| import OpenAI from 'openai'; // We're using the v4 SDK | |
| const openai = new OpenAI({ | |
| apiKey: 'OPENAI_API_KEY', // defaults to process.env["OPENAI_API_KEY"], | |
| baseURL: "https://api.portkey.ai", | |
| defaultHeaders: { | |
| "x-portkey-mode": "proxy openai", | |
| "x-portkey-api-key": "PORTKEY_API_KEY", | |
| "x-portkey-config": "CONFIG SLUG" // Optional to implement fallbacks, etc through a simple UI | |
| } |
| import portkey | |
| from portkey import Config, LLMOptions | |
| portkey.api_key = "<YOUR PORTKEY API KEY>" | |
| llm = LLMOptions(provider="anthropic", virtual_key="<YOUR VIRTUAL KEY>") | |
| portkey.config = Config(llms=[llm], mode="single") | |
| response = portkey.ChatCompletions.create( | |
| model="claude-instant-1.2", | |
| messages=[{"role":"user", "content": "What is the meaning of life, universe & everything?"}], |
Portkey.ai is a full-stack LLMOps platform to monitor & log all anyscale requests for latency, costs and accuracy. It also provides capabilities to use multiple models from different providers and setup load balancer, fallbacks and a/b tests between them.
To use Portkey with Anyscale endpoints, use the python SDK.
!pip install portkey-ai
| import requests | |
| import fitz # PyMuPDF | |
| from io import BytesIO | |
| import re | |
| def download_pdf(url): | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| return BytesIO(response.content) | |
| else: |
| %matplotlib inline | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from multielo import MultiElo | |
| # Initialize the Elo ratings and history | |
| recipes = ['Recipe 1', 'Recipe 2', 'Recipe 3'] | |
| elo_ratings = np.array([1500, 1500, 1500]) | |
| elo_history = [np.array([1500]), np.array([1500]), np.array([1500])] |