trustgraph
Discover the Knowledge Trapped in Data Silos with a Fully Agentic, No-code AI Systemโก๐
Stars: 219
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 โ๏ธ API Docs ๐งโ๐ป CLI Docs ๐บ YouTube ๐ฌ Discord ๐ Blog ๐ Use Cases
TrustGraph is a fully agentic AI system for complex unstructured data. Extract your documents to knowledge graphs and vector embeddings with customizable data extraction agents. Deploy AI agents that analyze your data to understand the complex relationships scattered across data silos.
- 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 GraphRAG Queries: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
- ๐ค Agent Flow: Define custom tools used by a ReAct style Agent Manager that fully controls the response flow including the ability to perform GraphRAG requests
- ๐ Multiple Knowledge Graph Options: Full integration with Memgraph, 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
- ๐๏ธ AI Powered Data Warehouse: Load only the subgraph and vector embeddings you use most often
- ๐ชด Customizable and Extensible: Tailor for your data and use cases
- ๐ฅ๏ธ Configuration UI: Build the
YAML
configuration with drop down menus and selectable parameters
There are four ways of interacting with TrustGraph:
The TrustGraph CLI
installs the commands for interacting with TrustGraph while running along with the Python SDK. The Configuration UI
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==0.17.16
[!NOTE] The
TrustGraph CLI
version must match the desiredTrustGraph
release version.
TrustGraph is endlessly customizable by editing the YAML
launch files. The Configuration UI
provides a quick and intuitive tool for building a custom configuration that deploys with Docker, Podman, Minikube, or Google Cloud. There is a Configuration UI
for the both the lastest and stable TrustGraph
releases.
The Configuration UI
has three 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
-
Finish Deployment ๐: Download the launch
YAML
files with deployment instructions
The Configuration UI
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
TrustGraph YAML
files are available here. Download deploy.zip
for the desired release version.
Release Type | Release Version |
---|---|
Latest | 0.18.7 |
Stable | 0.17.16 |
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
Config UI
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 knowledge core has been loaded, RAG queries are launched with a single line:
tg-query-graph-rag -q "Write a blog post about the 5 key takeaways from SB1047 and how they will impact AI development."
Invoking the Agent Flow will use a ReAct style approach the combines GraphRAG and text completion requests to think through a problem solution.
tg-invoke-agent -v -q "Write a blog post about the 5 key takeaways from SB1047 and how they will impact AI development."
[!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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