I'm building a Retrieval-Augmented Generation (RAG) pipeline for our financial analysts. The documents are full of complex charts and tables that the current "text-only" chunks completely miss. I want to use a Multimodal AI like Gemini or GPT-4o to "see" the charts and answer questions like "What was the growth trend in Figure 3?" How do I index images alongside text in a Vector Database? Do I need to generate text captions for every image, or is there a way to store "Multimodal Embeddings" directly?
3 answers
The modern way to handle this is using "Vision-Language Models" (VLMs) to create a unified index. Instead of just captioning, you use a model like "ColPali" or a CLIP-based encoder to generate embeddings for the entire document page as an image. This preserves the spatial relationship between the text and the charts. When a user asks a question, you embed the query and perform a similarity search in a vector DB like Pinecone or Weaviate. The retrieved "image patches" are then fed into a multimodal LLM (like GPT-4o) which can reason across both the pixels of the chart and the surrounding context to provide a precise answer.
How are you handling the "Optical Character Recognition" (OCR) for the text inside those charts? Often, standard embeddings miss the actual numbers written on the graph axes.
Keep it simple: use a tool like "Unstructured.io" to partition your PDFs. It automatically detects images and can send them to an LLM to generate a text summary for your index.
I second that, Susan. Using "Image-to-Text" summaries as a middle layer makes it much easier to use your existing text-based vector search infrastructure without a complete overhaul.