Power a Nexora RAG chatbot with Groq, Gemini, Pinecone, Cohere and Google Drive
Ready for 3.0What this workflow does
This AI workflow has 13 nodes and runs on Google Drive events (file created) (plus 1 other trigger). Along the way it connects Groq Chat Model, Pinecone Vector Store, Reranker Cohere and Google Drive and 1 more.
How it runs
Trigger: On Google Drive events (file created) (plus 1 other trigger).
- Download File from Drive (Google Drive): download
- Chatbot Response Agent (AI Agent): runs an AI agent, with model: Groq Chat Model; 1 tool (Main Pinecone Vector Store)
- Secondary Pinecone Vector Store (Pinecone Vector Store): stores documents in the Pinecone vector store, with embeddings: Generate Google Gemini Embeddings; document loader: Load Default Data Format
- Memory Buffer Window (Simple Memory): uses Simple Memory
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (11)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Groq Chat Model
@n8n/n8n-nodes-langchain.lmChatGroq - Pinecone Vector Store
@n8n/n8n-nodes-langchain.vectorStorePinecone - Reranker Cohere
@n8n/n8n-nodes-langchain.rerankerCohere - Google Drive Trigger
n8n-nodes-base.googleDriveTrigger - Google Drive
n8n-nodes-base.googleDrive - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow
From the author
Quick overview This workflow powers a RAG chatbot that answers chat messages using a Groq Llama model grounded in a Pinecone knowledge base, and automatically updates that knowledge base by ingesting new files added to a specific Google Drive folder using…