tjdify/api/core/agent/base_agent_runner.py

import json
import logging
import uuid
from collections.abc import Mapping, Sequence
from typing import Optional, Union, cast

from core.agent.entities import AgentEntity, AgentToolEntity
from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
from core.app.apps.base_app_queue_manager import AppQueueManager
from core.app.apps.base_app_runner import AppRunner
from core.app.entities.app_invoke_entities import (
    AgentChatAppGenerateEntity,
    ModelConfigWithCredentialsEntity,
)
from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackHandler
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
from core.file.message_file_parser import MessageFileParser
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_manager import ModelInstance
from core.model_runtime.entities.llm_entities import LLMUsage
from core.model_runtime.entities.message_entities import (
    AssistantPromptMessage,
    PromptMessage,
    PromptMessageContent,
    PromptMessageTool,
    SystemPromptMessage,
    TextPromptMessageContent,
    ToolPromptMessage,
    UserPromptMessage,
)
from core.model_runtime.entities.model_entities import ModelFeature
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.tools.__base.tool import Tool
from core.tools.entities.tool_entities import (
    ToolParameter,
)
from core.tools.tool_manager import ToolManager
from core.tools.utils.dataset_retriever_tool import DatasetRetrieverTool
from core.tools.utils.tool_parameter_converter import ToolParameterConverter
from extensions.ext_database import db
from models.model import Conversation, Message, MessageAgentThought

logger = logging.getLogger(__name__)


class BaseAgentRunner(AppRunner):
    def __init__(
        self,
        tenant_id: str,
        application_generate_entity: AgentChatAppGenerateEntity,
        conversation: Conversation,
        app_config: AgentChatAppConfig,
        model_config: ModelConfigWithCredentialsEntity,
        config: AgentEntity,
        queue_manager: AppQueueManager,
        message: Message,
        user_id: str,
        model_instance: ModelInstance,
        memory: Optional[TokenBufferMemory] = None,
        prompt_messages: Optional[list[PromptMessage]] = None,
    ) -> None:
        """
        Agent runner
        :param tenant_id: tenant id
        :param application_generate_entity: application generate entity
        :param conversation: conversation
        :param app_config: app generate entity
        :param model_config: model config
        :param config: dataset config
        :param queue_manager: queue manager
        :param message: message
        :param user_id: user id
        :param memory: memory
        :param prompt_messages: prompt messages
        :param variables_pool: variables pool
        :param db_variables: db variables
        :param model_instance: model instance
        """
        self.tenant_id = tenant_id
        self.application_generate_entity = application_generate_entity
        self.conversation = conversation
        self.app_config = app_config
        self.model_config = model_config
        self.config = config
        self.queue_manager = queue_manager
        self.message = message
        self.user_id = user_id
        self.memory = memory
        self.history_prompt_messages = self.organize_agent_history(prompt_messages=prompt_messages or [])
        self.model_instance = model_instance

        # init callback
        self.agent_callback = DifyAgentCallbackHandler()
        # init dataset tools
        hit_callback = DatasetIndexToolCallbackHandler(
            queue_manager=queue_manager,
            app_id=self.app_config.app_id,
            message_id=message.id,
            user_id=user_id,
            invoke_from=self.application_generate_entity.invoke_from,
        )
        self.dataset_tools = DatasetRetrieverTool.get_dataset_tools(
            tenant_id=tenant_id,
            dataset_ids=app_config.dataset.dataset_ids if app_config.dataset else [],
            retrieve_config=app_config.dataset.retrieve_config if app_config.dataset else None,
            return_resource=app_config.additional_features.show_retrieve_source,
            invoke_from=application_generate_entity.invoke_from,
            hit_callback=hit_callback,
        )
        # get how many agent thoughts have been created
        self.agent_thought_count = (
            db.session.query(MessageAgentThought)
            .filter(
                MessageAgentThought.message_id == self.message.id,
            )
            .count()
        )
        db.session.close()

        # check if model supports stream tool call
        llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
        model_schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
        if model_schema and ModelFeature.STREAM_TOOL_CALL in (model_schema.features or []):
            self.stream_tool_call = True
        else:
            self.stream_tool_call = False

        # check if model supports vision
        if model_schema and ModelFeature.VISION in (model_schema.features or []):
            self.files = application_generate_entity.files
        else:
            self.files = []
        self.query = None
        self._current_thoughts: list[PromptMessage] = []

    def _repack_app_generate_entity(
        self, app_generate_entity: AgentChatAppGenerateEntity
    ) -> AgentChatAppGenerateEntity:
        """
        Repack app generate entity
        """
        if app_generate_entity.app_config.prompt_template.simple_prompt_template is None:
            app_generate_entity.app_config.prompt_template.simple_prompt_template = ""

        return app_generate_entity

    def _convert_tool_to_prompt_message_tool(self, tool: AgentToolEntity) -> tuple[PromptMessageTool, Tool]:
        """
        convert tool to prompt message tool
        """
        tool_entity = ToolManager.get_agent_tool_runtime(
            tenant_id=self.tenant_id,
            app_id=self.app_config.app_id,
            agent_tool=tool,
            invoke_from=self.application_generate_entity.invoke_from,
        )
        assert tool_entity.entity.description
        message_tool = PromptMessageTool(
            name=tool.tool_name,
            description=tool_entity.entity.description.llm,
            parameters={
                "type": "object",
                "properties": {},
                "required": [],
            },
        )

        parameters = tool_entity.get_merged_runtime_parameters()
        for parameter in parameters:
            if parameter.form != ToolParameter.ToolParameterForm.LLM:
                continue

            parameter_type = ToolParameterConverter.get_parameter_type(parameter.type)
            enum = []
            if parameter.type == ToolParameter.ToolParameterType.SELECT:
                enum = [option.value for option in parameter.options]

            message_tool.parameters["properties"][parameter.name] = {
                "type": parameter_type,
                "description": parameter.llm_description or "",
            }

            if len(enum) > 0:
                message_tool.parameters["properties"][parameter.name]["enum"] = enum

            if parameter.required:
                message_tool.parameters["required"].append(parameter.name)

        return message_tool, tool_entity

    def _convert_dataset_retriever_tool_to_prompt_message_tool(self, tool: DatasetRetrieverTool) -> PromptMessageTool:
        """
        convert dataset retriever tool to prompt message tool
        """
        assert tool.entity.description

        prompt_tool = PromptMessageTool(
            name=tool.entity.identity.name,
            description=tool.entity.description.llm,
            parameters={
                "type": "object",
                "properties": {},
                "required": [],
            },
        )

        for parameter in tool.get_runtime_parameters():
            parameter_type = "string"

            prompt_tool.parameters["properties"][parameter.name] = {
                "type": parameter_type,
                "description": parameter.llm_description or "",
            }

            if parameter.required:
                if parameter.name not in prompt_tool.parameters["required"]:
                    prompt_tool.parameters["required"].append(parameter.name)

        return prompt_tool

    def _init_prompt_tools(self) -> tuple[Mapping[str, Tool], Sequence[PromptMessageTool]]:
        """
        Init tools
        """
        tool_instances = {}
        prompt_messages_tools = []

        for tool in self.app_config.agent.tools or [] if self.app_config.agent else []:
            try:
                prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
            except Exception:
                # api tool may be deleted
                continue
            # save tool entity
            tool_instances[tool.tool_name] = tool_entity
            # save prompt tool
            prompt_messages_tools.append(prompt_tool)

        # convert dataset tools into ModelRuntime Tool format
        for dataset_tool in self.dataset_tools:
            prompt_tool = self._convert_dataset_retriever_tool_to_prompt_message_tool(dataset_tool)
            # save prompt tool
            prompt_messages_tools.append(prompt_tool)
            # save tool entity
            tool_instances[dataset_tool.entity.identity.name] = dataset_tool

        return tool_instances, prompt_messages_tools

    def update_prompt_message_tool(self, tool: Tool, prompt_tool: PromptMessageTool) -> PromptMessageTool:
        """
        update prompt message tool
        """
        # try to get tool runtime parameters
        tool_runtime_parameters = tool.get_runtime_parameters() or []

        for parameter in tool_runtime_parameters:
            if parameter.form != ToolParameter.ToolParameterForm.LLM:
                continue

            parameter_type = ToolParameterConverter.get_parameter_type(parameter.type)
            enum = []
            if parameter.type == ToolParameter.ToolParameterType.SELECT:
                enum = [option.value for option in parameter.options]

            prompt_tool.parameters["properties"][parameter.name] = {
                "type": parameter_type,
                "description": parameter.llm_description or "",
            }

            if len(enum) > 0:
                prompt_tool.parameters["properties"][parameter.name]["enum"] = enum

            if parameter.required:
                if parameter.name not in prompt_tool.parameters["required"]:
                    prompt_tool.parameters["required"].append(parameter.name)

        return prompt_tool

    def create_agent_thought(
        self, message_id: str, message: str, tool_name: str, tool_input: str, messages_ids: list[str]
    ) -> MessageAgentThought:
        """
        Create agent thought
        """
        thought = MessageAgentThought(
            message_id=message_id,
            message_chain_id=None,
            thought="",
            tool=tool_name,
            tool_labels_str="{}",
            tool_meta_str="{}",
            tool_input=tool_input,
            message=message,
            message_token=0,
            message_unit_price=0,
            message_price_unit=0,
            message_files=json.dumps(messages_ids) if messages_ids else "",
            answer="",
            observation="",
            answer_token=0,
            answer_unit_price=0,
            answer_price_unit=0,
            tokens=0,
            total_price=0,
            position=self.agent_thought_count + 1,
            currency="USD",
            latency=0,
            created_by_role="account",
            created_by=self.user_id,
        )

        db.session.add(thought)
        db.session.commit()
        db.session.refresh(thought)
        db.session.close()

        self.agent_thought_count += 1

        return thought

    def save_agent_thought(
        self,
        agent_thought: MessageAgentThought,
        tool_name: str | None,
        tool_input: Union[str, dict, None],
        thought: str | None,
        observation: Union[str, dict, None],
        tool_invoke_meta: Union[str, dict, None],
        answer: str | None,
        messages_ids: list[str],
        llm_usage: LLMUsage | None = None,
    ):
        """
        Save agent thought
        """
        updated_agent_thought = (
            db.session.query(MessageAgentThought).filter(MessageAgentThought.id == agent_thought.id).first()
        )
        if not updated_agent_thought:
            raise ValueError("agent thought not found")

        if thought is not None:
            updated_agent_thought.thought = thought

        if tool_name is not None:
            updated_agent_thought.tool = tool_name

        if tool_input is not None:
            if isinstance(tool_input, dict):
                try:
                    tool_input = json.dumps(tool_input, ensure_ascii=False)
                except Exception as e:
                    tool_input = json.dumps(tool_input)

            updated_agent_thought.tool_input = tool_input

        if observation is not None:
            if isinstance(observation, dict):
                try:
                    observation = json.dumps(observation, ensure_ascii=False)
                except Exception as e:
                    observation = json.dumps(observation)

            updated_agent_thought.observation = observation

        if answer is not None:
            updated_agent_thought.answer = answer

        if messages_ids is not None and len(messages_ids) > 0:
            updated_agent_thought.message_files = json.dumps(messages_ids)

        if llm_usage:
            updated_agent_thought.message_token = llm_usage.prompt_tokens
            updated_agent_thought.message_price_unit = llm_usage.prompt_price_unit
            updated_agent_thought.message_unit_price = llm_usage.prompt_unit_price
            updated_agent_thought.answer_token = llm_usage.completion_tokens
            updated_agent_thought.answer_price_unit = llm_usage.completion_price_unit
            updated_agent_thought.answer_unit_price = llm_usage.completion_unit_price
            updated_agent_thought.tokens = llm_usage.total_tokens
            updated_agent_thought.total_price = llm_usage.total_price

        # check if tool labels is not empty
        labels = updated_agent_thought.tool_labels or {}
        tools = updated_agent_thought.tool.split(";") if updated_agent_thought.tool else []
        for tool in tools:
            if not tool:
                continue
            if tool not in labels:
                tool_label = ToolManager.get_tool_label(tool)
                if tool_label:
                    labels[tool] = tool_label.to_dict()
                else:
                    labels[tool] = {"en_US": tool, "zh_Hans": tool}

        updated_agent_thought.tool_labels_str = json.dumps(labels)

        if tool_invoke_meta is not None:
            if isinstance(tool_invoke_meta, dict):
                try:
                    tool_invoke_meta = json.dumps(tool_invoke_meta, ensure_ascii=False)
                except Exception as e:
                    tool_invoke_meta = json.dumps(tool_invoke_meta)

            updated_agent_thought.tool_meta_str = tool_invoke_meta

        db.session.commit()
        db.session.close()

    def organize_agent_history(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
        """
        Organize agent history
        """
        result = []
        # check if there is a system message in the beginning of the conversation
        for prompt_message in prompt_messages:
            if isinstance(prompt_message, SystemPromptMessage):
                result.append(prompt_message)

        messages: list[Message] = (
            db.session.query(Message)
            .filter(
                Message.conversation_id == self.message.conversation_id,
            )
            .order_by(Message.created_at.asc())
            .all()
        )

        for message in messages:
            if message.id == self.message.id:
                continue

            result.append(self.organize_agent_user_prompt(message))
            agent_thoughts: list[MessageAgentThought] = message.agent_thoughts
            if agent_thoughts:
                for agent_thought in agent_thoughts:
                    tools = agent_thought.tool
                    if tools:
                        tools = tools.split(";")
                        tool_calls: list[AssistantPromptMessage.ToolCall] = []
                        tool_call_response: list[ToolPromptMessage] = []
                        try:
                            tool_inputs = json.loads(agent_thought.tool_input)
                        except Exception as e:
                            tool_inputs = {tool: {} for tool in tools}
                        try:
                            tool_responses = json.loads(agent_thought.observation)
                        except Exception as e:
                            tool_responses = dict.fromkeys(tools, agent_thought.observation)

                        for tool in tools:
                            # generate a uuid for tool call
                            tool_call_id = str(uuid.uuid4())
                            tool_calls.append(
                                AssistantPromptMessage.ToolCall(
                                    id=tool_call_id,
                                    type="function",
                                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(
                                        name=tool,
                                        arguments=json.dumps(tool_inputs.get(tool, {})),
                                    ),
                                )
                            )
                            tool_call_response.append(
                                ToolPromptMessage(
                                    content=tool_responses.get(tool, agent_thought.observation),
                                    name=tool,
                                    tool_call_id=tool_call_id,
                                )
                            )

                        result.extend(
                            [
                                AssistantPromptMessage(
                                    content=agent_thought.thought,
                                    tool_calls=tool_calls,
                                ),
                                *tool_call_response,
                            ]
                        )
                    if not tools:
                        result.append(AssistantPromptMessage(content=agent_thought.thought))
            else:
                if message.answer:
                    result.append(AssistantPromptMessage(content=message.answer))

        db.session.close()

        return result

    def organize_agent_user_prompt(self, message: Message) -> UserPromptMessage:
        message_file_parser = MessageFileParser(
            tenant_id=self.tenant_id,
            app_id=self.app_config.app_id,
        )

        files = message.message_files
        if files:
            assert message.app_model_config
            file_extra_config = FileUploadConfigManager.convert(message.app_model_config.to_dict())

            if file_extra_config:
                file_objs = message_file_parser.transform_message_files(files, file_extra_config)
            else:
                file_objs = []

            if not file_objs:
                return UserPromptMessage(content=message.query)
            else:
                prompt_message_contents: list[PromptMessageContent] = [TextPromptMessageContent(data=message.query)]
                for file_obj in file_objs:
                    prompt_message_contents.append(file_obj.prompt_message_content)

                return UserPromptMessage(content=prompt_message_contents)
        else:
            return UserPromptMessage(content=message.query)