Multimodal RAG that ingests PDFs, generates grounded text, image outputs by retrieving relevant content from documents.
-
Updated
May 29, 2025 - Jupyter Notebook
#
multimodel-rag
Here are 7 public repositories matching this topic...
Cortex AI: Multi-Model Insights Hub is an advanced platform that leverages cutting-edge AI to empower your research, analysis, and data exploration. By integrating multiple Large Language Models (LLMs) with a sophisticated Retrieve-and-Generate (RAG) system
chatbot summarizer article-extractor pdf-document-processor rag vector-database streamlit-webapp llm chromadb deepseek-chat llama3 deepseek-r1 multimodel-rag youtube-video-analytics
-
Updated
Nov 22, 2025 - Python
A full-stack multimodal RAG system that intelligently ingests documents, performs semantic retrieval with hybrid reranking, and generates grounded, citation-backed answers using Ollama, Qdrant, Groq, and Python.
-
Updated
Jul 10, 2026 - TypeScript
This repository contains a full-stack Multimodal Retrieval-Augmented Generation (RAG) backend powered by FastAPI, PostgreSQL/pgvector, Google Gemini integration and Google's latest gemini-embedding-001
-
Updated
Mar 16, 2026 - Python
Curious how LLMs actually work? This is 10 hands-on notebooks that go from tokenization and generation to RAG, hallucinations, and building AI agents — all through experimentation.
memory gemini tokenization deepdive huggingface tools-for-thought prompting llms generative-ai reasoning-engine multimodel-rag
-
Updated
Jul 27, 2026 - Jupyter Notebook
A state-of-the-art Retrieval-Augmented Generation (RAG) system that transforms document processing and knowledge retrieval through hierarchical organization, advanced embedding techniques, and intelligent conversation management. This project combines cutting-edge AI technologies to create a sophisticated document intelligence platform.
microservices-architecture large-language-models vision-language-model retrieval-augmented-generation ai-deployment-and-integration weaviatedb multimodel-rag
-
Updated
Nov 20, 2025 - Python
An end-to-end Multimodal Retrieval-Augmented Generation (RAG) system using Docling, ChromaDB, CLIP, LangChain, and Google Gemini for intelligent PDF understanding.
python pdf clip rag streamlit document-ai llm generative-ai langchain google-gemini cromadb multimodel-rag
-
Updated
Jul 12, 2026 - Python
Add this topic to your repo
To associate your repository with the multimodel-rag topic, visit your repo's landing page and select "manage topics."