Chat with your business knowledge base using Google Gemini and Qdrant
UnknownWhat this workflow does
This AI workflow has 15 nodes and runs when someone submits an n8n form (“Upload Business Document”) (plus 2 other triggers). Along the way it connects Qdrant Vector Store, Embeddings Google Gemini, Google Gemini Chat Model and Qdrant (community).
How it runs
Trigger: When someone submits an n8n form (“Upload Business Document”) (plus 2 other triggers).
- Insert to Vector Store (Qdrant Vector Store): stores documents in the Qdrant vector store, with embeddings: Embeddings Google Gemini; document loader: Default Data Loader
- Delete from Vector Store (Qdrant): delete points
- Set Context (Edit Fields (Set)): sets 3 fields: bot_name, company_name, support_email
- AI Agent: runs an AI agent, with model: Google Gemini Chat Model; 1 tool (Knowledge Base); memory: Simple Memory
- Create fileGroup Index (Qdrant): create payload index
Services it uses
Before you upgrade to n8n 3.0
We can't fully verify this one: it relies on 1 community package, and n8n 3.0 turns unverified community packages off by default (N8N_UNVERIFIED_PACKAGES_ENABLED=false).
- Check that n8n-nodes-qdrant is verified or supports 3.0 (used in Delete from Vector Store, Create fileGroup Index).
Nodes used (11)
- n8n Form Trigger
n8n-nodes-base.formTrigger - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Qdrant
n8n-nodes-qdrant.qdrant - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Edit Fields (Set)
n8n-nodes-base.set - AI Agent
@n8n/n8n-nodes-langchain.agent - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow
From the author
This comprehensive Retrieval-Augmented Generation (RAG) system enables businesses to effectively manage and query their knowledge base. Users can seamlessly upload documents via a web form, automatically segment and chunk the content, generate high-quality…