mirror of
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255 lines
8 KiB
Markdown
255 lines
8 KiB
Markdown
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<div align="center">
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<img src="TG-fullname-logo.svg" width=100% />
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[](https://pypi.org/project/trustgraph/) 
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[](https://discord.gg/sQMwkRz5GX) [](https://deepwiki.com/trustgraph-ai/trustgraph)
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[**Website**](https://trustgraph.ai) | [**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)
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</div>
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# Data Preparation as the Foundation for AI Accuracy
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Build production-grade AI agents that reason, not hallucinate. TrustGraph is the open-source, full-stack platform for transforming raw data into precision-grounded intelligence through automated knowledge graph construction, custom ontology engineering, and intelligent context retrieval.
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Deploy anywhere. Own your data. Control your AI stack.
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<details>
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<summary>Table of Contents</summary>
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<br>
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- [**Key Features**](#key-features)<br>
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- [**Why TrustGraph?**](#why-trustgraph)<br>
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- [**Agentic MCP Demo**](#agentic-mcp-demo)<br>
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- [**Getting Started**](#getting-started)<br>
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- [**Configuration Builder**](#configuration-builder)<br>
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- [**Knowledge Cores**](#knowledge-cores)<br>
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- [**Integrations**](#integrations)<br>
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- [**Observability & Telemetry**](#observability--telemetry)<br>
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- [**Contributing**](#contributing)<br>
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- [**License**](#license)<br>
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- [**Support & Community**](#support--community)<br>
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</details>
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## Key Features
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- **Ontology-Driven Context Engineering**
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- **Unify Data Silos for Reliable, Accurate, and Precise AI**
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- **Automated Knowledge Graph Construction and Retrieval**
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- **3D GraphViz**
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- **Single Agent or Multi-Agent Systems**
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- **Interoperability with MCP**
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- **Run Anywhere from local to cloud**
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- **Observability and Telemetry**
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- **Serve Models for Private LLM Inference**
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- **Create Custom Workflows**
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- **Control Data Access for Users and Agents**
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- **Backend Orchestration for Knowledge Graphs, Datastores, and File and Object Storage**
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- **High Throughput Data Streaming**
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- **Fully Containerized**
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## Why TrustGraph?
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[](https://www.youtube.com/watch?v=Norboj8YP2M)
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## Agentic MCP Demo
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[](https://www.youtube.com/watch?v=mUCL1b1lmbA)
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## Getting Started
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- [**Quickstart Guide**](https://docs.trustgraph.ai/getting-started/)
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- [**Configuration Builder**](#configuration-builder)
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- [**Workbench**](#workbench)
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- [**Developer APIs and CLI**](https://docs.trustgraph.ai/reference/)
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- [**Deployment Guide**](https://docs.trustgraph.ai/deployment/)
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### Watch TrustGraph 101
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[](https://www.youtube.com/watch?v=rWYl_yhKCng)
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## Configuration Builder
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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:
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- **Version**: Select the version of TrustGraph you'd like to deploy
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- **Component Selection**: Choose from the available deployment platforms, LLMs, graph store, VectorDB, chunking algorithm, chunking parameters, and LLM parameters
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- **Customization**: Enable OCR pipelines and custom embeddings models
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- **Finish Deployment**: Download the launch `YAML` files with deployment instructions
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## Workbench
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The **Workbench** provides tools for all major features of TrustGraph. The **Workbench** is on port `8888` by default.
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- **Vector Search**: Search the installed knowledge bases
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- **Agentic, GraphRAG and LLM Chat**: Chat interface for agents, GraphRAG queries, or direct to LLMs
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- **Relationships**: Analyze deep relationships in the installed knowledge bases
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- **Graph Visualizer**: 3D GraphViz of the installed knowledge bases
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- **Library**: Staging area for installing knowledge bases
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- **Flow Classes**: Workflow preset configurations
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- **Flows**: Create custom workflows and adjust LLM parameters during runtime
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- **Knowledge Cores**: Manage resuable knowledge bases
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- **Prompts**: Manage and adjust prompts during runtime
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- **Schemas**: Define custom schemas for structured data knowledge bases
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- **Ontologies**: Define custom ontologies for unstructured data knowledge bases
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- **Agent Tools**: Define tools with collections, knowledge cores, MCP connections, and tool groups
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- **MCP Tools**: Connect to MCP servers
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## TypeScript Library for UIs
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There are 3 libraries for quick integration of TrustGraph services.
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- [@trustgraph/client](https://www.npmjs.com/package/@trustgraph/client)
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- [@trustgraph/react-state](https://www.npmjs.com/package/@trustgraph/react-state)
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- [@trustgraph/react-provider](https://www.npmjs.com/package/@trustgraph/react-provider)
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## Knowledge Cores
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A challenge facing GraphRAG architectures is the ability to reuse and remove datasets from agent workflows. TrustGraph can store the data ingest process as 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).
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A Knowledge Core has two components:
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- Knowledge graph triples
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- Vector embeddings mapped to the knowledge graph
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## Integrations
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TrustGraph provides component flexibility to optimize agent workflows.
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<details>
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<summary>LLM APIs</summary>
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<br>
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- Anthropic<br>
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- AWS Bedrock<br>
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- AzureAI<br>
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- AzureOpenAI<br>
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- Cohere<br>
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- Google AI Studio<br>
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- Google VertexAI<br>
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- Mistral<br>
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- OpenAI<br>
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</details>
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<details>
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<summary>LLM Orchestration</summary>
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<br>
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- LM Studio<br>
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- Llamafiles<br>
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- Ollama<br>
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- TGI<br>
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- vLLM<br>
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</details>
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<details>
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<summary>VectorDBs</summary>
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<br>
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- Qdrant (default)<br>
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- Pinecone<br>
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- Milvus<br>
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</details>
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<details>
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<summary>Graph Storage</summary>
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<br>
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- Apache Cassandra (default)<br>
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- Neo4j<br>
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- Memgraph<br>
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- FalkorDB<br>
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</details>
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<details>
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<summary>Observability</summary>
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<br>
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- Prometheus<br>
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- Grafana<br>
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</details>
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<details>
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<summary>Control Plane</summary>
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<br>
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- Apache Pulsar<br>
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</details>
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<details>
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<summary>Clouds</summary>
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<br>
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- AWS<br>
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- Azure<br>
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- Google Cloud<br>
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- OVHcloud<br>
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- Scaleway<br>
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</details>
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## Observability & Telemetry
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Once the platform is running, access the Grafana dashboard at:
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```
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http://localhost:3000
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```
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Default credentials are:
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```
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user: admin
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password: admin
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```
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The default Grafana dashboard tracks the following:
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<details>
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<summary>Telemetry</summary>
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<br>
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- LLM Latency<br>
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- Error Rate<br>
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- Service Request Rates<br>
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- Queue Backlogs<br>
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- Chunking Histogram<br>
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- Error Source by Service<br>
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- Rate Limit Events<br>
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- CPU usage by Service<br>
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- Memory usage by Service<br>
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- Models Deployed<br>
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- Token Throughput (Tokens/second)<br>
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- Cost Throughput (Cost/second)<br>
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</details>
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## Contributing
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[Developer's Guide](https://docs.trustgraph.ai/community/developer.html)
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## License
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**TrustGraph** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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Copyright 2024-2025 TrustGraph
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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## Support & Community
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- Bug Reports & Feature Requests: [Discord](https://discord.gg/sQMwkRz5GX)
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- Discussions & Questions: [Discord](https://discord.gg/sQMwkRz5GX)
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- Documentation: [Docs](https://docs.trustgraph.ai/)
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