Developing Generative AI (GenAI) technologies with Docker offers endless possibilities not only for summarizing lengthy documents but also for categorizing them and generating detailed descriptions and even providing prompt insights you may have missed. This multi-faceted approach, powered by AI, changes the way organizations interact with textual data, saving both time and effort.

In this article, we’ll look at how to integrate Alfresco, a robust document management system, with the GenAI Stack to open up possibilities such as enhancing document analysis, automating content classification, transforming search capabilities, and more.

High-level architecture of Alfresco document management 

Alfresco is an open source content management platform designed to help organizations manage, share, and collaborate on digital content and documents. It provides a range of features for document management, workflow automation, collaboration, and records management.

You can find the Alfresco Community platform on Docker Hub. The Docker image for the UI, named alfresco-content-app, has more than 10 million pulls, while other core platform services have more than 1 million pulls.

Alfresco Community platform (Figure 1) provides various open source technologies to create a Content Service Platform, including:

• Alfresco content repository is the core of Alfresco and is responsible for storing and managing content. This component exposes a REST API to perform operations in the repository.
• Database: PostgreSQL, among others, serves as the database management system, storing the metadata associated with a document.
• Apache Solr: Enhancing search capabilities, Solr enables efficient content and metadata searches within Alfresco.
• Apache ActiveMQ: As an open source message broker, ActiveMQ enables asynchronous communication between various Alfresco services. Its Messaging API handles asynchronous messages in the repository.
• UI reference applications: Share and Alfresco Content App provide intuitive interfaces for user interaction and accessibility.

For detailed instructions on deploying Alfresco Community with Docker Compose, refer to the official Alfresco documentation.

Why integrate Alfresco with the GenAI Stack?

Integrating Alfresco with the GenAI Stack unlocks a powerful suite of GenAI services, significantly enhancing document management capabilities. Enhancing Alfresco document management with the GenAI stack services has different benefits:

• Use different deployments according to resources available: Docker allows you to easily switch between different Large Language Models (LLMs) of different sizes. Additionally, if you have access to GPUs, you can deploy a container with a GPU-accelerated LLM for faster inference. Conversely, if GPU resources are limited or unavailable, you can deploy a container with a CPU-based LLM.
• Portability: Docker containers encapsulate the GenAI service, its dependencies, and runtime environment, ensuring consistent behavior across different environments. This portability allows you to develop and test the AI model locally and then deploy it seamlessly to various platforms.
• Production-ready: The stack provides support for GPU-accelerated computing, making it well suited for deploying GenAI models in production environments. Docker’s declarative approach to deployment allows you to define the desired state of the system and let Docker handle the deployment details, ensuring consistency and reliability.
• Integration with applications: Docker facilitates integration between GenAI services and other applications deployed as containers. You can deploy multiple containers within the same Docker environment and orchestrate communication between them using Docker networking. This integration enables you to build complex systems composed of microservices, where GenAI capabilities can be easily integrated into larger applications or workflows.

How does it work?

Alfresco provides two main APIs for integration purposes: the Alfresco REST API and the Alfresco Messaging API (Figure 2).

• The Alfresco REST API provides a set of endpoints that allow developers to interact with Alfresco content management functionalities over HTTP. It enables operations such as creating, reading, updating, and deleting documents, folders, users, groups, and permissions within Alfresco. 
• The Alfresco Messaging API provides a messaging infrastructure for asynchronous communication built on top of Apache ActiveMQ and follows the publish-subscribe messaging pattern. Integration with the Messaging API allows developers to build event-driven applications and workflows that respond dynamically to changes and updates within the Alfresco Repository.

The Alfresco Repository can be updated with the enrichment data provided by GenAI Service using both APIs:

• The Alfresco REST API may retrieve metadata and content from existing repository nodes to be sent to GenAI Service, and update back the node.
• The Alfresco Messaging API may be used to consume new and updated nodes in the repository and obtain the result from the GenAI Service.

Technically, Docker deployment includes both the Alfresco and GenAI Stack platforms running over the same Docker network (Figure 3). 

The GenAI Stack works as a REST API service with endpoints available in genai:8506, whereas Alfresco uses a REST API client (named alfresco-ai-applier) and a Messages API client (named alfresco-ai-listener) to integrate with AI services. Both clients can also be run as containers.

The GenAI Stack service provides the following endpoints:

• summary: Returns a summary of a document together with several tags. It allows some customization, like the language of the response, the number of words in the summary and the number of tags.
• classify: Returns a term from a list that best matches the document. It requires a list of terms as input in addition to the document to be classified.
• prompt: Replies to a custom user prompt using retrieval-augmented generation (RAG) for the document to limit the scope of the response.
• describe: Returns a text description for an input picture.

The implementation of GenAI Stack services loads the document text into chunks in Neo4j VectorDB to improve QA chains with embeddings and prevent hallucinations in the response. Pictures are processed using an LLM with a visual encoder (LlaVA) to generate descriptions (Figure 4). Note that Docker GenAI Stack allows for the use of multiple LLMs for different goals.

Getting started 

To get started, check the following:

• Ensure that you have installed the latest version of Docker Desktop with 20 GB of RAM allocated.
• Ensure that you have installed Java 17 and Maven 3.9.
• Ensure that you have Ollama running locally.

Obtaining the amount of RAM available for Docker Desktop can be done using following command:

docker info --format '{{json .MemTotal}}'

If the result is under 20 GiB, follow the instructions in Docker official documentation for your operating system to boost the memory limit for Docker Desktop.

Clone the repository

Use the following command to close the repository:

git clone https://github.com/aborroy/alfresco-genai.git

The project includes the following components:

• genai-stack folder is using https://github.com/docker/genai-stack project to build a REST endpoint that provides AI services for a given document.
• alfresco folder includes a Docker Compose template to deploy Alfresco Community 23.1.
• alfresco-ai folder includes a set of projects related to Alfresco integration.
  • alfresco-ai-model defines a custom Alfresco content model to store summaries, terms and prompts to be deployed in Alfresco Repository and Share App.
  • alfresco-ai-applier uses the Alfresco REST API to apply summaries or terms for a populated Alfresco Repository.
  • alfresco-ai-listener listens to messages and generates summaries for created or updated nodes in Alfresco Repository.
• compose.yaml file describes a deployment for Alfresco and GenAI Stack services using include directive.

Starting Docker GenAI service

The Docker GenAI Service for Alfresco, located in the genai-stack folder, is based on the Docker GenAI Stack project, and provides the summarization service as a REST endpoint to be consumed from Alfresco integration.

cd genai-stack

Before running the service, modify the .env file to adjust available preferences:

# Choose any of the on premise models supported by ollama
LLM=mistral
LLM_VISION=llava
# Any language name supported by chosen LLM
SUMMARY_LANGUAGE=English
# Number of words for the summary
SUMMARY_SIZE=120
# Number of tags to be identified with the summary
TAGS_NUMBER=3

Start the Docker Stack using the standard command:

docker compose up --build --force-recreate

After the service is up and ready, the summary REST endpoint becomes accessible. You can test its functionality using a curl command.

Use a local PDF file (file.pdf in the following sample) to obtain a summary and a number of tags.

curl --location 'http://localhost:8506/summary' \
--form 'file=@"./file.pdf"'
{ 
  "summary": " The text discusses...", 
  "tags": " Golang, Merkle, Difficulty", 
  "model": "mistral"
}

Use a local PDF file (file.pdf in the following sample) and a list of terms (such as Japanese or Spanish) to obtain a classification of the document.

curl --location \
'http://localhost:8506/classify?termList=%22Japanese%2CSpanish%22' \
--form 'file=@"./file.pdf"'
{
    "term": " Japanese",
    "model": "mistral"
}

Use a local PDF file (file.pdf in the following sample) and a prompt (such as “What is the name of the son?”) to obtain a response regarding the document.

curl --location \
'http://localhost:8506/prompt?prompt=%22What%20is%20the%20name%20of%20the%20son%3F%22' \
--form 'file=@"./file.pdf"'
{
    "answer": " The name of the son is Musuko.",
    "model": "mistral"
}

Use a local picture file (picture.jpg in the following sample) to obtain a text description of the image.

curl --location 'http://localhost:8506/describe' \
--form 'image=@"./picture.jpg"'
{
    "description": " The image features a man standing... ",
    "model": "llava"
}

Note that, in this case, LlaVA LLM is used instead of Mistral.

Make sure to stop Docker Compose before continuing to the next step.

Starting Alfresco

The Alfresco Platform, located in the alfresco folder, provides a sample deployment of the Alfresco Repository including a customized content model to store results obtained from the integration with the GenAI Service.

Because we want to run both Alfresco and GenAI together, we’ll use the compose.yaml file located in the project’s main folder.

include:
  - genai-stack/compose.yaml
  - alfresco/compose.yaml
#  - alfresco/compose-ai.yaml

In this step, we’re deploying only GenAI Stack and Alfresco, so make sure to leave the compose.ai.yaml line commented out.

Start the stack using the standard command:

docker compose up --build --force-recreate

After the service is up and ready, the Alfresco Repository becomes accessible. You can test the platform using default credentials (admin/admin) in the following URLs:

• Repository services: http://localhost:8080/alfresco
• UI: http://localhost:8080/share

Enhancing existing documents within Alfresco 

The AI Applier application, located in the alfresco-ai/alfresco-ai-applier folder, contains a Spring Boot application that retrieves documents stored in an Alfresco folder, obtains the response from the GenAI Service and updates the original document in Alfresco.

Before running the application for the first time, you’ll need to build the source code using Maven.

cd alfresco-ai/alfresco-ai-applier
mvn clean package

As we have GenAI Service and Alfresco Platform up and running from the previous steps, we can upload documents to the Alfresco Shared Files/summary folder and run the program to update the documents with the summary.

java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared/cm:summary \
--applier.action=SUMMARY
...
Processing 2 documents of a total of 2
END: All documents have been processed. The app may need to be executed again for nodes without existing PDF rendition.

Once the process has been completed, every Alfresco document in the Shared Files/summary folder will include the information obtained by the GenAI Stack service: summary, tags, and LLM used (Figure 5).

You can now upload documents to the Alfresco Shared Files/classify folder to prepare the repository for the next step.

Classifying action can be applied to documents in the Alfresco Shared Files/classify folder using the following command. GenAI Service will pick the term from the list (English, Spanish, Japanese) that best matches each document in the folder.

java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared/cm:classify \
--applier.action=CLASSIFY \
--applier.action.classify.term.list=English,Spanish,Japanese
...
Processing 2 documents of a total of 2
END: All documents have been processed. The app may need to be executed again for nodes without existing PDF rendition.

Upon completion, every Alfresco document in the Shared Files folder will include the information obtained by the GenAI Stack service: a term from the list of terms and the LLM used (Figure 6).

You can upload pictures to the Alfresco Shared Files/picture folder to prepare the repository for the next step.

To obtain a text description from pictures, create a new folder named picture under the Shared Files folder. Upload any image file to this folder and run the following command:

java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared/cm:picture \
--applier.action=DESCRIBE
...
Processing 1 documents of a total of 1
END: All documents have been processed. The app may need to be executed again for nodes without existing PDF rendition.

Following this process, every Alfresco image in the picture folder will include the information obtained by the GenAI Stack service: a text description and the LLM used (Figure 7).

Enhancing new documents uploaded to Alfresco

The AI Listener application, located in the alfresco-ai/alfresco-ai-listener folder, contains a Spring Boot application that listens to Alfresco messages, obtains the response from the GenAI Service and updates the original document in Alfresco.

Before running the application for the first time, you’ll need to build the source code using Maven and to build the Docker image.

cd alfresco-ai/alfresco-ai-listener
mvn clean package
docker build . -t alfresco-ai-listener

As we are using the AI Listener application as a container, stop the Alfresco deployment and uncomment the alfresco-ai-listener in the compose.yaml file.

include:
  - genai-stack/compose.yaml
  - alfresco/compose.yaml
  - alfresco/compose-ai.yaml

Start the stack using the standard command:

docker compose up --build --force-recreate

After the service is again up and ready, the Alfresco Repository becomes accessible. You can verify that the platform is working by using default credentials (admin/admin) in the following URLs:

• Repository services: http://localhost:8080/alfresco
• UI: http://localhost:8080/share

Summarization

Next, upload a new document and apply the “Summarizable with AI” aspect to the document. After a while, the document will include the information obtained by the GenAI Stack service: summary, tags, and LLM used.

Description

If you want to use AI enhancement, you might want to set up a folder that automatically applies the necessary aspect, instead of doing it manually.

Create a new folder named pictures in Alfresco Repository and create a rule with the following settings in it:

• Name: description
• When: Items are created or enter this folder
• If all criteria are met: All Items
• Perform Action: Add “Descriptable with AI” aspect

Upload a new picture to this folder. After a while, without manual setting of the aspect, the document will include the information obtained by the GenAI Stack service: description and LLM used.

Classification

Create a new folder named classifiable in Alfresco Repository. Apply the “Classifiable with AI” aspect to this folder and add a list of terms separated by comma in the “Terms” property (such as English, Japanese, Spanish).

Create a new rule for classifiable folder with the following settings:

• Name: classifiable
• When: Items are created or enter this folder
• If all criteria are met: All Items
• Perform Action: Add “Classified with AI” aspect

Upload a new document to this folder. After a while, the document will include the information obtained by the GenAI Stack service: term and LLM used.

A degree of automation can be achieved when using classification with AI. To do this, a simple Alfresco Repository script named classify.js needs to be created in the folder “Repository/Data Dictionary/Scripts” with following content.

document.move(
  document.parent.childByNamePath(    
    document.properties["genai:term"]));

Create a new rule for classifiable folder to apply this script with following settings:

• Name: move
• When: Items are updated
• If all criteria are met: All Items
• Perform Action: Execute classify.js script

Create a child folder of the classifiable folder with the name of every term defined in the “Terms” property. 

When you set up this configuration, any documents uploaded to the folder will automatically be moved to a subfolder based on the identified term. This means that the documents are classified automatically.

Prompting

Finally, to use the prompting GenAI feature, apply the “Promptable with AI” aspect to an existing document. Type your question in the “Question” property.

After a while, the document will include the information obtained by the GenAI Stack service: answer and LLM used.

A new era of document management

By embracing this framework, you can not only unlock a new level of efficiency, productivity, and user experience but also lay the foundation for limitless innovation. With Alfresco and GenAI Stack, the possibilities are endless — from enhancing document analysis and automating content classification to revolutionizing search capabilities and beyond.

If you’re unsure about any part of this process, check out the following video, which demonstrates all the steps live:

Learn more

• Read Introducing a New GenAI Stack: Streamlined AI/ML Integration Made Easy.
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