Langtail’s TypeScript SDK wraps OpenAI’s API client so that it can be used seamlessly with Langtail. You can store the entire prompt in Langtail and reference it by prompt slug and environment. By routing requests through Langtail, you can get logs, metrics, and many more beneifts. For more, check out the GitHub repository.

Installation

Use NPM or your package manager of choice to install the langtail package:

npm i langtail

yarn add langtail

pnpm add langtail

bun add langtail

Usage

Here are the different ways you can use the SDK.

1. Invoke a prompt that’s deployed on Langtail (recommended)

To invoke a deployed prompt, you can use lt.prompts.invoke like this:

Invoke a prompt

const deployedPromptCompletion = await lt.prompts.invoke({
  prompt: '<PROMPT_SLUG>', // required
  environment: 'staging',
  variables: {
    about: 'cowboy Bebop',
  },
}) // results in an openAI ChatCompletion

If you call a prompt that isn’t deployed yet, you will get an error thrown an error:

Error: Failed to fetch prompt: 404 {"error":"Prompt deployment not found"}

2. Invoke a prompt that’s stored in code

If you’re storing your prompt in your codebase and want to give Langtail a try, use this method. You can use lt.chat.completions.create as a light wrapper over the OpenAI API that still sends logs and metrics to Langtail. For more, see the example in this doc.

3. Fetch prompt from Langtail and invoke directly (proxyless)

If you’re storing your prompt in Langtail but want to invoke it directly from your code without using Langtail as a proxy, use this method. You can call LangtailPrompts.get to retrieve the contents of the prompt:

Fetch prompt from Langtail

import { LangtailPrompts } from 'langtail'

const lt = new LangtailPrompts({
  apiKey: '<LANGTAIL_API_KEY>',
})

const prompt = await lt.get({
  prompt: '<PROMPT_SLUG>',
  environment: 'preview',
  version: '<PROMPT_VERSION>', // optional
})

The response will return something like this:

Prompt response

{
  "chatInput": {
    "optionalExtra": ""
  },
  "state": {
    "args": {
      "frequency_penalty": 0,
      "jsonmode": false,
      "max_tokens": 800,
      "model": "gpt-3.5-turbo",
      "presence_penalty": 0,
      "stop": [],
      "stream": true,
      "temperature": 0.5,
      "top_p": 1
    },
    "functions": [],
    "template": [
      {
        "content": "I want you to tell me a joke. Topic of the joke: {{topic}}",
        "role": "system"
      }
    ],
    "tools": [],
    "type": "chat"
  }
}

Now you can build the final output:

Build the final output

const openAiBody = lt.build(prompt, {
  stream: true,
  variables: {
    topic: 'iron man',
  },
})

Which returns this object:

OpenAI body

{
  "frequency_penalty": 0,
  "max_tokens": 800,
  "messages": [
    {
      "content": "I want you to tell me a joke. Topic of the joke: iron man",
      "role": "system",
    },
  ],
  "model": "gpt-3.5-turbo",
  "presence_penalty": 0,
  "temperature": 0.5,
  "top_p": 1,
}

Finally, you can directly call the OpenAI SDK with the returned object:

Call OpenAI SDK

import OpenAI from 'openai'

const openai = new OpenAI()

const joke = await openai.chat.completions.create(openAiBody)

Using this method, you’re getting all of the power of Langtail prompts (like variables) and you’re able to send requests directly to OpenAI. This can be helpful if you’re especially sensitive to performance. If you’re taking this route for security reasons, let us know and give you a deeper look into Langtail.

API reference

LangtailNode

Constructor

The constructor accepts an options object with the following properties:

string

required

The API key for Langtail.

string

The base URL for the Langtail API.

boolean

A boolean indicating whether to record the API calls.

string

The organization ID.

string

The project ID.

Function

The fetch function to use for making HTTP requests. It is passed to openAI client under the hood.

Properties

Object

An object containing a completions object with a create method.

LangtailPrompts

An instance of the LangtailPrompts class.

Methods

chat.completions.create

This method accepts two parameters:

Object

required

An object that can be of type ChatCompletionCreateParamsNonStreaming & ILangtailExtraProps, ChatCompletionCreateParamsStreaming & ILangtailExtraProps, ChatCompletionCreateParamsBase & ILangtailExtraProps, or ChatCompletionCreateParams & ILangtailExtraProps.

Core.RequestOptions

OpenAI Core.RequestOptions object (optional).

It returns a promise that resolves to a ChatCompletion or a Stream<ChatCompletionChunk> depending whether you are using streaming or not.

Create a chat completion

const completion = await lt.chat.completions.create({
  messages: [{ role: 'system', content: 'You are a helpful assistant.' }],
  model: 'gpt-3.5-turbo',
})

console.log(completion.choices[0])

Exceptions

Throws an error if the apiKey is not provided in the options object or as an environment variable.

LangtailPrompts

Constructor

The constructor accepts an options object with the following properties:

string

required

The API key for Langtail.

string

The base URL for the Langtail API.

string

The organization ID.

string

The project ID.

Function

The fetch function to use for making HTTP requests. It is passed to openAI client under the hood.

Properties

string

required

The API key for Langtail.

string

The base URL for the Langtail API.

Object

An object containing the options for the Langtail API.

Methods

invoke

This method accepts:

IRequestParams | IRequestParamsStream

required

An IRequestParams or IRequestParamsStream object.

It returns a promise that resolves to an OpenAIResponseWithHttp or a StreamResponseType depending on whether you use streaming or not.

Invoke a prompt

const deployedPromptCompletion = await lt.prompts.invoke({
  prompt: '<PROMPT_SLUG>', // required
  environment: 'staging',
  variables: {
    about: 'cowboy Bebop',
  },
}) // results in an openAI ChatCompletion

get

This method accepts one parameter with these fields:

string

required

A string representing the prompt.

string

An Environment string identifier. Accepts values: "preview" | "staging" | "production". Defaults to production

string

String for version. Necessary for preview environment.

Returns Langtail state defined here:

Fetch prompt from Langtail

import { LangtailPrompts } from 'langtail'

const lt = new LangtailPrompts({
  apiKey: '<LANGTAIL_API_KEY>',
})

const prompt = await lt.get({
  prompt: '<PROMPT_SLUG>',
  environment: 'preview',
  version: '<PROMPT_VERSION>', // optional
})

build

This method accepts two parameters:

PromptState

required

The state object returned from the get method.

Object

An object containing the options for the Langtail API.

Returns an object that can be passed to the OpenAI SDK:

Build the final output

const openAiBody = lt.build(prompt, {
  stream: true,
  variables: {
    topic: 'iron man',
  },
})

Exceptions

Throws an error if the fetch operation fails.
Throws an error if there is no body in the response when streaming is enabled.

Introduction

Concepts and Examples

References

Media & Presentations

Support & Community

TypeScript SDK

Installation

Usage

1. Invoke a prompt that’s deployed on Langtail (recommended)

2. Invoke a prompt that’s stored in code

3. Fetch prompt from Langtail and invoke directly (proxyless)

API reference

LangtailNode

Constructor

Properties

Methods

chat.completions.create

Exceptions

LangtailPrompts

Constructor

Properties

Methods

invoke

get

build

Exceptions

​Installation

​Usage

​1. Invoke a prompt that’s deployed on Langtail (recommended)

​2. Invoke a prompt that’s stored in code

​3. Fetch prompt from Langtail and invoke directly (proxyless)

​API reference

​LangtailNode

​Constructor

​Properties

​Methods

​chat.completions.create

​Exceptions

​LangtailPrompts

​Constructor

​Properties

​Methods

​invoke

​get

​build

​Exceptions

Installation

Usage

1. Invoke a prompt that’s deployed on Langtail (recommended)

2. Invoke a prompt that’s stored in code

3. Fetch prompt from Langtail and invoke directly (proxyless)

API reference

LangtailNode

Constructor

Properties

Methods

chat.completions.create

Exceptions

LangtailPrompts

Constructor

Properties

Methods

invoke

get

build

Exceptions