Welcome to the repository for our project RAG4J.org. This project is a Java implementation of the Retrieval Augmented Generation framework. It is a framework that is simple to use and understand. But powerful enough to extend for your own projects.
Use one of the installers like:
Oh and we use maven, so install that if you do not have access to it yet.
Use an IDE of your choice, we use IntelliJ IDEA, but something like Eclipse or VS Code will work as well.
To test your installation, run the following command:
mvn clean packageWe try to limit accessing Large Language Models and vector stores to a minimum. This is enough to learn about all the elements of the Retrieval Augmented Generation framework, except for the generation part. In the workshop we use the LLM of Open AI, which is not publicly available. We will provide you with a key to access it.
Please use this key for the workshop only, and limit the amount of interaction, or we get blocked for exceeding our limits. The API key is obtained through a remote file, which is encrypted. Of course you can also use your own key if you have it.
For the workshop, we provided a key to use for OpenAI. This key is stored in a remote file, which is encrypted. To
decrypt this file, we need a secret key. This key is stored in an environment variable. The name of the environment
variable is secret_key. The value of this key will be provided during the workshop.
The provide an environment variable through a line in the file env.properties on the classpath.
secret_key=...If you have your own key, you can use the same file to store the following lines:
openai_api_key=sk-...
weaviate_api_key=...
weaviate_url=...If you omit the weaviate parameter, you still need to provided the secret_key in the file. Then, the weaviate
parameters will be read from the remote file.