🌿To WAŻNE! BGE-M3 + Qwen 2.5 = Magia! Jak działa RAG w lokalnym AI Modele embeddingowe w praktyce.

The video explains the fundamental concept of embedding models in AI, which translate human language into mathematical vectors. These vectors represent the semantic meaning of words or phrases, allowing AI models to understand relationships between them. The presenter demonstrates this using the 'Jan' LLM application, showcasing how embedding models enable Retrieval Augmented Generation (RAG). RAG is explained as a process where an LLM, instead of relying solely on its training data, uses an embedding model to retrieve relevant chunks of text from a provided knowledge base (like a PDF). These chunks are then used as context for the LLM to generate a more informed and accurate answer. The video uses a visual analogy of a librarian (embedding model) fetching books for a writer (LLM) and illustrates how embedding models place words with similar meanings close to each other in a vector space, enabling efficient retrieval. A demo shows how uploading a PDF allows the LLM to answer questions based on its content, highlighting the practical application of this technology, especially for Polish language understanding.