README.md

---
title: Multimodal RAG Kaggle Based
emoji: 👁
colorFrom: red
colorTo: pink
sdk: gradio
sdk_version: 5.25.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: Multimodal RAG to augment english recipes searches
---

# Multimodal Retrieval System with FAISS

This repository contains a prototype system for multimodal information retrieval using FAISS, capable of searching across text and images using vector similarity.

## Structure

- `notebook/` (or `.ipynb`): Contains the logic to generate the vector indexes for both text and images.
- `app.py`: Gradio-based interface for interacting with the system.
- `search_ocean.py`: Core logic for performing FAISS-based similarity search using precomputed indexes.
- `text_index.faiss`, `image_index.faiss`: The FAISS index files generated by the notebook (already included in the app).
- `metadata_text.json`, `metadata_image.json`: Associated metadata for mapping index results back to source information.

## What it does

- Loads precomputed FAISS indexes (for text and image).
- Performs retrieval based on a text or image query.
- Returns top matching results using cosine similarity.

## What it doesn't (yet) do

- No generation step (e.g., using LLMs) is implemented in this app.
- While the code for image retrieval is ready, image indexes must be built in the notebook beforehand.
- There is **no context overlap** implemented when chunking the data for indexing. Each chunk is indexed independently, which may affect the quality of retrieval in some use cases.

## Dependencies

- `faiss-cpu`
- `sentence-transformers`
- `openai-clip`
- `torch`
- `torchvision`
- `gradio`
- `Pillow`

## Notes

- The app is designed to separate concerns between indexing (offline, notebook) and retrieval (live, Gradio app).
- You can easily extend this to include LLM generation or contextual QA once relevant results are retrieved.