
trustgraph
Deploy reasoning AI agents powered by agentic graph RAG in minutes in a scalable and reliable infrastructure.
Stars: 281

TrustGraph is a tool that deploys private GraphRAG pipelines to build a RDF style knowledge graph from data, enabling accurate and secure `RAG` requests compatible with cloud LLMs and open-source SLMs. It showcases the reliability and efficiencies of GraphRAG algorithms, capturing contextual language flags missed in conventional RAG approaches. The tool offers features like PDF decoding, text chunking, inference of various LMs, RDF-aligned Knowledge Graph extraction, and more. TrustGraph is designed to be modular, supporting multiple Language Models and environments, with a plug'n'play architecture for easy customization.
README:
๐ Getting Started ๐บ YouTube โ๏ธ API Docs ๐งโ๐ป CLI Docs ๐ฌ Discord ๐ Blog ๐ Use Cases
TrustGraph empowers you to deploy reasoning AI Agents in minutes. Our Agentic Graph RAG platform allows you to leverage modular cognitive cores for complex reasoning and information retrieval, all within a scalable and reliable infrastructure. Forget lengthy development cycles โ TrustGraph delivers instant reasoning.
- ๐ Document Extraction: Bulk ingest documents such as
.pdf
,.txt
, and.md
- ๐ช Adjustable Chunking: Choose your chunking algorithm and parameters
- ๐ No-code LLM Integration: Anthropic, AWS Bedrock, AzureAI, AzureOpenAI, Cohere, Google AI Studio, Google VertexAI, Llamafiles, Ollama, and OpenAI
- ๐ Entity, Topic, and Relationship Knowledge Graphs
- ๐ข Mapped Vector Embeddings
- โNo-code Graph RAG Queries: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
- ๐ง Cognitive Cores: Modular data sets with semantic relationships that can saved and quickly loaded on demand
- ๐ค Agent Flow: Define custom tools used by a ReAct style Agent Manager that fully controls the response flow including the ability to perform Graph RAG requests
- ๐ Multiple Knowledge Graph Options: Full integration with Memgraph, FalkorDB, Neo4j, or Cassandra
- ๐งฎ Multiple VectorDB Options: Full integration with Pinecone, Qdrant, or Milvus
- ๐๏ธ Production-Grade reliability, scalability, and accuracy
- ๐ Observability: get insights into system performance with Prometheus and Grafana
- ๐ชด Customizable and Extensible: Tailor for your data and use cases
- ๐ฅ๏ธ Configuration Portal: Build the
YAML
configuration with drop down menus and selectable parameters - ๐ต๏ธ Data Workbench: Explore your data with a 3D semantic visualizer
See the API Developer's Guide for more information.
For users, TrustGraph has the following interfaces:
The TrustGraph CLI
installs the commands for interacting with TrustGraph while running along with the Python SDK. The Configuration Portal
enables customization of TrustGraph deployments prior to launching. The REST API can be accessed through port 8088
of the TrustGraph host machine with JSON request and response bodies.
pip3 install trustgraph-cli==0.19.22
[!NOTE] The
TrustGraph CLI
version must match the desiredTrustGraph
release version.
TrustGraph is endlessly customizable by editing the YAML
launch files. The Configuration Portal
provides a quick and intuitive tool for building a custom configuration that deploys with Docker, Podman, Minikube, or Google Cloud. There is a Configuration Portal
for the both the lastest and stable TrustGraph
releases.
The Configuration Portal
has 4 important sections:
- Component Selection โ : Choose from the available deployment platforms, LLMs, graph store, VectorDB, chunking algorithm, chunking parameters, and LLM parameters
- Customization ๐งฐ: Customize the prompts for the LLM System, Data Extraction Agents, and Agent Flow
-
Data Workbench ๐ต๏ธ: Add the Data Workbench to the configuration available on port
8888
-
Finish Deployment ๐: Download the launch
YAML
files with deployment instructions
The Configuration Portal
will generate the YAML
files in deploy.zip
. Once deploy.zip
has been downloaded and unzipped, launching TrustGraph is as simple as navigating to the deploy
directory and running:
docker compose up -d
[!TIP] Docker is the recommended container orchestration platform for first getting started with TrustGraph.
When finished, shutting down TrustGraph is as simple as:
docker compose down -v
The -v
flag will destroy all data on shut down. To restart the system, it's necessary to keep the volumes. To keep the volumes, shut down without the -v
flag:
docker compose down
With the volumes preserved, restarting the system is as simple as:
docker compose up -d
All data previously in TrustGraph will be saved and usable on restart.
If added to the build in the Configuration Portal
, the Data Workbench
will be available at port 8888
. The Data Workbench
has 4 capabilities:
- System Chat ๐ฌ: Graph RAG queries in a chat interface
- Data Explorer ๐ต๏ธ: See semantic relationships in a list structure
- Data Visualizer ๐: Visualize semantic relationships in 3D
-
Data Loader ๐: Directly load
.pdf
,.txt
, or.md
into the system
TrustGraph YAML
files are available here. Download deploy.zip
for the desired release version.
Release Type | Release Version |
---|---|
Latest | 0.20.5 |
Stable | 0.19.22 |
TrustGraph is fully containerized and is launched with a YAML
configuration file. Unzipping the deploy.zip
will add the deploy
directory with the following subdirectories:
docker-compose
minikube-k8s
gcp-k8s
[!NOTE] As more integrations have been added, the number of possible combinations of configurations has become quite large. It is recommended to use the
Configuration Portal
to build your deployment configuration. Each directory containsYAML
configuration files for the default component selections.
Docker:
docker compose -f <launch-file.yaml> up -d
Kubernetes:
kubectl apply -f <launch-file.yaml>
TrustGraph is designed to be modular to support as many LLMs and environments as possible. A natural fit for a modular architecture is to decompose functions into a set of modules connected through a pub/sub backbone. Apache Pulsar serves as this pub/sub backbone. Pulsar acts as the data broker managing data processing queues connected to procesing modules.
- For processing flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module.
- For services such as LLMs and embeddings, Pulsar provides a client/server model. A Pulsar queue is used as the input to the service. When processed, the output is then delivered to a separate queue where a client subscriber can request that output.
TrustGraph extracts knowledge documents to an ultra-dense knowledge graph using 3 automonous data extraction agents. These agents focus on individual elements needed to build the knowledge graph. The agents are:
- Topic Extraction Agent
- Entity Extraction Agent
- Relationship Extraction Agent
The agent prompts are built through templates, enabling customized data extraction agents for a specific use case. The data extraction agents are launched automatically with the loader commands.
PDF file:
tg-load-pdf <document.pdf>
Text or Markdown file:
tg-load-text <document.txt>
Once the knowledge graph and embeddings have been built or a cognitive core has been loaded, RAG queries are launched with a single line:
tg-invoke-graph-rag -q "What are the top 3 takeaways from the document?"
Invoking the Agent Flow will use a ReAct style approach the combines Graph RAG and text completion requests to think through a problem solution.
tg-invoke-agent -v -q "Write a blog post on the top 3 takeaways from the document."
[!TIP] Adding
-v
to the agent request will return all of the agent manager's thoughts and observations that led to the final response.
Developing on TrustGraph using APIs
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