This is a LlamaIndex project using Next.js bootstrapped with create-llama.

Custom Components from this tutorial

Most of the code generated is started code from create-llama. The custom code for this tutorial can be found in:

• app/components/integration-ui: These are frontend modifications that allow users to authenticate and enable integrations
• app/hooks: react hook that encapsulates the paragon authentication logic
• app/api/permissions: POST endpoint and custom logic for receiving and processing permissions data
• app/api/*-upload: POST endpoint to receive ingestion data for upserting to Pinecone
• app/api/route.ts: the default chat API will now look for permissions from authenticated users
• app/api/chat/engine/agents/*agent.ts: OpenAI Agent implementation with function tools
• utility/request-utilities.ts: Functions that send POST requests to Paragon endpoint triggers for Salesforce and Slack

Getting Started

npm install
npm run dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Inter, a custom Google Font.

Using Docker

1. Build an image for the Next.js app:

docker build -t <your_app_image_name> .

2. Generate embeddings:

Parse the data and generate the vector embeddings if the ./data folder exists - otherwise, skip this step:

docker run \
  --rm \
  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
  -v $(pwd)/config:/app/config \
  -v $(pwd)/data:/app/data \
  -v $(pwd)/cache:/app/cache \ # Use your file system to store the vector database
  <your_app_image_name> \
  npm run generate

3. Start the app:

docker run \
  --rm \
  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
  -v $(pwd)/config:/app/config \
  -v $(pwd)/cache:/app/cache \ # Use your file system to store gea vector database
  -p 3000:3000 \
  <your_app_image_name>