Basic RAG chat

This workflow demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline in n8n, split into two main sections: 🔹 Part 1: Load Data into Vector Store Reads files from disk (or Google Drive). Splits content into manageable chunks using a recursive text splitter. Generates embeddings using the Cohere Embedding API. Stores the vectors into an In-Memory Vector Store (for simplicity; can be replaced with Pinecone, Qdrant, etc.). 🔹 Part 2: Chat with the Vector Store Takes user input from a chat UI or trigger node. Embeds the query using the same Cohere embedding model. Retrieves similar chunks from the vector store via similarity search. Uses Groq-hosted LLM to generate a final answer ba

Help me set up the n8n workflow "Basic RAG chat" (https://n8n.io/workflows/5028). It uses: Question and Answer Chain, Embeddings Cohere, Vector Store Retriever, Recursive Character Text Splitter, Simple Vector Store, Default Data Loader, Groq Chat Model. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.