multimodal-rag

Index and retrieve text, image, audio, video, and PDF content via multimodal RAG.

1|Updated Feb 8, 2026

Search Tags:#pdf #indexing #image #search #multimodal #video #memory #audio

Ready to run

npx skills add https://github.com/framerslab/agentos-skills --skill multimodal-rag-framerslab

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multimodal-rag
Source: https://github.com/framerslab/agentos-skills/tree/main/registry/curated/multimodal-rag
Command: npx skills add https://github.com/framerslab/agentos-skills --skill multimodal-rag-framerslab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive solution for indexing, searching, and retrieving content across various modalities, including text, images, audio, video, and PDFs.

Core Features & Use Cases

Multi-modal Content Indexing: Seamlessly index text, images, audio, video, and PDF content.
Search Across Modalities: Perform a unified text query to find relevant content across different modalities.
Query-by-Image/Audio: Identify similar content by uploading images or audio clips.
Use Case: Enhance your knowledge base with this Skill by ingesting a collection of research papers, including their text, images, and supplementary PDF documents.

Quick Start

Ingest all the PDFs and images related to my project research into the multimodal search index.

Frequently Asked Questions about multimodal-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I index and search across text, image, audio, video, and PDF content simultaneously?▼

Cross-modal content indexing and retrieval is handled by ingesting text, images, audio, video, and PDFs into a unified pipeline, allowing a single text query to search and retrieve matching content across all these modalities.

What is the best way to find similar images or audio clips within a large knowledge base?▼

Query-by-image and query-by-audio capabilities allow you to upload an image or audio clip to identify and retrieve similar content across your indexed knowledge base.

Do I need speech to text and vision capabilities to build a multimodal RAG pipeline?▼

Yes, multimodal RAG requires image processing, speech to text, and vision capabilities to parse, extract, and index content from images, audio, video, and PDFs effectively.

Can I use a text query to retrieve relevant information from research papers and their supplementary images?▼

Yes, you can ingest research papers, supplementary PDFs, and images into the search index, enabling a unified text query to retrieve cross-modal matching content seamlessly.

How does cross-modal content matching work for cognitive agents and APIs?▼

Cross-modal matching works by indexing extracted features from text, images, audio, video, and PDFs, optimized for cognitive agents and APIs to efficiently query and retrieve unified content across modalities.

What are the limitations when processing video and PDF content for multimodal search?▼

Processing video and PDF content depends on adequate image processing and speech to text capabilities, meaning incomplete media extraction or missing prerequisite vision tools can limit cross-modal retrieval accuracy.