This workflow demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline in n8n, split into two main sections:
š¹ 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.).
š¹ 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.
š ļø Technologies Used:
š¦ Cohere Embedding API
ā” Groq LLM for fast inference
š§ n8n for orchestrating and visualizing the flow
š§² In-Memory Vector Store (for prototyping)
š§Ŗ 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.
