Spaces:
Sleeping
Sleeping
File size: 1,706 Bytes
ea86b97 818c1d2 a3ef303 818c1d2 a3ef303 818c1d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
title: RAGTesting
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.0.1
app_file: app.py
pinned: false
license: mit
short_description: A simple RAG demo
---
# Mini RAG Demo – Retrieval-Augmented Generation on Wikipedia
This is a lightweight Retrieval-Augmented Generation (RAG) app built with Gradio. It combines semantic search over a mini Wikipedia (`rag-datasets/rag-mini-wikipedia`) corpus with reranking and language generation to answer natural language questions using real documents.
---
## What It Does
- Embeds a query using a SentenceTransformer (`all-MiniLM-L6-v2`)
- Retrieves the top-5 most semantically similar Wikipedia passages using FAISS
- Reranks them using a CrossEncoder model (`cross-encoder/ms-marco-MiniLM-L-6-v2`)
- Generates an answer using a Hugging Face language model
---
## Tech Stack
- **Gradio** – Web interface
- **FAISS** – Fast dense vector retrieval
- **Sentence-Transformers** – Embedding & reranking
- **Transformers (Hugging Face)** – Language model for generation
- **Hugging Face Datasets** – Mini Wikipedia corpus (`rag-datasets/rag-mini-wikipedia`)
---
## Models Used
| Purpose | Model |
|---------------|---------------------------------------------|
| Embedding | `all-MiniLM-L6-v2` |
| Reranking | `cross-encoder/ms-marco-MiniLM-L-6-v2` |
| Generation | `mistralai/Mistral-7B-Instruct-v0.2` *(optional)* or a smaller model |
---
## 📦 Running Locally
To run the app locally:
```bash
git clone https://huggingface.co/spaces/YOUR_USERNAME/mini-rag-demo
cd mini-rag-demo
pip install -r requirements.txt
python app.py
```
|