Building Chatbots with LLM and Computer Vision

Multimodal chatbots that combine large language models with computer vision have moved from research demos to production requirements. Whether you are building a customer support agent that analyzes screenshots, a warehouse assistant that reads barcode labels, or a documentation tool that interprets diagrams, the architecture is straightforward: a vision-language model ingests images and text, reasons over both, and responds in natural language. The practical challenge is not the concept, but selecting infrastructure that keeps latency low and costs predictable as image payloads grow.

Architecture Pattern

A production vision chatbot usually follows a simple pipeline. The client uploads an image, the application encodes it as base64 or hosts it at a temporary URL, and the payload is sent to a chat completions endpoint alongside the conversation history. The model returns a text completion, and the cycle repeats.

Because Oxlo.ai exposes vision and language models through a single OpenAI-compatible endpoint at https://api.oxlo.ai/v1, you can route standard LLM tasks and vision tasks through the same client. There is no need to manage separate SDKs or switch providers when the conversation moves from text to image analysis.

Selecting Models

Oxlo.ai hosts multiple vision-capable models. For image understanding, Gemma 3 27B offers strong performance on general visual reasoning, while Kimi VL A3B provides advanced vision-language capabilities. If the chatbot needs deep reasoning after interpreting an image, you can switch the same thread to models such as Qwen 3 32B for multilingual agent workflows, Llama 3.3 70B for general-purpose follow-up, or DeepSeek R1 671B MoE for complex coding and logic tasks. All are accessible through identical chat/completions calls.

Implementation

The fastest way to start is with the OpenAI Python SDK pointed at Oxlo.ai. The following snippet encodes a local image and sends it in a user message.

python
import os
import base64
from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

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

base64_image = encode_image("receipt.jpg")

response = client.chat.completions.create(
    model=os.getenv("OXLO_VISION_MODEL"),