from typing import Any

from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import SystemMessage
from langchain_core.prompts.chat import MessagesPlaceholder
from langchain_core.tools import BaseTool

from langchain_classic.agents.agent import AgentExecutor
from langchain_classic.agents.openai_functions_agent.agent_token_buffer_memory import (
    AgentTokenBufferMemory,
)
from langchain_classic.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain_classic.base_memory import BaseMemory
from langchain_classic.memory.token_buffer import ConversationTokenBufferMemory


def _get_default_system_message() -> SystemMessage:
    return SystemMessage(
        content=(
            "Do your best to answer the questions. "
            "Feel free to use any tools available to look up "
            "relevant information, only if necessary"
        ),
    )


def create_conversational_retrieval_agent(
    llm: BaseLanguageModel,
    tools: list[BaseTool],
    remember_intermediate_steps: bool = True,  # noqa: FBT001,FBT002
    memory_key: str = "chat_history",
    system_message: SystemMessage | None = None,
    verbose: bool = False,  # noqa: FBT001,FBT002
    max_token_limit: int = 2000,
    **kwargs: Any,
) -> AgentExecutor:
    """A convenience method for creating a conversational retrieval agent.

    Args:
        llm: The language model to use, should be `ChatOpenAI`
        tools: A list of tools the agent has access to
        remember_intermediate_steps: Whether the agent should remember intermediate
            steps or not. Intermediate steps refer to prior action/observation
            pairs from previous questions. The benefit of remembering these is if
            there is relevant information in there, the agent can use it to answer
            follow up questions. The downside is it will take up more tokens.
        memory_key: The name of the memory key in the prompt.
        system_message: The system message to use. By default, a basic one will
            be used.
        verbose: Whether or not the final AgentExecutor should be verbose or not.
        max_token_limit: The max number of tokens to keep around in memory.
        **kwargs: Additional keyword arguments to pass to the `AgentExecutor`.

    Returns:
        An agent executor initialized appropriately
    """
    if remember_intermediate_steps:
        memory: BaseMemory = AgentTokenBufferMemory(
            memory_key=memory_key,
            llm=llm,
            max_token_limit=max_token_limit,
        )
    else:
        memory = ConversationTokenBufferMemory(
            memory_key=memory_key,
            return_messages=True,
            output_key="output",
            llm=llm,
            max_token_limit=max_token_limit,
        )

    _system_message = system_message or _get_default_system_message()
    prompt = OpenAIFunctionsAgent.create_prompt(
        system_message=_system_message,
        extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
    )
    agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
    return AgentExecutor(
        agent=agent,
        tools=tools,
        memory=memory,
        verbose=verbose,
        return_intermediate_steps=remember_intermediate_steps,
        **kwargs,
    )