---
title: Llm Project
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
- inference-api
---

An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

                                                    
                                                    
                                                    AI Tutor Chatbot 🤖
AI Tutor is an intelligent chatbot application built with LLamaIndex and RAG (Retrieval-Augmented Generation) techniques.
It is designed to answer questions strictly related to AI, ML, and their subfields. Questions outside of AI will not be answered.

                                                   Functionalities

Metadata Filtering:
Filter retrievals by keywords or metadata for more precise results.

Reranking for Accuracy:
Uses Cohere Reranker to improve the quality of retrieved nodes.

Context Augmentation via Perplexity API:
Enhances answers by providing additional context before generating a final response.

RAG Pipeline Evaluation:
Evaluates retrieval and answer quality using Faithfulness and Answer Relevance metrics.

Additional Data Sources:
Incorporates external sources beyond the course material to improve answer coverage.

                                                    Cost Estimation
The chatbot is optimized for low-cost experimentation.
Estimated cost to try all features: ~$0.50 or less
This includes all functionalities: LLM responses, reranking, and context augmentation.

                                                  Evaluation Results
The RAG system has been evaluated using faithfulness and answer relevance metrics:
        Metric	          Score
        Faithfulness	  0.93
        Answer Relevance  0.93
This indicates highly accurate, grounded, and relevant answers for AI-related queries.