This workflow demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline in n8n, split into two main sections: ๐น 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.). ๐น Part 2: Chat with the Vector Store Takes user input from

This workflow demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline in n8n, split into two main sections: ๐น 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.). ๐น 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. ๐ ๏ธ Technologies Used: ๐ฆ Cohere Embedding API โก Groq LLM for fast inference ๐ง n8n for orchestrating and visualizing the flow ๐งฒ In-Memory Vector Store (for prototyping) ๐งช 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.
Download the workflow JSON file after purchase.
Open n8n โ click the menu โ Import from File.
Select the downloaded JSON and import.
Set up credentials for each node that requires them.
Click Execute Workflow to test, then activate.
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