TrustGraph is a full AI powered data engineering platform. Extract your documents to knowledge graphs and vector embeddings with customizable data extraction agents. Deploy AI agents that leverage your data to generate reliable and accurate AI responses.
- 📄 **Document Extraction**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md`
- 🔁 **No-code LLM Integration**: Anthropic, AWS Bedrock, AzureAI, AzureOpenAI, Cohere, Google AI Studio, Google VertexAI, Llamafiles, Ollama, and OpenAI
- 📖 **Entity, Topic, and Relationship Knowledge Graphs**
- 🔢 **Mapped Vector Embeddings**
- ❔**No-code RAG Queries**: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
- 🤖 **AI Agent Generation**: Use AI to generate agent modules that autonomously run on the Apache pub/sub backbone
- 🎛️ **Production-Grade** reliability, scalability, and accuracy
- 🔍 **Observability**: get insights into system performance with Prometheus and Grafana
- 🗄️ **AI Powered Data Warehouse**: Load only the subgraph and vector embeddings you use most often
- 🪴 **Customizable and Extensible**: Tailor for your data and use cases
- 🖥️ **Configuration UI**: Build the `YAML` configuration with drop down menus and selectable parameters
The `TrustGraph CLI` installs the commands for interacting with TrustGraph while running. The `Configuration UI` enables customization of TrustGraph deployments prior to launching.
While TrustGraph is endlessly customizable through the `YAML` launch files, the `Configuration UI` can build a custom configuration in seconds that deploys with Docker, Podman, Minikube, or Google Cloud.
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:
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](https://github.com/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:
The agent prompts are built through templates, enabling customized extraction agents for a specific use case. The extraction agents are launched automatically with the loader commands.
