Get Started: Prompt Playground

You are a summarization tool. Summarize the provided paragraph.

You are a summarization tool. Summarize the provided paragraph in 2 sentences or less.

You are a summarization tool. Summarize the provided paragraph. Make sure not to leave out any key points.

In software engineering, more specifically in distributed computing, observability is the ability to collect data about programs' execution, modules' internal states, and the communication among components.[1][2] To improve observability, software engineers use a wide range of logging and tracing techniques to gather telemetry information, and tools to analyze and use it. Observability is foundational to site reliability engineering, as it is the first step in triaging a service outage. One of the goals of observability is to minimize the amount of prior knowledge needed to debug an issue.

pip install arize-phoenix-client openai

from phoenix.client import Client
from phoenix.client.types import PromptVersion

content = """
You're an expert educator in {{ topic }}. Summarize the following article
in a few concise bullet points that are easy for beginners to understand.

{{ article }}
"""

prompt_name = "article-bullet-summarizer"
prompt = Client().prompts.create(
    name=prompt_name,
    prompt_description="Summarize an article in a few bullet points",
    version=PromptVersion(
        [{"role": "user", "content": content}],
        model_name="gpt-4o-mini",
    ),
)

from phoenix.client import Client

client = Client()

# Pulling a prompt by name
prompt_name = "article-bullet-summarizer"
client.prompts.get(prompt_identifier=prompt_name)

# Pulling a prompt by version id
# The version ID can be found in the versions tab in the UI
prompt = client.prompts.get(prompt_version_id="UHJvbXB0VmVyc2lvbjoy")

# Pulling a prompt by tag
# Since tags don't uniquely identify a prompt version 
#  it must be paired with the prompt identifier (e.g. name)
prompt = client.prompts.get(prompt_identifier=prompt_name, tag="staging")

from openai import OpenAI

prompt_vars = {"topic": "Sports", "article": "Surrey have signed Australia all-rounder Moises Henriques for this summer's NatWest T20 Blast. Henriques will join Surrey immediately after the Indian Premier League season concludes at the end of next month and will be with them throughout their Blast campaign and also as overseas cover for Kumar Sangakkara - depending on the veteran Sri Lanka batsman's Test commitments in the second half of the summer. Australian all-rounder Moises Henriques has signed a deal to play in the T20 Blast for Surrey . Henriques, pictured in the Big Bash (left) and in ODI action for Australia (right), will join after the IPL . Twenty-eight-year-old Henriques, capped by his country in all formats but not selected for the forthcoming Ashes, said: 'I'm really looking forward to playing for Surrey this season. It's a club with a proud history and an exciting squad, and I hope to play my part in achieving success this summer. 'I've seen some of the names that are coming to England to be involved in the NatWest T20 Blast this summer, so am looking forward to testing myself against some of the best players in the world.' Surrey director of cricket Alec Stewart added: 'Moises is a fine all-round cricketer and will add great depth to our squad.'"}
formatted_prompt = prompt.format(variables=prompt_vars)

# Make a request with your Prompt
oai_client = OpenAI()
resp = oai_client.chat.completions.create(**formatted_prompt)

content = """
You're an expert educator in {{ topic }}. Summarize the following article
in a few concise bullet points that are easy for beginners to understand.

Be sure not to miss any key points.

{{ article }}
"""

prompt_name = "article-bullet-summarizer"
prompt = Client().prompts.create(
    name=prompt_name,
    prompt_description="Summarize an article in a few bullet points",
    version=PromptVersion(
        [{"role": "user", "content": content}],
        model_name="gpt-4o-mini",
    ),
)

npm install @arizeai/phoenix-client

import { createClient } from "@arizeai/phoenix-client";
import { createPrompt, promptVersion } from "@arizeai/phoenix-client/prompts";

// Create a Phoenix client 
// (optional, the createPrompt function will create one if not provided)
const client = createClient({
  options: {
    baseUrl: "http://localhost:6006", // Change to your Phoenix server URL
    // If your Phoenix instance requires authentication:
    // headers: {
    //   Authorization: "bearer YOUR_API_KEY",
    // }
  }
});

// Define a simple summarization prompt
const summarizationPrompt = await createPrompt({
  client,
  name: "article-summarizer",
  description: "Summarizes an article into concise bullet points",
  version: promptVersion({
    description: "Initial version",
    templateFormat: "MUSTACHE",
    modelProvider: "OPENAI", // Could also be ANTHROPIC, GEMINI, etc.
    modelName: "gpt-3.5-turbo",
    template: [
      {
        role: "system",
        content: "You are an expert summarizer. Create clear, concise bullet points highlighting the key information."
      },
      {
        role: "user",
        content: "Please summarize the following {{topic}} article:\n\n{{article}}"
      }
    ],
  })
});

console.dir(summarizationPrompt);

import { getPrompt } from "@arizeai/phoenix-client/prompts";

// Get by name (latest version)
const latestPrompt = await getPrompt({
  prompt: {
    name: "article-summarizer",
  }
});

// Get by specific version ID
const specificVersionPrompt = await getPrompt({ 
  prompt: {
    versionId: "abcd1234",
  },
});

// Get by tag (e.g., "production", "staging", "development")
const productionPrompt = await getPrompt({ 
  prompt: {
    name: "article-summarizer", 
    tag: "production", 
  }
});

import { getPrompt, toSDK } from "@arizeai/phoenix-client/prompts";
import OpenAI from "openai";

// Initialize your OpenAI client
const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

// Get your prompt
const prompt = await getPrompt({
  prompt: {
    name: "article-summarizer",
  },
});

// Make sure the prompt was properly fetched
if (!prompt) {
  throw new Error("Prompt not found");
}

// Transform the prompt to OpenAI format with variable values
const openaiParameters = toSDK({
  sdk: "openai", 
  prompt,
  variables: {
    topic: "technology",
    article:
      "Artificial intelligence has seen rapid advancement in recent years. Large language models like GPT-4 can now generate human-like text, code, and even create images from descriptions. This technology is being integrated into many industries, from healthcare to finance, transforming how businesses operate and people work.",
  },
});

// Make sure the prompt was successfully converted to parameters
if (!openaiParameters) {
  throw new Error("OpenAI parameters not found");
}

// Use the transformed parameters with OpenAI
const response = await openai.chat.completions.create({
  ...openaiParameters,
  // You can override any parameters here
  model: "gpt-4o-mini", // Override the model if needed
  stream: false,
});


console.log("Summary:", response.choices[0].message.content);