{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LLM: OpenAI Chat Endpoint Basic Examples\n",
    "\n",
    "This notebook demonstrates how to use the `OpenAIChatClient` in `dapr-agents` for basic tasks with the OpenAI Chat API. We will explore:\n",
    "\n",
    "* Initializing the OpenAI Chat client.\n",
    "* Generating responses to simple prompts.\n",
    "* Using a `.prompty` file to provide context/history for enhanced generation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install Required Libraries\n",
    "Before starting, ensure the required libraries are installed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install dapr-agents python-dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Environment Variables\n",
    "\n",
    "Load API keys or other configuration values from your `.env` file using `dotenv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dotenv import load_dotenv\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import OpenAIChatClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dapr_agents import OpenAIChatClient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Chat Completion\n",
    "\n",
    "Initialize the `OpenAIChatClient` and generate a response to a simple prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the client\n",
    "llm = OpenAIChatClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatCompletion(choices=[Choice(finish_reason='stop', index=0, message=MessageContent(content='One famous dog is Lassie, the Rough Collie from the television series and films that became iconic for her intelligence and heroic adventures.', role='assistant'), logprobs=None)], created=1741085405, id='chatcmpl-B7K8brL19kn1KgDTG9on7n7ICnt3P', model='gpt-4o-2024-08-06', object='chat.completion', usage={'completion_tokens': 28, 'prompt_tokens': 12, 'total_tokens': 40, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a response\n",
    "response = llm.generate('Name a famous dog!')\n",
    "\n",
    "# Display the response\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'content': 'One famous dog is Lassie, the Rough Collie from the television series and films that became iconic for her intelligence and heroic adventures.', 'role': 'assistant'}\n"
     ]
    }
   ],
   "source": [
    "print(response.get_message())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a Prompty File for Context\n",
    "\n",
    "Use a `.prompty` file to provide context for chat history or additional instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = OpenAIChatClient.from_prompty('basic-openai-chat-history.prompty')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptTemplate(input_variables=['chat_history', 'question'], pre_filled_variables={}, messages=[SystemMessage(content='You are an AI assistant who helps people find information.\\nAs the assistant, you answer questions briefly, succinctly, \\nand in a personable manner using markdown and even add some personal flair with appropriate emojis.\\n\\n{% for item in chat_history %}\\n{{item.role}}:\\n{{item.content}}\\n{% endfor %}', role='system'), UserMessage(content='{{question}}', role='user')], template_format='jinja2')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm.prompt_template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatCompletion(choices=[Choice(finish_reason='stop', index=0, message=MessageContent(content=\"Hey there! I'm your friendly AI assistant. You can call me whatever you'd like, but I don't have a specific name. 😊 How can I help you today?\", role='assistant'), logprobs=None)], created=1741085407, id='chatcmpl-B7K8dI84xY2hjaEspDtJL5EICbSLh', model='gpt-4o-2024-08-06', object='chat.completion', usage={'completion_tokens': 34, 'prompt_tokens': 57, 'total_tokens': 91, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm.generate(input_data={\"question\":\"What is your name?\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chat Completion with Messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dapr_agents.types import UserMessage\n",
    "\n",
    "# Initialize the client\n",
    "llm = OpenAIChatClient()\n",
    "\n",
    "# Generate a response using structured messages\n",
    "response = llm.generate(messages=[UserMessage(\"hello\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'content': 'Hello! How can I assist you today?', 'role': 'assistant'}\n"
     ]
    }
   ],
   "source": [
    "# Display the structured response\n",
    "print(response.get_message())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm.prompt_template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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