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standardized port and also included pre-req for all notebooks

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Justin Lee 2024-11-05 16:38:46 -08:00
parent d0baf24999
commit b556cd91fd
8 changed files with 177 additions and 42 deletions
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30
docs/zero_to_hero_guide/01_Prompt_Engineering101.ipynb
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@ -5,13 +5,13 @@
"id": "cd96f85a",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/Prompt_Engineering_with_Llama_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"# Prompt Engineering with Llama Stack\n",
"\n",
"Prompt engineering is using natural language to produce a desired response from a large language model (LLM).\n",
"\n",
"This interactive guide covers prompt engineering & best practices with Llama 3.1 and Llama Stack"
"This interactive guide covers prompt engineering & best practices with Llama 3.1 and Llama Stack.\n",
"\n",
"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)."
]
},
{
@ -30,6 +30,26 @@
"### Implementation"
]
},
{
"cell_type": "markdown",
"id": "e065af43",
"metadata": {},
"source": [
"### 0. Configuration\n",
"Set up your connection parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "df35d1e2",
"metadata": {},
"outputs": [],
"source": [
"HOST = \"localhost\" # Replace with your host\n",
"PORT = 5001 # Replace with your port"
]
},
{
"cell_type": "markdown",
"id": "a7a25a7e",
@ -49,7 +69,7 @@
"source": [
"from llama_stack_client import LlamaStackClient\n",
"\n",
"client = LlamaStackClient(base_url='http://localhost:5000')"
"client = LlamaStackClient(base_url='http://{HOST}:{PORT}')"
]
},
{
@ -180,7 +200,7 @@
"from llama_stack_client.types import CompletionMessage, UserMessage\n",
"from termcolor import cprint\n",
"\n",
"client = LlamaStackClient(base_url='http://localhost:5000')\n",
"client = LlamaStackClient(base_url='http://{HOST}:{PORT}')\n",
"\n",
"response = client.inference.chat_completion(\n",
" messages=[\n",