Building a Local RAG Chat App with Reflex, LangChain, Huggingface, and Ollama

A Retrieval-Augmented Generation (RAG) chat app was developed using Python, combining Reflex, LangChain, Ollama, FAISS, and Hugging Face. It retrieves context from a pre-indexed dataset, feeds it to a local language model, and displays answers via a web interface. Key components include data processing, embedding generation, vector store creation, and LLM setup. The app showcases RAG's ability to improve language model accuracy through retrieved information. Future enhancements could involve larger models, specialized vector databases, and custom datasets.