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refactor apps
This commit is contained in:
takatost
0
api/core/app/apps/agent_chat/__init__.py
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0
api/core/app/apps/agent_chat/__init__.py
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217
api/core/app/apps/agent_chat/app_config_manager.py
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api/core/app/apps/agent_chat/app_config_manager.py
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import uuid
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from typing import Optional
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from core.agent.entities import AgentEntity
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from core.app.app_config.base_app_config_manager import BaseAppConfigManager
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from core.app.app_config.easy_ui_based_app.agent.manager import AgentConfigManager
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from core.app.app_config.easy_ui_based_app.dataset.manager import DatasetConfigManager
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from core.app.app_config.easy_ui_based_app.model_config.manager import ModelConfigManager
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from core.app.app_config.easy_ui_based_app.prompt_template.manager import PromptTemplateConfigManager
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from core.app.app_config.easy_ui_based_app.variables.manager import BasicVariablesConfigManager
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from core.app.app_config.common.sensitive_word_avoidance.manager import SensitiveWordAvoidanceConfigManager
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from core.app.app_config.entities import EasyUIBasedAppConfig, EasyUIBasedAppModelConfigFrom, DatasetEntity
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from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
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from core.app.app_config.features.opening_statement.manager import OpeningStatementConfigManager
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from core.app.app_config.features.retrieval_resource.manager import RetrievalResourceConfigManager
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from core.app.app_config.features.speech_to_text.manager import SpeechToTextConfigManager
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from core.app.app_config.features.suggested_questions_after_answer.manager import \
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SuggestedQuestionsAfterAnswerConfigManager
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from core.app.app_config.features.text_to_speech.manager import TextToSpeechConfigManager
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from core.entities.agent_entities import PlanningStrategy
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from models.model import AppMode, App, AppModelConfig
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OLD_TOOLS = ["dataset", "google_search", "web_reader", "wikipedia", "current_datetime"]
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class AgentChatAppConfig(EasyUIBasedAppConfig):
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"""
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Agent Chatbot App Config Entity.
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"""
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agent: Optional[AgentEntity] = None
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class AgentChatAppConfigManager(BaseAppConfigManager):
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@classmethod
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def config_convert(cls, app_model: App,
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config_from: EasyUIBasedAppModelConfigFrom,
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app_model_config: AppModelConfig,
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config_dict: Optional[dict] = None) -> AgentChatAppConfig:
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"""
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Convert app model config to agent chat app config
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:param app_model: app model
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:param config_from: app model config from
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:param app_model_config: app model config
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:param config_dict: app model config dict
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:return:
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"""
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config_dict = cls.convert_to_config_dict(config_from, app_model_config, config_dict)
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app_config = AgentChatAppConfig(
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tenant_id=app_model.tenant_id,
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app_id=app_model.id,
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app_mode=AppMode.value_of(app_model.mode),
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app_model_config_from=config_from,
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app_model_config_id=app_model_config.id,
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app_model_config_dict=config_dict,
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model=ModelConfigManager.convert(
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config=config_dict
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),
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prompt_template=PromptTemplateConfigManager.convert(
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config=config_dict
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),
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sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(
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config=config_dict
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),
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dataset=DatasetConfigManager.convert(
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config=config_dict
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),
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agent=AgentConfigManager.convert(
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config=config_dict
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),
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additional_features=cls.convert_features(config_dict)
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)
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app_config.variables, app_config.external_data_variables = BasicVariablesConfigManager.convert(
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config=config_dict
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)
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return app_config
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@classmethod
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def config_validate(cls, tenant_id: str, config: dict) -> dict:
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"""
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Validate for agent chat app model config
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:param tenant_id: tenant id
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:param config: app model config args
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"""
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app_mode = AppMode.AGENT_CHAT
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related_config_keys = []
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# model
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config, current_related_config_keys = ModelConfigManager.validate_and_set_defaults(tenant_id, config)
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related_config_keys.extend(current_related_config_keys)
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# user_input_form
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config, current_related_config_keys = BasicVariablesConfigManager.validate_and_set_defaults(tenant_id, config)
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related_config_keys.extend(current_related_config_keys)
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# file upload validation
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config, current_related_config_keys = FileUploadConfigManager.validate_and_set_defaults(config)
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related_config_keys.extend(current_related_config_keys)
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# prompt
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config, current_related_config_keys = PromptTemplateConfigManager.validate_and_set_defaults(app_mode, config)
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related_config_keys.extend(current_related_config_keys)
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# agent_mode
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config, current_related_config_keys = cls.validate_agent_mode_and_set_defaults(tenant_id, config)
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related_config_keys.extend(current_related_config_keys)
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# opening_statement
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config, current_related_config_keys = OpeningStatementConfigManager.validate_and_set_defaults(config)
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related_config_keys.extend(current_related_config_keys)
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# suggested_questions_after_answer
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config, current_related_config_keys = SuggestedQuestionsAfterAnswerConfigManager.validate_and_set_defaults(
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config)
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related_config_keys.extend(current_related_config_keys)
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# speech_to_text
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config, current_related_config_keys = SpeechToTextConfigManager.validate_and_set_defaults(config)
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related_config_keys.extend(current_related_config_keys)
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# text_to_speech
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config, current_related_config_keys = TextToSpeechConfigManager.validate_and_set_defaults(config)
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related_config_keys.extend(current_related_config_keys)
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# return retriever resource
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config, current_related_config_keys = RetrievalResourceConfigManager.validate_and_set_defaults(config)
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related_config_keys.extend(current_related_config_keys)
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# moderation validation
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config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(tenant_id,
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config)
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related_config_keys.extend(current_related_config_keys)
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related_config_keys = list(set(related_config_keys))
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# Filter out extra parameters
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filtered_config = {key: config.get(key) for key in related_config_keys}
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return filtered_config
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@classmethod
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def validate_agent_mode_and_set_defaults(cls, tenant_id: str, config: dict) -> tuple[dict, list[str]]:
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"""
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Validate agent_mode and set defaults for agent feature
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:param tenant_id: tenant ID
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:param config: app model config args
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"""
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if not config.get("agent_mode"):
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config["agent_mode"] = {
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"enabled": False,
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"tools": []
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}
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if not isinstance(config["agent_mode"], dict):
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raise ValueError("agent_mode must be of object type")
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if "enabled" not in config["agent_mode"] or not config["agent_mode"]["enabled"]:
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config["agent_mode"]["enabled"] = False
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if not isinstance(config["agent_mode"]["enabled"], bool):
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raise ValueError("enabled in agent_mode must be of boolean type")
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if not config["agent_mode"].get("strategy"):
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config["agent_mode"]["strategy"] = PlanningStrategy.ROUTER.value
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if config["agent_mode"]["strategy"] not in [member.value for member in
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list(PlanningStrategy.__members__.values())]:
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raise ValueError("strategy in agent_mode must be in the specified strategy list")
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if not config["agent_mode"].get("tools"):
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config["agent_mode"]["tools"] = []
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if not isinstance(config["agent_mode"]["tools"], list):
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raise ValueError("tools in agent_mode must be a list of objects")
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for tool in config["agent_mode"]["tools"]:
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key = list(tool.keys())[0]
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if key in OLD_TOOLS:
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# old style, use tool name as key
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tool_item = tool[key]
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if "enabled" not in tool_item or not tool_item["enabled"]:
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tool_item["enabled"] = False
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if not isinstance(tool_item["enabled"], bool):
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raise ValueError("enabled in agent_mode.tools must be of boolean type")
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if key == "dataset":
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if 'id' not in tool_item:
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raise ValueError("id is required in dataset")
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try:
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uuid.UUID(tool_item["id"])
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except ValueError:
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raise ValueError("id in dataset must be of UUID type")
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if not DatasetConfigManager.is_dataset_exists(tenant_id, tool_item["id"]):
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raise ValueError("Dataset ID does not exist, please check your permission.")
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else:
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# latest style, use key-value pair
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if "enabled" not in tool or not tool["enabled"]:
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tool["enabled"] = False
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if "provider_type" not in tool:
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raise ValueError("provider_type is required in agent_mode.tools")
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if "provider_id" not in tool:
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raise ValueError("provider_id is required in agent_mode.tools")
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if "tool_name" not in tool:
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raise ValueError("tool_name is required in agent_mode.tools")
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if "tool_parameters" not in tool:
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raise ValueError("tool_parameters is required in agent_mode.tools")
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return config, ["agent_mode"]
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320
api/core/app/apps/agent_chat/app_runner.py
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320
api/core/app/apps/agent_chat/app_runner.py
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@ -0,0 +1,320 @@
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import logging
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from typing import cast
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from core.agent.cot_agent_runner import CotAgentRunner
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from core.agent.entities import AgentEntity
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from core.agent.fc_agent_runner import FunctionCallAgentRunner
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from core.app.app_queue_manager import AppQueueManager, PublishFrom
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from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
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from core.app.apps.base_app_runner import AppRunner
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from core.app.entities.app_invoke_entities import EasyUIBasedAppGenerateEntity, EasyUIBasedModelConfigEntity
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from core.memory.token_buffer_memory import TokenBufferMemory
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from core.model_manager import ModelInstance
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from core.model_runtime.entities.llm_entities import LLMUsage
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from core.model_runtime.entities.model_entities import ModelFeature
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from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
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from core.moderation.base import ModerationException
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from core.tools.entities.tool_entities import ToolRuntimeVariablePool
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from extensions.ext_database import db
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from models.model import App, Conversation, Message, MessageAgentThought
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from models.tools import ToolConversationVariables
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logger = logging.getLogger(__name__)
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class AgentChatAppRunner(AppRunner):
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"""
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Agent Application Runner
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"""
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def run(self, application_generate_entity: EasyUIBasedAppGenerateEntity,
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queue_manager: AppQueueManager,
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conversation: Conversation,
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message: Message) -> None:
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"""
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Run assistant application
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:param application_generate_entity: application generate entity
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:param queue_manager: application queue manager
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:param conversation: conversation
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:param message: message
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:return:
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"""
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app_config = application_generate_entity.app_config
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app_config = cast(AgentChatAppConfig, app_config)
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app_record = db.session.query(App).filter(App.id == app_config.app_id).first()
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if not app_record:
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raise ValueError("App not found")
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inputs = application_generate_entity.inputs
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query = application_generate_entity.query
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files = application_generate_entity.files
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# Pre-calculate the number of tokens of the prompt messages,
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# and return the rest number of tokens by model context token size limit and max token size limit.
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# If the rest number of tokens is not enough, raise exception.
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# Include: prompt template, inputs, query(optional), files(optional)
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# Not Include: memory, external data, dataset context
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self.get_pre_calculate_rest_tokens(
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app_record=app_record,
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model_config=application_generate_entity.model_config,
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prompt_template_entity=app_config.prompt_template,
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inputs=inputs,
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files=files,
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query=query
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)
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memory = None
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if application_generate_entity.conversation_id:
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# get memory of conversation (read-only)
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model_instance = ModelInstance(
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provider_model_bundle=application_generate_entity.model_config.provider_model_bundle,
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model=application_generate_entity.model_config.model
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)
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memory = TokenBufferMemory(
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conversation=conversation,
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model_instance=model_instance
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)
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# organize all inputs and template to prompt messages
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# Include: prompt template, inputs, query(optional), files(optional)
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# memory(optional)
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prompt_messages, _ = self.organize_prompt_messages(
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app_record=app_record,
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model_config=application_generate_entity.model_config,
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prompt_template_entity=app_config.prompt_template,
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inputs=inputs,
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files=files,
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query=query,
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memory=memory
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)
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# moderation
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try:
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# process sensitive_word_avoidance
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_, inputs, query = self.moderation_for_inputs(
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app_id=app_record.id,
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tenant_id=app_config.tenant_id,
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app_generate_entity=application_generate_entity,
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inputs=inputs,
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query=query,
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)
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except ModerationException as e:
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self.direct_output(
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queue_manager=queue_manager,
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app_generate_entity=application_generate_entity,
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prompt_messages=prompt_messages,
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text=str(e),
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stream=application_generate_entity.stream
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)
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return
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if query:
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# annotation reply
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annotation_reply = self.query_app_annotations_to_reply(
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app_record=app_record,
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message=message,
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query=query,
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user_id=application_generate_entity.user_id,
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invoke_from=application_generate_entity.invoke_from
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)
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if annotation_reply:
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queue_manager.publish_annotation_reply(
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message_annotation_id=annotation_reply.id,
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pub_from=PublishFrom.APPLICATION_MANAGER
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)
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self.direct_output(
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queue_manager=queue_manager,
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app_generate_entity=application_generate_entity,
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prompt_messages=prompt_messages,
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text=annotation_reply.content,
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stream=application_generate_entity.stream
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)
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return
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# fill in variable inputs from external data tools if exists
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external_data_tools = app_config.external_data_variables
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if external_data_tools:
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inputs = self.fill_in_inputs_from_external_data_tools(
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tenant_id=app_record.tenant_id,
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app_id=app_record.id,
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external_data_tools=external_data_tools,
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inputs=inputs,
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query=query
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)
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# reorganize all inputs and template to prompt messages
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# Include: prompt template, inputs, query(optional), files(optional)
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# memory(optional), external data, dataset context(optional)
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prompt_messages, _ = self.organize_prompt_messages(
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app_record=app_record,
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model_config=application_generate_entity.model_config,
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prompt_template_entity=app_config.prompt_template,
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inputs=inputs,
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files=files,
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query=query,
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memory=memory
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)
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# check hosting moderation
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hosting_moderation_result = self.check_hosting_moderation(
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application_generate_entity=application_generate_entity,
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queue_manager=queue_manager,
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prompt_messages=prompt_messages
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)
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if hosting_moderation_result:
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return
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agent_entity = app_config.agent
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# load tool variables
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tool_conversation_variables = self._load_tool_variables(conversation_id=conversation.id,
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user_id=application_generate_entity.user_id,
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tenant_id=app_config.tenant_id)
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# convert db variables to tool variables
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tool_variables = self._convert_db_variables_to_tool_variables(tool_conversation_variables)
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# init model instance
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model_instance = ModelInstance(
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provider_model_bundle=application_generate_entity.model_config.provider_model_bundle,
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model=application_generate_entity.model_config.model
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)
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prompt_message, _ = self.organize_prompt_messages(
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app_record=app_record,
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model_config=application_generate_entity.model_config,
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prompt_template_entity=app_config.prompt_template,
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inputs=inputs,
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files=files,
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query=query,
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memory=memory,
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)
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# change function call strategy based on LLM model
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llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
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model_schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
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if set([ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL]).intersection(model_schema.features or []):
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agent_entity.strategy = AgentEntity.Strategy.FUNCTION_CALLING
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db.session.refresh(conversation)
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db.session.refresh(message)
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db.session.close()
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# start agent runner
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if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
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assistant_cot_runner = CotAgentRunner(
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tenant_id=app_config.tenant_id,
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application_generate_entity=application_generate_entity,
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app_config=app_config,
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model_config=application_generate_entity.model_config,
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config=agent_entity,
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queue_manager=queue_manager,
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message=message,
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user_id=application_generate_entity.user_id,
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memory=memory,
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prompt_messages=prompt_message,
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variables_pool=tool_variables,
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db_variables=tool_conversation_variables,
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model_instance=model_instance
|
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)
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invoke_result = assistant_cot_runner.run(
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conversation=conversation,
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||||
message=message,
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||||
query=query,
|
||||
inputs=inputs,
|
||||
)
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elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
|
||||
assistant_fc_runner = FunctionCallAgentRunner(
|
||||
tenant_id=app_config.tenant_id,
|
||||
application_generate_entity=application_generate_entity,
|
||||
app_config=app_config,
|
||||
model_config=application_generate_entity.model_config,
|
||||
config=agent_entity,
|
||||
queue_manager=queue_manager,
|
||||
message=message,
|
||||
user_id=application_generate_entity.user_id,
|
||||
memory=memory,
|
||||
prompt_messages=prompt_message,
|
||||
variables_pool=tool_variables,
|
||||
db_variables=tool_conversation_variables,
|
||||
model_instance=model_instance
|
||||
)
|
||||
invoke_result = assistant_fc_runner.run(
|
||||
conversation=conversation,
|
||||
message=message,
|
||||
query=query,
|
||||
)
|
||||
|
||||
# handle invoke result
|
||||
self._handle_invoke_result(
|
||||
invoke_result=invoke_result,
|
||||
queue_manager=queue_manager,
|
||||
stream=application_generate_entity.stream,
|
||||
agent=True
|
||||
)
|
||||
|
||||
def _load_tool_variables(self, conversation_id: str, user_id: str, tenant_id: str) -> ToolConversationVariables:
|
||||
"""
|
||||
load tool variables from database
|
||||
"""
|
||||
tool_variables: ToolConversationVariables = db.session.query(ToolConversationVariables).filter(
|
||||
ToolConversationVariables.conversation_id == conversation_id,
|
||||
ToolConversationVariables.tenant_id == tenant_id
|
||||
).first()
|
||||
|
||||
if tool_variables:
|
||||
# save tool variables to session, so that we can update it later
|
||||
db.session.add(tool_variables)
|
||||
else:
|
||||
# create new tool variables
|
||||
tool_variables = ToolConversationVariables(
|
||||
conversation_id=conversation_id,
|
||||
user_id=user_id,
|
||||
tenant_id=tenant_id,
|
||||
variables_str='[]',
|
||||
)
|
||||
db.session.add(tool_variables)
|
||||
db.session.commit()
|
||||
|
||||
return tool_variables
|
||||
|
||||
def _convert_db_variables_to_tool_variables(self, db_variables: ToolConversationVariables) -> ToolRuntimeVariablePool:
|
||||
"""
|
||||
convert db variables to tool variables
|
||||
"""
|
||||
return ToolRuntimeVariablePool(**{
|
||||
'conversation_id': db_variables.conversation_id,
|
||||
'user_id': db_variables.user_id,
|
||||
'tenant_id': db_variables.tenant_id,
|
||||
'pool': db_variables.variables
|
||||
})
|
||||
|
||||
def _get_usage_of_all_agent_thoughts(self, model_config: EasyUIBasedModelConfigEntity,
|
||||
message: Message) -> LLMUsage:
|
||||
"""
|
||||
Get usage of all agent thoughts
|
||||
:param model_config: model config
|
||||
:param message: message
|
||||
:return:
|
||||
"""
|
||||
agent_thoughts = (db.session.query(MessageAgentThought)
|
||||
.filter(MessageAgentThought.message_id == message.id).all())
|
||||
|
||||
all_message_tokens = 0
|
||||
all_answer_tokens = 0
|
||||
for agent_thought in agent_thoughts:
|
||||
all_message_tokens += agent_thought.message_tokens
|
||||
all_answer_tokens += agent_thought.answer_tokens
|
||||
|
||||
model_type_instance = model_config.provider_model_bundle.model_type_instance
|
||||
model_type_instance = cast(LargeLanguageModel, model_type_instance)
|
||||
|
||||
return model_type_instance._calc_response_usage(
|
||||
model_config.model,
|
||||
model_config.credentials,
|
||||
all_message_tokens,
|
||||
all_answer_tokens
|
||||
)
|
||||
Reference in New Issue
Block a user