Multi Modal Rag With Image And Text

via “multi-modal-rag-with-image-and-text”

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

Unique: Implements multi-modal RAG using shared embedding spaces for text and images, enabling cross-modal retrieval where text queries find images and image queries find text — a unified approach that treats modalities symmetrically

vs others: More comprehensive than text-only RAG because it handles visual content, and more practical than separate text and image pipelines because it uses unified embeddings for symmetric cross-modal retrieval

via “multimodal rag with image and text retrieval fusion”

Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

Unique: Fuses image and text retrieval by maintaining separate modality-specific embeddings and using cross-modal reranking to score relevance — unique in providing reference implementations for multimodal RAG that handle both modalities without requiring unified embedding spaces

vs others: More practical than single-modality RAG for technical documents because it retrieves both diagrams and explanatory text, and more efficient than naive cross-modal embedding because separate modality-specific models avoid representation bottlenecks

via “multimodal rag with image understanding and visual document processing”

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

Unique: Extends RAG to handle images as first-class retrieval objects by generating image embeddings and indexing them alongside text, enabling unified retrieval of both text and visual content. Integrates vision-capable LLMs to generate answers based on visual understanding of retrieved images.

vs others: More comprehensive than text-only RAG for visual document collections; simpler than building custom multimodal pipelines. Pathway's unified indexing approach treats images and text symmetrically in retrieval.

via “multimodal generation support for image and text outputs”

⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)

Unique: Integrates multimodal generation (text + images) as a composable generator component following the same abstraction as text generation, enabling seamless multimodal RAG pipelines — most RAG frameworks support only text generation

vs others: Enables richer responses than text-only RAG, though adds complexity and latency compared to text-only approaches

via “multimodal rag with image understanding and processing”

Open-source Python library to build real-time LLM-enabled data pipeline.

Unique: Integrates image processing into the same reactive pipeline as text processing, enabling images to be indexed and retrieved alongside text without separate workflows. Vision model outputs (descriptions, embeddings) flow directly into the retrieval index.

vs others: More comprehensive than text-only RAG because it indexes visual content; simpler than building separate image and text pipelines because both are unified in one framework.