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synced 2025-08-14 09:36:13 +08:00
chore: refactor code in llm node
This commit is contained in:
Novice
parent
3e31dae1fd
commit
b3728bca52
@ -1,4 +1,3 @@
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import json
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import logging
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from collections.abc import Callable, Generator, Iterable, Sequence
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from typing import IO, Any, Literal, Optional, Union, cast, overload
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@ -10,8 +9,8 @@ from core.entities.provider_entities import ModelLoadBalancingConfiguration
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from core.errors.error import ProviderTokenNotInitError
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from core.model_runtime.callbacks.base_callback import Callback
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from core.model_runtime.entities.llm_entities import LLMResult
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from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, UserPromptMessage
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from core.model_runtime.entities.model_entities import AIModelEntity, ModelType
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from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
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from core.model_runtime.entities.model_entities import ModelType
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from core.model_runtime.entities.rerank_entities import RerankResult
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from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
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from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeConnectionError, InvokeRateLimitError
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@ -21,10 +20,7 @@ from core.model_runtime.model_providers.__base.rerank_model import RerankModel
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from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
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from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
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from core.model_runtime.model_providers.__base.tts_model import TTSModel
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from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
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from core.provider_manager import ProviderManager
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from core.workflow.utils.structured_output.entities import ResponseFormat, SpecialModelType
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from core.workflow.utils.structured_output.prompt import STRUCTURED_OUTPUT_PROMPT
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from extensions.ext_redis import redis_client
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from models.provider import ProviderType
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@ -164,13 +160,6 @@ class ModelInstance:
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raise Exception("Model type instance is not LargeLanguageModel")
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self.model_type_instance = cast(LargeLanguageModel, self.model_type_instance)
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if model_parameters and model_parameters.get("structured_output_schema"):
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result = self._handle_structured_output(
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model_parameters=model_parameters,
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prompt=prompt_messages,
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)
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prompt_messages = result["prompt"]
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model_parameters = result["parameters"]
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return cast(
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Union[LLMResult, Generator],
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self._round_robin_invoke(
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@ -421,190 +410,6 @@ class ModelInstance:
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model=self.model, credentials=self.credentials, language=language
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)
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def _handle_structured_output(self, model_parameters: dict, prompt: Sequence[PromptMessage]) -> dict:
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"""
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Handle structured output for language models.
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This function processes schema-based structured outputs by either:
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1. Using native JSON schema support of the model when available
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2. Falling back to prompt-based structured output for models without native support
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:param model_parameters: Model configuration parameters
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:param prompt: Sequence of prompt messages
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:return: Dictionary with updated prompt and parameters
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"""
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# Extract and validate schema
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structured_output_schema = model_parameters.pop("structured_output_schema")
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if not structured_output_schema:
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raise ValueError("Please provide a valid structured output schema")
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try:
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schema = json.loads(structured_output_schema)
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except json.JSONDecodeError:
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raise ValueError("structured_output_schema is not valid JSON format")
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# Fetch model schema and validate
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model_schema = self._fetch_model_schema(self.provider, self.model_type_instance.model_type, self.model)
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if not model_schema:
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raise ValueError("Unable to fetch model schema")
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rules = model_schema.parameter_rules
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rule_names = [rule.name for rule in rules]
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# Handle based on model's JSON schema support capability
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if "json_schema" in rule_names:
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return self._handle_native_json_schema(schema, model_parameters, prompt, rules)
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else:
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return self._handle_prompt_based_schema(structured_output_schema, model_parameters, prompt, rules)
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def _handle_native_json_schema(
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self, schema: dict, model_parameters: dict, prompt: Sequence[PromptMessage], rules: list
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) -> dict:
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"""
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Handle structured output for models with native JSON schema support.
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:param schema: Parsed JSON schema
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:param model_parameters: Model parameters to update
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:param prompt: Sequence of prompt messages
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:param rules: Model parameter rules
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:return: Updated prompt and parameters
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"""
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# Process schema according to model requirements
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schema_json = self._prepare_schema_for_model(schema)
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# Set JSON schema in parameters
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model_parameters["json_schema"] = json.dumps(schema_json, ensure_ascii=False)
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# Set appropriate response format if required by the model
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for rule in rules:
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if rule.name == "response_format" and ResponseFormat.JSON_SCHEMA.value in rule.options:
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model_parameters["response_format"] = ResponseFormat.JSON_SCHEMA.value
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return {"prompt": prompt, "parameters": model_parameters}
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def _handle_prompt_based_schema(
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self, schema_str: str, model_parameters: dict, prompt: Sequence[PromptMessage], rules: list
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) -> dict:
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"""
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Handle structured output for models without native JSON schema support.
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:param schema_str: JSON schema as string
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:param model_parameters: Model parameters to update
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:param prompt: Sequence of prompt messages
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:param rules: Model parameter rules
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:return: Updated prompt and parameters
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"""
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# Extract content from the last prompt message
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content = prompt[-1].content if isinstance(prompt[-1].content, str) else ""
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# Create structured output prompt
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structured_output_prompt = STRUCTURED_OUTPUT_PROMPT.replace("{{schema}}", schema_str).replace(
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"{{question}}", content
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)
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# Replace the last user message with our structured output prompt
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structured_prompt_message = UserPromptMessage(content=structured_output_prompt)
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updated_prompt = list(prompt[:-1]) + [structured_prompt_message]
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# Set appropriate response format based on model capabilities
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self._set_response_format(model_parameters, rules)
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return {"prompt": updated_prompt, "parameters": model_parameters}
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def _set_response_format(self, model_parameters: dict, rules: list) -> None:
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"""
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Set the appropriate response format parameter based on model rules.
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:param model_parameters: Model parameters to update
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:param rules: Model parameter rules
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"""
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for rule in rules:
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if rule.name == "response_format":
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if ResponseFormat.JSON.value in rule.options:
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model_parameters["response_format"] = ResponseFormat.JSON.value
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elif ResponseFormat.JSON_OBJECT.value in rule.options:
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model_parameters["response_format"] = ResponseFormat.JSON_OBJECT.value
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def _prepare_schema_for_model(self, schema: dict) -> dict:
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"""
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Prepare JSON schema based on model requirements.
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Different models have different requirements for JSON schema formatting.
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This function handles these differences.
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:param schema: The original JSON schema
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:return: Processed schema compatible with the current model
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"""
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def remove_additional_properties(schema: dict) -> None:
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"""
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Remove additionalProperties fields from JSON schema.
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Used for models like Gemini that don't support this property.
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:param schema: JSON schema to modify in-place
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"""
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if not isinstance(schema, dict):
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return
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# Remove additionalProperties at current level
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schema.pop("additionalProperties", None)
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# Process nested structures recursively
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for value in schema.values():
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if isinstance(value, dict):
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remove_additional_properties(value)
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elif isinstance(value, list):
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for item in value:
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if isinstance(item, dict):
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remove_additional_properties(item)
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def convert_boolean_to_string(schema: dict) -> None:
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"""
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Convert boolean type specifications to string in JSON schema.
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:param schema: JSON schema to modify in-place
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"""
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if not isinstance(schema, dict):
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return
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# Check for boolean type at current level
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if schema.get("type") == "boolean":
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schema["type"] = "string"
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# Process nested dictionaries and lists recursively
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for value in schema.values():
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if isinstance(value, dict):
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convert_boolean_to_string(value)
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elif isinstance(value, list):
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for item in value:
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if isinstance(item, dict):
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convert_boolean_to_string(item)
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# Deep copy to avoid modifying the original schema
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processed_schema = schema.copy()
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# Convert boolean types to string types (common requirement)
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convert_boolean_to_string(processed_schema)
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# Apply model-specific transformations
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if SpecialModelType.GEMINI in self.model:
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remove_additional_properties(processed_schema)
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return processed_schema
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elif SpecialModelType.OLLAMA in self.provider:
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return processed_schema
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else:
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# Default format with name field
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return {"schema": processed_schema, "name": "llm_response"}
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def _fetch_model_schema(self, provider: str, model_type: ModelType, model: str) -> AIModelEntity | None:
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"""
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Fetch model schema
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"""
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model_provider = ModelProviderFactory(self.model_type_instance.tenant_id)
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return model_provider.get_model_schema(
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provider=provider, model_type=model_type, model=model, credentials=self.credentials
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)
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class ModelManager:
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def __init__(self) -> None:
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@ -200,7 +200,7 @@ class AIModelEntity(ProviderModel):
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@model_validator(mode="after")
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def validate_model(self):
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supported_schema_keys = ["json_schema", "format"]
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supported_schema_keys = ["json_schema"]
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schema_key = next((rule.name for rule in self.parameter_rules if rule.name in supported_schema_keys), None)
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if schema_key:
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if self.features is None:
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@ -29,7 +29,13 @@ from core.model_runtime.entities.message_entities import (
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SystemPromptMessage,
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UserPromptMessage,
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)
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from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey, ModelType
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from core.model_runtime.entities.model_entities import (
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AIModelEntity,
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ModelFeature,
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ModelPropertyKey,
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ModelType,
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ParameterRule,
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)
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from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
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from core.model_runtime.utils.encoders import jsonable_encoder
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from core.plugin.entities.plugin import ModelProviderID
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@ -59,6 +65,8 @@ from core.workflow.nodes.event import (
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RunRetrieverResourceEvent,
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RunStreamChunkEvent,
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)
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from core.workflow.utils.structured_output.entities import ResponseFormat, SpecialModelType
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from core.workflow.utils.structured_output.prompt import STRUCTURED_OUTPUT_PROMPT
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from core.workflow.utils.variable_template_parser import VariableTemplateParser
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from extensions.ext_database import db
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from models.model import Conversation
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@ -132,7 +140,6 @@ class LLMNode(BaseNode[LLMNodeData]):
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if isinstance(event, RunRetrieverResourceEvent):
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context = event.context
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yield event
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if context:
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node_inputs["#context#"] = context
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@ -510,14 +517,6 @@ class LLMNode(BaseNode[LLMNodeData]):
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# model config
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completion_params = node_data_model.completion_params
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if (
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isinstance(self.node_data, LLMNodeData)
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and self.node_data.structured_output_enabled
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and self.node_data.structured_output
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):
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completion_params["structured_output_schema"] = json.dumps(
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self.node_data.structured_output.get("schema", {}), ensure_ascii=False
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)
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stop = []
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if "stop" in completion_params:
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stop = completion_params["stop"]
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@ -532,7 +531,12 @@ class LLMNode(BaseNode[LLMNodeData]):
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if not model_schema:
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raise ModelNotExistError(f"Model {model_name} not exist.")
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support_structured_output = self._check_model_structured_output_support()
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if support_structured_output:
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completion_params = self._handle_native_json_schema(completion_params, model_schema.parameter_rules)
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elif support_structured_output is False:
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# Set appropriate response format based on model capabilities
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self._set_response_format(completion_params, model_schema.parameter_rules)
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return model_instance, ModelConfigWithCredentialsEntity(
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provider=provider_name,
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model=model_name,
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@ -743,7 +747,11 @@ class LLMNode(BaseNode[LLMNodeData]):
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"No prompt found in the LLM configuration. "
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"Please ensure a prompt is properly configured before proceeding."
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)
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support_structured_output = self._check_model_structured_output_support()
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if support_structured_output is False:
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filtered_prompt_messages = self._handle_prompt_based_schema(
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prompt_messages=filtered_prompt_messages,
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)
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stop = model_config.stop
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return filtered_prompt_messages, stop
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@ -945,6 +953,167 @@ class LLMNode(BaseNode[LLMNodeData]):
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return prompt_messages
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def _handle_native_json_schema(self, model_parameters: dict, rules: list[ParameterRule]) -> dict:
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"""
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Handle structured output for models with native JSON schema support.
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:param model_parameters: Model parameters to update
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:param rules: Model parameter rules
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:return: Updated model parameters with JSON schema configuration
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"""
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# Process schema according to model requirements
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schema = self._fetch_structured_output_schema()
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schema_json = self._prepare_schema_for_model(schema)
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# Set JSON schema in parameters
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model_parameters["json_schema"] = json.dumps(schema_json, ensure_ascii=False)
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# Set appropriate response format if required by the model
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for rule in rules:
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if rule.name == "response_format" and ResponseFormat.JSON_SCHEMA.value in rule.options:
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model_parameters["response_format"] = ResponseFormat.JSON_SCHEMA.value
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return model_parameters
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def _handle_prompt_based_schema(self, prompt_messages: Sequence[PromptMessage]) -> list[PromptMessage]:
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"""
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Handle structured output for models without native JSON schema support.
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This function modifies the prompt messages to include schema-based output requirements.
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Args:
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prompt_messages: Original sequence of prompt messages
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Returns:
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list[PromptMessage]: Updated prompt messages with structured output requirements
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"""
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# Convert schema to string format
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schema_str = json.dumps(self._fetch_structured_output_schema(), ensure_ascii=False)
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# Find existing system prompt with schema placeholder
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system_prompt = next(
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(prompt for prompt in prompt_messages if isinstance(prompt, SystemPromptMessage)),
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None,
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)
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structured_output_prompt = STRUCTURED_OUTPUT_PROMPT.replace("{{schema}}", schema_str)
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# Prepare system prompt content
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system_prompt_content = (
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structured_output_prompt + "\n\n" + system_prompt.content
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if system_prompt and isinstance(system_prompt.content, str)
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else structured_output_prompt
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)
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system_prompt = SystemPromptMessage(content=system_prompt_content)
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# Extract content from the last user message
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filtered_prompts = [prompt for prompt in prompt_messages if not isinstance(prompt, SystemPromptMessage)]
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updated_prompt = [system_prompt] + filtered_prompts
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return updated_prompt
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def _set_response_format(self, model_parameters: dict, rules: list) -> None:
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"""
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Set the appropriate response format parameter based on model rules.
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:param model_parameters: Model parameters to update
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:param rules: Model parameter rules
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"""
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for rule in rules:
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if rule.name == "response_format":
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if ResponseFormat.JSON.value in rule.options:
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model_parameters["response_format"] = ResponseFormat.JSON.value
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elif ResponseFormat.JSON_OBJECT.value in rule.options:
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model_parameters["response_format"] = ResponseFormat.JSON_OBJECT.value
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def _prepare_schema_for_model(self, schema: dict) -> dict:
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"""
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Prepare JSON schema based on model requirements.
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Different models have different requirements for JSON schema formatting.
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This function handles these differences.
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:param schema: The original JSON schema
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:return: Processed schema compatible with the current model
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"""
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# Deep copy to avoid modifying the original schema
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processed_schema = schema.copy()
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# Convert boolean types to string types (common requirement)
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convert_boolean_to_string(processed_schema)
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# Apply model-specific transformations
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if SpecialModelType.GEMINI in self.node_data.model.name:
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remove_additional_properties(processed_schema)
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return processed_schema
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elif SpecialModelType.OLLAMA in self.node_data.model.provider:
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return processed_schema
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else:
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# Default format with name field
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return {"schema": processed_schema, "name": "llm_response"}
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def _fetch_model_schema(self, provider: str) -> AIModelEntity | None:
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"""
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Fetch model schema
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"""
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model_name = self.node_data.model.name
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model_manager = ModelManager()
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model_instance = model_manager.get_model_instance(
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tenant_id=self.tenant_id, model_type=ModelType.LLM, provider=provider, model=model_name
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)
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model_type_instance = model_instance.model_type_instance
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model_type_instance = cast(LargeLanguageModel, model_type_instance)
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model_credentials = model_instance.credentials
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model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
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return model_schema
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def _fetch_structured_output_schema(self) -> dict:
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"""
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Fetch the structured output schema for language models.
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This method retrieves and validates the JSON schema defined in the node data
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for structured output formatting. It ensures the schema is properly formatted
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and can be used for structured response generation.
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:return: The validated JSON schema as a dictionary
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"""
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# Extract and validate schema
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if not self.node_data.structured_output:
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raise LLMNodeError("Please provide a valid structured output schema")
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structured_output_schema = json.dumps(self.node_data.structured_output.get("schema", {}), ensure_ascii=False)
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if not structured_output_schema:
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raise LLMNodeError("Please provide a valid structured output schema")
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try:
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schema = json.loads(structured_output_schema)
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except json.JSONDecodeError:
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raise LLMNodeError("structured_output_schema is not valid JSON format")
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||||
return schema
|
||||
|
||||
def _check_model_structured_output_support(self) -> Optional[bool]:
|
||||
"""
|
||||
Check if the current model supports structured output.
|
||||
|
||||
Returns:
|
||||
Optional[bool]:
|
||||
- True if model supports structured output
|
||||
- False if model exists but doesn't support structured output
|
||||
- None if structured output is disabled or model doesn't exist
|
||||
"""
|
||||
# Early return if structured output is disabled
|
||||
if (
|
||||
not isinstance(self.node_data, LLMNodeData)
|
||||
or not self.node_data.structured_output_enabled
|
||||
or not self.node_data.structured_output
|
||||
):
|
||||
return None
|
||||
# Get model schema and check if it exists
|
||||
model_schema = self._fetch_model_schema(self.node_data.model.provider)
|
||||
if not model_schema:
|
||||
return None
|
||||
|
||||
# Check if model supports structured output feature
|
||||
return bool(model_schema.features and ModelFeature.STRUCTURED_OUTPUT in model_schema.features)
|
||||
|
||||
|
||||
def _combine_message_content_with_role(*, contents: Sequence[PromptMessageContent], role: PromptMessageRole):
|
||||
match role:
|
||||
@ -1083,3 +1252,49 @@ def _handle_completion_template(
|
||||
)
|
||||
prompt_messages.append(prompt_message)
|
||||
return prompt_messages
|
||||
|
||||
|
||||
def remove_additional_properties(schema: dict) -> None:
|
||||
"""
|
||||
Remove additionalProperties fields from JSON schema.
|
||||
Used for models like Gemini that don't support this property.
|
||||
|
||||
:param schema: JSON schema to modify in-place
|
||||
"""
|
||||
if not isinstance(schema, dict):
|
||||
return
|
||||
|
||||
# Remove additionalProperties at current level
|
||||
schema.pop("additionalProperties", None)
|
||||
|
||||
# Process nested structures recursively
|
||||
for value in schema.values():
|
||||
if isinstance(value, dict):
|
||||
remove_additional_properties(value)
|
||||
elif isinstance(value, list):
|
||||
for item in value:
|
||||
if isinstance(item, dict):
|
||||
remove_additional_properties(item)
|
||||
|
||||
|
||||
def convert_boolean_to_string(schema: dict) -> None:
|
||||
"""
|
||||
Convert boolean type specifications to string in JSON schema.
|
||||
|
||||
:param schema: JSON schema to modify in-place
|
||||
"""
|
||||
if not isinstance(schema, dict):
|
||||
return
|
||||
|
||||
# Check for boolean type at current level
|
||||
if schema.get("type") == "boolean":
|
||||
schema["type"] = "string"
|
||||
|
||||
# Process nested dictionaries and lists recursively
|
||||
for value in schema.values():
|
||||
if isinstance(value, dict):
|
||||
convert_boolean_to_string(value)
|
||||
elif isinstance(value, list):
|
||||
for item in value:
|
||||
if isinstance(item, dict):
|
||||
convert_boolean_to_string(item)
|
||||
|
||||
@ -1,10 +1,9 @@
|
||||
STRUCTURED_OUTPUT_PROMPT = """
|
||||
You’re a helpful AI assistant. You could answer questions and output in JSON format.
|
||||
STRUCTURED_OUTPUT_PROMPT = """You’re a helpful AI assistant. You could answer questions and output in JSON format.
|
||||
constrant:
|
||||
- You must output in JSON format.
|
||||
- Do not output boolean value, use string type instead.
|
||||
- Do not output integer or float value, use number type instead.
|
||||
eg1:
|
||||
eg:
|
||||
Here is the JSON schema:
|
||||
{"additionalProperties": false, "properties": {"age": {"type": "number"}, "name": {"type": "string"}}, "required": ["name", "age"], "type": "object"}
|
||||
|
||||
@ -13,12 +12,6 @@ eg1:
|
||||
|
||||
output:
|
||||
{"name": "John Doe", "age": 30}
|
||||
|
||||
Here is the JSON schema:
|
||||
{{schema}}
|
||||
|
||||
Here is the user's question:
|
||||
{{question}}
|
||||
output:
|
||||
|
||||
""" # noqa: E501
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user