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TrustGraph Documentation
Welcome to the TrustGraph documentation. This directory contains comprehensive guides for using TrustGraph's APIs and command-line tools.
Documentation Overview
📚 API Documentation
Complete reference for TrustGraph's APIs, including REST, WebSocket, Pulsar, and Python SDK interfaces. Learn how to integrate TrustGraph services into your applications.
🖥️ CLI Documentation
Comprehensive guide to TrustGraph's command-line interface. Includes detailed documentation for all CLI commands, from system administration to knowledge graph management.
🚀 Quick Start Guide
Step-by-step guide to get TrustGraph running using Docker Compose. Perfect for first-time users who want to quickly deploy and test TrustGraph.
Getting Started
If you're new to TrustGraph, we recommend starting with the Compose - Quick Start Guide to get a working system up and running quickly.
For developers integrating TrustGraph into applications, check out the API Documentation to understand the available interfaces.
For system administrators and power users, the CLI Documentation provides detailed information about all command-line tools.
Ways to deploy
If you haven't deployed TrustGraph before, the 'compose' deployment mentioned above is going to be the least commitment of setting things up: See Quick Start Guide
Other deployment mechanisms include:
- Scaleway Kubernetes deployment using Pulumi
- Intel Gaudi and GPU - tested on Intel Tiber cloud
- Azure Kubernetes deployment using Pulumi
- AWS EC2 single instance deployment using Pulumi
- GCP GKE cloud deployment using Pulumi
- RKE Kubernetes on AWS deployment using Pulumi
- It should be possible to deploy on AWS EKS, but we haven't been able to script anything reliable so far.
What is TrustGraph?
TrustGraph is a comprehensive knowledge graph and retrieval-augmented generation (RAG) platform that enables:
- Knowledge Extraction: Extract structured knowledge from documents and text
- Graph RAG: Advanced retrieval-augmented generation using knowledge graphs
- Multi-Model Support: Integration with various AI models (OpenAI, Claude, Ollama, etc.)
- Scalable Architecture: Built on Apache Pulsar for high-performance processing
- Flexible Deployment: Docker Compose files for various configurations
Key Features
- Graph-Based Knowledge Representation: Store and query knowledge as interconnected graphs
- Document Processing: Extract knowledge from PDFs, text files, and other documents
- Agent-Based Interactions: Conversational AI with access to knowledge graphs
- Multi-Modal APIs: REST, WebSocket, Pulsar, and Python SDK interfaces
- Comprehensive CLI: Command-line tools for all operations
- Monitoring & Observability: Built-in metrics and monitoring capabilities
Architecture
TrustGraph consists of several key components:
- Processing Pipeline: Document ingestion, chunking, vectorization, and knowledge extraction
- Storage Systems: Vector databases, graph databases (Neo4j/Cassandra), and document storage
- AI Services: Text completion, embeddings, and specialized processing services
- API Gateway: REST and WebSocket interfaces for external integration
- Message System: Apache Pulsar for reliable, scalable message processing
Support
For questions, issues, or contributions:
- GitHub Issues: Report bugs and feature requests
- Documentation: This documentation covers most use cases
- Community: Join discussions and share experiences
Related Resources
- TrustGraph GitHub Repository
- Docker Hub Images
- Example Notebooks - shows some example use of various APIs.