tool_call.id or ""
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if tool_call.function.arguments:
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tool_call_args[call_id] = tool_call_args.get(call_id, "") + tool_call.function.arguments
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yield LLMResultChunkWithStructuredOutput(
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model=model_schema.model,
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prompt_messages=prompt_messages,
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@ -210,7 +229,11 @@ def invoke_llm_with_structured_output(
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delta=event.delta,
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)
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structured_output = _parse_structured_output(result_text)
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# Extract structured output: prefer tool call, fallback to text
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structured_output = _extract_structured_output_from_stream(result_text, tool_call_args)
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# Fill missing fields with default values
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structured_output = fill_defaults_from_schema(structured_output, json_schema)
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# Convert file references if tenant_id is provided
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if tenant_id is not None:
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@ -299,6 +322,144 @@ def _validate_structured_output(
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return validated_output
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def _supports_tool_call(model_schema: AIModelEntity) -> bool:
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"""Check if model supports tool call feature."""
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return bool(set(model_schema.features or []) & TOOL_CALL_FEATURES)
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def _create_structured_output_tool(json_schema: Mapping[str, Any]) -> PromptMessageTool:
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"""Create a tool definition for structured output."""
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return PromptMessageTool(
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name=STRUCTURED_OUTPUT_TOOL_NAME,
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description="Generate structured output according to the provided schema. "
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"You MUST call this function to provide your response in the required format.",
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parameters=dict(json_schema),
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)
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def _extract_structured_output(llm_result: LLMResult) -> Mapping[str, Any]:
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"""
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Extract structured output from LLM result (non-streaming).
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First tries to extract from tool_calls (if present), then falls back to text content.
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"""
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# Try to extract from tool call first
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tool_calls = llm_result.message.tool_calls
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if tool_calls:
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for tool_call in tool_calls:
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if tool_call.function.name == STRUCTURED_OUTPUT_TOOL_NAME:
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return _parse_tool_call_arguments(tool_call.function.arguments)
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# Fallback to text content parsing
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content = llm_result.message.content
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if not isinstance(content, str):
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raise OutputParserError(f"Failed to parse structured output, LLM result is not a string: {content}")
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return _parse_structured_output(content)
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def _extract_structured_output_from_stream(
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result_text: str,
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tool_call_args: dict[str, str],
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) -> Mapping[str, Any]:
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"""
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Extract structured output from streaming collected data.
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First tries to parse from collected tool call arguments, then falls back to text content.
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"""
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# Try to parse from tool call arguments first
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if tool_call_args:
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# Use the first non-empty tool call arguments
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for arguments in tool_call_args.values():
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if arguments.strip():
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return _parse_tool_call_arguments(arguments)
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# Fallback to text content parsing
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if not result_text:
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raise OutputParserError("No tool call arguments and no text content to parse")
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return _parse_structured_output(result_text)
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def _parse_tool_call_arguments(arguments: str) -> Mapping[str, Any]:
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"""Parse JSON from tool call arguments."""
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if not arguments:
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raise OutputParserError("Tool call arguments is empty")
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try:
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parsed = json.loads(arguments)
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if not isinstance(parsed, dict):
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raise OutputParserError(f"Tool call arguments is not a dict: {arguments}")
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return parsed
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except json.JSONDecodeError:
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# Try to repair malformed JSON
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repaired = json_repair.loads(arguments)
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if not isinstance(repaired, dict):
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raise OutputParserError(f"Failed to parse tool call arguments: {arguments}")
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return cast(dict, repaired)
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def _get_default_value_for_type(type_name: str | list[str] | None) -> Any:
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"""Get default empty value for a JSON schema type."""
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# Handle array of types (e.g., ["string", "null"])
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if isinstance(type_name, list):
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# Use the first non-null type
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type_name = next((t for t in type_name if t != "null"), None)
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if type_name == "string":
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return ""
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elif type_name == "object":
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return {}
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elif type_name == "array":
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return []
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elif type_name in {"number", "integer"}:
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return 0
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elif type_name == "boolean":
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return False
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elif type_name == "null" or type_name is None:
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return None
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else:
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return None
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def fill_defaults_from_schema(
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output: Mapping[str, Any],
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json_schema: Mapping[str, Any],
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) -> dict[str, Any]:
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"""
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Fill missing required fields in output with default empty values based on JSON schema.
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Only fills default values for fields that are marked as required in the schema.
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Recursively processes nested objects to fill their required fields as well.
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Default values by type:
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- string → ""
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- object → {} (with nested required fields filled)
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- array → []
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- number/integer → 0
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- boolean → False
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- null → None
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"""
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result = dict(output)
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properties = json_schema.get("properties", {})
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required_fields = set(json_schema.get("required", []))
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for prop_name, prop_schema in properties.items():
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prop_type = prop_schema.get("type")
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is_required = prop_name in required_fields
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if prop_name not in result:
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# Field is missing from output
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if is_required:
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# Only fill default value for required fields
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if prop_type == "object" and "properties" in prop_schema:
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# Create empty object and recursively fill its required fields
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result[prop_name] = fill_defaults_from_schema({}, prop_schema)
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else:
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result[prop_name] = _get_default_value_for_type(prop_type)
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elif isinstance(result[prop_name], dict) and prop_type == "object" and "properties" in prop_schema:
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# Field exists and is an object, recursively fill nested required fields
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result[prop_name] = fill_defaults_from_schema(result[prop_name], prop_schema)
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return result
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def _handle_native_json_schema(
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provider: str,
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model_schema: AIModelEntity,
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@ -6,6 +6,8 @@ from pydantic import BaseModel, ConfigDict
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from core.llm_generator.output_parser.errors import OutputParserError
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from core.llm_generator.output_parser.structured_output import (
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_get_default_value_for_type,
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fill_defaults_from_schema,
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invoke_llm_with_pydantic_model,
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invoke_llm_with_structured_output,
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)
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@ -530,3 +532,304 @@ def test_structured_output_with_pydantic_model_validation_error():
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output_model=ExampleOutput,
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stream=False,
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)
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class TestGetDefaultValueForType:
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"""Test cases for _get_default_value_for_type function"""
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def test_string_type(self):
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assert _get_default_value_for_type("string") == ""
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def test_object_type(self):
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assert _get_default_value_for_type("object") == {}
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def test_array_type(self):
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assert _get_default_value_for_type("array") == []
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def test_number_type(self):
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assert _get_default_value_for_type("number") == 0
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def test_integer_type(self):
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assert _get_default_value_for_type("integer") == 0
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def test_boolean_type(self):
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assert _get_default_value_for_type("boolean") is False
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def test_null_type(self):
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assert _get_default_value_for_type("null") is None
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def test_none_type(self):
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assert _get_default_value_for_type(None) is None
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def test_unknown_type(self):
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assert _get_default_value_for_type("unknown") is None
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def test_union_type_string_null(self):
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# ["string", "null"] should return "" (first non-null type)
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assert _get_default_value_for_type(["string", "null"]) == ""
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def test_union_type_null_first(self):
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# ["null", "integer"] should return 0 (first non-null type)
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assert _get_default_value_for_type(["null", "integer"]) == 0
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def test_union_type_only_null(self):
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# ["null"] should return None
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assert _get_default_value_for_type(["null"]) is None
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class TestFillDefaultsFromSchema:
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"""Test cases for fill_defaults_from_schema function"""
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def test_simple_required_fields(self):
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"""Test filling simple required fields"""
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schema = {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"age": {"type": "integer"},
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"email": {"type": "string"},
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},
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"required": ["name", "age"],
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}
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output = {"name": "Alice"}
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result = fill_defaults_from_schema(output, schema)
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assert result == {"name": "Alice", "age": 0}
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# email is not required, so it should not be added
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assert "email" not in result
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def test_non_required_fields_not_filled(self):
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"""Test that non-required fields are not filled"""
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schema = {
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"type": "object",
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"properties": {
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"required_field": {"type": "string"},
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"optional_field": {"type": "string"},
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},
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"required": ["required_field"],
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}
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output = {}
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result = fill_defaults_from_schema(output, schema)
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assert result == {"required_field": ""}
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assert "optional_field" not in result
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def test_nested_object_required_fields(self):
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"""Test filling nested object required fields"""
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schema = {
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"type": "object",
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"properties": {
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"user": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"email": {"type": "string"},
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"address": {
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"type": "object",
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"properties": {
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"city": {"type": "string"},
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"street": {"type": "string"},
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"zipcode": {"type": "string"},
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},
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"required": ["city", "street"],
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},
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},
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"required": ["name", "email", "address"],
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},
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},
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"required": ["user"],
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}
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output = {
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"user": {
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"name": "Alice",
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"address": {
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"city": "Beijing",
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},
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}
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}
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result = fill_defaults_from_schema(output, schema)
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assert result == {
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"user": {
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"name": "Alice",
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"email": "", # filled because required
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"address": {
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"city": "Beijing",
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"street": "", # filled because required
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# zipcode not filled because not required
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},
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}
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}
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def test_missing_nested_object_created(self):
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"""Test that missing required nested objects are created"""
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schema = {
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"type": "object",
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"properties": {
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"metadata": {
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"type": "object",
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"properties": {
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"created_at": {"type": "string"},
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"updated_at": {"type": "string"},
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},
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"required": ["created_at"],
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},
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},
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"required": ["metadata"],
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}
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output = {}
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result = fill_defaults_from_schema(output, schema)
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assert result == {
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"metadata": {
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"created_at": "",
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}
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}
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def test_all_types_default_values(self):
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"""Test default values for all types"""
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schema = {
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"type": "object",
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"properties": {
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"str_field": {"type": "string"},
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"int_field": {"type": "integer"},
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"num_field": {"type": "number"},
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"bool_field": {"type": "boolean"},
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"arr_field": {"type": "array"},
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"obj_field": {"type": "object"},
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},
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"required": ["str_field", "int_field", "num_field", "bool_field", "arr_field", "obj_field"],
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}
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output = {}
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result = fill_defaults_from_schema(output, schema)
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assert result == {
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"str_field": "",
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"int_field": 0,
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"num_field": 0,
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"bool_field": False,
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"arr_field": [],
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"obj_field": {},
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}
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def test_existing_values_preserved(self):
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"""Test that existing values are not overwritten"""
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schema = {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"count": {"type": "integer"},
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},
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"required": ["name", "count"],
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}
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output = {"name": "Bob", "count": 42}
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result = fill_defaults_from_schema(output, schema)
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assert result == {"name": "Bob", "count": 42}
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def test_complex_nested_structure(self):
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"""Test complex nested structure with multiple levels"""
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schema = {
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"type": "object",
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"properties": {
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"user": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"email": {"type": "string"},
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"age": {"type": "integer"},
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"address": {
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"type": "object",
|
||||
"properties": {
|
||||
"city": {"type": "string"},
|
||||
"street": {"type": "string"},
|
||||
"zipcode": {"type": "string"},
|
||||
},
|
||||
"required": ["city", "street"],
|
||||
},
|
||||
},
|
||||
"required": ["name", "email", "address"],
|
||||
},
|
||||
"tags": {"type": "array"},
|
||||
"orders": {"type": "array"},
|
||||
"metadata": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"created_at": {"type": "string"},
|
||||
"updated_at": {"type": "string"},
|
||||
},
|
||||
"required": ["created_at"],
|
||||
},
|
||||
"is_active": {"type": "boolean"},
|
||||
"notes": {"type": "string"},
|
||||
},
|
||||
"required": ["user", "tags", "metadata", "is_active"],
|
||||
}
|
||||
output = {
|
||||
"user": {
|
||||
"name": "Alice",
|
||||
"age": 25,
|
||||
"address": {
|
||||
"city": "Beijing",
|
||||
},
|
||||
},
|
||||
"orders": [{"id": 1}],
|
||||
"metadata": {
|
||||
"updated_at": "2024-01-01",
|
||||
},
|
||||
}
|
||||
|
||||
result = fill_defaults_from_schema(output, schema)
|
||||
|
||||
expected = {
|
||||
"user": {
|
||||
"name": "Alice",
|
||||
"email": "", # required, filled
|
||||
"age": 25, # not required but exists
|
||||
"address": {
|
||||
"city": "Beijing",
|
||||
"street": "", # required, filled
|
||||
# zipcode not required
|
||||
},
|
||||
},
|
||||
"tags": [], # required, filled
|
||||
"orders": [{"id": 1}], # not required but exists
|
||||
"metadata": {
|
||||
"created_at": "", # required, filled
|
||||
"updated_at": "2024-01-01", # exists
|
||||
},
|
||||
"is_active": False, # required, filled
|
||||
# notes not required
|
||||
}
|
||||
assert result == expected
|
||||
|
||||
def test_empty_schema(self):
|
||||
"""Test with empty schema"""
|
||||
schema = {}
|
||||
output = {"any": "value"}
|
||||
|
||||
result = fill_defaults_from_schema(output, schema)
|
||||
|
||||
assert result == {"any": "value"}
|
||||
|
||||
def test_schema_without_required(self):
|
||||
"""Test schema without required field"""
|
||||
schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"optional1": {"type": "string"},
|
||||
"optional2": {"type": "integer"},
|
||||
},
|
||||
}
|
||||
output = {}
|
||||
|
||||
result = fill_defaults_from_schema(output, schema)
|
||||
|
||||
# No required fields, so nothing should be added
|
||||
assert result == {}
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import type { FC } from 'react'
|
||||
import type { LLMNodeType } from './types'
|
||||
import type { NodePanelProps } from '@/app/components/workflow/types'
|
||||
import { RiAlertFill, RiQuestionLine } from '@remixicon/react'
|
||||
import { RiAlertFill, RiInformationLine, RiQuestionLine } from '@remixicon/react'
|
||||
import * as React from 'react'
|
||||
import { useCallback } from 'react'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
|
|
@ -63,6 +63,7 @@ const Panel: FC<NodePanelProps<LLMNodeType>> = ({
|
|||
handleVisionResolutionEnabledChange,
|
||||
handleVisionResolutionChange,
|
||||
isModelSupportStructuredOutput,
|
||||
isModelSupportToolCall,
|
||||
structuredOutputCollapsed,
|
||||
setStructuredOutputCollapsed,
|
||||
handleStructureOutputEnableChange,
|
||||
|
|
@ -299,19 +300,37 @@ const Panel: FC<NodePanelProps<LLMNodeType>> = ({
|
|||
operations={(
|
||||
<div className="mr-4 flex shrink-0 items-center">
|
||||
{(!isModelSupportStructuredOutput && !!inputs.structured_output_enabled) && (
|
||||
<Tooltip
|
||||
noDecoration
|
||||
popupContent={(
|
||||
<div className="w-[232px] rounded-xl border-[0.5px] border-components-panel-border bg-components-tooltip-bg px-4 py-3.5 shadow-lg backdrop-blur-[5px]">
|
||||
<div className="title-xs-semi-bold text-text-primary">{t('structOutput.modelNotSupported', { ns: 'app' })}</div>
|
||||
<div className="body-xs-regular mt-1 text-text-secondary">{t('structOutput.modelNotSupportedTip', { ns: 'app' })}</div>
|
||||
</div>
|
||||
)}
|
||||
>
|
||||
<div>
|
||||
<RiAlertFill className="mr-1 size-4 text-text-warning-secondary" />
|
||||
</div>
|
||||
</Tooltip>
|
||||
isModelSupportToolCall
|
||||
? (
|
||||
<Tooltip
|
||||
noDecoration
|
||||
popupContent={(
|
||||
<div className="w-[232px] rounded-xl border-[0.5px] border-components-panel-border bg-components-tooltip-bg px-4 py-3.5 shadow-lg backdrop-blur-[5px]">
|
||||
<div className="title-xs-semi-bold text-text-primary">{t('structOutput.toolCallFallback', { ns: 'app' })}</div>
|
||||
<div className="body-xs-regular mt-1 text-text-secondary">{t('structOutput.toolCallFallbackTip', { ns: 'app' })}</div>
|
||||
</div>
|
||||
)}
|
||||
>
|
||||
<div>
|
||||
<RiInformationLine className="mr-1 size-4 text-text-tertiary" />
|
||||
</div>
|
||||
</Tooltip>
|
||||
)
|
||||
: (
|
||||
<Tooltip
|
||||
noDecoration
|
||||
popupContent={(
|
||||
<div className="w-[232px] rounded-xl border-[0.5px] border-components-panel-border bg-components-tooltip-bg px-4 py-3.5 shadow-lg backdrop-blur-[5px]">
|
||||
<div className="title-xs-semi-bold text-text-primary">{t('structOutput.modelNotSupported', { ns: 'app' })}</div>
|
||||
<div className="body-xs-regular mt-1 text-text-secondary">{t('structOutput.modelNotSupportedTip', { ns: 'app' })}</div>
|
||||
</div>
|
||||
)}
|
||||
>
|
||||
<div>
|
||||
<RiAlertFill className="mr-1 size-4 text-text-warning-secondary" />
|
||||
</div>
|
||||
</Tooltip>
|
||||
)
|
||||
)}
|
||||
<div className="system-xs-medium-uppercase mr-0.5 text-text-tertiary">{t('structOutput.structured', { ns: 'app' })}</div>
|
||||
<Tooltip popupContent={
|
||||
|
|
|
|||
|
|
@ -313,12 +313,17 @@ const useConfig = (id: string, payload: LLMNodeType) => {
|
|||
|
||||
// structure output
|
||||
const { data: modelList } = useModelList(ModelTypeEnum.textGeneration)
|
||||
const isModelSupportStructuredOutput = modelList
|
||||
const currentModelFeatures = modelList
|
||||
?.find(provideItem => provideItem.provider === model?.provider)
|
||||
?.models
|
||||
.find(modelItem => modelItem.model === model?.name)
|
||||
?.features
|
||||
?.includes(ModelFeatureEnum.StructuredOutput)
|
||||
|
||||
const isModelSupportStructuredOutput = currentModelFeatures?.includes(ModelFeatureEnum.StructuredOutput)
|
||||
|
||||
const isModelSupportToolCall = currentModelFeatures?.some(
|
||||
feature => [ModelFeatureEnum.toolCall, ModelFeatureEnum.multiToolCall, ModelFeatureEnum.streamToolCall].includes(feature),
|
||||
)
|
||||
|
||||
const [structuredOutputCollapsed, setStructuredOutputCollapsed] = useState(true)
|
||||
const handleStructureOutputEnableChange = useCallback((enabled: boolean) => {
|
||||
|
|
@ -394,6 +399,7 @@ const useConfig = (id: string, payload: LLMNodeType) => {
|
|||
handleVisionResolutionEnabledChange,
|
||||
handleVisionResolutionChange,
|
||||
isModelSupportStructuredOutput,
|
||||
isModelSupportToolCall,
|
||||
handleStructureOutputChange,
|
||||
structuredOutputCollapsed,
|
||||
setStructuredOutputCollapsed,
|
||||
|
|
|
|||
|
|
@ -217,6 +217,8 @@
|
|||
"structOutput.required": "Required",
|
||||
"structOutput.structured": "Structured",
|
||||
"structOutput.structuredTip": "Structured Outputs is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema",
|
||||
"structOutput.toolCallFallback": "Using Tool Call mode",
|
||||
"structOutput.toolCallFallbackTip": "The current model does not support native structured output, but supports tool calling. Structured output will be achieved via tool call.",
|
||||
"switch": "Switch to Workflow Orchestrate",
|
||||
"switchLabel": "The app copy to be created",
|
||||
"switchStart": "Start switch",
|
||||
|
|
|
|||
|
|
@ -215,6 +215,8 @@
|
|||
"structOutput.required": "必填",
|
||||
"structOutput.structured": "结构化输出",
|
||||
"structOutput.structuredTip": "结构化输出是一项功能，可确保模型始终生成符合您提供的 JSON 模式的响应",
|
||||
"structOutput.toolCallFallback": "使用工具调用模式",
|
||||
"structOutput.toolCallFallbackTip": "当前模型不支持原生结构化输出，但支持工具调用。将通过工具调用实现结构化输出。",
|
||||
"switch": "迁移为工作流编排",
|
||||
"switchLabel": "新应用创建为",
|
||||
"switchStart": "开始迁移",
|
||||
|
|
|
|||
Loading…
Reference in New Issue