Generative AI with local or cloud LLMs for Alfresco. Provides: summarization, categorization, image description, chat prompting about doc content.
This adds NER / entity linking auto tagging of documents in Alfresco to Wikidata and DBpedia terms. The dbpedia and wikidata custom aspects have multivalue properties for the entity link labels, link urls, and super type lists. Tags are added using the labels of the entity links. Regular alfresco tags are used to work with existing Share and ACA clients. Currently, a hierarchy of categories with the type and super types are not created.
The spaCy NLP python library along with spaCy projects are used. The spaCy-entity-linker python library is used for getting Wikidata entity links without having to call a public serivce api. It was created before spaCy had its own entity linking system. It still has the advantage of not needing to do training. This project had used spaCyOpenTapioca for Wikidata entity linking, but the service it uses recently gives SSL certificate error. Setting up a local OpenTapioca is an involved process. Local is better anyway for security and performance.
DBpedia Spotlight for SpaCy is used for getting DBpedia entity links, The public service it was using by default, also recently has a SSL certificate issue. So a local dbpedia-spotlight docker container is now composed into alfresco-genai-semantic and the local url is passed to the spacy-dbpedia-spotlight python apis.
The spaCy-LLM python package integrates Large Language Models (LLMs) into spaCy pipelines. This project currently doesn't use spacy-llm.
Note alfresco community docker 23.2 is included instead of 23.1
Note: performance could be improved by changing things to send text renditions from alfresco instead of pdf renditions to the python genai and genai-semantic rest apis. These apis just turn around and get text of the pdfs. Although, with things like LlamaIndex LlamaParse and LLMSherpa, having pdf and their structure would help in how things are passed to LLMs.
- To use, in Share UI client, add either a DBpedia Entity Links (genai:entitylinks-dbpedia) or a Wikidata Entity Links (genai:entitylinks-wikidata) aspect in Manage Aspects.
- Tags on a doc are displayed below doc info in file lists in both Share and ACA. The entity link multi-value properties (labels, links, super type lists) can be seen in the in Share document details right side in the properties section. The best way to see the properties in the ACA Content App with View Details with the Expand Panel clicked.
- Note the original genai aspects show up better in Share document details. ACA content app by default, only has a single line for single value properties.
- To use the original alfresco-genai actions, in the Manage Aspects of Share add any of the following aspects:
genai:summarizableaspect is used to storesummaryandtagsgenerated with AI. Note the tag terms come back from the summary prompt. By default, they are stored in the multivalue property genai:tags of genai:summarizable aspect. They can be configured to create tags instead (in application.properties of ai-applier and ai-listener)genai:promptableaspect is used to store thequestionprovided by the user and theanswergenerated with AIgenai:classifiableaspect is used to store the list of terms available for the AI to classify a document. It should be applied to a foldergenai:classifiedaspect is used to store the term selected by the AI. It should be applied to a documentgenai:descriptableaspect is used to store the description generated with AI. It should be applied to a picture
To Build alfresco-genai-semantic, use the same steps as alfesco-genai but with some added steps:
sudo alfresco/create_volumes.shcan be used to prepare for first time startup of alfresco onLinux, and I assumeMac. This creates log and data folders and sets their permissions. Not needed onWindows. See alfresco-docker-install project- Start with the
top level compose.yamlintially with alfresco-ai-listener and dbpedia-spotlight commented out - Run
docker compose up - In alfresco-ai-applier dir:
mvn clean package - Now the alfresco-ai-applier jar can be tested with exsting alfresco content to apply the Wikidata aspect with
-applier.root.folder--applier.action=ENTITYLINKWIKIDATA. Note the Wikidata code will the first time download a 595 MB model file that expands to to 1.2 GB? automatically. Sometimes you need to wait for pdf renditions to be created and run the applier jar again. docker compose down- in
top level compose.yaml, uncomment the line to include composing indbpedia-spotlight/compose.yaml - Run
docker volume create spotlight-modelsfor storing models for multiple languages (thedbpedia-spotlight/compose.yamlis only configured for one english service). - Run
docker compose up, can look inside dbpedia-spotlight.en container at files and see the 2.2 GB tar being downloaded and then extracted the first time to thespotlight-modelsvolume. This can be as fast as 2.5 minutes or up to 30 minutes for me on Windows. (Just now on Ubuntu its taking me over hour but then speeded up) The englishenfolder in the spotlight-models' volume will be 4.3 GB. - Now the alfresco-ai-applier jar can be tested with exsting alfresco content to apply the DBpedia aspect with
-applier.root.folder--applier.action=ENTITYLINKDBPEDIA - In alfresco-ai-listener dir:
mvn clean package - In alfresco-ai-listener dir:
docker build . -t alfresco-ai-listener - in
top level compose.yaml, uncomment the line to include composing in alfresco-ai-listener - Run
docker compose up, now can add dbpedia or wikidata aspects to new or existing content in share and see results in share and or dthe content app.
Note: After other changes sometimes can't go wrong with
docker compose down, then docker compose build --no-cache, then docker compose up --force-recreate
- The changes to alfresco-genai comtent-model.xml is already built in "alfresco-genai-semantic\alfresco\alfresco\modules\jars\genai-model-repo-1.0.0.jar" If changed again would need to mvn clean package in "alfresco-genai-semantic\alfresco-ai\alfresco-ai-model\genai-model-repo" and replace the jar in alfresco\alfresco and rebuild the project
- The changes to alfresco-genai genai-model-share.xml is already built in "alfresco-genai-semantic\alfresco\share\modules\jars\genai-model-share-1.0.0.jar" If changed again, would need to mvn clean package in "alfresco-genai-semantic\alfresco-ai\alfresco-ai-model\genai-model-share" and replace the jar in alfresco\share and rebuild the project
alfresco-ai-applier jar can be used when ai-listener is not composed in, or you might be able to even if ai-listener is composed in to add aspects not thru the ui or from a rule, although ai-listener interaction may cause problems. Note you usally have to run it twice to get it when pdf renditions are ready.
$ java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared \
--applier.action=ENTITYLINKDBPEDIA$ java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared \
--applier.action=ENTITYLINKWIKIDATAThe genai-stack folder / docker container has two new REST apis for entity linking
curl --location 'http://localhost:8506/entitylink-wikidata' --form 'file=@"./file.pdf"'curl --location 'http://localhost:8506/entitylink-dbpedia' --form 'file=@"./file.pdf"'Following tools can be used to build and deploy this project (same as alfresco-genai)
- Docker 4.25 (with 20 GB of RAM allocated)
- ollama
- Java 17
- Maven 3.9
- Not required, but if you want to work separately with python used in previous genai and with
spacy code added, setup a new python virtual env with a python 3.12 and activate it. Then use the requirements.txt file in alfresco-genai-semantic\genai-stack:
Note the document.DockerFile is now setup to use python 3.12 (FROM python:3.12) instead of the python 3.11 you got previously with docker by default with "FROM langchain/langchain" Now requirements.txt has versions of every package that works with python 3.12 and 3.12.x versions. Note docker when it builds gets versions of python on its own and is not impacted by what you have installed in your enviroment.
pip install -r requirements.txt
- Note: when alfresco-genai-semantic is up and running correctly 12/14 images / sub-containers will continue. These can be seen in docker desktop, or with docker compose ps at the command line. The pull-model and pull-vision-model will do their jobs and exit.
- In the alfresco-genai-semantic/test folder, upload space-station.txt to your folder in alfresco and apply the genai:entitylinks-dbpedia aspect
- In the alfresco-genai-semantic/test folder, upload space-station.txt again to your folder in alfresco and apply the genai:entitylinks-wikidata aspect to the duplicate file. (Sometimes there is an issue doing both on the same doc and they would have huge lists of properties)
- In the share client, the first doc with the dbpedia aspect should have property values matching test/dbpedia-expected.txt
- In the share client, the second doc with the wikidata aspect, should have property values matching test/wikidata-expected.txt
- When the view details of the ACA content app is expanded on the first doc, it should look like test/dbpedia-expected-aca.jpg
- When the view details of the ACA content app is expanded on the second doc, it should like test/wikidata-expected-aca.jpg
This project provides a collection of resources to enable the utilization of Private Generative AI in conjunction with Alfresco. Each service within the project is designed to operate locally, offering flexibility for usage in a development environment.
The primary scenarios covered by this project are centered around a document:
- Summarize a document in any language and recognize various tags
- Select a term from a provided list that characterizes a document
- Answer to a question related to the document
In addition, it includes a use case related to images:
- Provide a description of a picture
Following tools can be used to build and deploy this project:
- Docker 4.25 (with 20 GB of RAM allocated)
- ollama
- Java 17
- Maven 3.9
Deploying this project in a production environment could require additional steps to ensure minimal performance and successful execution of actions
The project includes 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
- 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 based on the application of the
genai:summarizableaspect - alfresco-ai-listener listens to messages and generates summaries, apply terms and reply answers for create or updated nodes in Alfresco Repository
- compose.yaml file describes a deployment for Alfresco and GenAI Stack services using
includedirective
┌────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ ┌─Compose────────────────────┐ ┌─Compose────────────────────┐ │
│ │ │ │ │ │
│ │ A L F R E S C O │ │ G E N A I Stack │ │
│ │ │ │ │ │
│ │ ┌──────────┐ ┌───────────┐ │ │ ┌───┐ ┌─────┐ ┌─────────┐ │ │
│ ┌────────────┤ │model-repo│ │model-share│ │ │ │llm│ │neo4j│ │langchain│ │ │
│ │ │ └──────────┘ └───────────┘ │ │ └───┘ └─────┘ └─────────┘ │ │
│ │ │ │ │ ollama │ │
│ │ └──────────────▲─────────────┘ └─────────────▲──────────────┘ │
│ │ │ │ │
│ │ │ http://alfresco:8080 │ http://genai:8506/summary │
│ │ ┌─App──────────┴─────────────┐ │ http://genai:8506/classify │
│ │ │ │ │ http://genai:8506/prompt │
│ │ │ alfresco-ai-applier ├────────────────────────┤ http://genai:8506/describe │
│ │ │ │ │ │
│ │ └──────────────┬─────────────┘ │ │
│ │ │ │ │
│ │ │ │ │
│ │ ┌─Service──────┴─────────────┐ │ │
│ │ │ │ │ │
│ └────────────► alfresco-ai-listener ├────────────────────────┘ │
│ tcp://activemq:61616 │ │ │
│ └────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────────────────────────────────────DOCKER NETWORK──────┘
This service, available in genai-stack folder, offers various REST endpoints for applying AI operations to a given document.
┌─Compose────────────────────┐
│ │
│ G E N A I Stack │
│ │
│ ┌───┐ ┌─────┐ ┌─────────┐ │
│ │llm│ │neo4j│ │langchain│ │
│ └───┘ └─────┘ └─────────┘ │
│ ollama │
└─────────────▲──────────────┘
│
│ http://genai:8506/
- Summarizing a document and extracting tags from it
curl --location 'http://localhost:8506/summary' --form 'file=@"./file.pdf"'
{
"summary": " The text discusses...",
"tags": " Golang, Merkle, Difficulty",
"model": "mistral"
}- Selecting a term from a list that best matches the document
curl --location 'http://localhost:8506/classify?termList="Japanese,Spanish,Korean,English,Vietnamese"' --form 'file=./file.pdf"'
{
"term": " English",
"model": "mistral"
}- Responding to questions related to the document
curl --location 'http://localhost:8506/prompt?prompt="What is the name of the son?"' --form 'file=./file.pdf"'
{
"answer": "The name of the son is Musuko.",
"model": "mistral"
}- Describing a picture
curl --location 'http://localhost:8506/describe' --form 'image=@"file.jpg"'
{
"description": " In the image, a man with a beard is standing in an indoor setting.",
"model": "llava"
}Modify .env file values:
# 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
Note that LLM_VISION must be a LLM with vision encoder
Alfresco service, available in alfresco folder, includes custom content model definition and additional events configuration.
┌─Compose────────────────────┐
│ │
│ A L F R E S C O │
│ │
│ ┌──────────┐ ┌───────────┐ │
│ │model-repo│ │model-share│ │
│ └──────────┘ └───────────┘ │
│ │
└──────────────▲─────────────┘
│
│ http://alfresco:8080
-
Content Model for Repository is available in genai-model-repo
genai:summarizableaspect is used to storesummaryandtagsgenerated with AIgenai:promptableaspect is used to store thequestionprovided by the user and theanswergenerated with AIgenai:classifiableaspect is used to store the list of terms available for the AI to classify a document. It should be applied to a foldergenai:classifiedaspect is used to store the term selected by the AI. It should be applied to a documentgenai:descriptableaspect is used to store the description generated with AI. It should be applied to a picture
-
Forms and configuration to handle custom Content Model from Share are available in genai-model-share
-
Additional configuration for Repository
- Since
alfresco-ai-listeneris listening to renditions, defaultevent2filter should be modified. Following configuration has been added toalfrescoservice incompose.yaml
- Since
-Drepo.event2.filter.nodeTypes="sys:*, fm:*, cm:failedThumbnail, cm:rating, rma:rmsite include_subtypes, usr:user"
This Spring Boot application utilizes the Alfresco REST API to fetch all documents from a given Alfresco folder and apply a single action:
- The
Summarizingaction involves retrieving documents from a folder using the Alfresco Search API, checking for the availability of PDF renditions, and updating document nodes with summaries obtained from the GenAi service. - The
Classifyingaction retrieves documents from a folder using the Alfresco Search API, checks for the availability of PDF renditions, and updates document nodes by selecting a term, from a list of terms using the GenAi service. - The
Describingactions retrieves pictures from a folder using the Alfresco Search API and updates image nodes with descriptions obtained from the GenAi service.
┌─Compose────────────────────┐ ┌─Compose────────────────────┐
│ │ │ │
│ A L F R E S C O │ │ G E N A I Stack │
│ │ │ │
│ ┌──────────┐ ┌───────────┐ │ │ ┌───┐ ┌─────┐ ┌─────────┐ │
│ │model-repo│ │model-share│ │ │ │llm│ │neo4j│ │langchain│ │
│ └──────────┘ └───────────┘ │ │ └───┘ └─────┘ └─────────┘ │
│ │ │ ollama │
└──────────────▲─────────────┘ └─────────────▲──────────────┘
│ │
│ http://alfresco:8080 │ http://genai:8506/summary
┌─App──────────┴─────────────┐ │ http://genai:8506/classify
│ │ │ http://genai:8506/describe
│ alfresco-ai-applier ├────────────────────────┘
│ │
└────────────────────────────┘
Modify property values in application.properties file:
# Spring Boot properties
# Disable Spring Boot banner
spring.main.banner-mode=off
# Logging Configuration
logging.level.org.springframework=ERROR
logging.level.org.alfresco=INFO
logging.pattern.console=%msg%n
# Alfresco Server Configuration
# Basic authentication credentials for Alfresco Server
content.service.security.basicAuth.username=admin
content.service.security.basicAuth.password=admin
# URL and path for Alfresco Server API
content.service.url=http://localhost:8080
content.service.path=/alfresco/api/-default-/public/alfresco/versions/1
# Alfresco Repository Content Model (Summary)
# Aspect that triggers the summarization task
content.service.summary.aspect=genai:summarizable
# Node property to store the summary obtained from GenAI Stack
content.service.summary.summary.property=genai:summary
# Node property to store tags obtained from GenAI Stack; use TAG as a value to use a tag instead of a property
content.service.summary.tags.property=genai:tags
# Node property to store the Large Language Model (LLM) used; use TAG as a value to use a tag instead of a property
content.service.summary.model.property=genai:llmSummary
# Alfresco Repository Content Model (Classify)
# Node property that includes terms for classification
content.service.classify.terms.property=genai:terms
# Aspect that enables classification task
content.service.classify.aspect=genai:classified
# Node property to fill with the term
content.service.classify.term.property=genai:term
# Node property to fill with the model
content.service.classify.model.property=genai:llmClassify
# Alfresco Repository Content Model (Description)
# Aspect that triggers the description task
content.service.description.aspect=genai:descriptable
# Node property to store the description obtained from GenAI Stack
content.service.description.description.property=genai:description
# Node property to store the Large Language Model (LLM) used; use TAG as a value to use a tag instead of a property
content.service.description.model.property=genai:llmDescription
# GenAI Client Configuration
# Host URL for the Document GenAI service
genai.url=http://localhost:8506
# Request timeout in seconds for GenAI service requests
genai.request.timeout=1200
# Alfresco AI Applier Configuration
# Root folder in Alfresco Repository to apply GenAI action
applier.root.folder=/app:company_home/app:shared
# Choose one action: SUMMARY, CLASSIFY, DESCRIBE
applier.action=SUMMARY
# List of terms to be applied for CLASSIFY action (ignored when using SUMMARY action)
applier.action.classify.term.list=English,Spanish,Japanese,Vietnamese
# Maximum number of items to be retrieved from Alfresco Repository in each iteration
request.max.items=20
Configuration parameters can be also used as command line arguments or Docker environment variables, like in the following sample:
$ java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared/cm:picture \
--applier.action=DESCRIBE \
--logging.level.org.alfresco=DEBUGThis Spring Boot application is designed to capture summary, classification, or prompting aspect settings by listening to ActiveMQ events. The application then forwards the request to the GenAI Stack and subsequently updates the Alfresco Node using the REST API.
┌─Compose────────────────────┐ ┌─Compose────────────────────┐
│ │ │ │
│ A L F R E S C O │ │ G E N A I Stack │
│ │ │ │
│ ┌──────────┐ ┌───────────┐ │ │ ┌───┐ ┌─────┐ ┌─────────┐ │
┌────────────┤ │model-repo│ │model-share│ │ │ │llm│ │neo4j│ │langchain│ │
│ │ └──────────┘ └───────────┘ │ │ └───┘ └─────┘ └─────────┘ │
│ │ │ │ ollama │
│ └──────────────▲─────────────┘ └─────────────▲──────────────┘
│ tcp://activemq:61616 │ │
│ │ http://alfresco:8080 │ http://genai:8506/summary
│ ┌─Service──────┴─────────────┐ │ http://genai:8506/classify
│ │ │ │ http://genai:8506/prompt
└────────────► alfresco-ai-listener ├────────────────────────┘ http://genai:8506/describe
│ │
└────────────────────────────┘
Modify property values in application.properties file or use Docker environment setings:
# Spring Boot properties
spring.main.banner-mode=off
logging.level.org.springframework=ERROR
logging.level.org.alfresco=INFO
logging.pattern.console=%msg%n
# Alfresco Server Configuration
# Basic authentication credentials for Alfresco Server
content.service.security.basicAuth.username=admin
content.service.security.basicAuth.password=admin
# URL and path for Alfresco Server API
content.service.url=http://localhost:8080
content.service.path=/alfresco/api/-default-/public/alfresco/versions/1
# Alfresco Repository Content Model (Summary)
# Aspect that triggers the summarization task
content.service.summary.aspect=genai:summarizable
# Node property to store the summary obtained from GenAI Stack
content.service.summary.summary.property=genai:summary
# Node property to store tags obtained from GenAI Stack; use TAG as a value to use a tag instead of a property
content.service.summary.tags.property=genai:tags
# Node property to store the Large Language Model (LLM) used; use TAG as a value to use a tag instead of a property
content.service.summary.model.property=genai:llmSummary
# Alfresco Repository Content Model (Prompt)
# Aspect that enables prompt task
content.service.prompt.aspect=genai:promptable
# Node property that contains a question
content.service.prompt.question.property=genai:question
# Node property to fill with the answer
content.service.prompt.answer.property=genai:answer
# Node property to fill with the model
content.service.prompt.model.property=genai:llmPrompt
# Alfresco Repository Content Model (Classify)
# Node property that includes terms for classification
content.service.classify.terms.property=genai:terms
# Aspect that enables classification task
content.service.classify.aspect=genai:classified
# Node property to fill with the term
content.service.classify.term.property=genai:term
# Node property to fill with the model
content.service.classify.model.property=genai:llmClassify
# Alfresco Repository Content Model (Description)
# Aspect that triggers the description task
content.service.description.aspect=genai:descriptable
# Node property to store the description obtained from GenAI Stack
content.service.description.description.property=genai:description
# Node property to store the Large Language Model (LLM) used; use TAG as a value to use a tag instead of a property
content.service.description.model.property=genai:llmDescription
# ActiveMQ Server
spring.activemq.brokerUrl=tcp://localhost:61616
spring.jms.cache.enabled=false
alfresco.events.enableSpringIntegration=false
alfresco.events.enableHandlers=true
# GenAI Client Configuration
# Host URL for the Document GenAI service
genai.url=http://localhost:8506
# Request timeout in seconds for GenAI service requests
genai.request.timeout=1200
Configuration parameters can be also used as command line arguments or Docker environment variables, like in the following sample:
$ java -jar target/alfresco-ai-listener-0.8.0.jar --logging.level.org.alfresco=DEBUG- Before proceeding, ensure that Docker, ollama, Java, and Maven are up and working:
$ ollama -v
ollama version is 0.1.31
$ docker -v
Docker version 25.0.3, build 4debf41
$ java -version
openjdk version "17.0.5" 2022-10-18
$ mvn -v
Apache Maven 3.9.6- Verify that
compose-ai.yamlis commented in compose.yaml
$ cat compose.yaml
include:
- genai-stack/compose.yaml
- alfresco/compose.yaml
# - alfresco/compose-ai.yaml- Start Docker containers for Alfresco and GenAI Stack
$ docker compose up-
Once Alfresco is up & running, upload a number of documents to a given folder, for instance
/app:company_home/app:shared. You may use the Legacy UI with default credentials (admin/admin) available in http://localhost:8080/share -
Compile the Alfresco AI Applier (if required)
$ cd alfresco-ai/alfresco-ai-applier
$ mvn clean package- Run the Alfresco AI Applier to summarize the documents in a given folder
$ java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared \
--applier.action=SUMMARYOnce this command has finished, every document in the folder should include a populated
Summaryproperty (accessible in "view" mode)
- Run the Alfresco AI Applier to classify the documents based on a list of terms in a specific folder
$ cd alfresco-ai/alfresco-ai-applier
$ java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared \
--applier.action=CLASSIFY \
--applier.action.classify.term.list=English,Spanish,Japanese,VietnameseOnce this command has finished, every document in the folder should include a populated
Termproperty selected from the Term List (accessible in "view" mode)
-
Upload a number of pictures to an specific folder, for instance
/app:company_home/app:shared/cm:pictures -
Run the Alfresco AI Applier to summarize the documents, note that
applier.root.folderuses this folder already created
$ cd alfresco-ai/alfresco-ai-applier
$ java -jar target/alfresco-ai-applier-0.8.0.jar \
--applier.root.folder=/app:company_home/app:shared/cm:pictures \
--applier.action=DESCRIBEOnce this command has finished, every picture in the folder should include a populated
Descriptionproperty (accessible in "view" mode)
- Before proceeding, ensure that Docker, ollama, Java, and Maven are up and working:
$ ollama -v
ollama version is 0.1.31
$ docker -v
Docker version 25.0.3, build 4debf41
$ java -version
openjdk version "17.0.5" 2022-10-18
$ mvn -v
Apache Maven 3.9.6- Build
alfresco-ai-listenerDocker Image if required
$ cd alfresco-ai/alfresco-ai-listener
$ mvn clean package
$ docker build . -t alfresco-ai-listener- Verify that
compose-ai.yamlis uncommented in compose.yaml
$ cat compose.yaml
include:
- genai-stack/compose.yaml
- alfresco/compose.yaml
- alfresco/compose-ai.yaml- Start Docker containers for Alfresco and GenAI Stack
$ docker compose up- Use Alfresco Legacy UI, available in http://localhost:8080/share, to get a summary for a document
- Apply Summarizable with AI (
genai:summarizable) aspect to a node - Wait until GenAI populates
Summaryproperty (accesible in "view" mode)
- Classify a document
- Apply Classifiable with AI (
genai:classifiable) aspect to a folder - Add a list of terms separated by comma in property Terms (
genai:terms) of the folder - Add a document inside this folder
- Apply the aspect Classified with AI (
genai:classified) to the document - Wait until GenAI selects one term from the list and populates
Termproperty of the document (accesible in "view" mode)
- Ask a question
- Apply Promptable with AI (
genai:promptable) aspect to a document - Type your question in the property Question
- Wait until GenAI populates
Answerproperty (accesible in "view" mode)
- Describe a picture
- Apply Descriptable with AI (
genai:descriptable) aspect to a picture - Wait until GenAI populates
Descriptionproperty (accesible in "view" mode)
These operations may be automated by creating folder rules that apply required aspects to documents uploaded to an specific folder