mirror of
http://112.124.100.131/huang.ze/ebiz-dify-ai.git
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FEAT: NEW WORKFLOW ENGINE (#3160)
Co-authored-by: Joel <iamjoel007@gmail.com> Co-authored-by: Yeuoly <admin@srmxy.cn> Co-authored-by: JzoNg <jzongcode@gmail.com> Co-authored-by: StyleZhang <jasonapring2015@outlook.com> Co-authored-by: jyong <jyong@dify.ai> Co-authored-by: nite-knite <nkCoding@gmail.com> Co-authored-by: jyong <718720800@qq.com>
This commit is contained in:
takatost
0
api/core/workflow/nodes/llm/__init__.py
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0
api/core/workflow/nodes/llm/__init__.py
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49
api/core/workflow/nodes/llm/entities.py
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49
api/core/workflow/nodes/llm/entities.py
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from typing import Any, Literal, Optional, Union
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from pydantic import BaseModel
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from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
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from core.workflow.entities.base_node_data_entities import BaseNodeData
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class ModelConfig(BaseModel):
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"""
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Model Config.
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"""
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provider: str
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name: str
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mode: str
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completion_params: dict[str, Any] = {}
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class ContextConfig(BaseModel):
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"""
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Context Config.
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"""
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enabled: bool
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variable_selector: Optional[list[str]] = None
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class VisionConfig(BaseModel):
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"""
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Vision Config.
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"""
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class Configs(BaseModel):
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"""
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Configs.
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"""
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detail: Literal['low', 'high']
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enabled: bool
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configs: Optional[Configs] = None
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class LLMNodeData(BaseNodeData):
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"""
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LLM Node Data.
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"""
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model: ModelConfig
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prompt_template: Union[list[ChatModelMessage], CompletionModelPromptTemplate]
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memory: Optional[MemoryConfig] = None
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context: ContextConfig
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vision: VisionConfig
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554
api/core/workflow/nodes/llm/llm_node.py
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554
api/core/workflow/nodes/llm/llm_node.py
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from collections.abc import Generator
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from typing import Optional, cast
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from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
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from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
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from core.entities.model_entities import ModelStatus
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from core.entities.provider_entities import QuotaUnit
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from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
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from core.file.file_obj import FileVar
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from core.memory.token_buffer_memory import TokenBufferMemory
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from core.model_manager import ModelInstance, ModelManager
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from core.model_runtime.entities.llm_entities import LLMUsage
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from core.model_runtime.entities.message_entities import PromptMessage
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from core.model_runtime.entities.model_entities import ModelType
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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.prompt.advanced_prompt_transform import AdvancedPromptTransform
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from core.prompt.entities.advanced_prompt_entities import CompletionModelPromptTemplate, MemoryConfig
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from core.prompt.utils.prompt_message_util import PromptMessageUtil
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from core.workflow.entities.base_node_data_entities import BaseNodeData
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from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType, SystemVariable
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from core.workflow.entities.variable_pool import VariablePool
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from core.workflow.nodes.base_node import BaseNode
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from core.workflow.nodes.llm.entities import LLMNodeData, ModelConfig
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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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from models.provider import Provider, ProviderType
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from models.workflow import WorkflowNodeExecutionStatus
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class LLMNode(BaseNode):
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_node_data_cls = LLMNodeData
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node_type = NodeType.LLM
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def _run(self, variable_pool: VariablePool) -> NodeRunResult:
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"""
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Run node
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:param variable_pool: variable pool
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:return:
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"""
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node_data = self.node_data
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node_data = cast(self._node_data_cls, node_data)
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node_inputs = None
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process_data = None
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try:
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# fetch variables and fetch values from variable pool
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inputs = self._fetch_inputs(node_data, variable_pool)
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node_inputs = {}
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# fetch files
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files: list[FileVar] = self._fetch_files(node_data, variable_pool)
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if files:
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node_inputs['#files#'] = [file.to_dict() for file in files]
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# fetch context value
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context = self._fetch_context(node_data, variable_pool)
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if context:
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node_inputs['#context#'] = context
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# fetch model config
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model_instance, model_config = self._fetch_model_config(node_data.model)
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# fetch memory
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memory = self._fetch_memory(node_data.memory, variable_pool, model_instance)
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# fetch prompt messages
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prompt_messages, stop = self._fetch_prompt_messages(
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node_data=node_data,
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query=variable_pool.get_variable_value(['sys', SystemVariable.QUERY.value])
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if node_data.memory else None,
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inputs=inputs,
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files=files,
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context=context,
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memory=memory,
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model_config=model_config
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)
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process_data = {
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'model_mode': model_config.mode,
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'prompts': PromptMessageUtil.prompt_messages_to_prompt_for_saving(
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model_mode=model_config.mode,
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prompt_messages=prompt_messages
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)
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}
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# handle invoke result
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result_text, usage = self._invoke_llm(
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node_data_model=node_data.model,
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model_instance=model_instance,
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prompt_messages=prompt_messages,
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stop=stop
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)
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except Exception as e:
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return NodeRunResult(
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status=WorkflowNodeExecutionStatus.FAILED,
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error=str(e),
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inputs=node_inputs,
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process_data=process_data
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)
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outputs = {
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'text': result_text,
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'usage': jsonable_encoder(usage)
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}
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return NodeRunResult(
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status=WorkflowNodeExecutionStatus.SUCCEEDED,
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inputs=node_inputs,
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process_data=process_data,
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outputs=outputs,
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metadata={
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NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
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NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
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NodeRunMetadataKey.CURRENCY: usage.currency
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}
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)
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def _invoke_llm(self, node_data_model: ModelConfig,
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model_instance: ModelInstance,
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prompt_messages: list[PromptMessage],
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stop: list[str]) -> tuple[str, LLMUsage]:
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"""
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Invoke large language model
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:param node_data_model: node data model
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:param model_instance: model instance
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:param prompt_messages: prompt messages
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:param stop: stop
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:return:
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"""
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db.session.close()
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invoke_result = model_instance.invoke_llm(
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prompt_messages=prompt_messages,
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model_parameters=node_data_model.completion_params,
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stop=stop,
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stream=True,
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user=self.user_id,
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)
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# handle invoke result
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text, usage = self._handle_invoke_result(
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invoke_result=invoke_result
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)
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# deduct quota
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self.deduct_llm_quota(tenant_id=self.tenant_id, model_instance=model_instance, usage=usage)
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return text, usage
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def _handle_invoke_result(self, invoke_result: Generator) -> tuple[str, LLMUsage]:
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"""
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Handle invoke result
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:param invoke_result: invoke result
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:return:
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"""
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model = None
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prompt_messages = []
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full_text = ''
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usage = None
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for result in invoke_result:
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text = result.delta.message.content
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full_text += text
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self.publish_text_chunk(text=text, value_selector=[self.node_id, 'text'])
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if not model:
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model = result.model
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if not prompt_messages:
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prompt_messages = result.prompt_messages
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if not usage and result.delta.usage:
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usage = result.delta.usage
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if not usage:
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usage = LLMUsage.empty_usage()
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return full_text, usage
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def _fetch_inputs(self, node_data: LLMNodeData, variable_pool: VariablePool) -> dict[str, str]:
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"""
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Fetch inputs
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:param node_data: node data
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:param variable_pool: variable pool
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:return:
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"""
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inputs = {}
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prompt_template = node_data.prompt_template
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variable_selectors = []
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if isinstance(prompt_template, list):
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for prompt in prompt_template:
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variable_template_parser = VariableTemplateParser(template=prompt.text)
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variable_selectors.extend(variable_template_parser.extract_variable_selectors())
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elif isinstance(prompt_template, CompletionModelPromptTemplate):
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variable_template_parser = VariableTemplateParser(template=prompt_template.text)
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variable_selectors = variable_template_parser.extract_variable_selectors()
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for variable_selector in variable_selectors:
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variable_value = variable_pool.get_variable_value(variable_selector.value_selector)
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if variable_value is None:
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raise ValueError(f'Variable {variable_selector.variable} not found')
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inputs[variable_selector.variable] = variable_value
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return inputs
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def _fetch_files(self, node_data: LLMNodeData, variable_pool: VariablePool) -> list[FileVar]:
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"""
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Fetch files
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:param node_data: node data
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:param variable_pool: variable pool
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:return:
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"""
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if not node_data.vision.enabled:
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return []
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files = variable_pool.get_variable_value(['sys', SystemVariable.FILES.value])
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if not files:
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return []
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return files
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def _fetch_context(self, node_data: LLMNodeData, variable_pool: VariablePool) -> Optional[str]:
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"""
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Fetch context
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:param node_data: node data
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:param variable_pool: variable pool
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:return:
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"""
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if not node_data.context.enabled:
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return None
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if not node_data.context.variable_selector:
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return None
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context_value = variable_pool.get_variable_value(node_data.context.variable_selector)
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if context_value:
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if isinstance(context_value, str):
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return context_value
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elif isinstance(context_value, list):
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context_str = ''
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original_retriever_resource = []
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for item in context_value:
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if 'content' not in item:
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raise ValueError(f'Invalid context structure: {item}')
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context_str += item['content'] + '\n'
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retriever_resource = self._convert_to_original_retriever_resource(item)
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if retriever_resource:
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original_retriever_resource.append(retriever_resource)
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if self.callbacks:
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for callback in self.callbacks:
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callback.on_event(
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event=QueueRetrieverResourcesEvent(
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retriever_resources=original_retriever_resource
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)
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)
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return context_str.strip()
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return None
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def _convert_to_original_retriever_resource(self, context_dict: dict) -> Optional[dict]:
|
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"""
|
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Convert to original retriever resource, temp.
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:param context_dict: context dict
|
||||
:return:
|
||||
"""
|
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if ('metadata' in context_dict and '_source' in context_dict['metadata']
|
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and context_dict['metadata']['_source'] == 'knowledge'):
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metadata = context_dict.get('metadata', {})
|
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source = {
|
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'position': metadata.get('position'),
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'dataset_id': metadata.get('dataset_id'),
|
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'dataset_name': metadata.get('dataset_name'),
|
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'document_id': metadata.get('document_id'),
|
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'document_name': metadata.get('document_name'),
|
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'data_source_type': metadata.get('document_data_source_type'),
|
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'segment_id': metadata.get('segment_id'),
|
||||
'retriever_from': metadata.get('retriever_from'),
|
||||
'score': metadata.get('score'),
|
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'hit_count': metadata.get('segment_hit_count'),
|
||||
'word_count': metadata.get('segment_word_count'),
|
||||
'segment_position': metadata.get('segment_position'),
|
||||
'index_node_hash': metadata.get('segment_index_node_hash'),
|
||||
'content': context_dict.get('content'),
|
||||
}
|
||||
|
||||
return source
|
||||
|
||||
return None
|
||||
|
||||
def _fetch_model_config(self, node_data_model: ModelConfig) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
|
||||
"""
|
||||
Fetch model config
|
||||
:param node_data_model: node data model
|
||||
:return:
|
||||
"""
|
||||
model_name = node_data_model.name
|
||||
provider_name = node_data_model.provider
|
||||
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_model_instance(
|
||||
tenant_id=self.tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
provider=provider_name,
|
||||
model=model_name
|
||||
)
|
||||
|
||||
provider_model_bundle = model_instance.provider_model_bundle
|
||||
model_type_instance = model_instance.model_type_instance
|
||||
model_type_instance = cast(LargeLanguageModel, model_type_instance)
|
||||
|
||||
model_credentials = model_instance.credentials
|
||||
|
||||
# check model
|
||||
provider_model = provider_model_bundle.configuration.get_provider_model(
|
||||
model=model_name,
|
||||
model_type=ModelType.LLM
|
||||
)
|
||||
|
||||
if provider_model is None:
|
||||
raise ValueError(f"Model {model_name} not exist.")
|
||||
|
||||
if provider_model.status == ModelStatus.NO_CONFIGURE:
|
||||
raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
|
||||
elif provider_model.status == ModelStatus.NO_PERMISSION:
|
||||
raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.")
|
||||
elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
|
||||
raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
|
||||
|
||||
# model config
|
||||
completion_params = node_data_model.completion_params
|
||||
stop = []
|
||||
if 'stop' in completion_params:
|
||||
stop = completion_params['stop']
|
||||
del completion_params['stop']
|
||||
|
||||
# get model mode
|
||||
model_mode = node_data_model.mode
|
||||
if not model_mode:
|
||||
raise ValueError("LLM mode is required.")
|
||||
|
||||
model_schema = model_type_instance.get_model_schema(
|
||||
model_name,
|
||||
model_credentials
|
||||
)
|
||||
|
||||
if not model_schema:
|
||||
raise ValueError(f"Model {model_name} not exist.")
|
||||
|
||||
return model_instance, ModelConfigWithCredentialsEntity(
|
||||
provider=provider_name,
|
||||
model=model_name,
|
||||
model_schema=model_schema,
|
||||
mode=model_mode,
|
||||
provider_model_bundle=provider_model_bundle,
|
||||
credentials=model_credentials,
|
||||
parameters=completion_params,
|
||||
stop=stop,
|
||||
)
|
||||
|
||||
def _fetch_memory(self, node_data_memory: Optional[MemoryConfig],
|
||||
variable_pool: VariablePool,
|
||||
model_instance: ModelInstance) -> Optional[TokenBufferMemory]:
|
||||
"""
|
||||
Fetch memory
|
||||
:param node_data_memory: node data memory
|
||||
:param variable_pool: variable pool
|
||||
:return:
|
||||
"""
|
||||
if not node_data_memory:
|
||||
return None
|
||||
|
||||
# get conversation id
|
||||
conversation_id = variable_pool.get_variable_value(['sys', SystemVariable.CONVERSATION.value])
|
||||
if conversation_id is None:
|
||||
return None
|
||||
|
||||
# get conversation
|
||||
conversation = db.session.query(Conversation).filter(
|
||||
Conversation.app_id == self.app_id,
|
||||
Conversation.id == conversation_id
|
||||
).first()
|
||||
|
||||
if not conversation:
|
||||
return None
|
||||
|
||||
memory = TokenBufferMemory(
|
||||
conversation=conversation,
|
||||
model_instance=model_instance
|
||||
)
|
||||
|
||||
return memory
|
||||
|
||||
def _fetch_prompt_messages(self, node_data: LLMNodeData,
|
||||
query: Optional[str],
|
||||
inputs: dict[str, str],
|
||||
files: list[FileVar],
|
||||
context: Optional[str],
|
||||
memory: Optional[TokenBufferMemory],
|
||||
model_config: ModelConfigWithCredentialsEntity) \
|
||||
-> tuple[list[PromptMessage], Optional[list[str]]]:
|
||||
"""
|
||||
Fetch prompt messages
|
||||
:param node_data: node data
|
||||
:param query: query
|
||||
:param inputs: inputs
|
||||
:param files: files
|
||||
:param context: context
|
||||
:param memory: memory
|
||||
:param model_config: model config
|
||||
:return:
|
||||
"""
|
||||
prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
|
||||
prompt_messages = prompt_transform.get_prompt(
|
||||
prompt_template=node_data.prompt_template,
|
||||
inputs=inputs,
|
||||
query=query if query else '',
|
||||
files=files,
|
||||
context=context,
|
||||
memory_config=node_data.memory,
|
||||
memory=memory,
|
||||
model_config=model_config
|
||||
)
|
||||
stop = model_config.stop
|
||||
|
||||
return prompt_messages, stop
|
||||
|
||||
@classmethod
|
||||
def deduct_llm_quota(cls, tenant_id: str, model_instance: ModelInstance, usage: LLMUsage) -> None:
|
||||
"""
|
||||
Deduct LLM quota
|
||||
:param tenant_id: tenant id
|
||||
:param model_instance: model instance
|
||||
:param usage: usage
|
||||
:return:
|
||||
"""
|
||||
provider_model_bundle = model_instance.provider_model_bundle
|
||||
provider_configuration = provider_model_bundle.configuration
|
||||
|
||||
if provider_configuration.using_provider_type != ProviderType.SYSTEM:
|
||||
return
|
||||
|
||||
system_configuration = provider_configuration.system_configuration
|
||||
|
||||
quota_unit = None
|
||||
for quota_configuration in system_configuration.quota_configurations:
|
||||
if quota_configuration.quota_type == system_configuration.current_quota_type:
|
||||
quota_unit = quota_configuration.quota_unit
|
||||
|
||||
if quota_configuration.quota_limit == -1:
|
||||
return
|
||||
|
||||
break
|
||||
|
||||
used_quota = None
|
||||
if quota_unit:
|
||||
if quota_unit == QuotaUnit.TOKENS:
|
||||
used_quota = usage.total_tokens
|
||||
elif quota_unit == QuotaUnit.CREDITS:
|
||||
used_quota = 1
|
||||
|
||||
if 'gpt-4' in model_instance.model:
|
||||
used_quota = 20
|
||||
else:
|
||||
used_quota = 1
|
||||
|
||||
if used_quota is not None:
|
||||
db.session.query(Provider).filter(
|
||||
Provider.tenant_id == tenant_id,
|
||||
Provider.provider_name == model_instance.provider,
|
||||
Provider.provider_type == ProviderType.SYSTEM.value,
|
||||
Provider.quota_type == system_configuration.current_quota_type.value,
|
||||
Provider.quota_limit > Provider.quota_used
|
||||
).update({'quota_used': Provider.quota_used + used_quota})
|
||||
db.session.commit()
|
||||
|
||||
@classmethod
|
||||
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
|
||||
"""
|
||||
Extract variable selector to variable mapping
|
||||
:param node_data: node data
|
||||
:return:
|
||||
"""
|
||||
node_data = node_data
|
||||
node_data = cast(cls._node_data_cls, node_data)
|
||||
|
||||
prompt_template = node_data.prompt_template
|
||||
|
||||
variable_selectors = []
|
||||
if isinstance(prompt_template, list):
|
||||
for prompt in prompt_template:
|
||||
variable_template_parser = VariableTemplateParser(template=prompt.text)
|
||||
variable_selectors.extend(variable_template_parser.extract_variable_selectors())
|
||||
else:
|
||||
variable_template_parser = VariableTemplateParser(template=prompt_template.text)
|
||||
variable_selectors = variable_template_parser.extract_variable_selectors()
|
||||
|
||||
variable_mapping = {}
|
||||
for variable_selector in variable_selectors:
|
||||
variable_mapping[variable_selector.variable] = variable_selector.value_selector
|
||||
|
||||
if node_data.context.enabled:
|
||||
variable_mapping['#context#'] = node_data.context.variable_selector
|
||||
|
||||
if node_data.vision.enabled:
|
||||
variable_mapping['#files#'] = ['sys', SystemVariable.FILES.value]
|
||||
|
||||
return variable_mapping
|
||||
|
||||
@classmethod
|
||||
def get_default_config(cls, filters: Optional[dict] = None) -> dict:
|
||||
"""
|
||||
Get default config of node.
|
||||
:param filters: filter by node config parameters.
|
||||
:return:
|
||||
"""
|
||||
return {
|
||||
"type": "llm",
|
||||
"config": {
|
||||
"prompt_templates": {
|
||||
"chat_model": {
|
||||
"prompts": [
|
||||
{
|
||||
"role": "system",
|
||||
"text": "You are a helpful AI assistant."
|
||||
}
|
||||
]
|
||||
},
|
||||
"completion_model": {
|
||||
"conversation_histories_role": {
|
||||
"user_prefix": "Human",
|
||||
"assistant_prefix": "Assistant"
|
||||
},
|
||||
"prompt": {
|
||||
"text": "Here is the chat histories between human and assistant, inside "
|
||||
"<histories></histories> XML tags.\n\n<histories>\n{{"
|
||||
"#histories#}}\n</histories>\n\n\nHuman: {{#sys.query#}}\n\nAssistant:"
|
||||
},
|
||||
"stop": ["Human:"]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user