Tag: Alfresco

New Tabbed UI for Flexible GraphRAG (and Flexible RAG)

See Flexible GraphRAG Initial Version Blog Post

Flexible GraphRAG on GitHub

X.com Steve Reiner @stevereiner LinkedIn Steve Reiner LinkedIn

The Angular, React, and Vue frontend clients now have different stages organized into different tabs so they have room. They all can be switched between a dark and light theme using the slider at the top right corner. New functionality beyond the old UI includes a file upload dialog, drag/drop upload, a table with file processing progress bars, and a new Chat UI. Note the github readme.md page has collapse / expand sections to look at screenshots with dark and light themes for React, and only shows the light theme for Angular and Vue.

Sources Tab


Allows you to choose file to upload from the file system, or paths file or folder path in Alfresco or CMIS repositories. For filesystem files you can now use a file upload dialog and drag/drop files onto the drop area in the source tab view.

For Alfresco and CMIS their no file picker UI currently (only a field for folder or file path) Note the file path is a basic CMIS style path like /Shared/GraphRAG/cmispress.txt. You also specify username, password and base URL like prefilled http://localhost:8080/alfresco for Alfresco and http://localhost:8080/alfresco/api/-default-/public/cmis/versions/1.1/atom for CMIS.

You then click on “Configure Processing“


Processing Tab

Here you can modify what files get processed by unselecting / selecting file checkboxes, Remove from processing list by using x a on file row, our use the remove selected button.
The click on Start Processing to process selected files.
There is an overall progress bar, and per file progress bars. Note currently all files are processed as one batch in the backend, so the file progress bars will be showing the same status.
You can cancel processing by using the cancel button

Search Tab

Here you can do a Hybrid Search (Fulltext+Vector RAG+GraphRAG) or (Fulltext+Vector RAG) depending on configuration. This gives you a traditional results list. For now ignore the scores and extra results just check results order.

The Q&A Query, Here you ask a question using conversational style (This is an AI query using the configured LLM and the information submitted in the processing tab (and in full text, vector, and graph “memory”)

Chat Tab

This a traditional chat style UI allowing you the enter multiple conversational Q&A queries (AI queries like the one at a time in the Search Tab). You hit enter or click the arrow button to submit a query. You can also use Shift+Enter to get a extra new line for your question. The chat view area displays a history of questions and answers. The you can clear things with the Clear History button

Flexible RAG

I used Flexible RAG in the title to indicate that Flexible GraphRAG can be configured to just be a RAG system. This would still have the flexibility that LlamaIndex abstractions provide to be able to plug in different search engines/databases, vector databases, and LLMs. You still get Angular, React, and Vue frontends, have MCP server support, a FastAPI backend, and Docker support. You could just configure a search engine. You could just configure a Graph database for auto graph building of knowledge graphs using the configurable schema support.

For RAG configuration:
Flexible GraphRAG can be setup to do RAG only without the GraphRAG (see env-sample.txt and setup your environment in .env, etc.):

  • Have SEARCH_DB and SEARCH_DB_CONFIG set for elasticsearch, opensearch, or bm25
  • Have VECTOR_DB and VECTOR_DB_CONFIG setup for neo4j, qdrant, elasticsearch, or opensearch
  • Have GRAPH_DB set to none and ENABLE_KNOWLEDGE_GRAPH=false.

Server Monitoring and Management UI

Basically you can use the docker setup and get a docker compose that run all the following at the same time (or a subset by commenting out a compose include) without having to these up individually: Alfresco docker compose (which has Share and ACA), Neo4j docker (which has a console URL), Kuzu API server (not used, used embedded), Kuzu explorer, Qdrant (which has a dashboard), Elasticsearch, Elasticsearch Kibana dashboard, OpenSearch which has a OpenSearch Dashboards URL.

So you can setup a browser window with tabs for all these dashboards, Alfresco Share / ACA, and Neo4J console. This is your monitoring and management UI.

You can uses the Neo4j, Elasticsearch Kibana, Qdrant dashboard, OpenSearch dashboards to delete full text indexes (Elasticsearch, OpenSearch), delete vector indexes (Qdrant, Neo4j, Elasticsearch, OpenSearch) and delete nodes and relationships (Neo4j and Kuzu consoles).

Alfresco GenAI Semantic project updated: now adds regular Alfresco tags, uses local Wikidata and DBpedia entity recognizers

The Alfresco GenAI Semantic  github project  now adds regular Alfresco tags when performing auto tagging when enhancing with links to Wikidata and DBpedia. Semantic entity linking info is kept in 3 parallel multi-value properties (labels, links, super type lists) in the WikiData and DBpedia custom aspects. The labels values are used for the tag labels.

I switched to a local, private Wikidata recognizer.  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. Had previously used the  spaCyOpenTapioca library, which calls an OpenTapioca public web service api URL. Note the URLs in the links properties do go to the public website wikidata.org if used in your application.

I also switched to a local, private DBpedia Spotlight entity recognizer in a docker composed in. The local URL to this docker is given the to the spacy DBpedia Spotlight for SpaCy library. This library was using a public Spotlight web service api URL by default previously. Note the URLs in the links properties do go to to the public website dbpeda.org if used in your application.

For documents with the Wikidata or DBpedia aspects added to them, tags will show up in the Alfresco clients (ACA, ADW, Share) after PDF rendition creation and alfresco-genai-semantic AI Listener gets responses from REST apis in the genai-stack. Shown below are tags in the ACA community content app:

Multi-value Wikidata aspect properties of a document in the ACA client are shown below in the view details expanded out. The labels property repeats what the labels of the tags have. The links properties have URLs to wikidata.org. The super types properties have the zero “” or one or multiple comma separated super types in wikidata for each entity. These supertypes are wikidata ids (are links once you add “http://www.wikidata.org/wiki/” in front of the ids).

The same style DBpedia aspect multivalue properties are shown below in the ACA client. Note that the super types can be from Wikidata, DBpedia, Schema (schema.org), foaf, or DUL (ontologydesignpatterns.org DUL.owl), etc.