mirror of
https://github.com/meta-llama/llama-stack.git
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283 lines
9 KiB
Text
283 lines
9 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c1e7571c",
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"metadata": {},
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"source": [
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"# Llama Stack Inference Guide\n",
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"\n",
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"This document provides instructions on how to use Llama Stack's `chat_completion` function for generating text using the `Llama3.2-11B-Vision-Instruct` model. \n",
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"\n",
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"Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).\n",
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"\n",
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"\n",
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"### Table of Contents\n",
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"1. [Quickstart](#quickstart)\n",
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"2. [Building Effective Prompts](#building-effective-prompts)\n",
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"3. [Conversation Loop](#conversation-loop)\n",
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"4. [Conversation History](#conversation-history)\n",
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"5. [Streaming Responses](#streaming-responses)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "414301dc",
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"metadata": {},
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"source": [
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"## Quickstart\n",
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"\n",
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"This section walks through each step to set up and make a simple text generation request.\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "25b97dfe",
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"metadata": {},
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"source": [
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"### 0. Configuration\n",
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"Set up your connection parameters:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "38a39e44",
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"metadata": {},
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"outputs": [],
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"source": [
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"HOST = \"localhost\" # Replace with your host\n",
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"PORT = 5001 # Replace with your port"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d1d097ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"### 1. Set Up the Client\n",
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"\n",
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"Begin by importing the necessary components from Llama Stack’s client library:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7a573752",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_stack_client import LlamaStackClient\n",
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"from llama_stack_client.types import SystemMessage, UserMessage\n",
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"\n",
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"client = LlamaStackClient(base_url='http://{HOST}:{PORT}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "86366383",
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"metadata": {},
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"source": [
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"### 2. Create a Chat Completion Request\n",
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"\n",
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"Use the `chat_completion` function to define the conversation context. Each message you include should have a specific role and content:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "77c29dba",
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"metadata": {},
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"outputs": [],
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"source": [
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"response = client.inference.chat_completion(\n",
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" messages=[\n",
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" SystemMessage(content='You are a friendly assistant.', role='system'),\n",
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" UserMessage(content='Write a two-sentence poem about llama.', role='user')\n",
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" ],\n",
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" model='Llama3.2-11B-Vision-Instruct',\n",
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")\n",
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"\n",
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"print(response.completion_message.content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e5f16949",
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"metadata": {},
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"source": [
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"## Building Effective Prompts\n",
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"\n",
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"Effective prompt creation (often called 'prompt engineering') is essential for quality responses. Here are best practices for structuring your prompts to get the most out of the Llama Stack model:\n",
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"\n",
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"1. **System Messages**: Use `SystemMessage` to set the model's behavior. This is similar to providing top-level instructions for tone, format, or specific behavior.\n",
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" - **Example**: `SystemMessage(content='You are a friendly assistant that explains complex topics simply.')`\n",
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"2. **User Messages**: Define the task or question you want to ask the model with a `UserMessage`. The clearer and more direct you are, the better the response.\n",
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" - **Example**: `UserMessage(content='Explain recursion in programming in simple terms.')`\n",
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"\n",
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"### Sample Prompt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5c6812da",
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"metadata": {},
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"outputs": [],
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"source": [
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"response = client.inference.chat_completion(\n",
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" messages=[\n",
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" SystemMessage(content='You are shakespeare.', role='system'),\n",
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" UserMessage(content='Write a two-sentence poem about llama.', role='user')\n",
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" ],\n",
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" model='Llama3.2-11B-Vision-Instruct',\n",
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")\n",
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"\n",
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"print(response.completion_message.content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c8690ef0",
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"metadata": {},
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"source": [
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"## Conversation Loop\n",
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"\n",
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"To create a continuous conversation loop, where users can input multiple messages in a session, use the following structure. This example runs an asynchronous loop, ending when the user types 'exit,' 'quit,' or 'bye.'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "02211625",
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"metadata": {},
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"outputs": [],
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"source": [
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"import asyncio\n",
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"from llama_stack_client import LlamaStackClient\n",
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"from llama_stack_client.types import UserMessage\n",
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"from termcolor import cprint\n",
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"\n",
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"client = LlamaStackClient(base_url='http://{HOST}:{PORT}')\n",
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"\n",
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"async def chat_loop():\n",
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" while True:\n",
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" user_input = input('User> ')\n",
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" if user_input.lower() in ['exit', 'quit', 'bye']:\n",
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" cprint('Ending conversation. Goodbye!', 'yellow')\n",
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" break\n",
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"\n",
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" message = UserMessage(content=user_input, role='user')\n",
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" response = client.inference.chat_completion(\n",
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" messages=[message],\n",
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" model='Llama3.2-11B-Vision-Instruct',\n",
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" )\n",
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" cprint(f'> Response: {response.completion_message.content}', 'cyan')\n",
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"\n",
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"asyncio.run(chat_loop())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8cf0d555",
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"metadata": {},
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"source": [
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"## Conversation History\n",
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"\n",
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"Maintaining a conversation history allows the model to retain context from previous interactions. Use a list to accumulate messages, enabling continuity throughout the chat session."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9496f75c",
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"metadata": {},
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"outputs": [],
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"source": [
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"async def chat_loop():\n",
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" conversation_history = []\n",
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" while True:\n",
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" user_input = input('User> ')\n",
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" if user_input.lower() in ['exit', 'quit', 'bye']:\n",
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" cprint('Ending conversation. Goodbye!', 'yellow')\n",
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" break\n",
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"\n",
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" user_message = UserMessage(content=user_input, role='user')\n",
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" conversation_history.append(user_message)\n",
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"\n",
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" response = client.inference.chat_completion(\n",
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" messages=conversation_history,\n",
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" model='Llama3.2-11B-Vision-Instruct',\n",
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" )\n",
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" cprint(f'> Response: {response.completion_message.content}', 'cyan')\n",
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"\n",
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" assistant_message = UserMessage(content=response.completion_message.content, role='user')\n",
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" conversation_history.append(assistant_message)\n",
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"\n",
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"asyncio.run(chat_loop())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "03fcf5e0",
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"metadata": {},
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"source": [
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"## Streaming Responses\n",
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"\n",
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"Llama Stack offers a `stream` parameter in the `chat_completion` function, which allows partial responses to be returned progressively as they are generated. This can enhance user experience by providing immediate feedback without waiting for the entire response to be processed.\n",
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"\n",
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"### Example: Streaming Responses"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d119026e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import asyncio\n",
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"from llama_stack_client import LlamaStackClient\n",
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"from llama_stack_client.lib.inference.event_logger import EventLogger\n",
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"from llama_stack_client.types import UserMessage\n",
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"from termcolor import cprint\n",
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"\n",
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"async def run_main(stream: bool = True):\n",
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" client = LlamaStackClient(base_url='http://{HOST}:{PORT}')\n",
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"\n",
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" message = UserMessage(\n",
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" content='hello world, write me a 2 sentence poem about the moon', role='user'\n",
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" )\n",
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" print(f'User>{message.content}', 'green')\n",
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"\n",
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" response = client.inference.chat_completion(\n",
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" messages=[message],\n",
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" model='Llama3.2-11B-Vision-Instruct',\n",
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" stream=stream,\n",
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" )\n",
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"\n",
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" if not stream:\n",
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" cprint(f'> Response: {response}', 'cyan')\n",
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" else:\n",
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" async for log in EventLogger().log(response):\n",
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" log.print()\n",
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"\n",
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" models_response = client.models.list()\n",
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" print(models_response)\n",
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"\n",
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"if __name__ == '__main__':\n",
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" asyncio.run(run_main())"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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