This sample helps you use AI to take over your business's email customer support.

These genkit flows take an email as input. If the email is a question that can be answered with AI, the provided flow will run a genkit flow that:

• classifies the intent
• extracts relevant IDs from the inquiry
• loads corresponding data from a SQLite database
• drafts a response to send back

If the email cannot be answered with AI or requires taking some actions, it will terminate and escalate the inquiry.

This sample demonstrates:

• Answering questions using a database + LLM
• Conditional branching based on intent (different data is added to the prompt based on whether the inquiry was an Order vs. Refund vs. Product question)
• Using an LLM call to escalate the request to a human based on certain conditions

Before you begin, make sure you have the following installed:

• Node (v18+)
• npm
• Genkit CLI (v1.0+)

1. Install dependencies:

   pnpm i
   

2. Set up your database: a. Set up your environment variables: Create a .env file in the root directory and add the following:

   PROJECT_ID=[YOUR PROJECT ID]
   LOCATION=[YOUR LOCATION]
   

   b. Set up the database and seed with sample data:

   cd src/
   npx prisma generate
   npx prisma migrate dev --name init
   npm run prisma:seed
   cd ../
   

3. Test the email responder with sample data

   After seeding the database, you can test the chatbot with these example queries that match our seed data:

   # In a different terminal
   npm run genkit:dev
   
   # Test the classify inquiry flow to detect intent
   genkit flow:run classifyInquiryFlow '{
     "inquiry": "Is the Classic Blue T-Shirt for $19.99 still in stock?"
   }'
   
   # Test e2e CS flow (generates a email response to the input inquiry)
   genkit flow:run customerServiceFlow '{
     "from": "john.doe@example.com",
     "to": "support@company.com",
     "subject": "Product Catalog",
     "body": "Do you guys carry shirts?",
     "sentAt": "2024-03-14T12:00:00Z",
     "threadHistory": []
   }'

   Seeded Data Reference

   The SQLite database comes pre-seeded with some data so that you can easily test out your queries.

   Products:

   • Classic Blue T-Shirt ($19.99, SKU: BLU-TSHIRT-M)
   • Running Shoes ($89.99, SKU: RUN-SHOE-42)
   • Denim Jeans ($49.99, SKU: DEN-JEAN-32)
   • Leather Wallet ($29.99, SKU: LEA-WALL-01)
   • Wireless Headphones ($149.99, SKU: WIR-HEAD-BK)

   Customers and Their Orders:

   • John Doe (john.doe@example.com)
     • Order TRACK123456: 2 Blue T-Shirts, 1 Running Shoes (DELIVERED)
   • Jane Smith (jane.smith@example.com)
     • Order TRACK789012: 1 Denim Jeans (PROCESSING)
   • Bob Wilson (bob.wilson@example.com)
     • Order TRACK345678: 1 Leather Wallet, 1 Wireless Headphones (PENDING)