Automated workflow
Workflow Description
Intelligent workflow combining an AI agent with vector search to process user queries based on stored documents. Leverages Gemini language model with MongoDB memory for accurate, contextual responses through semantic understanding.
How it works
- 1.Receive user query through chat interface
- 2.Split uploaded documents into processable text chunks
- 3.Search MongoDB for documents most relevant to the query
- 4.Process query and context using AI agent with Gemini model
- 5.Store conversation history in MongoDB for retrieval
- 6.Send response back to user through chat
Use cases
- Smart customer support system answering questions from company knowledge base
- Document and contract analysis through intelligent questioning
- Enhanced internal search engine that understands query intent and context
Requirements
- Active Google Cloud account with Gemini API access
- OpenAI account for embeddings generation
- MongoDB Atlas database with vector index configured
- Documents or text files for indexing and knowledge base
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (14)
When chat message received
Chat Trigger
MongoDB Chat Memory
Memory Mongo Db Chat
Google Gemini Chat Model
Gemini Model
MongoDB Atlas Vector Store
Vector Store Mongo D B Atlas
Embeddings OpenAI
OpenAI
Sticky Note
Sticky Note
AI Traveling Planner Agent
Agent
Webhook
Webhook
Default Data Loader
Document Default Data Loader
Recursive Character Text Splitter
Text Splitter Recursive Character Text Splitter
MongoDB Atlas Vector Store1
Vector Store Mongo D B Atlas
Embeddings OpenAI1
OpenAI
Sticky Note1
Sticky Note
Sticky Note2
Sticky Note