AI SDK

// neon.ts
import { defineConfig } from "@neon/config/v1";

export default defineConfig({
  aiGateway: true,
  buckets: { images: {} },
  functions: {
    agent: { name: "ai agent", source: "src/index.ts" },
  },
});

// src/index.ts
import { neon } from "@neon/ai-sdk-provider";
import { attachDatabasePool } from "@neon/functions";
import { streamText, tool, stepCountIs, type ModelMessage } from "ai";
import { z } from "zod";
import { drizzle } from "drizzle-orm/node-postgres";
import { Pool } from "pg";
import { todos } from "./db/schema";

const pool = new Pool({ connectionString: process.env.DATABASE_URL, max: 5 });
attachDatabasePool(pool);
const db = drizzle(pool);

export default {
  async fetch(request: Request) {
    if (request.method !== "POST") {
      return new Response("POST chat messages here", { status: 405 });
    }
    const { messages } = (await request.json()) as { messages: ModelMessage[] };

    const result = streamText({
      model: neon("claude-sonnet-4-6"), // swap to gpt-5-mini, gemini-3-flash, …
      system: "You are a concise assistant with access to the user's todos.",
      messages,
      tools: {
        countOpenTodos: tool({
          description: "Count the user's open todos.",
          inputSchema: z.object({}),
          execute: async () => ({ open: await db.$count(todos) }),
        }),
      },
      // Let the model call tools and then summarize, instead of stopping after
      // the first tool call. The loop runs in-process — no host timeout.
      stopWhen: stepCountIs(5),
      onError({ error }) {
        console.error("[streamText] error:", error);
      },
    });

    return result.toUIMessageStreamResponse({
      onError: (error) =>
        error instanceof Error ? error.message : String(error),
    });
  },
};

import { neon } from "@neon/ai-sdk-provider";
import { streamText } from "ai";
import { Files } from "files-sdk";
import { neon as neonFiles } from "files-sdk/neon";
import { randomUUID } from "node:crypto";

const files = new Files({ adapter: neonFiles({ bucket: "images" }) });

const result = streamText({
  model: neon("gpt-5-mini"),
  system:
    "Use image_generation when the user asks for a picture, then describe it.",
  messages,
  tools: {
    image_generation: neon.tools.imageGeneration({
      outputFormat: "jpeg",
      quality: "low", // the gateway caps a response near 640 KB — keep images small
      size: "1024x1024",
    }),
  },
  async onStepFinish({ toolResults }) {
    for (const tr of toolResults) {
      if (tr.toolName !== "image_generation") continue;
      const base64 = imageResultBase64(tr.output);
      if (!base64) continue;
      const key = `generated/${randomUUID()}.jpg`;
      await files.upload(key, Buffer.from(base64, "base64"), {
        contentType: "image/jpeg",
      });
      // …insert a row keyed by `key` into Postgres; serve later via files.url(key)
    }
  },
});

neon dev      # injects DATABASE_URL + the gateway/storage creds; hot reload
neon deploy   # provisions the gateway + bucket and deploys the function

curl -N -X POST "$(neon functions get agent -o json | jq -r .invocation_url)" \
  -H "content-type: application/json" \
  -d '{"messages":[{"role":"user","content":"How many open todos do I have?"}]}'