Demo on how easy it is to build AI-driven applications with Spring Boot and Spring AI. It also shows how to implement advanced techniques for the adaption of foundation models with Function Calling and RAG.
Slides: Building AI-Driven Spring Applications with Spring AI
As Ollama doesn't yet provide a text-to-image model, recipe image generation is not available with this setup. Also Function calling is not supported by the Ollama models.
- Download and install Ollama on your local machine link
- Start llama3 model
ollama run llama3
By enabling the "ollama-compose" Spring profile, the llama3 model will be automatically started and configured with docker compose. Depending on your system (e.g. ARM macs) this is not a recommended setup due to performance reasons.
export SPRING_PROFILES_ACTIVE=ollama-compose
Set the API key via an environment variable or in application-openai.yaml.
export SPRING_AI_OPENAI_API_KEY=<INSERT KEY HERE>
Run your application with the "openai" Spring Profile.
export SPRING_PROFILES_ACTIVE=openai
Set the API key and endpoint via environment variables or in application-azure.yaml.
export SPRING_AI_AZURE_OPENAI_API_KEY=<INSERT KEY HERE>
export SPRING_AI_AZURE_OPENAI_ENDPOINT=<INSERT ENDPOINT URL HERE>
Run your application with the "azure" Spring Profile.
export SPRING_PROFILES_ACTIVE=azure
On your local machine, a Redis database is automatically started and configured with docker compose.
./gradlew bootRun
Open http://localhost:8080 in your browser. Enter the ingredients (e.g. "Potatoes") you want to find a recipe for in the form and press the "find" button.
By checking the "Prefer available ingredients" checkbox, Function Calling will be enabled.
As the functionalities to add always available ingredients and for the API call to check the available ingredients in the fridge are not yet implemented, they can be configured via the
app.always-available-ingredients and app.available-ingredients-in-fridge properties in application.yaml.
Bacon and onions are currently configured for available ingredients in fridge.
With the input "Potatoes", you should get a recipe with potatoes and bacon.
By checking the "Prefer own recipes" checkbox, Retrieval-Augmented Generation will be enabled.
To upload your own PDF documents for recipes to the vector database, there is a REST API endpoint implemented.
curl -XPOST -F "file=@$PWD/my-recipe.pdf" http://localhost:8080/api/v1/recipes/upload
The sample recipe part of this repository is a potato soup. With the input "Potatoes", you should get a recipe that goes in the direction of a potato soup.
kubectl apply -f deployment/kubernetes/ollama.yaml
export SPRING_AI_AZURE_OPENAI_API_KEY=<INSERT KEY HERE>
export SPRING_AI_AZURE_OPENAI_ENDPOINT=<INSERT ENDPOINT URL HERE>
envsubst < deployment/kubernetes/openai.yaml | kubectl apply -f -
export SPRING_AI_OPENAI_API_KEY=<INSERT KEY HERE>
envsubst < deployment/kubernetes/azure-openai.yaml | kubectl apply -f -