Simple Spring AI QA application

This project contains a web service with the following endpoints under http://localhost:8080

• POST /data/load
• GET /data/count
• DELETE /data/delete
• GET /question

The /question endpoint takes a question parameter which is the question you want to ask the AI model. The /question endpoint also takes a prompstuff boolean parameter, whose default it true, that will 'stuff the prompt' with similar documents to the question. When stuffing the prompt, this follows the RAG pattern.

Prerequisites

OpenAI Credentials

Create an account at OpenAI # and generate the token at API Keys.

The Spring AI project defines a configuration property named spring.ai.openai.api-key that you should set to the value of the API Key obtained from openai.com.

You can set this in the projects /resources/application.yml file or by exporting an environment variable, for example.

export SPRING_AI_OPENAI_API_KEY=<INSERT KEY HERE>

The /resources/application.yml references the environment variable ${OPENAI_API_KEY} as that is what the onboarding instructions for OpenAI suggest.

In short, Spring Boot provides many ways to set this property, pick a method that works for your needs.

VectorStore

To run the PgVectorStore locally, using docker-compose. From the top project directory and run:

docker-compose up

Later starts Postgres DB on localhost and port 5432.

Then you can connect to the database (password: postgres) and inspect or alter the vector_store table content:

psql -U postgres -h localhost -p 5432

\l
\c vector_db
\dt

select count(*) from vector_store;

delete from vector_store;

You can connect to the pgAdmin on http://localhost:5050 as user: pgadmin4@pgadmin.org and pass: admin. Then navigate to the Databases/vector_store/Schemas/public/Tables/vector_store.

The UI tool DBeaver is also a useful GUI for postgres.

Building and running

./mvnw spring-boot:run