What session id should I use in LangChain?

Any stable identifier for the conversation — reuse your existing LangChain session or thread id so turns are grouped correctly.

Is passing the prompt required for LangChain?

It is optional but strongly recommended: the prompt is what makes the injected ad contextually relevant to the current turn.

How-to guide

How to Pass Session Context to Ads in LangChain

This guide shows how to pass session context to ads in a LangChain app, the goal being to supply session id, prompt, and metadata so injected ads are relevant. It builds on the Monetzly server SDK, so the approach is specific to how LangChain produces and streams responses.

Overview

Ad relevance in a LangChain app comes from context. When you inject, you pass a session id, the live prompt, and optional metadata — that is what lets Monetzly match a recommendation to what the user is actually asking about in that turn.

The session id groups turns from the same conversation; the prompt drives per-turn relevance; metadata carries anything extra you want to target on. All are supplied per injection call.

LangChain apps are usually built for RAG chatbots, tool-using agents, multi-step reasoning pipelines, and document QA, so pass session context to ads typically comes up while a user is mid-conversation, the moment where monetization has to be additive rather than disruptive.

How this works on LangChain

Grounded in this repo (src/app/api/agents/chat/route.ts) and the SDK's own LangChain example. A LangChain.js chat model returns a stream of message chunks that already carry a `content` field, so it can be passed straight into sdk.inject() with no adapter.

Because Monetzly's inject() accepts any async token stream, the LangChain side of this task is just mapping your output to it, no rewrite of your LangChain model call.

Steps

Choose a stable session id for the conversation (reuse your LangChain session or thread id).
Pass the current user prompt on each injection call.
Add optional metadata (topic, user type) to sharpen targeting.

Integration snippet

import { MonetzlySDK, MonetzlyConfig } from "@monetzly/server-sdk";
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const config: MonetzlyConfig = {
  apiKey: process.env.MONETZLY_API_KEY!,
  serverAddress: process.env.MONETZLY_SERVER_ADDRESS || "localhost:8080",
  useSSL: !process.env.MONETZLY_SERVER_ADDRESS?.startsWith("localhost"),
};
const sdk = new MonetzlySDK(config);
const llm = new ChatGoogleGenerativeAI({ model: "gemini-2.0-flash" });

await sdk.connect();
const llmStream = await llm.stream(messages); // chunks already have .content

for await (const token of sdk.inject(llmStream, {
  sessionId,
  prompt: messages[messages.length - 1]?.content ?? "",
})) {
  send(token.content ?? ""); // forward to client (SSE / WebSocket)
}
await sdk.disconnect();