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| 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. | |