{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LLM: NVIDIA Chat Completion with Structured Output\n", "\n", "This notebook demonstrates how to use the `NVIDIAChatClient` from `dapr_agents` to generate structured output using `Pydantic` models.\n", "\n", "We will:\n", "\n", "* Initialize the `NVIDIAChatClient` with the `meta/llama-3.1-8b-instruct` model.\n", "* Define a Pydantic model to structure the response.\n", "* Use the `response_model` parameter to get structured output from the LLM." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install Required Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install dapr-agents python-dotenv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Environment Variables\n", "\n", "Load your API keys or other configuration values 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() # Load environment variables from a `.env` file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Enable Logging" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import logging\n", "\n", "logging.basicConfig(level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Libraries" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from dapr_agents import NVIDIAChatClient\n", "from dapr_agents.types import UserMessage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize LLM Client\n", "\n", "Create an instance of the `NVIDIAChatClient`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:dapr_agents.llm.nvidia.client:Initializing NVIDIA API client...\n" ] } ], "source": [ "llmClient = NVIDIAChatClient(\n", " model=\"meta/llama-3.1-8b-instruct\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the Pydantic Model\n", "\n", "Define a Pydantic model to represent the structured response from the LLM." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from pydantic import BaseModel\n", "\n", "class Dog(BaseModel):\n", " name: str\n", " breed: str\n", " reason: str" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Structured Output (JSON)\n", "\n", "Use the generate method of the `NVIDIAChatClient` with the `response_model` parameter to enforce the structure of the response." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:dapr_agents.llm.utils.request:A response model has been passed to structure the response of the LLM.\n", "INFO:dapr_agents.llm.utils.structure:Structured response enabled.\n", "INFO:dapr_agents.llm.nvidia.chat:Invoking ChatCompletion API.\n", "INFO:httpx:HTTP Request: POST https://integrate.api.nvidia.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", "INFO:dapr_agents.llm.nvidia.chat:Chat completion retrieved successfully.\n", "INFO:dapr_agents.llm.utils.response:Structured output was successfully validated.\n", "INFO:dapr_agents.llm.utils.response:Returning an instance of .\n" ] } ], "source": [ "response = llmClient.generate(\n", " messages=[UserMessage(\"One famous dog in history.\")],\n", " response_model=Dog\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dog(name='Laika', breed='Soviet space dog (mixed breeds)', reason='First animal in space')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.12.1" } }, "nbformat": 4, "nbformat_minor": 2 }