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AI Question Answering
A customer message reaches the RAG assistant. The agent uses OpenRouter for response generation and calls the knowledge base search tool to retrieve relevant document content before answering.
AI Automation Case Study
RAG Knowledge Base Agent
A document grounded AI assistant that searches approved business information before answering and keeps its searchable knowledge base synchronized when files are created, updated, or deleted.
Project Overview
The solution separates question answering from document management. Business documents are processed into searchable embeddings, stored in a vector database, and retrieved only when they are relevant to the user’s question.
Business Challenge
- Teams spend time searching across multiple documents.
- General AI responses may not reflect approved company information.
- Updated files can leave outdated knowledge in the system.
- Deleted documents must also be removed from search results.
Automated Solution
- Uses retrieval before generating an answer.
- Creates embeddings from newly uploaded documents.
- Removes old document chunks before processing an update.
- Deletes removed files from the knowledge base.
Grounded Answers
Responses use relevant document content
Automatic Updates
Knowledge follows document changes
Less Searching
Users ask questions in natural language
Project Walkthrough
Watch the RAG agent search approved business documents, retrieve relevant information, and generate a grounded response through one connected workflow.
How the Process Works
Four connected pipelines manage the full knowledge lifecycle and keep the answering experience aligned with the available source documents.
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Document Creation Pipeline
When a new file is uploaded to Google Drive, the workflow downloads it, extracts its content, generates embeddings, and stores the searchable document chunks in Supabase.
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Document Update Pipeline
When a source file changes, previous document chunks are removed first. The updated file is downloaded and passes through the creation pipeline again so search results use current information.
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Document Deletion Pipeline
When a document is deleted from Google Drive, its related records are removed from the knowledge base so unavailable content is no longer returned.
Project Visuals
The overview explains the business value while the full workflow shows the complete answering and document lifecycle.
Solution Overview
Full n8n Workflow
Important Automation Logic
Retrieval Before Response
The assistant searches the knowledge base before generating
an answer.
Vector Search
Embeddings make semantically relevant document chunks searchable.
Document Identity
Stored metadata connects each chunk to its source file for
later updates and deletion.
Remove Before Reindex
Old chunks are deleted before an updated document is processed again.
Lifecycle Synchronization
Create, update, and delete events keep the vector store aligned
with Google Drive.
Source Controlled Knowledge
Only approved documents placed in the connected source are indexed.
Technology and Integrations
| Tool | Role in the Solution |
|---|---|
| n8n | Coordinates triggers, document processing, retrieval tools, and AI responses. |
| Google Drive | Provides the source documents and file lifecycle events. |
| Gemini Embeddings | Converts document content into searchable numeric representations. |
| Supabase | Stores document chunks, metadata, and vector embeddings. |
| OpenRouter | Provides the chat model used by the RAG assistant. |
Production Considerations
A production RAG system needs controls for access, data quality, synchronization, cost, and monitoring.
Access Control
Index only approved files and protect the connected document source.
Index only approved files and protect the connected document source.
Credential Security
Protect Google, Supabase, model, and n8n credentials.
Protect Google, Supabase, model, and n8n credentials.
Duplicate Prevention
Ensure repeated file events do not create duplicate document chunks.
Ensure repeated file events do not create duplicate document chunks.
Failed Update Recovery
Handle cases where old chunks are removed but reindexing fails.
Handle cases where old chunks are removed but reindexing fails.
Rate Limits and Billing
Monitor embedding, chat model, database, and workflow usage.
Monitor embedding, chat model, database, and workflow usage.
Testing and Monitoring
Validate retrieval quality and review failed document events.
Validate retrieval quality and review failed document events.
Explore the Project
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