TypeScript SDK Overview

// 1. Set up tracing with OTEL
import { register } from '@arizeai/phoenix-otel';

const provider = register({
  projectName: 'my-llm-app',
  batch: true
});

// 2. Your LLM application runs and generates traces automatically
import OpenAI from 'openai';

const client = new OpenAI();
const response = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [{ role: 'user', content: 'Hello!' }]
});

// 3. Use the client to query traces
import { createClient } from '@arizeai/phoenix-client';

const phoenixClient = createClient();
const projects = await phoenixClient.GET('/v1/projects');

// 4. Run evaluations on your data
import { createHallucinationEvaluator } from '@arizeai/phoenix-evals';

const evaluator = createHallucinationEvaluator({
  model: 'gpt-4o'
});

const result = await evaluator({
  output: response.choices[0].message.content,
  context: 'System context'
});

import type { PhoenixClient } from '@arizeai/phoenix-client';
import type { RegisterParams } from '@arizeai/phoenix-otel';
import type { Evaluator } from '@arizeai/phoenix-evals';

// Fully typed client
const client = createClient();
const response = await client.GET('/v1/projects');
// ^? response is fully typed based on OpenAPI spec

// Type-safe evaluator configuration
const evaluator: Evaluator = createHallucinationEvaluator({
  model: 'gpt-4o',
  temperature: 0.0
});