https://github.com/leotodisco/smart-study-agent
https://github.com/leotodisco/smart-study-agent
agentic-ai llamaindex-rag ollama python rag
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/leotodisco/smart-study-agent
- Owner: leotodisco
- Created: 2025-04-06T20:25:00.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-06T21:11:09.000Z (2 months ago)
- Last Synced: 2025-04-09T22:02:52.367Z (about 2 months ago)
- Topics: agentic-ai, llamaindex-rag, ollama, python, rag
- Language: Python
- Homepage:
- Size: 6.84 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# 📚 AI-Powered Study Assistant using RAG
This is a project developed as a practical exercise for the **Agents course by Hugging Face**.
The main goal is to create an **AI agent** capable of supporting the learning process through **Retrieval-Augmented Generation (RAG)**, leveraging **LlamaIndex** to ingest, index, and query personal study documents.
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## 🎯 Project Goals
- ✅ Practice concepts from the Hugging Face *Agents* course.
- 📄 Build an intelligent assistant that can answer questions based on personal documents.
- 🤖 Integrate **RAG**, **LlamaIndex**, **ChromaDB**, and **Ollama** into a seamless workflow.
- 📚 Enhance study effectiveness by turning static notes into an interactive tool.
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## 🛠️ Tech Stack
- [LlamaIndex](https://www.llamaindex.ai/) – document ingestion, indexing, and retrieval
- [ChromaDB](https://www.trychroma.com/) – persistent vector store
- [Ollama](https://ollama.ai/) – local LLMs for fast, private responses
- Hugging Face Embeddings (BAAI/bge-small-en-v1.5)
- Python 3.10+
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## 🚀 How It Works
1. Documents are loaded from a specified folder.
2. They are split into sentences and converted into embeddings.
3. The embeddings are stored in ChromaDB.
4. You can ask questions in natural language and get context-aware responses powered by a local LLM.