# Deploying Chained OpenAI LLM Calls to Vercel with the Inngest SDK

**Author:** Steven Tey

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## Introduction to LLMs

[Large-language modals (LLMs)](/guides/what-is-a-large-language-model) like [OpenAI GPT-4](/docs/integrations/openai) are revolutionizing many sectors due to their ability to comprehend and generate human-like text. They are pre-trained on vast amounts of data, allowing them to generate relevant responses to any given prompt.

> Fluid compute is a hybrid approach that bridges the gap between serverless and traditional servers. To learn more, read [Fluid Compute](https://vercel.com/docs/functions/fluid-compute) and to get started with using it, read [How to enable fluid compute](https://vercel.com/docs/functions/fluid-compute#how-to-enable-fluid-compute).

## Why Chain LLMs?

However, for more complex applications, single responses from an LLM might not suffice. Chaining, or executing multiple LLM calls in a series or parallel, can offer refined results.

Here are some of the benefits of chaining LLMs:

- Maintains contextual continuity in conversations.
  
- Allows for elaboration and expansion on a topic.
  
- Facilitates multi-turn interactions for a dynamic conversation.
  
- Aids in problem solving and planning by breaking down tasks.
  
- Enables error correction and feedback to improve model responses.
  

## Using Inngest SDK

[Inngest](https://www.inngest.com/) offers a solution to chain LLMs efficiently without infrastructure overhead. It provides:

- [Automatic retries](https://www.inngest.com/docs/functions/retries) and state preservation
  
- [Optional parallelization](https://www.inngest.com/docs/guides/step-parallelism) for performance
  
- Built-in [observability](https://www.inngest.com/docs/reference/functions/step-send-event) and transparency
  
- [Function cancellation](https://www.inngest.com/docs/functions/cancellation) capability
  

Here's an example:

``import { Inngest } from "inngest"; const inngest = new Inngest({ name: "Your Application Name" }); export const chain = inngest.createFunction( { name: "Function Name" }, { event: "api/function.endpoint" }, async ({ event, step }) => { const llm = new OpenAI(); const task1 = await step.run("Task 1 Name", async () => { const prompt = "..."; return await llm.createCompletion({ model: "...", prompt: `${prompt}: ${event.data.input}`, }); }); const task2 = await step.run("Task 2 Name", async () => { const prompt = "..."; return await llm.createCompletion({ model: "...", prompt: `${prompt}: ${task1}`, }); }); await step.run("Save to DB", async () => { await db.collection.create({ task1, task2, requestID: event.data.requestID }); }); return { task1, task2 }; }, )``

In this example, we define a chained function that is triggered by a specific event.

It consists of two tasks – each generating a completion using [OpenAI's language model](/docs/integrations/openai) based on a given prompt and input.

The results of these tasks are then saved to a [database](https://vercel.com/storage/postgres).

## Deploying to Vercel

Functions created with Inngest can be served via HTTP and hosted on any platform, such as Vercel, a popular deployment platform for front-end and serverless functions.

For example, here's the code above as used in an [API endpoint on Vercel](https://vercel.com/guides/using-express-with-vercel):

`import { serve } from "inngest/next"; const inngest = new Inngest({ name: "Your Application Name" }); export const chain = inngest.createFunction( ... ) // chain code from above export const handler = serve(inngest, [chain]);` You can then trigger this endpoint by sending a HTTP POST request: `await inngest.send({ name: "api/function.endpoint", data: { requestID: "...", input: "...", } });` ## Further Reading For more information on chained LLMs, you can refer to the following resources: - [Running chained LLMs with TypeScript in production](https://www.inngest.com/blog/running-chained-llms-typescript-in-production)
  
- [Inngest GitHub repository](https://github.com/inngest/inngest-js)

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