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The language model is accessed through the **Groq API** using the configured Qwen model.\n\n## 🧠 Architecture\n\n```text\nUser\n  ↓\nUpload PDF\n  ↓\nExtract Text\n  ↓\nSentence Transformer Embeddings\n  ↓\nChromaDB\n  ↓\nResearch Agent\n  ↓\nAnalysis Agent\n  ↓\nReview Agent\n  ↓\nVerified Final Answer\n  ↓\nUser\n```\n\n## 🤖 Agents\n\n### Research Agent\nRetrieves and organizes the most relevant evidence from the ChromaDB search results.\n\n### Analysis Agent\nAnalyzes the retrieved evidence and prepares a clear answer to the student's question.\n\n### Review Agent\nChecks whether the generated answer is supported by the uploaded study material and removes unsupported claims.\n\n## 🛠️ Technologies\n\n- Python\n- Flask\n- CrewAI\n- Groq API\n- Qwen LLM\n- ChromaDB\n- Sentence Transformers\n- PyPDF\n- HTML/CSS/JavaScript\n\n## 🔍 How It Works\n\n1. User uploads a PDF.\n2. PyPDF extracts the text.\n3. Text is split into smaller chunks.\n4. Sentence Transformers converts chunks into embeddings.\n5. Embeddings are stored in ChromaDB.\n6. User asks a question.\n7. ChromaDB performs semantic similarity search.\n8. Research Agent identifies relevant evidence.\n9. Analysis Agent generates an answer.\n10. Review Agent verifies the answer against the retrieved content.\n11. The verified answer is displayed.\n\n## ⚙️ Installation\n\nCreate a virtual environment:\n\n```bash\npython -m venv venv\n```\n\nWindows:\n\n```bash\nvenv\\Scripts\\activate\n```\n\nLinux/macOS:\n\n```bash\nsource venv/bin/activate\n```\n\nInstall dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\nCreate `.env` from `.env.example`:\n\n```text\nGROQ_API_KEY=your_groq_api_key\nGROQ_MODEL=qwen/qwen3.8-27b\nCHROMA_DIR=./data/chroma_db\nEMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2\nTOP_K=5\n```\n\nRun:\n\n```bash\npython app.py\n```\n\nOpen:\n\n```text\nhttp://127.0.0.1:5000\n```\n\n## ⚠️ Model Note\n\nThe project uses the model identifier supplied for this project:\n\n```text\nqwen/qwen3.8-27b\n```\n\nThe exact model identifier available through Groq can change. If your Groq account exposes a different Qwen model identifier, update `GROQ_MODEL` in `.env`.\n\n## 📁 Project Structure\n\n```text\nai_study_assistant_crewai_chromadb/\n│\n├── app.py\n├── config.py\n├── requirements.txt\n├── .env.example\n├── README.md\n│\n├── agents/\n│   ├── __init__.py\n│   └── crew_workflow.py\n│\n├── services/\n│   ├── __init__.py\n│   ├── document_service.py\n│   └── vector_store.py\n│\n├── data/\n│   └── chroma_db/\n│\n├── templates/\n│   └── index.html\n│\n└── uploads/\n```\n\n## 🔮 Future Enhancements\n\n- Support DOCX, PPTX and TXT files\n- User authentication\n- Multiple document collections\n- Chat history\n- Source/page citations\n- Improved RAG retrieval\n- Hybrid keyword + semantic search\n- Quiz generation\n- Automatic summaries\n- Voice-based questions\n- Student progress tracking\n- Production database and cloud deployment\n\n## 🎯 Objective\n\nThe objective of this project is to demonstrate how **multi-agent AI and Retrieval-Augmented Generation (RAG)** can be used to build a reliable study assistant that answers questions using uploaded educational materials rather than relying only on the model's general knowledge.\n","readmeExcerpt":"📚 AI Study Assistant Using CrewAI & ChromaDB An AI-powered Study Assistant that allows students to upload study materials in PDF format and ask questions about the uploaded content. 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