shehzada / aidocumentlibrarychat

A project to show howto use SpringAI with OpenAI to chat with the documents in a library. Documents are stored in a normal/vector database. The AI is used to create embeddings from documents that are stored in the vector database. The vector database is used to query for the nearest document. That document is used by the AI to generate the answer.

AIDocumentLibraryChat

This is a project to show howto use SpringAI to chat with the documents in a library. Document can be uploaded are then stored in a normal and vector database. The AI is used to create embeddings from the chunks of the documents that are stored in the vector database. In the chat questions can be asked that are turned in embeddings and are used for requests to the vector database. The document vector with the lowest distance is used to load the document from the database and to request an answer from the AI based on document content tokens. That enables the AI to generate the answer based on the content of the document and limits hallucinations. A link to the source document is provided for further research.

The project uses Spring Boot with Spring AI to access OpenAI and the vector database. The Postgresql DB is used with the vector, hstore and the uuid-ossp extensions. Liquibase is used to manage the database migrations. Jpa is used for database access in the services by the rest controllers. For the frontend Angular, Angular Material and Typescript is used to create the table of documents, the upload dialog and the document chat. Angular Cli is used for the frontend build and Gradle for the project build.

Author: Sven Loesekann

Technologies: Angular, Angular-Cli, Angular-Material, Typescript, Spring Boot, Spring AI, OpenAI, Postgresql(vector,hstore), Liquibase, Jpa, Gradle, Java

Articles

• Making Spring AI and OpenAI GPT useful with RAG on your own Documents

Features

1. It shows the list of the documents.
2. It uploads new documents and creates the embeddings.
3. It provides a chat box and shows the AI answers based on the nearest document with a link.

Mission Statement

The project shows howto use Spring AI to generate answers based on a provided set of documents with a link to the source. The Angular frontend provides the use interface for the backend and shows the responses. Spring AI makes using OpenAI services simple and useful and this project demonstrates that.

Postgresql setup

In the runPostgresql.sh file are the commands to pull and run the Postgresql Docker image with vector extension locally.

OpenAI Api key

To get the OpenAI api key you need to create a account at https://platform.openai.com and create a key in the user settings. That key has to be added in the application.properties at the key: 'spring.ai.openai.api-key'. As alternative it can be provided as the environment variable 'OPEN-API-KEY'.

Setup

Postgresql with Vector Extension 0.5.1 or newer

Java 21 or newer

Gradle 8.3 or newer

NodeJs 18.13.x or newer

Npm 8.19.x or newer

Angular Cli 17 or newer