◈ API / SDK/2026-06-20Advanced

Building a Type-Safe AI Backend with Gemini API, tRPC v11, and Prisma — Real-Time Streaming, Auth Middleware, and Production Deployment

Learn how to integrate Gemini API streaming into tRPC v11 subscriptions, persist conversations type-safely with Prisma, and handle auth middleware, rate limiting, and common production pitfalls — all with working code examples.

gemini¹¹⁹ gemini-api²⁸⁶trpcprismatypescript¹⁶ nextjs³ backend⁴

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If you've used tRPC to build a type-safe API and then tried to add Gemini API streaming on top of it, you've probably hit a wall somewhere. Maybe the observable types don't line up with your response shape, or the for await loop and Prisma updates don't play well together, or the whole thing breaks the moment you try Edge Runtime. Each piece works individually — but combining them reliably takes some trial and error.

I ran into all of these issues while integrating this exact stack into a personal SaaS project. This guide shares what I learned: not just the happy path, but the reasons behind each design decision, and the specific spots where things tend to fall apart in production.

Why tRPC + Gemini API Works Well Together

The main value of tRPC is that your client and server share types automatically. When you call Gemini API directly with fetch, the response shape tends to be loosely typed, which means your frontend handling becomes brittle. Routing your Gemini calls through tRPC lets you normalize and shape the response on the server, then push those types all the way to the client.

Prisma pairs naturally with this setup too. AI chat data has a clear hierarchical structure — sessions contain messages, messages have roles — and that maps cleanly to a relational schema. When Prisma's generated types flow into tRPC's type inference, you get IDE autocompletion for things like session.messages[0].role on the client without any manual type definitions.

The combination isn't without tradeoffs, and we'll get to those later. But for a Next.js-based AI product with a TypeScript-first team, this stack is genuinely ergonomic once it's wired up correctly.

Project Setup

Install the core dependencies. tRPC v11 splits its packages between server and client:

npm install @trpc/server@^11 @trpc/client@^11 @trpc/react-query@^11
npm install @tanstack/react-query@^5
npm install @prisma/client prisma
npm install @google/genai
npm install zod superjson
 
# Initialize Prisma
npx prisma init

The directory structure for a Next.js App Router project:

src/
  server/
    trpc.ts             # tRPC instance and procedure definitions
    routers/
      _app.ts           # Root router
      chat.ts           # Chat feature router
  app/
    api/
      trpc/
        [trpc]/
          route.ts      # Next.js Route Handler
  lib/
    prisma.ts           # Prisma client singleton
  trpc/
    client.ts           # Client-side tRPC setup

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WHAT YOU'LL LEARN

✦A production design that wires tRPC v11 subscriptions to Gemini API streaming with full type safety

✦Persisting conversations with Prisma and recording token counts at write time for cost visibility

✦Auth middleware, multi-session isolation, and the Edge Runtime pitfalls to avoid

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Prisma Schema Design for AI Chat

The schema needs to track three things: users, chat sessions, and individual messages. The key design choice here is adding a status field to messages — this lets you track whether a message is still streaming (PENDING), finished (COMPLETED), or failed (FAILED).

// prisma/schema.prisma
generator client {
  provider = "prisma-client-js"
}
 
datasource db {
  provider = "postgresql"
  url      = env("DATABASE_URL")
}
 
enum Role {
  USER
  MODEL
  SYSTEM
}
 
enum MessageStatus {
  PENDING    // Currently streaming
  COMPLETED  // Stream finished
  FAILED     // Error during streaming
}
 
model User {
  id        String        @id @default(cuid())
  email     String        @unique
  createdAt DateTime      @default(now())
  sessions  ChatSession[]
}
 
model ChatSession {
  id        String    @id @default(cuid())
  userId    String
  title     String    @default("New conversation")
  createdAt DateTime  @default(now())
  updatedAt DateTime  @updatedAt
  user      User      @relation(fields: [userId], references: [id], onDelete: Cascade)
  messages  Message[]
 
  @@index([userId, createdAt(sort: Desc)])
}
 
model Message {
  id         String        @id @default(cuid())
  sessionId  String
  role       Role
  content    String        @db.Text
  status     MessageStatus @default(PENDING)
  tokenCount Int?
  createdAt  DateTime      @default(now())
  session    ChatSession   @relation(fields: [sessionId], references: [id], onDelete: Cascade)
 
  @@index([sessionId, createdAt])
}

The PENDING → COMPLETED transition matters for recovery scenarios. If a user reloads mid-stream, the frontend can check the message status and know to show a "this message was interrupted" indicator rather than empty content.

Run the migration:

npx prisma migrate dev --name init
npx prisma generate

tRPC Instance, Context, and Middleware

The context wires authentication into every tRPC procedure. We read a Bearer token from the Authorization header and look up the user ID. For the rate limiter, we track request counts per user with an in-memory map — fine for development and single-instance deployments, but needs Redis in a multi-instance setup.

// src/server/trpc.ts
import { initTRPC, TRPCError } from "@trpc/server";
import type { FetchCreateContextFnOptions } from "@trpc/server/adapters/fetch";
import { prisma } from "@/lib/prisma";
import superjson from "superjson";
 
export async function createContext({ req }: FetchCreateContextFnOptions) {
  const authHeader = req.headers.get("Authorization");
  const token = authHeader?.replace("Bearer ", "");
  const userId = token ? await verifyToken(token) : null;
 
  return { userId, prisma, req };
}
 
export type Context = Awaited<ReturnType<typeof createContext>>;
 
const t = initTRPC.context<Context>().create({
  transformer: superjson,
  errorFormatter({ shape, error }) {
    return {
      ...shape,
      data: {
        ...shape.data,
        zodError:
          error.cause instanceof ZodError ? error.cause.flatten() : null,
      },
    };
  },
});
 
export const router = t.router;
export const publicProcedure = t.procedure;
 
// Require authentication
export const protectedProcedure = t.procedure.use(({ ctx, next }) => {
  if (!ctx.userId) {
    throw new TRPCError({ code: "UNAUTHORIZED" });
  }
  return next({ ctx: { ...ctx, userId: ctx.userId } });
});
 
// In-memory rate limiter (replace with Redis in production)
const rateLimitMap = new Map<string, { count: number; resetAt: number }>();
 
export const rateLimitedProcedure = protectedProcedure.use(({ ctx, next }) => {
  const now = Date.now();
  const windowMs = 60_000;
  const limit = 20;
 
  const entry = rateLimitMap.get(ctx.userId) ?? {
    count: 0,
    resetAt: now + windowMs,
  };
 
  if (now > entry.resetAt) {
    entry.count = 0;
    entry.resetAt = now + windowMs;
  }
  entry.count++;
  rateLimitMap.set(ctx.userId, entry);
 
  if (entry.count > limit) {
    throw new TRPCError({
      code: "TOO_MANY_REQUESTS",
      message: `Rate limit exceeded. Retry in ${Math.ceil((entry.resetAt - now) / 1000)}s`,
    });
  }
 
  return next({ ctx });
});
 
async function verifyToken(token: string): Promise<string | null> {
  try {
    // Replace with JWT verification or NextAuth.js session lookup
    return Buffer.from(token, "base64").toString("utf-8") || null;
  } catch {
    return null;
  }
}

Chat Router: Integrating Gemini API

The chat router handles session management, message persistence, and Gemini API calls. The non-streaming sendMessage mutation is a good starting point before tackling subscriptions:

// src/server/routers/chat.ts
import { z } from "zod";
import { router, protectedProcedure, rateLimitedProcedure } from "../trpc";
import { GoogleGenAI } from "@google/genai";
import { observable } from "@trpc/server/observable";
import { TRPCError } from "@trpc/server";
 
const genai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY! });
 
export const chatRouter = router({
  listSessions: protectedProcedure.query(async ({ ctx }) => {
    return ctx.prisma.chatSession.findMany({
      where: { userId: ctx.userId },
      orderBy: { updatedAt: "desc" },
      take: 50,
      include: { _count: { select: { messages: true } } },
    });
  }),
 
  createSession: protectedProcedure
    .input(z.object({ title: z.string().max(100).optional() }))
    .mutation(async ({ ctx, input }) => {
      return ctx.prisma.chatSession.create({
        data: { userId: ctx.userId, title: input.title ?? "New conversation" },
      });
    }),
 
  sendMessage: rateLimitedProcedure
    .input(
      z.object({
        sessionId: z.string().cuid(),
        content: z.string().min(1).max(10_000),
      })
    )
    .mutation(async ({ ctx, input }) => {
      const session = await ctx.prisma.chatSession.findFirst({
        where: { id: input.sessionId, userId: ctx.userId },
        include: {
          messages: {
            orderBy: { createdAt: "asc" },
            take: 20,
            where: { status: "COMPLETED" },
          },
        },
      });
 
      if (!session) throw new TRPCError({ code: "NOT_FOUND" });
 
      const userMessage = await ctx.prisma.message.create({
        data: {
          sessionId: input.sessionId,
          role: "USER",
          content: input.content,
          status: "COMPLETED",
        },
      });
 
      const aiMessage = await ctx.prisma.message.create({
        data: {
          sessionId: input.sessionId,
          role: "MODEL",
          content: "",
          status: "PENDING",
        },
      });
 
      try {
        const history = session.messages.map((msg) => ({
          role: msg.role === "USER" ? "user" : "model",
          parts: [{ text: msg.content }],
        }));
 
        const chat = genai
          .getGenerativeModel({ model: "gemini-2.5-pro" })
          .startChat({ history });
 
        const result = await chat.sendMessage(input.content);
        const responseText = result.response.text();
        const tokenCount = result.response.usageMetadata?.candidatesTokenCount;
 
        const updated = await ctx.prisma.message.update({
          where: { id: aiMessage.id },
          data: { content: responseText, status: "COMPLETED", tokenCount },
        });
 
        await ctx.prisma.chatSession.update({
          where: { id: input.sessionId },
          data: { updatedAt: new Date() },
        });
 
        return { userMessage, aiMessage: updated };
      } catch (error) {
        await ctx.prisma.message.update({
          where: { id: aiMessage.id },
          data: { status: "FAILED", content: "An error occurred." },
        });
        throw new TRPCError({
          code: "INTERNAL_SERVER_ERROR",
          message: "Failed to get AI response",
        });
      }
    }),

Real-Time Streaming with tRPC Subscriptions

This is the part most developers struggle with. The key is being explicit about the union type passed to observable — without it, type narrowing on the client breaks.

  streamMessage: rateLimitedProcedure
    .input(
      z.object({
        sessionId: z.string().cuid(),
        content: z.string().min(1).max(10_000),
      })
    )
    .subscription(({ ctx, input }) => {
      return observable<
        | { type: "chunk"; text: string }
        | { type: "done"; messageId: string; tokenCount: number }
        | { type: "error"; message: string }
      >((emit) => {
        let cancelled = false;
        let aiMessageId: string | null = null;
        let fullText = "";
 
        const run = async () => {
          try {
            const session = await ctx.prisma.chatSession.findFirst({
              where: { id: input.sessionId, userId: ctx.userId },
              include: {
                messages: {
                  orderBy: { createdAt: "asc" },
                  take: 20,
                  where: { status: "COMPLETED" },
                },
              },
            });
 
            if (!session) {
              emit.next({ type: "error", message: "Session not found" });
              emit.complete();
              return;
            }
 
            await ctx.prisma.message.create({
              data: {
                sessionId: input.sessionId,
                role: "USER",
                content: input.content,
                status: "COMPLETED",
              },
            });
 
            const placeholder = await ctx.prisma.message.create({
              data: {
                sessionId: input.sessionId,
                role: "MODEL",
                content: "",
                status: "PENDING",
              },
            });
            aiMessageId = placeholder.id;
 
            const history = session.messages.map((msg) => ({
              role: msg.role === "USER" ? "user" : "model",
              parts: [{ text: msg.content }],
            }));
 
            const chat = genai
              .getGenerativeModel({ model: "gemini-2.5-pro" })
              .startChat({ history });
 
            const stream = await chat.sendMessageStream(input.content);
 
            for await (const chunk of stream.stream) {
              if (cancelled) break;
              const text = chunk.text();
              if (text) {
                fullText += text;
                emit.next({ type: "chunk", text });
              }
            }
 
            const finalResponse = await stream.response;
            const tokenCount =
              finalResponse.usageMetadata?.candidatesTokenCount ?? 0;
 
            if (!cancelled) {
              await ctx.prisma.message.update({
                where: { id: aiMessageId },
                data: {
                  content: fullText,
                  status: "COMPLETED",
                  tokenCount,
                },
              });
 
              await ctx.prisma.chatSession.update({
                where: { id: input.sessionId },
                data: { updatedAt: new Date() },
              });
 
              emit.next({ type: "done", messageId: aiMessageId, tokenCount });
              emit.complete();
            }
          } catch (error) {
            if (aiMessageId) {
              await ctx.prisma.message
                .update({
                  where: { id: aiMessageId },
                  data: {
                    status: "FAILED",
                    content: fullText || "An error occurred.",
                  },
                })
                .catch(() => {});
            }
 
            emit.next({
              type: "error",
              message:
                error instanceof Error ? error.message : "Streaming failed",
            });
            emit.complete();
          }
        };
 
        run();
 
        // Called when the client disconnects
        return () => {
          cancelled = true;
        };
      });
    }),
});

The cancelled flag is essential. When a client disconnects, tRPC calls the cleanup function returned from observable. Setting cancelled = true breaks the for await loop on the next iteration, so you stop processing chunks even if Gemini is still returning data. Note that Gemini's API has already received the request by this point, so billing still applies — but you avoid wasting server CPU and creating orphaned DB writes.

Route Handler and Client Configuration

The Next.js Route Handler wires tRPC into the App Router:

// src/app/api/trpc/[trpc]/route.ts
import { fetchRequestHandler } from "@trpc/server/adapters/fetch";
import { appRouter } from "@/server/routers/_app";
import { createContext } from "@/server/trpc";
 
const handler = (req: Request) =>
  fetchRequestHandler({
    endpoint: "/api/trpc",
    req,
    router: appRouter,
    createContext: ({ req, resHeaders }) => createContext({ req, resHeaders }),
    onError:
      process.env.NODE_ENV === "development"
        ? ({ path, error }) => console.error(`tRPC error on ${path}:`, error)
        : undefined,
  });
 
export { handler as GET, handler as POST };

The client uses splitLink to route subscriptions through SSE and regular queries/mutations through HTTP batch:

// src/trpc/client.ts
"use client";
import {
  createTRPCClient,
  httpBatchStreamLink,
  unstable_httpSubscriptionLink,
  splitLink,
} from "@trpc/client";
import type { AppRouter } from "@/server/routers/_app";
import superjson from "superjson";
 
const url = `${process.env.NEXT_PUBLIC_APP_URL}/api/trpc`;
 
export const trpc = createTRPCClient<AppRouter>({
  links: [
    splitLink({
      condition: (op) => op.type === "subscription",
      true: unstable_httpSubscriptionLink({ url, transformer: superjson }),
      false: httpBatchStreamLink({
        url,
        transformer: superjson,
        headers() {
          const token =
            typeof window !== "undefined"
              ? localStorage.getItem("auth_token")
              : null;
          return token ? { Authorization: `Bearer ${token}` } : {};
        },
      }),
    }),
  ],
});

A minimal React component to kick off a streaming session:

// src/components/ChatInput.tsx
"use client";
import { useState } from "react";
import { trpc } from "@/trpc/client";
 
export function ChatInput({ sessionId }: { sessionId: string }) {
  const [input, setInput] = useState("");
  const [streaming, setStreaming] = useState(false);
  const [streamedText, setStreamedText] = useState("");
 
  const handleSubmit = async (e: React.FormEvent) => {
    e.preventDefault();
    if (!input.trim() || streaming) return;
 
    setStreaming(true);
    setStreamedText("");
 
    trpc.chat.streamMessage.subscribe(
      { sessionId, content: input },
      {
        onData(data) {
          if (data.type === "chunk") {
            setStreamedText((prev) => prev + data.text);
          } else if (data.type === "done" || data.type === "error") {
            setStreaming(false);
          }
        },
        onError() {
          setStreaming(false);
        },
        onComplete() {
          setStreaming(false);
        },
      }
    );
 
    setInput("");
  };
 
  return (
    <form onSubmit={handleSubmit}>
      <input
        value={input}
        onChange={(e) => setInput(e.target.value)}
        disabled={streaming}
        placeholder="Type a message..."
      />
      <button type="submit" disabled={streaming || !input.trim()}>
        {streaming ? "Sending..." : "Send"}
      </button>
      {streamedText && <p className="streaming-text">{streamedText}</p>}
    </form>
  );
}

Common Pitfalls (and How to Avoid Them)

Pitfall 1: Edge Runtime breaks Prisma

If you add export const runtime = "edge" to your Route Handler, Prisma stops working. Prisma Client depends on Node.js APIs (fs, native bindings) that aren't available in the V8 isolate environment that Edge Runtime uses.

// ❌ Breaks Prisma
export const runtime = "edge";
 
// ✅ Use Node.js runtime (or omit — it's the default)
export const runtime = "nodejs";

Gemini API itself works fine on Edge Runtime, so if you want the performance benefits of Edge for some routes, split your API: put Gemini-only calls on Edge and DB-touching calls on Node.js. Alternatively, use Prisma Accelerate or an HTTP-based database driver (Neon serverless, PlanetScale) which do work on Edge.

Pitfall 2: Missing type annotation on observable breaks client narrowing

Without an explicit union type on observable, emit.next() accepts any, and the client loses the ability to narrow data.type:

// ❌ Types are lost — client can't narrow data.type
return observable((emit) => { ... });
 
// ✅ Explicit union type — narrowing works on the client
return observable<
  | { type: "chunk"; text: string }
  | { type: "done"; messageId: string; tokenCount: number }
  | { type: "error"; message: string }
>((emit) => { ... });

This is the single most common tRPC subscription mistake I've seen in codebases. The missing type doesn't cause a runtime error — it just silently disables the type safety you were counting on.

Pitfall 3: Calling Prisma inside the for await loop

It's tempting to update the message content in the database on every chunk — "so the user can resume if they close the tab." But this creates a DB query for every streaming chunk, which can easily mean 50–100 queries per message.

// ❌ 100 DB writes for a 100-chunk response
for await (const chunk of stream.stream) {
  fullText += chunk.text();
  await prisma.message.update({ where: { id }, data: { content: fullText } });
}
 
// ✅ One DB write after the stream completes
for await (const chunk of stream.stream) {
  fullText += chunk.text();
  emit.next({ type: "chunk", text: chunk.text() });
}
await prisma.message.update({
  where: { id: aiMessageId },
  data: { content: fullText, status: "COMPLETED" },
});

If you genuinely need mid-stream persistence for recovery, write a snapshot at intervals (every 10 seconds or every 500 characters) rather than every chunk. Most chat applications don't need this.

Handling Conversation History Correctly

One area where Gemini API integrations go wrong in production is conversation history management. The naive approach — sending all messages in a session as history — breaks down in two ways: it increases latency as sessions grow, and it risks hitting the context window limit on very long conversations.

The schema above uses take: 20 to limit history to the last 20 completed messages. This is a reasonable default for most chat applications. If you need longer context, use take: 50 but watch your average response times — each additional message adds tokens that Gemini must process.

A more sophisticated approach is to implement a "sliding window with summary" pattern:

// src/server/lib/buildHistory.ts
import type { Message } from "@prisma/client";
 
const MAX_HISTORY_MESSAGES = 20;
const SUMMARIZE_THRESHOLD = 40; // summarize when we exceed this
 
export async function buildConversationHistory(
  messages: Message[],
  summarizer?: (text: string) => Promise<string>
) {
  const completed = messages.filter((m) => m.status === "COMPLETED");
 
  if (completed.length <= MAX_HISTORY_MESSAGES) {
    // Short enough — use directly
    return completed.map((msg) => ({
      role: msg.role === "USER" ? "user" : "model",
      parts: [{ text: msg.content }],
    }));
  }
 
  if (completed.length > SUMMARIZE_THRESHOLD && summarizer) {
    // Summarize the oldest half, keep the recent half verbatim
    const splitAt = Math.floor(completed.length / 2);
    const oldMessages = completed.slice(0, splitAt);
    const recentMessages = completed.slice(splitAt);
 
    const oldText = oldMessages
      .map((m) => `${m.role}: ${m.content}`)
      .join("
");
    const summary = await summarizer(oldText);
 
    return [
      { role: "user" as const, parts: [{ text: "Earlier in our conversation:" }] },
      { role: "model" as const, parts: [{ text: summary }] },
      ...recentMessages.map((msg) => ({
        role: msg.role === "USER" ? "user" as const : "model" as const,
        parts: [{ text: msg.content }],
      })),
    ];
  }
 
  // Default: use the most recent MAX_HISTORY_MESSAGES
  return completed.slice(-MAX_HISTORY_MESSAGES).map((msg) => ({
    role: msg.role === "USER" ? "user" as const : "model" as const,
    parts: [{ text: msg.content }],
  }));
}

The summarizer function can itself call Gemini API with a cheap model like gemini-2.5-flash to produce the summary. This keeps conversation context meaningful without blowing up token costs.

Multi-Session Isolation and Security

One subtle security issue with the chat router above: it queries the session with userId in the where clause, which correctly prevents users from accessing each other's sessions. But make sure this pattern is consistent everywhere. A common mistake is checking ownership in some mutations but not others:

// ❌ Forgot ownership check — any authenticated user can delete any session
deleteSession: protectedProcedure
  .input(z.object({ sessionId: z.string().cuid() }))
  .mutation(async ({ ctx, input }) => {
    await ctx.prisma.chatSession.delete({ where: { id: input.sessionId } });
  }),
 
// ✅ Always include userId in the where clause
deleteSession: protectedProcedure
  .input(z.object({ sessionId: z.string().cuid() }))
  .mutation(async ({ ctx, input }) => {
    const deleted = await ctx.prisma.chatSession.deleteMany({
      where: { id: input.sessionId, userId: ctx.userId },
    });
    if (deleted.count === 0) {
      throw new TRPCError({ code: "NOT_FOUND" });
    }
  }),

Using deleteMany instead of delete lets you silently handle the case where the record doesn't exist or belongs to someone else — returning count: 0 instead of throwing a Prisma RecordNotFound error that would bubble up as a 500.

Token Usage Tracking and Cost Visibility

If you're building a product where users have token budgets or you want to track API costs, the tokenCount field in the Message model is your foundation. Here's a simple aggregation to show per-user usage:

// Usage stats procedure
getUsageStats: protectedProcedure
  .input(
    z.object({
      since: z.date().optional(), // e.g., start of current month
    })
  )
  .query(async ({ ctx, input }) => {
    const since = input.since ?? new Date(new Date().setDate(1)); // Default: this month
 
    const result = await ctx.prisma.message.aggregate({
      _sum: { tokenCount: true },
      _count: { id: true },
      where: {
        session: { userId: ctx.userId },
        role: "MODEL", // Count output tokens only
        status: "COMPLETED",
        createdAt: { gte: since },
      },
    });
 
    const outputTokens = result._sum.tokenCount ?? 0;
    const messageCount = result._count.id;
 
    // Gemini 2.5 Pro pricing (output): approximately $15 per 1M tokens
    const estimatedCostUsd = (outputTokens / 1_000_000) * 15;
 
    return {
      outputTokens,
      messageCount,
      estimatedCostUsd,
      period: { from: since, to: new Date() },
    };
  }),

As an indie developer who runs this exact stack in my own SaaS, this is the kind of data that makes your per-user pricing model defensible — whether you're charging a flat monthly fee or building a usage-based system. Storing token counts at write time is much cheaper than re-computing them later from API logs.

Testing tRPC Procedures with Gemini API

Testing procedures that call Gemini API requires mocking the AI client to avoid incurring costs in CI. Here's a pattern that keeps tests fast and deterministic:

// src/server/routers/chat.test.ts
import { describe, it, expect, vi, beforeEach } from "vitest";
import { createCallerFactory } from "@trpc/server";
import { appRouter } from "./_app";
import { prisma } from "@/lib/prisma";
 
// Mock the Gemini AI client
vi.mock("@google/genai", () => ({
  GoogleGenAI: vi.fn().mockImplementation(() => ({
    getGenerativeModel: vi.fn().mockReturnValue({
      startChat: vi.fn().mockReturnValue({
        sendMessage: vi.fn().mockResolvedValue({
          response: {
            text: () => "Mocked AI response",
            usageMetadata: { candidatesTokenCount: 42 },
          },
        }),
      }),
    }),
  })),
}));
 
const createCaller = createCallerFactory(appRouter);
 
describe("chatRouter.sendMessage", () => {
  beforeEach(async () => {
    // Clean up test data between tests
    await prisma.chatSession.deleteMany({ where: { userId: "test-user" } });
  });
 
  it("creates user and AI messages in the database", async () => {
    // Create a test context with a known userId
    const caller = createCaller({
      userId: "test-user",
      prisma,
      req: new Request("http://localhost"),
    });
 
    // Create a session first
    const session = await caller.chat.createSession({ title: "Test" });
 
    // Send a message
    const result = await caller.chat.sendMessage({
      sessionId: session.id,
      content: "Hello",
    });
 
    expect(result.userMessage.content).toBe("Hello");
    expect(result.aiMessage.content).toBe("Mocked AI response");
    expect(result.aiMessage.status).toBe("COMPLETED");
    expect(result.aiMessage.tokenCount).toBe(42);
  });
 
  it("throws NOT_FOUND when session belongs to another user", async () => {
    const caller = createCaller({
      userId: "different-user",
      prisma,
      req: new Request("http://localhost"),
    });
 
    const otherUserSession = await prisma.chatSession.create({
      data: { userId: "test-user", title: "Private session" },
    });
 
    await expect(
      caller.chat.sendMessage({
        sessionId: otherUserSession.id,
        content: "Trying to access",
      })
    ).rejects.toThrow("NOT_FOUND");
  });
});

The createCallerFactory pattern from tRPC v11 gives you a direct function caller for tests — no HTTP server needed, and the full middleware chain (including auth checks) still runs. This is particularly useful for verifying that the ownership checks we discussed earlier are actually enforced.

Production Deployment Considerations

On Vercel, tRPC's SSE subscriptions use Vercel's Streaming Response feature. The free tier has a 60-second response timeout, which means very long Gemini responses may hit the limit. The Gemini 2.5 Pro model typically completes in 5–15 seconds for conversational inputs, so this usually isn't a problem — but watch for it if you're generating long-form content.

For the database connection, add Prisma's migration step to your build:

{
  "scripts": {
    "build": "prisma generate && prisma migrate deploy && next build",
    "postinstall": "prisma generate"
  }
}

The in-memory rate limiter in the middleware above is intentionally simple. In production with multiple server instances, replace rateLimitMap with Redis using Upstash or similar. The interface stays the same — just swap the underlying store.

For error handling strategies specific to Gemini API (exponential backoff, retry logic for 429s), see Gemini API Error Handling and Rate Limiting in Production — it covers the patterns that work best when Gemini is embedded inside a tRPC mutation or subscription.

Structuring System Instructions per Session

One feature worth adding early is per-session system instructions. Rather than hardcoding a single system prompt, storing it in the session lets you support different AI personas, languages, or task modes without schema changes.

Add a systemInstruction field to ChatSession:

model ChatSession {
  id                String   @id @default(cuid())
  userId            String
  title             String   @default("New conversation")
  systemInstruction String?  @db.Text  // Optional, per-session persona
  createdAt         DateTime @default(now())
  updatedAt         DateTime @updatedAt
  user              User     @relation(fields: [userId], references: [id], onDelete: Cascade)
  messages          Message[]
 
  @@index([userId, createdAt(sort: Desc)])
}

Then pass it to the Gemini model initialization:

const model = genai.getGenerativeModel({
  model: "gemini-2.5-pro",
  systemInstruction: session.systemInstruction ?? undefined,
});
const chat = model.startChat({ history });

This opens the door to use cases like "a coding assistant that only answers in Python" or "a customer support bot with a specific tone guide" — all controlled per session, not per deployment.

For the full-stack implementation connecting these patterns to a production Next.js application, Gemini API with Next.js 15 App Router — Full-Stack Production Guide walks through the deployment configuration and server component integration in more detail.

Next Step

Get the sendMessage mutation working first — make sure the type flows from Prisma through tRPC to your React component before adding streaming. Once you see IDE autocomplete on aiMessage.tokenCount in the frontend, you'll know the type chain is working. From there, swap in the streamMessage subscription and you'll already understand why each piece is there.

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