Basic RAG Pipeline

This example shows how to use Qdrant Vector Store in a basic Retrieval Augmented Generation (RAG) pipeline:

1. Connect a Document Parser to extract text from documents
2. Connect a Preprocessor to clean and prepare the text
3. Connect an Embeddings node to convert text to vector embeddings
4. Connect the Embeddings output to the Qdrant Vector Store Documents input
5. Connect a question input to the Qdrant Vector Store Questions input
6. Connect the Qdrant Answers output to an LLM for generating responses