Effortless AI Chatbot: Building Internal RAG with Gemini, GAS, and Slack
infrastructure#rag📝 Blog|Analyzed: Mar 19, 2026 04:00•
Published: Mar 19, 2026 03:58
•1 min read
•Qiita LLMAnalysis
This article unveils a streamlined approach to building a Retrieval-Augmented Generation (RAG) chatbot using Google's Gemini API, Google Apps Script (GAS), and Slack. It showcases how to simplify the traditionally complex process of managing vector databases, making it accessible even without specialized infrastructure knowledge. The integration of URL links to source documents is a brilliant touch, enhancing both the reliability and user trust in the AI's responses.
Key Takeaways
- •Leverages Google's File Search Tool to eliminate the need for manual vector database management.
- •Employs Google Apps Script (GAS) for serverless operation, simplifying deployment and maintenance.
- •Integrates with Slack for an intuitive user interface, enhancing accessibility for all employees.
Reference / Citation
View Original"This method's biggest advantage is that all the troublesome 'vector database management' can be entrusted to Google."