This project relies on OpenAI to generate a newsletter. Enter your favorite topics, add your own links, and let the app generates a digest for you. Behind the scenes, webpages are downloaded, and the content is summarized using the LLM. The app takes care of rendering the result in a single page.
This app is powered by Spring AI, an open-source Spring project which simplifies the use of AI technologies for your Java project.
Use Java 17+ to build and run this app.
Use this command to build the app:
./mvnw clean packageThe application was tested with 3 AI providers: OpenAI, Azure OpenAI, AWS Bedrock based on Llama2, and Ollama. Choose the one you preferred and enable it based on the options below.
You need an OpenAI API key to run this app. Refer to this page to get an API key.
Set your API key as an environment variable:
export OPENAI_API_KEY=xxxxxxSet newsletter.ai.model to openai in application.properties.
Only uncomment spring-ai-openai-spring-boot-starter in pom.xml, comment the other three spring-ai dependencies.
It appears that currently this model is only available at us-east-1. Enable the API at https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/
Then export your AWS key and secret:
export AWS_CLIENT_ID=xxxxxxxxxx
export AWS_SECRET=xxxxxxxxxChange the propety spring.ai.bedrock.llama2.chat.enabled to true in application.properties.
Set newsletter.ai.model to bedrock-llama2 in application.properties.
Only uncomment spring-ai-bedrock-ai-spring-boot-starter in pom.xml, comment the other three spring-ai dependencies.
Enable the Azure OpenAI service in the Azure portal. This requires to fill out a form at the moment, which usually takes at most 24 hours. Create the service at https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI. Create an Azure OpenAI deployment at https://oai.azure.com/portal.
Export the following environment variables:
export AZURE_OPENAPI_KEY=xxxxxxx
export AZURE_OPENAPI_ENDPOINT=https://xxxxxx.openai.azure.com/
export AZURE_OPENAPI_DEPLOYMENT=xxxxxxxSet newsletter.ai.model to azure-openai in application.properties.
Only uncomment spring-ai-azure-openai-spring-boot-starter in pom.xml, comment the other two spring-ai dependencies.
The application relies on Ollama for providing LLMs. You can run Ollama locally on your laptop (macOS, Linux or Windows-preview).
First make sure you have Ollama installed on your laptop (macOS, Linux or Windows-preview).
For macOS, the simplest is to use brew, e.g.
brew install ollama
Start the ollama process, e.g.
ollama start
Run the model, e.g.
ollama run llama2
This will download the LLM first time you run it.
The most popular LLM is llama2 - see documentation and is about 3.8GB download.
The default model for Spring AI is mistral, and can be modified with spring.ai.ollama.chat.options.model property.
For more details, see Spring AI Ollama documentation.
To get the list of available models, please refer to Ollama documentation.
Default ollama serving port locally is 11434, surprisingly :)
Only uncomment spring-ai-ollama-spring-boot-starter in pom.xml, comment any other Spring AI LLM service starters.
Use this command to run the app on your local workstation:
./mvnw spring-boot:runThe app is available at http://localhost:8080.
Contributions are always welcome!
Feel free to open issues & send PR.
Copyright © 2024 Broadcom, Inc. or its affiliates.
This project is licensed under the Apache Software License version 2.0.