https://github.com/aborroy/alfresco-ai-framework

Sample application to apply Spring AI services to Alfresco Repository
https://github.com/aborroy/alfresco-ai-framework

alfresco docker docker-compose genai spring-ai

Last synced: about 1 year ago
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Sample application to apply Spring AI services to Alfresco Repository

• Host: GitHub
• URL: https://github.com/aborroy/alfresco-ai-framework
• Owner: aborroy
• License: apache-2.0
• Created: 2024-10-31T15:03:07.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-20T11:59:30.000Z (over 1 year ago)
• Last Synced: 2025-02-14T19:56:02.732Z (over 1 year ago)
• Topics: alfresco, docker, docker-compose, genai, spring-ai
• Language: Java
• Homepage:
• Size: 801 KB
• Stars: 10
• Watchers: 2
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Alfresco AI Framework  

[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)  

The **Alfresco AI Framework** is a robust framework designed to integrate AI capabilities into Alfresco, leveraging Java and [Spring AI](https://spring.io/projects/spring-ai). It provides a suite of tools and services to process, analyze, and enhance document content in Alfresco using AI and machine learning models.  

> **Note**: This project uses the Spring AI SNAPSHOT version, as a final RELEASE is not yet available.  

## Key Features  

- **AI-Driven Document Processing**: Enable Retrieval-Augmented Generation (RAG) chat functionality within the Alfresco repository.  

- **Modular Architecture**: Built on a Spring Boot-based architecture, offering flexibility in deployment and scalability.  

- **Seamless Integration**: Easily extend Alfresco Content Services with AI capabilities.  

## Getting Started  

### Prerequisites  

- **Java 17**: Required for building and running the services  

- **Maven**: For building the backend services  

- **Angular 18**: For developing and running the UI  

- **Node.js 18**: Required for building the UI  

- **Docker**: For containerized deployment  

### Architecture  

![Architecture Diagram](docs/alfresco-ai-framework.png)  

## Projects  

- **[ai-rag-framework](ai-rag-framework)**:  

  A REST API built on top of [Spring AI](https://spring.io/projects/spring-ai) for ingesting documents into a Generative AI (GenAI) model and providing a Retrieval-Augmented Generation (RAG) chat service.

  - **Requirements**:  

    - A local Large Language Model (LLM) powered by Ollama  

    - Elasticsearch as the vector database  

- **[alfresco-ai-sync](alfresco-ai-sync)**:  

  A service built on top of [Alfresco Java SDK](https://github.com/Alfresco/alfresco-java-sdk) that retrieves documents from the Alfresco Repository and ingests them into the vector database through the `ai-rag-framework` API.  

  - **Requirements**:

    - A running Alfresco Repository  

    - The `ai-rag-framework` service  

- **[alfresco-ai-ui](alfresco-ai-ui)**:  

  A user interface built on top of [Alfresco ADF](https://github.com/Alfresco/alfresco-ng2-components) for interacting with the RAG chat service provided by `ai-rag-framework`.  

  - **Requirements**:  

    - The `ai-rag-framework` service must be running

- **[alfresco-docker](alfresco-docker)**:

  Container-oriented Alfresco Community 23.3 deployment     

## Laboratories

This series of tutorials will guide you through the key features of the project, including data ingestion, chat integration, and the overall operation of the system.

### **Lab 1: Ingestion Pipeline**

![Ingestion Pipeline Diagram](docs/alfresco-ai-framework-ingestion.png)

In this lab, you'll learn how to populate the **Vector Database** (Elasticsearch) with selected content from the **Knowledge Base** stored in Alfresco. This involves extracting vectors from content using the **Embedding** module `nomic-embed-text` via Ollama.

Begin the lab by following [Lab 1: Ingestion Pipeline](docs/lab1-ingestion.md).

### **Lab 2: Chat Functionality**

![Chatting Diagram](docs/alfresco-ai-framework-chatting.png)

This lab focuses on enabling chat functionality with the **LLM** `qwen2.5` via Ollama, using Alfresco UI apps like Share and ADF. The process includes transforming the user's prompt into vectors using the **Embedding** module `nomic-embed-text` via Ollama, then searching for relevant content in the **Vector Database** (Elasticsearch). The retrieved text is used to provide context to the **LLM**, which helps generate more accurate responses.

Start this lab by following [Lab 2: Chat Functionality](docs/lab2-chat.md).

### **Lab 3: Running All Components Together**

![Architecture Diagram](docs/alfresco-ai-framework.png)

In this lab, you'll integrate all the components (ingestion and chat functionality) with a live Alfresco Repository. The system will automatically update the **Vector Database** whenever there are changes in the repository, eliminating the need for manual intervention.

You can begin this lab by following [Lab 3: Running All Components Together](docs/lab3-all.md).

## License  

This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.  

## Acknowledgments  

Special thanks to the Alfresco and Hyland teams for their continuous support and contributions to open-source initiatives in the content management and AI domains.