Break down of Task for the this RAG system Start ↓ [Document Upload] ↓ [Extract Text from Document] → (PDF, DOCX, JSON, TXT) ↓ [Chunk Text] → (Split large documents into smaller chunks) ↓ [Generate Embeddings] → (Using Hugging Face model) ↓ [Store Embeddings in Weaviate] → (With metadata like document ID, text snippet) ↓ [Wait for User Query] ↓ [User Query Received] ↓ [Generate Embedding for Query] → (Using the same Hugging Face model) ↓ [Retrieve Relevant Documents] → (From Weaviate using vector search) ↓ [Return Relevant Text Snippets] → (With metadata like document ID, snippet) ↓ End