X.com Steve Reiner @stevereiner  LinkedIn Steve Reiner LinkedIn Posts

Flexible GraphRAG or Flexible RAG, an Apache 2.0 open source python platform, is now flexing to the max using LlamaIndex, in terms of supporting more databases and data sources: supports 8 graph databases, 10 vector databases, 3 search engines, and 13 data sources,. Also supports knowledge graph auto-building, schemas, LlamaIndex LLMs, Docling doc processing (LlamaParse coming soon), GraphRAG mode, RAG only mode, Hybrid search, and AI query / chat. Has React, Vue, and Angular frontends, and a FastAPI backend. React, Vue, Angular, Backend now work on Windows, Mac, Linux (standalone or in docker). Also has a FastMCP MCP server. Has a convenient docker compose that can include any of the databases (Vector, Graph, Search, Alfresco) and Dashboards / Consoles.

(Flexible GraphRAG AI chat shown with Hyland products web page(s) used with web pages data source to auto-generated a Neo4j graph).

Convenient docker compose: you can choose to include from all supported 10 vector and 8 graph databases, Elasticsearch, OpenSearch, and Hyland Alfresco Community. Include in the docker-compose.yaml by just removing the # comment in front of their includes. Dashboards / Consoles for these databases, as much as possible are also included in the docker compose choices (either in the yaml file for the database or for some a separate yaml file include).

You can run the docker with the databases with the backend and frontends (React, Angular, Vue) running stand alone in separate terminal windows. In addition to running the databases in docker, you can include the backend and frontends in the dock compose by including the app-stack.yaml and proxy.yaml includes. Now have no config duplication for standalone backend+frontends vs full docker mode: previously had to repeat all config in app-stack.yaml now use env_file: include standalone backend .env and overrides with include of docker.env (for configs that need host.docker.internal)

All 8 Graph database working: Neo4j, ArcadeDB, FalkorDB, archived Kuzu (LadybugDB fork todo), NebulaGraph, Memgraph, Amazon Neptune, Amazon Neptune Analytics

All 10 Vector databases working: Qdrant, Elasticsearch vector, OpenSearch vector, Neo4j vector, Milvus, Weaviate, Chroma (both http, embedded), Pinecone, PostgreSQL + pgvector, LanceDB

All 3 Search engines working: Elasticsearch, OpenSearch, LlamaIndex built-in BM25

New Data Sources: using LlamaIndex readers: 1. working ones that don’t use document processing: Web Pages, Wikipedia, Youtube, 2. working using document processing: S3, 3. ones using document processing still to test: Google Drive, Microsoft OneDrive, Azure Blob, GCS, Box, SharePoint.

Support for Docling document processing is currently available. Being able configure to use LlamaParse coming soon.

Original data sources with document processing that don’t use LlamaIndex readers: filesystem, Alfresco, CMIS. Hyland Alfresco Community can be included in the docker compose by taking the “#” comment off the beginning of its include.

LLMs: LlamaIndex LLMs (LlamaIndex has support for very many), Flexible GraphRAG currently has config for 1. tested, working: OpenAI, Ollama, 2. untested: Anthropic Claude, Google Gemini, Azure OpenAI.

Previous Flexible GraphRAG posts:

See Flexible GraphRAG Initial Version Blog Post

See New Tabbed UI for Flexible GraphRAG (and Flexible RAG)

See Flexible GraphRAG: Performance improvements, FalkorDB graph database support added

See Flexible GraphRAG: Supports ArcadeDB Graph Database with new LlamaIndex Integration

See Flexible GraphRAG: Amazon Neptune, Neptune Analytics, and Graph Explorer support added

X.com Steve Reiner @stevereiner  LinkedIn Steve Reiner LinkedIn Posts

Amazon Neptune, and Amazon Neptune Analytics support is working and checked int0 the Flexible GraphRAG github.

Graph Explorer is supported and working with these graph databases and is also checked in. It runs in a docker and can be used to query and visualize with both Amazon Neptune and Amazon Neptune Analytics.

Gremlin and openCypher can be used in Amazon Neptune with Graph Explorer, while openCypher is the primary language for Neptune Analytics. SPARQL is available for graph queries in the general Neptune database, SPARQL support for Graph Explorer is officially on the AWS development roadmap, but no firm timeline has been announced as of October 2025

Note that for Neptune Analytics, Flexible GraphRAG had to put in a wrapper class to filter out vector queries from its LamaIndex integration that were causing errors in Neptune Analytics. This wasn’t an issue with regular Neptune.

Flexible GraphRAG or Flexible RAG , an Apache 2.0 open source python platform, supports 8 graph databases, 10 vector databases, 3 search engines, and 13 data sources,. Supports knowledge graph auto-building, schemas, LlamaIndex LLMs, Docling doc processing (LlamaParse coming soon), GraphRAG mode, RAG only mode, Hybrid search, and AI query / chat. Has React, Vue, and Angular frontends, and a FastAPI backend. React, Vue, Angular, and Backend now work on Windows, Mac, Linux (standalone or in docker). Has a convenient docker compose that can include any of the databases (vector, graph, search, alfresco) and dashboards / consoles. There is also a Flexible GraphRAG MCP server.

Previous Flexible GraphRAG posts:

See Flexible GraphRAG Initial Version Blog Post

See New Tabbed UI for Flexible GraphRAG (and Flexible RAG)

See Flexible GraphRAG: Performance improvements, FalkorDB graph database support added

See Flexible GraphRAG: Supports ArcadeDB Graph Database with new LlamaIndex Integration

X.com Steve Reiner @stevereiner  LinkedIn Steve Reiner LinkedIn Posts

Flexible GraphRAG added support for the ArcadeDB graph database using this new integration:

ArcadeDB LlamaIndex Integration and arcadedb-python available:

arcadedb-llama-index Github

arcadedb-python Github

ArcadeDB (Apache 2.0) is a next generation Multi-Model Database for Graphs, Documents, Key/Value and Time-Series. Supports SQL, Cypher, Gremlin and MongoDB queries

arcadedb.com

ArcadeDB Github

Flexible GraphRAG is open source python platform supporting Docling document processing, knowledge graph auto-building, schemas, 13 data sources, 10 Vector databases, 7 Graph databases, ElasticSearch and OpenSearch search engines, RAG, GraphRAG, hybrid search, and AI query / chat. Has React, Vue, and Angular frontends, and a FastAPI backend. Also has a FastMCP MCP server.

Previous Flexible GraphRAG posts:

See Flexible GraphRAG Initial Version Blog Post

See New Tabbed UI for Flexible GraphRAG (and Flexible RAG)

See Flexible GraphRAG: Performance improvements, FalkorDB graph database support added

See New Tabbed UI for Flexible GraphRAG (and Flexible RAG)

Flexible GraphRAG on GitHub

X.com Steve Reiner @stevereiner LinkedIn Steve Reiner LinkedIn

1. Improved the performance of flexible-graphrag
   • Added doing parallel Docling document conversion helped pipeline timing
   • Now not doing KeywordExtractor/SummaryExtractor also helped pipeline timing
   • Ollama Parallel Processing (need OLLAMA_NUM_PARALLEL=4)
   • Async PropertyGraphIndex with use_async=True
   • Increased kg_batch_size from 10 to 20 chunk
   • Logging added for performance timing
2. Added performance testing results to readme.md (6 docs with openai with each graph database (neo4j, kuzu, falkordb)
3. Added docs/performance.md: has performance testing results for each graph database with 2,4,6 docs with openai and 2,4 docs with ollama
4. Added support for FalkorDB graph database https://www.falkordb.com/ and https://github.com/FalkorDB/falkordb The abstractions of LlamaIndex, LlamaIndex support for FalkorDB, and the configurability of flexible-graphrag made this a relatively straightforward process.
5. Added LlamaIndex DynamicLLMPathExtractor support (works on openai, not on ollama currently)
6. Added config of kg extractor type (simple, schema, or dynamic) to set which LlamaIndex extractor to use (SimpleLLMPathExtractor, SchemaLLMPathExtractor, or DynamicLLMPathExtractor)
7. Added config of MAX_TRIPLETS_PER_CHUNK and MAX_PATHS_PER_CHUNK
8. Added readme.md info on system environment setup of ollama for performance and parallelism (OLLAMA_CONTEXT_LENGTH, OLLAMA_NUM_PARALLEL, etc.)
9. Added new default schema with 35+ relationship combinations, more relations, and entity types: PERSON, ORGANIZATION, TECHNOLOGY, PROJECT, LOCATION
10. Fixed file upload dialog performance in all 3 front ends: React, Angular, and Vue (chosen files display quickly after dialog ok)

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).

Flexible GraphRAG is an open source python platform supporting document processing, Knowledge Graph auto-building, Schema support, RAG and GraphRAG setup, hybrid search (fulltext, vector, graph), and AI Q&A query capabilities.

X.com Steve Reiner @stevereiner LinkedIn Steve Reiner LinkedIn

Has a MCP Server, Fast API Backend, Docker support, Angular, React, and Vue UI clients

Built with LlamaIndex which provides abstractions for allowing multiple vector, search graph databases, LLMs to be supported.

Supports currently:

Graph Databases: Neo4j, Kuzu

Vector Databases: Neo4j, Qdrant, Elasticsearch, OpenSearch

Search Databases/Engines: Elasticsearch, OpenSearch, LlamaIndex built-in BM25

LLMs: OpenAI, Ollama

Data Sources: File System, Hyland Alfresco, CMIS

A configurable hybrid search system that optionally combines vector similarity search, full-text search, and knowledge graph GraphRAG on document processed (Docling) from multiple data sources (filesystem, Alfresco, CMIS, etc.). It has both a FastAPI backend with REST endpoints and a Model Context Protocol (MCP) server for MCP clients like Claude Desktop, etc. Also has simple Angular, React, and Vue UI clients (which use the REST APIs of the FastAPI backend) for using interacting with the system.

• Hybrid Search: Combines vector embeddings, BM25 full-text search, and graph traversal for comprehensive document retrieval

Knowledge Graph GraphRAG: Extracts entities and relationships from documents to create graphs in graph databases for graph-based reasoning

• Configurable Architecture: LlamaIndex provides abstractions for vector databases, graph databases, search engines, and LLM providers
• Multi-Source Ingestion: Processes documents from filesystems, CMIS repositories, and Alfresco systems
• FastAPI Server with REST API: FastAPI server with REST API for document ingesting, hybrid search, and AI Q&A query
• MCP Server: MCP server that provides MCP Clients like Claude Desktop, etc. tools for document and text ingesting, hybrid search and AI Q&A query.
• UI Clients: Angular, React, and Vue UI clients support choosing the data source (filesystem, Alfresco, CMIS, etc.), ingesting documents, performing hybrid searches and AI Q&A Queries.
• Deployment Flexibility: Supports both standalone and Docker deployment modes. Docker infrastructure provides modular database selection via docker-compose includes – vector, graph, and search databases can be included or excluded with a single comment. Choose between hybrid deployment (databases in Docker, backend and UIs standalone) or full containerization.

Check-ins 8/5/25 thru 8/9/25 provided:
1. Added LlamaIndex support, configurability, KG Building, GraphRAG, Hybrid Search, AI Q&A Query, Angular, React, and Vue UIs. Based on CMIS GraphRAG UI and CMIS GraphRAG which didn’t use LlamaIndex (used neo4j-graphrag python package)
2. Also added a FastMCP based MCP Server that uses the FastAPI server.

Check-in today 8/15/25 provided:

Added: Multiple Databases Support, Docker, Schemas, and Ollama support

1. Leveraging LlamaIndex abstractions, added support for more search, vector and graph databases (beyond previous Neo4j, built-in BM25). Now support:
   Neo4j graph database, or Neo4j graph and vectors (also Neo4j browser / console)
   Elasticsearch search, or search and separate vector (also Kibana dashboard)
   OpenSearch search, or search+vector hybrid search (also OpenSearch Dashboards)
   Qdrant vector database (also its dashboard)
   Kuzu graph database support (also Kuzu explorer)
   LlamaIndex built-in local BM25 full text search
   (Note: LlamaIndex supports additonal vector and graph databases which we could support)
2. Added composable Docker support
   a. As way to run search, graph, and vector databases. Also dashboards, and alfreso
   (comment out includes for what you have exernally or don’t use)
   b. Databases together with Flexible GraphRAG backend, and Angular, React, and Vue UIs
3. Added Schema support for Neo4j (optional), and Kuzu (needed). Support default and custom
   schemas you configure in your environment (.env file, etc.)
4. Added Ollama support in addition to OpenAI. Tested thru Ollama gpt-oss:20b, llama3.1, llama3.2.
   (Note: LlamaIndex supports additonal LLMs which we could support)

Model Context Protocol Server (MCP) for Alfresco Content Services (Community and Enterprise)

This uses FastMCP 2.0 and Python-Alfresco-API

A full featured MCP server for Alfresco in search and content management areas. Features complete documentation, tests, examples,
and config samples for various MCP clients (Claude Desktop, MCP Inspector, references to configuring others).

Python-Alfresco-MCP-Server on Github
https://github.com/stevereiner/python-alfresco-mcp-server

Tools:
Basic search, advanced search, metadata search, and cmis query,
upload, download, check-in, checkout, cancel checkout,
create folder, folder browse, delete node,
get/set properties, repository info.

(With python-alfresco-api having full coverage of the 7 Alfresco REST APIs
you could customize with what tools you want from 191 in core, 29 in workflow,
3 in authentication, 1 in search, 1 in discovery, 18 in model, 1 search sql for solr)

Resources: repository info repeated

Prompts: search and analyze

Latest on Github 7/29/25

• readme.md focuses on install with uv and uvx
• docs\install_with_pip_pipx.md covers install with pip and pipx
• sample configs for Claude Deskop (stdio) with uv, uvx, pipx for windows and mac
• sample configs for mcp-inspector with uv, uvx, pipx for both http and stdio

Python-Alfresco-MCP-Server v1.1.0 7/25/25

• Refactored code into single file per tool (organized in tools/search/,
  tools/core/, resources/, prompts/, utils/
• Changes for python-afresco-api 1.1.1
• Must better testing (143/143 passing)
• Added uv support (latest readme and config samples also have uvx)
• First version on PyPI.org

Python-Alfresco-MCP-Server v1.0 6/24/25
Changed to use FastMCP vs original code

Python-Alfresco-MCP-Server on PyPI
https://pypi.org/project/python-alfresco-mcp-server/
(On PyPI so don’t need source, still need python and optionally fast uv installed)

Thse can be used to test install or run one thing
# Tests that installation worked
uv tool run python-alfresco-mcp-server –help
uvx python-alfresco-mcp-server –help # alias for uv tool run

This install may not be needed
uv tool install python-alfresco-mcp-server

Python-Alfresco-API on Github
https://github.com/stevereiner/python-alfresco-api

Python-Alfresco-API on PyPI
https://pypi.org/project/python-alfresco-api/

X.com
https://x.com/stevereiner

LinkedIn
https://www.linkedin.com/in/steve-reiner-abbb5320/

 This is a complete Python client package for developing python code and apps for Alfresco. It supports using all 7 Alfresco REST APIs: Core, Search, Authentication, Discovery, Model, Workflow, Search SQL (Solr admin). It has Event support (activemq or Event Gateway). The project has extensive documentation, examples, and tests.

See Python-Alfresco-MCP-Server . This is a Model Context Protocol (MCP) Server that uses Python Alfresco API

https://github.com/stevereiner/python-alfresco-api

https://pypi.org/project/python-alfresco-api

You need Python 3.10+ installed.

This can be used to install:

pip install python-alfresco-api

The released v1.1.1 version goes well beyond the previous 1.0.x version.

It has a generated well organized hierarchical structure for higher level clients (1.0.x only had 7 wrapper files). Its generated from the openapi-python-client generated low level “raw clients”

Pydantic v2 models are now used the high level clients. Hopefully in v1.2 the low level clients will too. This can be done by configuring the openapi-python-client generator with templates. Some things need to be worked out, so no guarantees. This will simplify things and avoid model conversions.

Added utilities for upload, download, versioning, searching. etc. Using the utilities reduced the amount of code you need to do these operations.

A well organized hierarchical structure of linked md docs for the high level client apis and models documentation is also generated.

Documentation now has diagrams for overall architecture, model levels, and client type.

Readme now covers how to install an Alfresco Community docker from GitHub. In case you don’t already a Enterprise or Community version of Alfresco Content Services. Also see Hyland Alfresco .

Neo4j Labs LLM Knowledge Graph Builder main info site

Short Youtube demo video

The Online LLM Graph Builder can be used. You need to provide it with your Aura Neo4j connection info (you can create an account for a free Aura DB). It only has Diffbot, OpenAI, and Gemini LLM models available.

Graph Builder can upload from local files, AWS S3, web pages, Wikipedia, and Youtube. Google GCS can be a source if configured.

First choose the LLM model to use. Then upload one or more files. Then choose generate graph. You can view the graphs with the basic viewer (which allows hiding chunk nodes, community nodes, so you can see the entities and relationships). The Bloom viewer is also available, which is more complicated.

You can also chat with the data using GraphRAG and your chosen LLM. Answers have a icon below them that when clicked, provides info on graph doc sources, what entities, and what chunks were used to answer.

LLM Graph Builder Github project (Apache 2.0 open source)

The online version doesn’t have the llama3 models. So you need to clone the github project and build locally. To add using Meta Llama3 models, you need to configure it. You use the example.env to create a .env file and then add an optional OpenAI key, LLM model configuration, and indicate you initial Neo4j database info. Neo4j connection info can also be provided in the UI. Then do docker compose up. I have a fork of the main branch in my LLM Graph Builder that has added: configuration for lllama3, llama3.1, llama3.2, and openai gpt-4 choices, some neo4j connection config examples, switched to 8090 to not conflict with Alfresco 8080, has an additional debug log to so you can check on model config. and has a sample files folder with space-station.txt.

Speaking of Alfresco, I could add to my Alfresco GenAI Semantic project to call the separable backend of Graph Builder to generate a knowledge graph of new or updated Alfresco documents that have a new custom aspect. The backend may only have support for sources coming for the app’s kinds of sources currently. Also note in terms of UI integration, Alfresco’s ADF components and the ACA client use Angular. Neo4j Graph Builder’s front end uses React (and so does some of their other software projects).

space-station.txt with OpenAI GPT-4o:

space-station.txt with Meta Llama3:

space-station.txt with Meta Llama3.1:

space-station.txt with smaller Meta Llama3.2:

OpenAI GPT-4o with Albert Einstein Wikipedia page (340 nodes, 230 relationships):

Meta Llama3 with Albert Einstein Wikipedia page (150 nodes, 150 relationships), not shown: Llama3.1 (had 161 nodes, 85 relationships), not shown Llama3.2 (125 nodes, 76 relationships)

I first investigated how NLP could be used for both entity recognition and relation extraction for creating a knowledge graphs of content. Tomaz Bratanic’s Neo4j blog article  used Relik for NLP along with LlamaIndex for creating a graph in Neo4j, and setting up an embedding model for use with LLM queries.

In my llama_relik github project, I used the  notebook from the blog article and changed it to use fastcoref instead of coreferee. Fastcoref was mentioned in the medium article version of the Neo4j blog article in the comments. It’s supposed to work better. There is also a python file in this project than can be used instead of the notebook.

I submitted some fixes to Relik on Windows, but it performs best on Linux in general and was more able to use the GPU “cuda” mode instead of “cpu”.

Similar work has been done using Rebel for NLP by Neo4j / Tomaz Bratanic, Saurav Joshi, and Qrious Kamal

Note that Relik has closed information extraction (CIE) models that do both entity linking (EL) and relation extraction (RE) . It also has models focused on either EL or RE.

Below is a screenshot from Neo4j with a knowledge graph created with the python file from the llama_relik project using the “relik-cie-small” model with the spacy space station sample text (ignore chunk node and it’s mentions relations). Notice how it has separate entities for “ISS” and “International Space Station” .

The “relik-cie-large” model finds more relations in screenshot below. It also has separate entities for “ISS” and “International Space Station” (and throws in second “International Space Station”).