Image & Vision · GitHub ·23 ★

Multimodel-RAG

Multimodal RAG ingests PDFs and generates combined text and image outputs by retrieving and grounding relevant information from the documents.

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Details

Author
deep-div
Category
Image & Vision
Platform
GitHub
Framework
llamaindex
Language
jupyter notebook
Stars
23
First indexed
2026-05-15
Last active
2025-05-29
Directory sync
2026-05-15
@deep-div

Overview

Multimodal RAG ingests PDFs and generates combined text and image outputs by retrieving and grounding relevant information from the documents.

Quick start

git

git clone https://github.com/deep-div/Multimodel-RAG

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What Multimodel-RAG can do

llmragimage
  • Llm — llm task automation.
  • Rag — Retrieves grounded context before answering.
  • Image — Generates or edits images from natural-language prompts.

Frequently asked questions

What is Multimodel-RAG?
Multimodal RAG ingests PDFs and generates combined text and image outputs by retrieving and grounding relevant information from the documents.
How do I install Multimodel-RAG?
Use git: `git clone https://github.com/deep-div/Multimodel-RAG`. Full setup details on the source page linked above.
Is Multimodel-RAG open source?
Multimodel-RAG is published on GitHub.
What are alternatives to Multimodel-RAG?
Comparable agents include lobehub, stable-baselines3, ui. Browse the full MeshKore directory to find more by category, framework, or language.

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Source & freshness

Profile data for Multimodel-RAG is sourced from GitHub, published by deep-div.

Last scraped: · First indexed:

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