Local Chatbot with Retrieval Augmented Generation (RAG)

Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial Click here to view the YouTube Tutorial How it works Build a chatbot that answers based on documents you provide it (Retrieval Augmented Generation). You can upload as many PDF files as you want to the Qdrant database. The chatbot will use its retrieval tool to fetch the chunks and use them to answer questions. Installation Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. How to use it First run the "Data Ingestion" part and upload as many PDF files as you want

Help me set up the n8n workflow "Local Chatbot with Retrieval Augmented Generation (RAG)" (https://n8n.io/workflows/5148). It uses: AI Agent, Ollama Chat Model, Simple Memory, Recursive Character Text Splitter, Default Data Loader, Qdrant Vector Store, Embeddings Ollama. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.