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 complex relationships visualized in 3D.
- ❔**No-code GraphRAG Queries**: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
- 🤖 **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
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.
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` 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:
TrustGraph `YAML` files are available [here](https://github.com/trustgraph-ai/trustgraph/releases). Download `deploy.zip` for the desired release version.
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:
> 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 contains `YAML` configuration files for the default component selections.
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 data extraction agents for a specific use case. The data extraction agents are launched automatically with the loader commands.
