Use the Context Mode API with the Vercel AI SDK

With the Inkeep context mode, you can leverage all of the capabilities provided by a normal OpenAI API endpoint but "grounded" with context about your product.

This gives you the full flexibility of creating any LLM application: custom copilots, AI workflows, etc., all backed by your own data. Structured data can be particularly powerful in rendering dynamic UI components as part of a conversational experience.

As a basic example, instead of having a support bot that answers with a single content payload, we can instead define a response to be returned as a series of structured steps. The example shown below illustrates how to accomplish that with streamObject and Inkeep's context model.

index.ts

import { z } from "zod";
 
import { streamObject } from "ai";
import { createOpenAI } from "@ai-sdk/openai";
import dotenv from "dotenv";
dotenv.config();
 
if (!process.env.INKEEP_API_KEY) {
  throw new Error("INKEEP_API_KEY is required");
}
 
const openai = createOpenAI({
  apiKey: process.env.INKEEP_API_KEY,
  baseURL: "https://api.inkeep.com/v1",
});
 
const StepSchema = z.object({
  steps: z.array(
    z.object({
      step: z.string(),
      description: z.string(),
    })
  ),
});
 
async function getResponseFromAI() {
  const result = await streamObject({
    model: openai("inkeep-context-sonnet-3-5"),
    schema: StepSchema,
    messages: [
      {
        role: "system",
        content:
          "Generate step-by-step instructions to answer the user question about Inkeep only based on the information sources. Break it down to be as granular as possible. Always generate more than one step.",
      },
    ],
  });
 
  const { partialObjectStream } = result;
  for await (const partialObject of partialObjectStream) {
    console.clear();
    console.log(partialObject.steps);
  }
}
 
getResponseFromAI();