Ollama’s multimodal capabilities let you feed images directly into LLMs, transforming how we interact with documents.
Let’s see it in action. Imagine you have a PDF invoice and you want to extract the total amount.
First, you need to convert the PDF pages into images. You can use a tool like pdftoppm for this:
pdftoppm -jpeg invoice.pdf invoice_page
This creates invoice_page-01.jpeg, invoice_page-02.jpeg, and so on.
Now, you can load an Ollama multimodal model, like llava, and send the image along with your prompt.
ollama run llava "What is the total amount on this invoice? Respond with only the number." -f invoice_page-01.jpeg
The model will process the image and, if successful, return just the total amount.
This works because multimodal models like LLaVA are trained on vast datasets of images paired with text descriptions. They learn to associate visual features with linguistic concepts. When you send an image and a prompt, the model internally creates a representation of the image and then uses its language understanding to connect that visual representation to the words in your prompt. It’s not just "seeing" pixels; it’s understanding the semantic content of those pixels.
The core of this capability lies in the model’s architecture, often a combination of a vision encoder (like CLIP) and a language model. The vision encoder transforms the image into a sequence of numerical representations (embeddings) that the language model can understand. This bridge allows the LLM to "reason" about visual information.
To get the best results, the quality of the input image is paramount. Clear, high-resolution images with good contrast will yield much better results than blurry or low-resolution scans. The prompt itself also plays a crucial role; be specific about what you want the model to identify or extract from the image.
When prompting, you can ask a wide range of questions. For instance, if you had a diagram, you could ask:
ollama run llava "Describe the flow of information in this diagram." -f diagram.png
Or for a screenshot of an application:
ollama run llava "What is the current status of the order shown in this screenshot?" -f order_status.png
The system handles the heavy lifting of encoding the image into a format the LLM can process, making it seem like the LLM is directly "seeing" the image. You don’t need to manually embed the image; Ollama abstracts that complexity away.
A detail that often trips people up is the exact format and pathway for the -f flag. It expects a file path to the image. If you’re working with multiple images or dynamically generated ones, ensuring the path is correct and the file is accessible is key. Errors here often manifest as generic "model not found" or "input error" messages that don’t immediately point to an image loading issue.
The next frontier is streaming multimodal output, allowing you to get responses as the model processes the image and text.