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 based on the context. 🛠️ Technologies Used: 📦 Cohere Embedding API ⚡ Groq LLM for fast inference 🧠 n8n for orchestrating and visualizing the flow 🧲 In-Memory Vector Store (for prototyping) 🧪 Usage: Upload or point to your source documents. Embed them and populate the vector store. Ask questions through the chat trigger node. Receive context-aware responses based on retrieved content.