51 lines
1.7 KiB
TypeScript
51 lines
1.7 KiB
TypeScript
import { LLM_CONFIG } from "../config/llm.js";
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/**
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* Pings the local llama.cpp server to ensure it's up, running, and has a model loaded.
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* If the server is offline or still loading, it terminates the pipeline gracefully.
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* Skipped entirely when using a cloud provider.
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*/
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export async function checkLlmServer(
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url = "http://127.0.0.1:8080/health",
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): Promise<void> {
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if (LLM_CONFIG.provider !== "local") {
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console.log("🌐 Using cloud provider — skipping local health check.");
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return;
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}
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try {
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const response = await fetch(url);
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// llama.cpp returns a 503 status if the server is up but the model weights are still loading
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if (response.status === 503) {
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console.error(
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"\n ⏳ Local AI engine is starting up, but the model is still loading into memory.",
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);
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console.error(
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"👉 Please wait a minute for the weights to load, then run the pipeline again.\n",
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);
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process.exit(1);
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}
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// Parse the JSON health response (expected: { status: "ok" })
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const data = (await response.json()) as { status?: string };
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if (response.ok && data.status === "ok") {
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console.log("🟢 Local AI engine is connected and ready for inference!");
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return;
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}
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// Catch-all for unexpected active server responses
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console.error(
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`\n ❌ Unknown response from local AI engine health check (Status: ${response.status}).`,
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);
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process.exit(1);
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} catch (_error: unknown) {
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console.error("\n ❌ Could not connect to the local AI engine.");
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console.error(`🔗 Attempted endpoint: ${url}`);
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console.error(
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"👉 Make sure your './llama-server' command is actively running in another terminal tab!\n",
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);
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process.exit(1);
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}
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}
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