{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the modules of smolagent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Optional\n",
    "\n",
    "from smolagents import HfApiModel, LiteLLMModel, TransformersModel, tool\n",
    "from smolagents.agents import CodeAgent, ToolCallingAgent\n",
    "from smolagents import DuckDuckGoSearchTool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the LLM model from ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LiteLLMModel(\n",
    "        model_id=\"ollama_chat/llama3.1\",\n",
    "        api_base=\"http://192.168.0.117:11434\",  # replace with remote open-ai compatible server if necessary\n",
    "        #api_key=\"your-api-key\",  # replace with API key if necessary\n",
    "        #num_ctx=8192,  # ollama default is 2048 which will often fail horribly. 8192 works for easy tasks, more is better. Check https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator to calculate how much VRAM this will need for the selected model.\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign the tools and model to agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = ToolCallingAgent(tools=[DuckDuckGoSearchTool()], model=model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create task for agent (prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prompt = \"What is the capital of France?\"\n",
    "# promt = \"What is the capital of Germany?\"\n",
    "prompt = \"who is the current world champion in football?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.run(prompt)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "smolagent",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}