AlekSystem Workflow Detail

Search hardware inventory with Supabase vector RAG and Google Gemini Workflow Solution

Search hardware inventory with Supabase vector RAG and Google Gemini

Advanced AI Inventory Agent: Supabase Vector RAG & Gemini This workflow upgrades your AI agent from simple sheet reading to high-performance Vector RAG.

Rank 61 Verified workflow

Workflow overview

Why this workflow matters

Helpful for business development and pipeline building. Improves internal consulting operations and productivity.

Advanced AI Inventory Agent: Supabase Vector RAG & Gemini This workflow upgrades your AI agent from simple sheet reading to high-performance Vector RAG. It allows your assistant to search through thousands of items with lightning speed and high accuracy. Purpose: To provide a scalable, professional-grade retrieval system for hardware inventory. It uses "semantic search" to find products even when the user makes typos or uses different terminology. Setup Instructions: Supabase: Run the provided SQL to create the documents table and the match_documents function. Credentials: Connect your Supabase (Service Role Key) and Google Gemini API credentials. Sync Workflow: Run the "Path A" workflow to index your Google Sheets data into the vector database. Chat Workflow: Use the "Path B" workflow as your production chat interface. Prompt: Customize the System Prompt to define your brand's specific tone and sales rules. Ideal for: Large product catalogs (100+ items), technical hardware inventories, and high-traffic customer support bots. To learn more about how to build and optimize this workflow, read the full blog post here.

Best fit

Categories

AI/MLCommunicationSalesDocument OpsProductivity

Services

Google SheetsAI AgentSimple MemorySupabase Vector StoreDefault Data LoaderEmbeddings Google GeminiGoogle Gemini Chat Model

Use cases

sales automationsupport automationdocument intelligence