This GenAI application stack will get you started building your own GenAI application in no time. The demo applications can serve as inspiration or as a starting point.

Create a .env file from the environment template file env.example

MacOS and Linux users can use any LLM that's available via Ollama. Check the "tags" section under the model page you want to use on https://ollama.ai/library and write the tag for the value of the environment variable LLM= in th e.env file. All platforms can use GPT-3.5-turbo and GPT-4 (bring your own API keys for OpenAIs models).

MacOS Install Ollama in MacOS and start it before running docker compose up.

Linux No need to install Ollama manually, it will run in a container as part of the stack when running with the Linus profile: run docker compose --profile linux up.

Windows Not supported by Ollama, so Windows users need to generate a OpenAI API key and configure the stack to use gpt-3.5 or gpt-4 in the .env file.

To start everything

docker compose up

If changes to build scripts has been made, rebuild

docker compose up --build

To enter watch mode (auto rebuild on file changes). First start everything, then in new terminal:

docker compose alpha watch

Shutdown Is health check fails or containers doesn't start up as expected, shutdown completely to start up again.

docker compose down

UI: https://localhost:8501 DB client: https://localhost:7474

• answer support question based on recent entries
• provide summarized answers with sources
• demonstrate difference between
  • RAG Disabled (pure LLM response)
  • RAG Enabled (vector + knowledge graph context)
• allow to generate a high quality support ticket for the current conversation based on the style of highly rated questions in the database.

(Chat input + RAG mode selector)

(CTA to auto generate support ticket draft)

(UI of the auto generated support ticket draft)

UI: https://localhost:8502 DB client: https://localhost:7474

• import recent SO data for certain tags into a KG
• embed questions and answers and store in vector index
• UI: choose tags, run import, see progress, some stats of data in the database
• Load high ranked questions (regardless of tags) to support the ticket generation feature of App 1.

UI: https://localhost:8503
DB client: https://localhost:7474

This application lets you load a local PDF into text chunks and embed it into Neo4j so you can ask questions about its contents and have the LLM answer them using vector similarity search.