Building Multimodal Chatbots with LLMs: A Step-by-Step Guide

Multimodal chatbots are moving from demo to production. Instead of chaining separate pipelines for vision, speech, and text, developers now expect a single conversational interface that accepts images, audio, and text, then responds in kind. This guide walks through the architecture and implementation of a multimodal chatbot using modern LLM inference platforms, with concrete code you can run today.

What Makes a Chatbot Multimodal

A truly multimodal system does more than append an image to a text prompt. It maintains context across media types, handles tool use with visual inputs, and generates outputs that mix text, images, or audio. The core requirement is an inference backend that exposes unified endpoints for chat, vision, audio, and image generation through a single API surface.

Architecture Overview

A typical production architecture has three layers. The orchestration layer manages conversation state and routes requests. The inference layer handles model execution. The modality layer encodes and decodes media, converting images to base64, audio to compatible formats, and rendered text back to speech.

For the inference layer, you want an API that behaves like the OpenAI SDK so you can swap backends without rewriting client logic. Oxlo.ai provides fully OpenAI SDK compatible endpoints for chat, vision, audio, and image generation, which means your orchestration code stays portable.

Choosing Your Models

Model selection depends on which modalities you need.

For vision and language, you need a model that accepts image inputs alongside text. Oxlo.ai hosts Gemma 3 27B and Kimi VL A3B for vision tasks, while Kimi K2.6 offers advanced reasoning, agentic coding, and vision support with a 131K context window. For general text reasoning and tool use, Qwen 3 32B handles multilingual agent workflows, and Llama 3.3 70B serves as a reliable general-purpose flagship.

For audio, Whisper Large v3, Whisper Turbo, and Whisper Medium handle transcriptions, while Kokoro 82M provides lightweight text-to-speech. For image generation, Oxlo.ai Image Pro and Ultra, Flux.1, SDXL, and Stable Diffusion 3.5 are available through a single images/generations endpoint.

Because Oxlo.ai organizes 45+ open-source and proprietary models across 7 categories under one API key, you can mix and match without managing multiple provider accounts.

Implementation: Processing Vision and Text

The simplest way to add vision is to base64-encode an image and pass it inside the chat messages payload. Since Oxlo.ai is fully OpenAI SDK compatible, you can use the standard Python client with the Oxlo.ai base URL.

import base64
from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

def encode_image(path):
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

image_b64 = encode_image("diagram.png")

response = client.chat.completions.create(
    model="kimi-k2.6",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Explain this architecture diagram and suggest improvements."
                },
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{image_b64}"}
                }
            ]
        }
    ],
    stream=False
)

print(response.choices[0].message.content)

This pattern also supports multi-turn conversations, streaming responses, JSON mode, and function calling without changing the request