workflowmanager.py

import copy
import os
from typing import List, Optional, Union, Dict

from requests import Session

import autogen
from .datamodel import (
    AgentConfig,
    AgentFlowSpec,
    AgentWorkFlowConfig,
    Message,
    SocketMessage,
)
from .utils import get_skills_prompt, clear_folder, sanitize_model, save_skills_to_file
from datetime import datetime


class AutoGenWorkFlowManager:
    """
    AutoGenWorkFlowManager class to load agents from a provided configuration and run a chat between them
    """

    def __init__(
        self,
        config: AgentWorkFlowConfig,
        history: Optional[List[Message]] = None,
        work_dir: str = None,
        clear_work_dir: bool = True,
        send_message_function: Optional[callable] = None,
        connection_id: Optional[str] = None,
    ) -> None:
        """
        Initializes the AutoGenFlow with agents specified in the config and optional
        message history.

        Args:
            config: The configuration settings for the sender and receiver agents.
            history: An optional list of previous messages to populate the agents' history.

        """
        self.workflow_skills = []
        self.send_message_function = send_message_function
        self.connection_id = connection_id
        self.work_dir = work_dir or "work_dir"
        if clear_work_dir:
            clear_folder(self.work_dir)
        self.config = config
        # given the config, return an AutoGen agent object
        self.sender = self.load(config.sender)
        # given the config, return an AutoGen agent object
        self.receiver = self.load(config.receiver)
        # save all agent skills to skills.py
        save_skills_to_file(self.workflow_skills, self.work_dir)
        self.agent_history = []

        if history:
            self.populate_history(history)

    def process_message(
        self,
        sender: autogen.Agent,
        receiver: autogen.Agent,
        message: Dict,
        request_reply: bool = False,
        silent: bool = False,
        sender_type: str = "agent",
    ) -> None:
        """
        Processes the message and adds it to the agent history.

        Args:

            sender: The sender of the message.
            receiver: The receiver of the message.
            message: The message content.
            request_reply: If set to True, the message will be added to agent history.
            silent: determining verbosity.
            sender_type: The type of the sender of the message.
        """

        message = (
            message
            if isinstance(message, dict)
            else {"content": message, "role": "user"}
        )
        message_payload = {
            "recipient": receiver.name,
            "sender": sender.name,
            "message": message,
            "timestamp": datetime.now().isoformat(),
            "sender_type": sender_type,
            "connection_id": self.connection_id,
            "message_type": "agent_message",
        }

        # if the agent will respond to the message, or the message is sent by a groupchat agent. This avoids adding groupchat broadcast messages to the history (which are sent with request_reply=False), or when agent populated from history
        if request_reply is not False or (sender_type == "groupchat" and not silent):
            print(
                "Request reply",
                request_reply,
                "sender_type",
                sender_type,
                "silent",
                silent,
                "sender",
                sender.name,
            )
            self.agent_history.append(message_payload)  # add to history
            if self.send_message_function:  # send over the message queue
                socket_msg = SocketMessage(
                    type="agent_message",
                    data=message_payload,
                    connection_id=self.connection_id,
                )
                self.send_message_function(socket_msg.dict())

    def _sanitize_history_message(self, message: str) -> str:
        """
        Sanitizes the message e.g. remove references to execution completed

        Args:
            message: The message to be sanitized.

        Returns:
            The sanitized message.
        """
        to_replace = ["execution succeeded", "exitcode"]
        for replace in to_replace:
            message = message.replace(replace, "")
        return message

    def populate_history(self, history: List[Message]) -> None:
        """
        Populates the agent message history from the provided list of messages.

        Args:
            history: A list of messages to populate the agents' history.
        """
        for msg in history:
            if isinstance(msg, dict):
                msg = Message(**msg)
            if msg.role == "user":
                self.sender.send(
                    msg.content,
                    self.receiver,
                    request_reply=False,
                    silent=True,
                )
            elif msg.role == "assistant":
                self.receiver.send(
                    msg.content,
                    self.sender,
                    request_reply=False,
                    silent=True,
                )

    def sanitize_agent_spec(self, agent_spec: AgentFlowSpec) -> AgentFlowSpec:
        """
        Sanitizes the agent spec by setting loading defaults

        Args:
            config: The agent configuration to be sanitized.
            agent_type: The type of the agent.

        Returns:
            The sanitized agent configuration.
        """

        agent_spec.config.is_termination_msg = agent_spec.config.is_termination_msg or (
            lambda x: "TERMINATE" in x.get("content", "").rstrip()[-20:]
        )

        def get_default_system_message(agent_type: str) -> str:
            if agent_type == "assistant":
                return autogen.AssistantAgent.DEFAULT_SYSTEM_MESSAGE
            else:
                return "You are a helpful AI Assistant."

        # sanitize llm_config if present
        if agent_spec.config.llm_config is not False:
            config_list = []
            for llm in agent_spec.config.llm_config.config_list:
                # check if api_key is present either in llm or env variable
                if "api_key" not in llm and "OPENAI_API_KEY" not in os.environ:
                    error_message = f"api_key is not present in llm_config or OPENAI_API_KEY env variable for agent ** {agent_spec.config.name}**. Update your workflow to provide an api_key to use the LLM."
                    raise ValueError(error_message)

                # only add key if value is not None
                sanitized_llm = sanitize_model(llm)
                config_list.append(sanitized_llm)
            agent_spec.config.llm_config.config_list = config_list

        if agent_spec.config.code_execution_config is not False:
            code_execution_config = agent_spec.config.code_execution_config or {}
            code_execution_config["work_dir"] = self.work_dir
            # tbd check if docker is installed
            code_execution_config["use_docker"] = False
            agent_spec.config.code_execution_config = code_execution_config

        if agent_spec.skills:
            # add skills to workflow_skills
            print("processing skills -- ", len(agent_spec.skills))
            self.workflow_skills.extend(agent_spec.skills)
            # get skill prompt
            skills_prompt = get_skills_prompt(agent_spec.skills, self.work_dir)
            if agent_spec.config.system_message:
                agent_spec.config.system_message = (
                    agent_spec.config.system_message + "\n\n" + skills_prompt
                )
            else:
                agent_spec.config.system_message = (
                    get_default_system_message(agent_spec.type) + "\n\n" + skills_prompt
                )

        return agent_spec

    def load(self, agent_spec: AgentFlowSpec) -> autogen.Agent:
        """
        Loads an agent based on the provided agent specification.

        Args:
            agent_spec: The specification of the agent to be loaded.

        Returns:
            An instance of the loaded agent.
        """
        agent_spec = self.sanitize_agent_spec(agent_spec)
        if agent_spec.type == "groupchat":
            print(
                "loading groupchat ....",
                "# agents : ",
                len(agent_spec.groupchat_config.agents),
            )
            agents = []
            for group_agent_spec in agent_spec.groupchat_config.agents:
                print("processing group agents ....")
                agent = self.load(group_agent_spec)
                agents.append(agent)

            group_chat_config = agent_spec.groupchat_config.dict()
            group_chat_config["agents"] = agents
            groupchat = autogen.GroupChat(**group_chat_config)
            agent = ExtendedGroupChatManager(
                groupchat=groupchat,
                **agent_spec.config.dict(),
                message_processor=self.process_message,
            )
            return agent

        else:
            agent = self.load_agent_config(agent_spec.config, agent_spec.type)
            return agent

    def load_agent_config(
        self, agent_config: AgentConfig, agent_type: str
    ) -> autogen.Agent:
        """
        Loads an agent based on the provided agent configuration.

        Args:
            agent_config: The configuration of the agent to be loaded.
            agent_type: The type of the agent to be loaded.

        Returns:
            An instance of the loaded agent.
        """
        if agent_type == "assistant":
            agent = ExtendedConversableAgent(
                **agent_config.dict(), message_processor=self.process_message
            )
        elif agent_type == "userproxy":
            agent = ExtendedConversableAgent(
                **agent_config.dict(), message_processor=self.process_message
            )
        else:
            raise ValueError(f"Unknown agent type: {agent_type}")

        return agent

    def run(self, message: str, clear_history: bool = False) -> None:
        """
        Initiates a chat between the sender and receiver agents with an initial message
        and an option to clear the history.

        Args:
            message: The initial message to start the chat.
            clear_history: If set to True, clears the chat history before initiating.
        """
        self.sender.initiate_chat(
            self.receiver,
            message=message,
            clear_history=clear_history,
        )


class ExtendedConversableAgent(autogen.ConversableAgent):
    def __init__(self, message_processor=None, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.message_processor = message_processor

    def receive(
        self,
        message: Union[Dict, str],
        sender: autogen.Agent,
        request_reply: Optional[bool] = None,
        silent: Optional[bool] = False,
    ):
        if self.message_processor:
            self.message_processor(
                sender, self, message, request_reply, silent, sender_type="agent"
            )
        super().receive(message, sender, request_reply, silent)


class ExtendedGroupChatManager(autogen.GroupChatManager):
    def __init__(self, message_processor=None, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.message_processor = message_processor

    def receive(
        self,
        message: Union[Dict, str],
        sender: autogen.Agent,
        request_reply: Optional[bool] = None,
        silent: Optional[bool] = False,
    ):
        if self.message_processor:
            self.message_processor(
                sender, self, message, request_reply, silent, sender_type="groupchat"
            )
        super().receive(message, sender, request_reply, silent)