Most retrieval-augmented generation systems today feel a bit stiff.

You ask a question. You wait. You get an answer.

It works, but it doesn’t “feel” like a conversation.

That’s fine for document search. But the moment you try to build something more human, voice assistants, live customer support, AI tutors, the cracks start to show. Latency becomes noticeable. Context feels fragile. Conversations don’t flow.

This is the gap that real-time RAG is meant to close.

At its core, RAG is still: Retrieve → Augment → Generate. What changes is how generation happens.

rag

From “Ask & Wait” to “Stay Connected”

Traditional RAG follows a request–response rhythm. Each question is a fresh start: authenticate, retrieve context, generate a response, disconnect. Then repeat.

Humans don’t talk like that. When you talk to a support agent or a tutor, the conversation stays open. There’s continuity. You interrupt. They clarify. You go back and forth. The system doesn’t “reset” after every sentence.

Gemini 2.0’s Multimodal Live API is designed for this exact interaction model. Instead of treating generation as a one-off call, it keeps the pipeline alive. Context flows in, and responses flow out, continuously, in text or audio, with very low latency.

Why Gemini 2.0 Flash Changes the Equation

Latency is the silent killer of conversational AI. Gemini 2.0 Flash dramatically reduces time-to-first-token, making responses feel immediate instead of computed. More importantly, it’s natively multimodal. Text, audio, images, even video can coexist in the same interaction.

The Multimodal Live API builds on that by maintaining a persistent connection, typically over WebSockets, so you’re not reinitializing the model every time the user speaks. This single detail unlocks an entirely different class of applications.

Setting the Groundwork

To get started practically, lets set up a google-genai client and a model.

pip install --upgrade google-genai PyPDF2

from google import genai

client = genai.Client(
    vertexai=True,
    project=PROJECT_ID,
    location="us-central1"
)

MODEL_ID = "gemini-2.0-flash-live-preview-04-09"

Gemini 2.0 Flash is fast; noticeably fast. That speed matters because latency is the first thing users notice in conversational systems, even if they can’t name it.

A Simple, Real Problem: Retail Support

Imagine you’re building a support assistant for Northstar Outfitters, an outdoor retail brand that sells camping gear, backpacks, and seasonal equipment. Internally, the company has a handful of PDFs that support agents rely on every day:

A customer asks: “Do you offer maintenance for hiking backpacks, and how much does it cost?”

If you ask a plain LLM, it will confidently make something up. That confidence is the bug. So we ground the model in the documents.

Turning Documents into Something the Model Can Use

First, we extract and embed the documents. This part looks like classic RAG, and that’s intentional.

vector_db = build_index(
    docs,
    embedding_client=client,
    embedding_model="text-embedding-005"
)

What this really does is simple: it turns messy human documents into a searchable semantic space. Now we can ask, “Which parts of our knowledge base matter right now?”

Retrieval: Finding the Right Context

When the user asks a question, we don’t rush to generate an answer. We pause, just briefly, to retrieve the most relevant chunks.

context = get_relevant_chunks(
    query,
    vector_db,
    client,
    "text-embedding-005"
)

This step is quiet but critical. It’s the difference between an assistant that sounds confident and one that is actually correct.

Where Real-Time RAG Starts to Feel Different

Here’s where things change. Instead of sending a single prompt and waiting, we open a live session with Gemini. The context is fed in, and the model begins responding immediately, streaming tokens as it reasons.

For text output:

await generate_answer(
    query=query,
    context=context,
    client=client,
    modality="text"
)

For audio output, the exact same pipeline works:

await generate_answer(
    query=query,
    context=context,
    client=client,
    modality="audio"
)

That symmetry is powerful. Text is no longer the default, just one option.

Putting It All Together

Once the pieces are in place, the entire Real-Time RAG flow becomes surprisingly small.

answer = await rag(
    question="Do you offer maintenance for hiking backpacks?",
    vector_db=vector_db,
    embedding_client=client,
    embedding_model="text-embedding-005",
    llm_client=client,
    llm_model=MODEL_ID,
    top_k=3,
    modality="text"
)

The important thing isn’t the function signature, it’s what happens under the hood:

To the user, it just feels… responsive.

Why the Multimodal Live API Changes Everything

The Live API isn’t just about speed. It’s about presence.

Because the connection is persistent, you don’t re-authenticate on every turn; you don’t re-initialize the model; you can stream text, audio, or both!

This is what makes AI call agents, live tutors, and assistive companions finally feel viable instead of gimmicky.

You’re no longer stitching together systems. You’re holding a conversation.

What Comes Next

Once you’ve built this once, the next steps feel obvious: You can swap in larger vector stores like Vertex AI Search, or add speech input for full duplex conversations, or even embed the system into web or Android apps, and extend grounding beyond PDFs to images or video.

But, the core idea doesn’t change.

That’s Real-Time RAG, and with Gemini 2.0 and the Multimodal Live API, it’s no longer experimental. It’s practical.