Feeding Reader output to an LLM

import { ReaderClient } from "@vakra-dev/reader-js";
import Anthropic from "@anthropic-ai/sdk";

const reader = new ReaderClient({ apiKey: process.env.READER_KEY! });
const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY! });

async function summarize(url: string): Promise<string> {
  const result = await reader.read({ url });
  if (result.kind !== "scrape") throw new Error("unexpected async result");

  const content = result.data.markdown ?? "";

  const response = await anthropic.messages.create({
    model: "claude-opus-4-6",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: `Summarize this article in 3 sentences:\n\n${content}`,
      },
    ],
  });

  return response.content[0].type === "text" ? response.content[0].text : "";
}

const summary = await summarize("https://example.com/long-article");
console.log(summary);

await reader.read({ url, onlyMainContent: true }); // default

const truncated = content.slice(0, 40_000); // rough chars → tokens estimate

const prompt = `You are a technical writer. Summarize the following article for busy engineers.

The article is in markdown. Preserve code blocks in your response if they're important.

---
${content}
---

Summary:`;

const result = await reader.read({ url });
if (result.kind === "scrape") {
  const source = `[${result.data.metadata.title}](${result.data.url})`;
  // Include `source` in your final LLM response to the user
}

import { UpstreamUnavailableError, ScrapeTimeoutError } from "@vakra-dev/reader-js";

try {
  const result = await reader.read({ url });
  // ...
} catch (err) {
  if (err instanceof UpstreamUnavailableError || err instanceof ScrapeTimeoutError) {
    // retry with premium
    const result = await reader.read({ url, proxyMode: "premium" });
    // ...
  } else {
    throw err;
  }
}