mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 00:16:23 +02:00
270 lines
8.7 KiB
Markdown
270 lines
8.7 KiB
Markdown
|
|
<div align="center">
|
|
|
|
[](https://pypi.org/project/trustgraph/) 
|
|
[](https://discord.gg/sQMwkRz5GX) [](https://deepwiki.com/trustgraph-ai/trustgraph)
|
|
|
|
[**Docs**](https://docs.trustgraph.ai) | [**YouTube**](https://www.youtube.com/@TrustGraphAI?sub_confirmation=1) | [**Configuration Builder**](https://config-ui.demo.trustgraph.ai/) | [**Discord**](https://discord.gg/sQMwkRz5GX) | [**Blog**](https://blog.trustgraph.ai/subscribe)
|
|
|
|
<img src="TG-fullname-logo.svg" width=100% />
|
|
|
|
</div>
|
|
|
|
# AI-Ready Data Infrastructure
|
|
|
|
TrustGraph provides an event-driven data-to-AI platform that transforms data into AI-ready datasets through automated structuring, knowledge graph construction, and vector embeddings mapping — all deployable privately, on-prem, or in your cloud. Deploy and manage open LLMs within the same platform, ensuring complete data sovereignty while enabling agents that generate real, actionable insights.
|
|
|
|
<details>
|
|
<summary>Table of Contents</summary>
|
|
<br>
|
|
|
|
- [**Key Features**](#key-features)<br>
|
|
- [**Why TrustGraph?**](#why-trustgraph)<br>
|
|
- [**Agentic MCP Demo**](#agentic-mcp-demo)<br>
|
|
- [**Getting Started**](#getting-started)<br>
|
|
- [**Configuration Builder**](#configuration-builder)<br>
|
|
- [**Knowledge Cores**](#knowledge-cores)<br>
|
|
- [**Integrations**](#integrations)<br>
|
|
- [**Observability & Telemetry**](#observability--telemetry)<br>
|
|
- [**Contributing**](#contributing)<br>
|
|
- [**License**](#license)<br>
|
|
- [**Support & Community**](#support--community)<br>
|
|
|
|
</details>
|
|
|
|
## Key Features
|
|
|
|
TrustGraph is not just another AI framework but a complete, production-ready platform that bridges the gap between raw data and intelligent, adaptable agent deployments.
|
|
|
|
- **AI-Ready Data Transformation**
|
|
|
|
*Convert unstructured and structured (bring your own schema) data into AI-optimized formats*.
|
|
|
|
- **Automated Knowledge Graph Construction**
|
|
|
|
*Transform unstructured data into interconnected knowledge graphs that capture relationships, context, and meaning*.
|
|
|
|
- **Semantic Retrieval**
|
|
|
|
*TrustGraph combines multiple retrieval methods optimized for each data type and use case*.
|
|
|
|
- **Event-Driven**
|
|
|
|
*Built with Apache Pulsar for high-throughput and reliable messaging*.
|
|
|
|
- **Datastore Orchestration**
|
|
|
|
*Deploy stores like Apache Cassandra, Neo4j, Qdrant, Milvus, Memgraph, or FalkorDB for structured and unstructured data storage*.
|
|
|
|
- **Data Sovereignty**
|
|
|
|
*Deploy the entire stack—data pipelines, knowledge graphs, vector stores, and LLMs—on-premises, in your VPC, or across hybrid environments*.
|
|
|
|
- **Private LLM Inferencing**
|
|
|
|
*In addition to support for all major LLM APIs, deploy and manage open models connected to all of the agentic data infrastructure*.
|
|
|
|
- **Agentic GraphRAG**
|
|
|
|
*Deploy intelligent agents with context awareness. Bring your own ontology for easy integration into interconnected systems*.
|
|
|
|
- **Production Ready**
|
|
|
|
*Containerized deployment with Docker/Kubernetes support. Built for enterprise scale with monitoring, observability, and management*.
|
|
|
|
- **MCP Integration**
|
|
|
|
*Native support for MCP enables standardized agent communication with third-party tools and services while maintaining data sovereignty*.
|
|
|
|
- **Full Stack Visibility**
|
|
|
|
*3D visualization of knowledge graphs. Grafana dashboard for observability*.
|
|
|
|
## Why TrustGraph?
|
|
|
|
[](https://www.youtube.com/watch?v=Norboj8YP2M)
|
|
|
|
## Agentic MCP Demo
|
|
|
|
[](https://www.youtube.com/watch?v=mUCL1b1lmbA)
|
|
|
|
## Getting Started
|
|
|
|
- [**Quickstart Guide**](https://docs.trustgraph.ai/getting-started/)
|
|
- [**Configuration Builder**](#configuration-builder)
|
|
- [**Workbench**](#workbench)
|
|
- [**Developer APIs and CLI**](https://docs.trustgraph.ai/reference/)
|
|
- [**Deployment Guide**](https://docs.trustgraph.ai/deployment/)
|
|
|
|
### Watch TrustGraph 101
|
|
|
|
[](https://www.youtube.com/watch?v=rWYl_yhKCng)
|
|
|
|
## Configuration Builder
|
|
|
|
The [**Configuration Builder**](https://config-ui.demo.trustgraph.ai/) assembles all of the selected components and builds them into a deployable package. It has 4 sections:
|
|
|
|
- **Version**: Select the version of TrustGraph you'd like to deploy
|
|
- **Component Selection**: Choose from the available deployment platforms, LLMs, graph store, VectorDB, chunking algorithm, chunking parameters, and LLM parameters
|
|
- **Customization**: Enable OCR pipelines and custom embeddings models
|
|
- **Finish Deployment**: Download the launch `YAML` files with deployment instructions
|
|
|
|
## Workbench
|
|
|
|
The **Workbench** is a UI that provides tools for interacting with all major features of the platform. The **Workbench** is enabled by default in the **Configuration Builder** and is available at port `8888` on deployment. The **Workbench** has the following capabilities:
|
|
|
|
- **Agentic, GraphRAG and LLM Chat**: Chat interface for agentic flows, GraphRAG queries, or directly interfacing with a LLM
|
|
- **Semantic Discovery**: Analyze semantic relationships with vector search, knowledge graph relationships, and 3D graph visualization
|
|
- **Data Management**: Load data into the **Librarian** for processing, create and upload **Knowledge Packages**
|
|
- **Flow Management**: Create and delete processing flow patterns
|
|
- **Prompt Management**: Edit all LLM prompts used in the platform during runtime
|
|
- **Agent Tools**: Define tools used by the Agent Flow including MCP tools
|
|
- **MCP Tools**: Connect to MCP servers
|
|
|
|
## Knowledge Cores
|
|
|
|
A challenge facing RAG architectures is the ability to quickly reuse and remove datasets from pipelines. **TrustGraph** stores the results of the data ingestion process in reusable Knowledge Cores. Knowledge cores can be loaded and removed during runtime. Some sample knowledge cores are [here](https://github.com/trustgraph-ai/catalog/tree/master/v3).
|
|
|
|
A Knowledge Core has two components:
|
|
|
|
- Knowledge graph triples
|
|
- Vector embeddings mapped to the knowledge graph
|
|
|
|
## Integrations
|
|
TrustGraph provides maximum flexibility to avoid vendor lock-in.
|
|
|
|
<details>
|
|
<summary>LLM APIs</summary>
|
|
<br>
|
|
|
|
- Anthropic<br>
|
|
- AWS Bedrock<br>
|
|
- AzureAI<br>
|
|
- AzureOpenAI<br>
|
|
- Cohere<br>
|
|
- Google AI Studio<br>
|
|
- Google VertexAI<br>
|
|
- Mistral<br>
|
|
- OpenAI<br>
|
|
|
|
</details>
|
|
<details>
|
|
<summary>LLM Orchestration</summary>
|
|
<br>
|
|
|
|
- LM Studio<br>
|
|
- Llamafiles<br>
|
|
- Ollama<br>
|
|
- TGI<br>
|
|
- vLLM<br>
|
|
|
|
</details>
|
|
<details>
|
|
<summary>VectorDBs</summary>
|
|
<br>
|
|
|
|
- Qdrant (default)<br>
|
|
- Pinecone<br>
|
|
- Milvus<br>
|
|
|
|
</details>
|
|
<details>
|
|
<summary>Graph Storage</summary>
|
|
<br>
|
|
|
|
- Apache Cassandra (default)<br>
|
|
- Neo4j<br>
|
|
- Memgraph<br>
|
|
- FalkorDB<br>
|
|
|
|
</details>
|
|
<details>
|
|
<summary>Observability</summary>
|
|
<br>
|
|
|
|
- Prometheus<br>
|
|
- Grafana<br>
|
|
|
|
</details>
|
|
<details>
|
|
<summary>Control Plane</summary>
|
|
<br>
|
|
|
|
- Apache Pulsar<br>
|
|
|
|
</details>
|
|
<details>
|
|
<summary>Clouds</summary>
|
|
<br>
|
|
|
|
- AWS<br>
|
|
- Azure<br>
|
|
- Google Cloud<br>
|
|
- OVHcloud<br>
|
|
- Scaleway<br>
|
|
|
|
</details>
|
|
|
|
## Observability & Telemetry
|
|
|
|
Once the platform is running, access the Grafana dashboard at:
|
|
|
|
```
|
|
http://localhost:3000
|
|
```
|
|
|
|
Default credentials are:
|
|
|
|
```
|
|
user: admin
|
|
password: admin
|
|
```
|
|
|
|
The default Grafana dashboard tracks the following:
|
|
|
|
<details>
|
|
<summary>Telemetry</summary>
|
|
<br>
|
|
|
|
- LLM Latency<br>
|
|
- Error Rate<br>
|
|
- Service Request Rates<br>
|
|
- Queue Backlogs<br>
|
|
- Chunking Histogram<br>
|
|
- Error Source by Service<br>
|
|
- Rate Limit Events<br>
|
|
- CPU usage by Service<br>
|
|
- Memory usage by Service<br>
|
|
- Models Deployed<br>
|
|
- Token Throughput (Tokens/second)<br>
|
|
- Cost Throughput (Cost/second)<br>
|
|
|
|
</details>
|
|
|
|
## Contributing
|
|
|
|
[Developer's Guide](https://docs.trustgraph.ai/community/developer.html)
|
|
|
|
## License
|
|
|
|
**TrustGraph** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
|
|
|
|
Copyright 2024-2025 TrustGraph
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
|
|
## Support & Community
|
|
- Bug Reports & Feature Requests: [Discord](https://discord.gg/sQMwkRz5GX)
|
|
- Discussions & Questions: [Discord](https://discord.gg/sQMwkRz5GX)
|
|
- Documentation: [Docs](https://docs.trustgraph.ai/)
|