https://github.com/deliciousboy/rag-finder

https://github.com/deliciousboy/rag-finder

nlp rag streamlit webapp

Last synced: about 2 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/deliciousboy/rag-finder
• Owner: DeliciousBoy
• License: mit
• Created: 2025-03-05T19:44:59.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-22T17:15:54.000Z (about 1 year ago)
• Last Synced: 2025-07-03T02:03:36.733Z (12 months ago)
• Topics: nlp, rag, streamlit, webapp
• Language: Python
• Homepage:
• Size: 1.32 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: docs/README.md
  • License: LICENSE

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README

          


  



# Fantastic RAGs

## Overview

Fantastic RAGs is an AI-powered chatbot that uses Retrieval-Augmented Generation (RAG) to answer user queries about magical creatures from the Fantastic Beasts universe. Leveraging large language models and vector search, it provides context-aware and lore-accurate responses in natural language.

## Response RAG  chatbot

### Question 1

![Question 1](../.github/img/Question_1.png)

### Question 2

![Question 2](../.github/img/Question_2.png)

### Question 3

![Question 3](../.github/img/Question_3.png)

### Question 4

![Question 4](../.github/img/Question_4.png)

### Question 5

![Question 5](../.github/img/Question_5.png)

### Question 6

![Question 6](../.github/img/Question_6.png)

## Technologies Used

- Python 

- LangChain

- FAISS

- microsoft/Phi-4-mini-instruct

- BAAI/bge-m3

- Streamlit

## Vector Store Creation

This notebook demonstrates how to create a FAISS vector store from the Fantastic Beasts knowledge base. Vector embeddings are generated using the BAAI/bge-m3 model for efficient semantic search capabilities.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DeliciousBoy/rag-finder/blob/main/notebooks/create_vector_store.ipynb)