Author: Sven Loesekann
Technologies: Angular, Angular-Cli, Angular-Material, Typescript, Spring Boot, Spring AI, OpenAI, Ollama, Postgresql(vector,hstore), Liquibase, Jpa, Gradle, Java
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.
Ollama is used to use locally run AI/LLM models. Ollama can be installed or run as a Docker image in a local machine. Ollama supports a library of AI/LLM models for different use cases. Models like LLava for image description, falcon for RAG based question answering, sqlcoder for Sql generation and mixtral for function calling can be used. Ollama can use GPUs if they are available and works on CPU without them. Spring AI has Ollama support that make the use similar to using an AI service. On current CPUs there are often performance issues. The CPU providers want to add AI engines to their CPUs in the future to solve these issues.
The project uses Spring Boot with Spring AI to question a image database that uses Postgresql with the PGVector extension and Ollama. The for the imported and resized images are descriptions generated with the LLava model that runs locally on Ollama. These descriptions are turned in Embeddings and are stored with the description in the vector database. The image and metadata is stored in the relational database. A question to the image database is turned in Embeddings and the vector database is queried for the nearest neighbor. The best matches are returned with image and description. The result is displayed in the frontend. This enables are new kind of image search that was not possible before AI/LLMs became usable.
The project uses Spring AI to turn questions with a LLMs into Sql queries and display the result in the frontend. Based on metadata that is provided for the relational dataset the LLM is able to create embeddings for the metadata of the tables and columns. The project also creates embeddings for the content of certain columns to be able to decide if the column should be used as join in the Sql query. With that embedding metadata the LLM is able to turn a question in a reasonable Sql query and display the result in the frontend. The frontend uses a Angular Material Table with a flexible amount of columns and rows.
The project uses Spring AI to turn questions about books into a rest function call to the OpenLibrary Api. It uses the Llama3.1 model and the Spring AI function calling api. The api response is used to create the response.
The project uses Spring AI to generate test classes. To do that the class to test is provided and the classes the class to test depends on. A test example class can also be provided. The ollama based AI/LLM then gets a prompt with all the information and generates a draft of the source of the test class.
The project uses Spring AI to generate summaries of books. To generate the summaries the chapter headings and the heading after the last chapter have to be provided. Then summaries of the chapters in form of bullet points are generated. A book summary is created of the chapter summaries.
- Using Spring AI with LLMs to generate Java tests
- Questioning an Image Database with local AI/LLM on Ollama and Spring AI
- Extending AI/LLM Capabilities with Rag and Function calls
- Using Spring AI with LLMs to query relational databases
- Making Spring AI and OpenAI GPT useful with RAG on your own Documents
- Implementing RAG With Spring AI and Ollama Using Local AI/LLM Models
- It shows the list of the documents.
- It uploads new documents and creates the embeddings.
- It provides a chat box and shows the AI answers based on the nearest document with a link.
- It displays the result of the Sql query based on the question.
- It displays the responses based on the results of the api requested based on the user question.
- It displays the results of the questions to the image database.
- It generates tests for sources in public Github repositories
- It generates book summaries of epub of pdf books.
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 user interface for the backend and shows the responses.
The project shows howto use Spring Ai to generate descriptions for uploaded images and to store the image, the descriptions and the description embeddings in the database. The questions asked to the image database are turned in embeddings and the best fitting descriptions with the images are returned. The Angular frontend provides a user interface to display the result and to upload the images.
The project shows howto use Spring AI to generate Sql queries based on provided metadata for the tables/columns. The Angular frontend provides a user interface to display the result in table.
The project shows howto use Spring AI to select a Rest interface to call and to provide the parameters for the Rest call. The Angular frontend provides the user interface to ask the question and to display the result of the Rest call.
The project shows howto use Spring AI to create a image database that can be queried with natural language questions and returns the closest matching images with their descriptions.
Spring AI makes using OpenAI / Ollama services simple and useful and this project demonstrates that.
The project has a System Context Diagram, a Container Diagram and a Component Diagram. The Diagrams have been created with Structurizr. The file runStructurizr.sh contains the commands to use Structurizr and the directory structurizr contains the dsl file.
In the helm directory is a kubernetes setup to run the AIDocumentLibraryChat project with minikube. The Helm chart deploys the postgres database and the AIDocumentLibraryChat with the needed parameters(SpringProfile is in values.yaml) to run. It uses the resource limit support of Jdk 16 to limit memory. Kubernetes limits the cpu use and uses the startupprobes and livenessprobes that Spring Actuator provides.
In the runPostgresql.sh file are the commands to pull and run the Postgresql Docker image with vector extension locally.
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'.
Ollama can run the AI model locally. The file runOllama.sh has the commands to run it as Docker container. The application needs to be build with the 'useOllama=true' Gradle build property to include the dependencies. The application needs to be started with the 'ollama' profile to switch on the configs/features to use Ollama based models. Ollama has support for GPU acceleration.
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
![@dependabot[bot]](https://avatars.githubusercontent.com/in/29110?s=64&v=4){kind=small}
