lab3-all.md

Lab 3: Running All Components Together

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 changes occur in the repository, eliminating the need for manual intervention. Additionally, a user interface will be provided for interacting with the chat service.

Components

• Alfresco acts as the Knowledge Base, storing documents within folders that have the cm:syndication aspect (1)
• alfresco-ai-sync listens to changes in sync folder from the Alfresco Repository and updates the content to the Vector Database through the AI RAG Framework REST API (2)
• ai-rag-framework exposes the REST API for ingestion and utilizes Ollama to access the nomic-embed-text embedding model and the qwen-2.5 LLM (3)
• alfresco-ai-ui provides a UI, built on top of ADF, for interacting with the chat service (4)

Configuration Files

• (1) The aspect and properties to be used can be configured in the application.properties file of the alfresco-ai-sync application.
• (2) Alfresco credentials, protocol, host, and port can be configured in the application.properties file of the alfresco-ai-sync application.
• (3) Ollama and Elasticsearch settings can be configured in the application.yml file of the ai-rag-framework application.
• (4) Chat service and Alfresco Share servers can be configured using environment variables

Step 1: Use the UI to get answers

It is required to complete Lab 1: Ingestion Pipeline before proceeding with this lab to ensure that the Vector Database is already populated.

1. Start the stack by running the following command:

   docker compose up --build --force-recreate

2. After a while, all the services will be up & ready

   docker ps
   
   CONTAINER ID   IMAGE                                                   PORTS
   92db86206d73   alfresco-ai-framework                                   0.0.0.0:9999->9999/tcp
   f27e5fb2079a   kibana:8.15.3                                           0.0.0.0:5601->5601/tcp                     
   7a8acc029270   elasticsearch:8.15.3                                    9200/tcp, 9300/tcp
   6d6c6e50967c   alfresco-ai-sync                                        
   ba7f3cf0fd39   alfresco-ai-ui                                          0.0.0.0:80->80/tcp
   de55e97b01f8   alfresco/alfresco-acs-nginx:3.4.2                       80/tcp, 0.0.0.0:8080->8080/tcp
   c309d41d8878   alfresco/alfresco-search-services:2.0.12                8983/tcp, 10001/tcp
   db1fba8aecd3   alfresco/alfresco-share:23.3.0                          8000/tcp, 8080/tcp
   e87a98d1e421   alfresco/alfresco-content-repository-community:23.3.0   8000/tcp, 8080/tcp, 10001/tcp
   cc9b9518fd68   alfresco/alfresco-transform-core-aio:5.1.4              8090/tcp
   3a07a3d2c698   postgres:14.4                                           5432/tcp
   a3c4c59c4ce0   alfresco/alfresco-activemq:5.18-jre17-rockylinux8       5672/tcp, 8161/tcp, 61613/tcp, 61616/tcp

3. Use the UI, available in http://localhost, to get answers to prompts

Source document details can be accessed by clicking the icon in the list below the answer. This will open the view page in Alfresco Share application.

Step 2: Update the Knowledge Base

1. Log into Alfresco Share using the default credentials:

   Username: admin
   Password: admin
   Access Alfresco Share at: http://localhost:8080/share

2. Access to the "Knowledge Base" folder and add some documents to the folder RAG within the Knowledge Base folder.

   Knowledge Base is being updated with the changes by alfresco-ai-sync service

3. Verify the Vector Database Population

Access Kibana Developer Tools at http://localhost:5601/app/dev_tools#/console and type following request:

GET /alfresco-ai-document-index/_search
{
  "hits": {
    "total": {
      "value": 7,
      "relation": "eq"
    },
    "hits": [
      { ... }
      ...
    ]
  }
}

Added document should be indexed and ready to be used by the ai-rag-framework service