"""
Agent creation using LangChain 1.x `create_agent` API.

This module exposes a single helper `create_agent_executor` that builds an
AgentExecutor configured with the custom tools defined in :mod:`tools`.
"""

from langchain_ollama import ChatOllama
from langchain.agents import create_agent, AgentExecutor
from langchain.tools import BaseTool
from typing import List

from .tools import search, write_file

# Define the list of tools that the agent can use
TOOLS: List[BaseTool] = [search, write_file]

# LLM configuration – Ollama local model
LLM = ChatOllama(model="llama3.1:latest", temperature=0.0)

# Create the agent executor using the new LangChain 1.x API
# The `create_agent` function returns an AgentExecutor instance
# that can be called like a normal function.

def create_agent_executor() -> AgentExecutor:
    """Instantiate and return an AgentExecutor.

    The executor is configured with:
    * The Ollama Chat model.
    * The custom tools defined in :mod:`tools`.
    * The default agent type "openai-retrieval-qa" is not used – we rely on
      the automatically selected agent type by `create_agent`.
    """
    agent = create_agent(
        llm=LLM,
        tools=TOOLS,
        verbose=True,
    )
    # The returned object is already an AgentExecutor
    return agent

# Expose the executor for external use
agent_executor = create_agent_executor()

# For convenience, a small helper that runs a single prompt

def run_prompt(prompt: str) -> str:
    """Run the prompt through the agent and return the final answer."""
    result = agent_executor.invoke({"input": prompt})
    # The result is a dict with keys: "output" and possibly others
    return result.get("output", "")