Multimodal chatbots that accept images alongside text are now standard infrastructure for customer support, document analysis, and autonomous agents. The implementation pattern is straightforward if you already work with LLM chat APIs, but vision introduces unique constraints around context length, token accounting, and model selection. This guide walks through building a production ready vision chatbot using Python and the OpenAI SDK, with Oxlo.ai as the inference backend.
How Vision Changes the Payload
Vision inputs are typically base64 encoded images or publicly accessible image URLs. Inside the model, each image is converted into a sequence of tokens, often thousands, which inflates the context window far beyond the text prompt. Unlike text, you cannot estimate token count from character length, so cost and latency become harder to predict on token-based platforms.
Choosing a Vision Model
Oxlo.ai hosts several vision-capable models through a single OpenAI compatible endpoint
Top comments (0)