Daethyra/Build-RAGAI
📚 TutorialDaethyra
Interactive Jupyter Notebooks for mastering Retrieval-Augmented Generation (RAG) and building AI-powered applications.
Build-RAGAI provides a comprehensive, hands-on learning path for developers aiming to master the intricacies of RAG architectures. The repository utilizes Jupyter Notebooks to break down complex AI workflows into manageable, executable segments. It covers the full lifecycle of RAG development, from document ingestion and vector database integration to advanced retrieval strategies and agentic orchestration using LangGraph.
The project integrates industry-standard tools such as LangChain for pipeline orchestration, Hugging Face for model deployment, and LangSmith for observability. By experimenting with these notebooks, users gain practical experience in prompt engineering, context management, and the integration of diverse LLMs like DeepSeek and OpenAI. The modular nature of the notebooks allows developers to swap components easily, facilitating rapid prototyping and experimentation with different retrieval techniques, such as hybrid search and re-ranking, which are critical for building production-grade AI applications.
💡Highlights
- ├─Interactive RAG workflows
- ├─LangGraph & LangChain integration
- └─Hands-on LLM implementation
🎯For
- ├─AI Developers
- ├─Machine Learning Engineers
- └─Students