@arizeai/phoenix-client (original) (raw)

@arizeai/phoenix-client

This package provides a TypeSript client for the Arize Phoenix API. It is still under active development and is subject to change.

Installation

or yarn, pnpm, bun, etc...

npm install @arizeai/phoenix-client

Configuration

The client will automatically read environment variables from your environment, if available.

The following environment variables are used:

• PHOENIX_HOST - The base URL of the Phoenix API.
• PHOENIX_API_KEY - The API key to use for authentication.
• PHOENIX_CLIENT_HEADERS - Custom headers to add to all requests. A JSON stringified object.

PHOENIX_HOST='http://localhost:12345' PHOENIX_API_KEY='xxxxxx' pnpx tsx examples/list_datasets.ts

emits the following request:

GET http://localhost:12345/v1/datasets

headers: {

"Authorization": "Bearer xxxxxx",

}

Alternatively, you can pass configuration options to the client directly, and they will be prioritized over environment variables and default values.

const phoenix = createClient({ options: { baseUrl: "http://localhost:6006", headers: { Authorization: "Bearer xxxxxx", }, }, });

Prompts

@arizeai/phoenix-client provides a prompts export that exposes utilities for working with prompts for LLMs.

Creating a Prompt and push it to Phoenix

The createPrompt function can be used to create a prompt in Phoenix for version control and reuse.

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

const version = createPrompt({ name: "my-prompt", description: "test-description", version: promptVersion({ description: "version description here", modelProvider: "OPENAI", modelName: "gpt-3.5-turbo", template: [ { role: "user", content: "{{ question }}", }, ], invocationParameters: { temperature: 0.8, }, }), });

Prompts that are pushed to Phoenix are versioned and can be tagged.

Pulling a Prompt from Phoenix

The getPrompt function can be used to pull a prompt from Phoenix based on some Prompt Identifier and returns it in the Phoenix SDK Prompt type.

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

const prompt = await getPrompt({ name: "my-prompt" }); // ^ you now have a strongly-typed prompt object, in the Phoenix SDK Prompt type

const promptByTag = await getPrompt({ tag: "production", name: "my-prompt" }); // ^ you can optionally specify a tag to filter by

const promptByVersionId = await getPrompt({ versionId: "1234567890", }); // ^ you can optionally specify a prompt version Id to filter by

Using a Phoenix Prompt with an LLM Provider SDK

The toSDK helper function can be used to convert a Phoenix Prompt to the format expected by an LLM provider SDK. You can then use the LLM provider SDK as normal, with your prompt.

If your Prompt is saved in Phoenix as openai, you can use the toSDK function to convert the prompt to the format expected by OpenAI, or even Anthropic and Vercel AI SDK. We will do a best effort conversion to your LLM provider SDK of choice.

The following LLM provider SDKs are supported:

• Vercel AI SDK: ai ai
• OpenAI: openai openai
• Anthropic: anthropic @anthropic-ai/sdk

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

const prompt = await getPrompt({ name: "my-prompt" }); const promptAsAI = toSDK({ sdk: "ai", // ^ the SDK you want to convert the prompt to, supported SDKs are listed above variables: { "my-variable": "my-value", }, // ^ you can format the prompt with variables, if the prompt has any variables in its template // the format (Mustache, F-string, etc.) is specified in the Prompt itself prompt, }); // ^ promptAsAI is now in the format expected by the Vercel AI SDK generateText function

const response = await generateText({ model: openai(prompt.model_name), // ^ the model adapter provided by the Vercel AI SDK can be swapped out for any other model // adapter supported by the Vercel AI SDK. Take care to use the correct model name for the // LLM provider you are using. ...promptAsAI, });

REST Endpoints

The client provides a REST API for all endpoints defined in the Phoenix OpenAPI spec.

Endpoints are accessible via strongly-typed string literals and TypeScript auto-completion inside of the client object.

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

const phoenix = createClient();

// Get all datasets const datasets = await phoenix.GET("/v1/datasets");

// Get specific prompt const prompt = await phoenix.GET("/v1/prompts/{prompt_identifier}/latest", { params: { path: { prompt_identifier: "my-prompt", }, }, });

A comprehensive overview of the available endpoints and their parameters is available in the OpenAPI viewer within Phoenix, or in the Phoenix OpenAPI spec.

Datasets

The @arizeai/phoenix-client package allows you to create and manage datasets, which are collections of examples used for experiments and evaluation.

Creating a Dataset

You can create a dataset by providing a name, description, and an array of examples (each with input, output, and optional metadata).

import { createDataset } from "@arizeai/phoenix-client/datasets";

const { datasetId } = await createDataset({ name: "questions", description: "a simple dataset of questions", examples: [ { input: { question: "What is the capital of France" }, output: { answer: "Paris" }, metadata: {}, }, { input: { question: "What is the capital of the USA" }, output: { answer: "Washington D.C." }, metadata: {}, }, ], }); // You can now use datasetId to run experiments or add more examples

Experiments

The @arizeai/phoenix-client package provides an experiments API for running and evaluating tasks on datasets. This is useful for benchmarking models, evaluating outputs, and tracking experiment results in Phoenix.

Running an Experiment

To run an experiment, you typically:

1. Create a dataset (or use an existing one)
2. Define a task function to run on each example
3. Define one or more evaluators to score or label the outputs
4. Run the experiment and inspect the results

Below is a complete example:

import { createDataset } from "@arizeai/phoenix-client/datasets"; import { asEvaluator, runExperiment, } from "@arizeai/phoenix-client/experiments";

// 1. Create a dataset const { datasetId } = await createDataset({ name: "names-dataset", description: "a simple dataset of names", examples: [ { input: { name: "John" }, output: { text: "Hello, John!" }, metadata: {}, }, { input: { name: "Jane" }, output: { text: "Hello, Jane!" }, metadata: {}, }, ], });

// 2. Define a task to run on each example const task = async (example) => hello ${example.input.name};

// 3. Define evaluators const evaluators = [ asEvaluator({ name: "matches", kind: "CODE", evaluate: async ({ output, expected }) => { const matches = output === expected?.text; return { label: matches ? "matches" : "does not match", score: matches ? 1 : 0, explanation: matches ? "output matches expected" : "output does not match expected", metadata: {}, }; }, }), asEvaluator({ name: "contains-hello", kind: "CODE", evaluate: async ({ output }) => { const matches = typeof output === "string" && output.includes("hello"); return { label: matches ? "contains hello" : "does not contain hello", score: matches ? 1 : 0, explanation: matches ? "output contains hello" : "output does not contain hello", metadata: {}, }; }, }), ];

// 4. Run the experiment const experiment = await runExperiment({ dataset: { datasetId }, task, evaluators, });

Hint: Tasks and evaluators are instrumented using OpenTelemetry. You can view detailed traces of experiment runs and evaluations directly in the Phoenix UI for debugging and performance analysis.

Examples

To run examples, install dependencies using pnpm and run:

pnpm install pnpx tsx examples/list_datasets.ts

change the file name to run other examples

Compatibility

This package utilizes openapi-ts to generate the types from the Phoenix OpenAPI spec.

Because of this, this package only works with the arize-phonix server 8.0.0 and above.

Compatibility Table: