langtail

Langtail SDK

Typescript SDK for Langtail.

Install

npm i langtail

Usage

OpenAI chat completion

basic completion without any prompt. This just wraps openAI api and adds a few extra parameters you can use to affect how the request gets logged in langtail.

import OpenAI from "openai"
import { createOpenAIProxy } from "langtail/openai"

const openai = new OpenAI({
  apiKey: "<LANGTAIL_API_KEY>",
})
const lt = createOpenAIProxy(openai)

const rawCompletion = await lt.chat.completions.create({
  // Required
  messages: [{ role: "system", content: "You are a helpful assistant." }],
  model: "gpt-3.5-turbo",
  // Optional:
  // All OpenAI fields (temperature, top_p, tools,...)
  prompt: "<prompt-slug>",
  doNotRecord: false, // false will ensure logs do not contain any info about payloads. You can still see the request in the logs, but you cannot see the variables etc.
  metadata: {
    "custom-field": "1",
  },
})

Deployed prompts

Completion from a deployed prompt can be called with lt.prompts.invoke:

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

Of course this assumes that you have already deployed your prompt to staging environment. If not, you will get an error thrown an error: Error: Failed to fetch prompt: 404 {"error":"Prompt deployment not found"}

LangtailPrompts

In case you only need deployed prompts, you can import just LangtailPrompts like this:

import { LangtailPrompts } from "langtail"

const lt = new LangtailPrompts({
  apiKey: "<LANGTAIL_API_KEY>",
})
// usage
const deployedPromptCompletion = await lt.invoke({
  prompt: "<PROMPT_SLUG>",
  environment: "staging",
  variables: {
    about: "cowboy Bebop",
  },
})

You can initialize LangtailPrompts with workspace and project slugs like so:

import { Langtail } from "langtail"

const lt = new Langtail({
  apiKey: "<LANGTAIL_API_KEY>",
  workspace: "<WORKSPACE_SLUG>",
  project: "<PROJECT_SLUG>",
})

which is necessary if your API key is workspace wide. For a project api key this is not necessary.

Streaming responses

both chat.prompts.create and prompts.invoke support streaming responses. All you need to enable it is { stream: true } flag like this:

const deployedPromptCompletion = await lt.prompts.invoke({
  prompt: "<PROMPT_SLUG>",
  environment: "staging",
  stream: true, // changes result to be a streaming OpenAI response
}) // results in an openAI Stream<ChatCompletionChunk>

Full API reference is in API.md

We support the same runtimes as OpenAI.

Proxyless usage

You can avoid langtail API all together by constructing your prompt locally and calling your provider like openAI directly.

let's suppose you have a prompt called joke-teller deployed on staging in langtail. You can get it's template and all the playground config by calling get method like this:

import { LangtailPrompts } from "langtail"

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

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

get will return something like this depending on how your prompt configured when it was deployed:

          {
            "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",
            },
          }

render your template and builds the final open AI compatible payload:

import { getOpenAIBody } from "langtail/getOpenAIBody"

const openAiBody = getOpenAIBody(playgroundState, {
  stream: true,
  variables: {
    topic: "iron man",
  },
})

openAiBody now contains this object:

{
            "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,
          }

Notice that your langtail template was replaced with a variable passed in. You can directly call openAI SDK with this object:

import OpenAI from "openai"

const openai = new OpenAI()

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

This way you are still using langtail prompts without exposing potentially sensitive data in your variables.

Typed inputs

You can override input types to improve IntelliSense for the prompt, environment, version and variables when calling a prompt. Use the command npx langtail generate-types.

Vercel AI provider

You can use Langtail with Vercel AI SDK. Import langtail from langtail/vercel-ai and provide your prompt slug as an argument.

import { generateText } from 'ai'
import { langtail } from 'langtail/vercel-ai'

async function main() {
  const result = await generateText({
    // API key is loaded from env variable LANGTAIL_API_KEY
    model: langtail('stock-simple', {
      // Optional Langtail options:
      variables: { 'ticker': 'TSLA' },
      environment: "production",
      version: "2",
      doNotRecord: false,
      metadata: {},
    }),
    // Optional LLM options:
    prompt: 'show me the price',
    temperature: 0,  // overrides setting in Langtail
  })

  console.log(result.text)
}

main().catch(console.error);

You can also use aiBridge from langtail/vercel-ai to use already existing Langtail instance:

const langtail = new Langtail({ apiKey })
const lt = aiBridge(langtail)

const result = await generateText({
  model: lt('stock-simple', {
    variables: { 'ticker': 'TSLA' },
  }),
  prompt: 'show me the price',
})

Using tools from Langtail

If your prompts in Langtail contain tools, you can generate a file containing tool parameters for every prompt deployment in your project. Run npx langtail generate-tools --out [output_filepath] to generate the file. For typings of the tools helper to work correctly, you also need to generate types.

After the file is generated, you can provide the Langtail tools to AI SDK like this:

import { generateText } from 'ai'
import { langtail } from 'langtail/vercel-ai'
import tools from './langtailTools';  // generated langtailTools.ts file

const ltModel = langtail('stock-simple',
  {
    environment: "production",
    version: "3"  // pinning the version is recommended
  }
);
const result = await generateText({
  model: ltModel,
  prompt: 'Show me the current price!',
  tools: tools(ltModel),  // loads all the tools for the specified prompt version
});

You can also define custom execute functions for your tools as follows:

tools(ltModel, {
  get_current_stock_price: {
    execute: async ({ ticker }) => {
      return ({
        ticker,
        price: 200 + Math.floor(Math.random() * 50),
      });
    },
  },
})

Stream helpers

The AI streams are delivered as JSON objects, which are split into chunks. This can pose a challenge because JSON objects might be distributed across multiple chunks. We have provide you with helper functions to manage these JSON streams more effectively.

Here's an example:

import {
  chatStreamToRunner,
  type ChatCompletionStream,
} from "langtail/stream"

const stream = await fetch(`/api/langtail`, {
  method: "POST",
  body: JSON.stringify({ messages: localMessages }),
  headers: {
    "Content-Type": "application/json",
  },
}).then((res) => res.body)

// NOTE: await res.body => ReadableStream
const runner = chatStreamToRunner(stream)

runner.on("message", (messageDelta: string) => {
  // NOTE: this is a string delta directly from the AI you can put together
  console.log(messageDelta)
})

runner.on("chunk", (chunk: ChatCompletionChunk) => {
  // NOTE: chunk here is always a proper JSON even with parts of the message
})