A Step-by-Step Guide to Build a Fast Semantic Search and RAG QA Engine on Web-Scraped Data Using Together AI Embeddings, FAISS Retrieval, and LangChain

The post provides a tutorial on building a semantic search and retrieval-augmented generation (RAG) system using Together AI embeddings, FAISS retrieval, and LangChain. It explains how to transform web-scraped data into a question-answering service by embedding chunks of text and storing them in a millisecond-level vector index. The modular approach allows flexibility in swapping components while maintaining efficiency through the unified Together AI backend.