Skip to main content

​
Vision RAG

Vision RAG extends traditional RAG to handle images, diagrams, charts, and visual content using multimodal embeddings and vision-language models.

​
Overview

Vision RAG capabilities:
  • Image understanding: Extract information from images
  • Multimodal embeddings: Embed text and images in same space
  • Visual question answering: Query visual content naturally
  • Document analysis: Process PDFs with charts and diagrams

Multimodal Embeddings

CLIP or OpenAI vision embeddings for image-text alignment

Vision Models

GPT-4V, Claude 3, or Gemini for image understanding

Document Parsing

Extract text, images, and tables from complex PDFs

Visual Retrieval

Search across text and visual content simultaneously

​
Architecture

​
Implementation Example

from langchain_community.document_loaders import UnstructuredImageLoader
from langchain_openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma

# Load images
loader = UnstructuredImageLoader("path/to/images")
images = loader.load()

# Create multimodal embeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = Chroma.from_documents(images, embeddings)

# Query with vision model
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4-vision-preview")
response = llm.invoke([
    {"type": "text", "text": query},
    {"type": "image_url", "image_url": retrieved_image_url}
])

​
Use Cases

  • Analyze X-rays, MRIs, and CT scans
  • Retrieve similar cases from image database
  • Combine imaging with patient records
  • Process engineering diagrams and schematics
  • Search across text and visual instructions
  • Answer questions about product designs
  • Understand charts, graphs, and figures
  • Extract data from visualizations
  • Synthesize findings across visual and text content

​
Best Practices

Image Quality: Ensure images are high resolution and properly preprocessed for best embedding quality.
Use separate indices for text and images, then merge results for more control over retrieval.

Basic RAG

Start with text-only RAG

Multimodal Agents

Build agents with vision capabilities

Build docs developers (and LLMs) love

Get started for freeTalk to us