Tweak page order

Author: cybermaggedon

getting-started/concepts.md

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 title: Core Concepts
 layout: default
-nav_order: 2
+nav_order: 1
 parent: Getting Started
 grand_parent: TrustGraph Documentation
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 # Core Concepts
 
-Understand the fundamental concepts and terminology used in TrustGraph.
+Understand the fundamental concepts and architecture that make TrustGraph a powerful AI agent intelligence platform.
 
-## Key Concepts
+## What is TrustGraph?
 
-### Graphs
-Coming soon - graph concept explanation
+TrustGraph is an **Open Source Agent Intelligence Platform** that transforms AI agents from simple task executors into intelligent, contextually-aware systems. Unlike traditional AI approaches that work with isolated data points, TrustGraph creates interconnected knowledge structures that enable agents to understand relationships and context.
 
-### Nodes
-Coming soon - node concept explanation
+## Core Concepts
 
-### Edges
-Coming soon - edge concept explanation
+### Knowledge Graphs
 
-### Trust Relationships
-Coming soon - trust relationship explanation
+**Knowledge Graphs** are the foundation of TrustGraph's intelligence. They represent information as interconnected networks of entities and relationships, rather than isolated documents or data points.
 
-### Reputation Systems
-Coming soon - reputation system explanation
+- **Entities**: People, places, concepts, or objects in your data
+- **Relationships**: How entities connect and relate to each other
+- **Context**: The meaning that emerges from understanding these connections
+
+### GraphRAG (Graph Retrieval-Augmented Generation)
+
+**GraphRAG** is TrustGraph's advanced approach to information retrieval that goes beyond traditional RAG systems:
+
+**Traditional RAG:**
+- Retrieves similar documents based on vector similarity
+- Works with isolated pieces of information
+- Limited contextual understanding
+
+**GraphRAG:**
+- Understands relationships between different pieces of information
+- Retrieves contextually relevant knowledge based on graph structure
+- Provides more accurate, nuanced responses
+- Significantly reduces AI hallucinations
+
+### Knowledge Packages
+
+**Knowledge Packages** combine the best of both worlds:
+- **Knowledge Graphs**: For structured relationships and context
+- **Vector Embeddings**: For semantic similarity search
+- **Unified Access**: Single interface for complex knowledge retrieval
+
+This hybrid approach enables both precise relationship-based queries and flexible semantic search.
+
+### AI Agent Intelligence
+
+TrustGraph enables AI agents to:
+- **Reason about relationships**: Understand how different facts connect
+- **Provide contextual responses**: Draw insights from interconnected knowledge
+- **Reduce hallucinations**: Ground responses in structured knowledge
+- **Learn continuously**: Build and refine knowledge over time
 
 ## Architecture Overview
 
-### Data Model
-Coming soon - data model explanation
+### Knowledge Graph Builder
+
+Extracts entities and relationships from your enterprise data:
+- **Document Processing**: Analyzes text, PDFs, and other formats
+- **Entity Extraction**: Identifies key concepts and objects
+- **Relationship Mapping**: Discovers how entities connect
+- **Graph Construction**: Builds interconnected knowledge structures
+
+### Vector Embedding Engine
+
+Creates semantic representations of knowledge elements:
+- **Semantic Encoding**: Converts text into mathematical representations
+- **Similarity Mapping**: Enables finding related concepts
+- **Hybrid Search**: Combines with graph structure for powerful queries
+
+### GraphRAG Processor
+
+Combines graph and vector search for contextual retrieval:
+- **Relationship-Aware Retrieval**: Finds information based on connections
+- **Context Assembly**: Builds comprehensive context for AI responses
+- **Multi-Hop Reasoning**: Follows relationship chains for deeper insights
+
+### AI Agent Runtime
+
+Executes intelligent agents with access to knowledge graphs:
+- **Contextual Understanding**: Agents know how information relates
+- **Grounded Responses**: Answers based on structured knowledge
+- **Transparent Reasoning**: Clear path from question to answer
+
+### Integration Layer
+
+Connects with existing enterprise infrastructure:
+- **LLM Integration**: Works with multiple AI models
+- **Data Connectors**: Integrates with databases, documents, APIs
+- **API Gateway**: Provides unified access to all capabilities
+
+## How TrustGraph Works
+
+### 1. Knowledge Ingestion
+```
+Documents → Entity Extraction → Relationship Discovery → Knowledge Graph
+```
+
+### 2. Query Processing
+```
+User Question → GraphRAG → Contextual Retrieval → AI Response
+```
+
+### 3. Continuous Learning
+```
+New Data → Graph Updates → Enhanced Knowledge → Better Responses
+```
+
+## Key Benefits
+
+### Reduced Hallucinations
+By grounding AI responses in structured knowledge graphs, TrustGraph significantly reduces the likelihood of AI generating false or misleading information.
+
+### Contextual Intelligence
+Agents understand not just what information exists, but how different pieces of information relate to each other.
+
+### Enterprise Integration
+Unifies fragmented organizational knowledge into coherent, queryable knowledge systems.
+
+### Transparency
+Full visibility into how data is processed and how AI agents arrive at their responses.
+
+### Flexibility
+Open-source architecture prevents vendor lock-in and enables customization.
+
+## From Your First Steps
+
+When you followed the [First Steps](first-steps.md) guide, you experienced these concepts in action:
 
-### Query Engine
-Coming soon - query engine explanation
+- **Document Loading**: Your PDFs became entities and relationships in a knowledge graph
+- **Graph Visualization**: You saw how TrustGraph represents knowledge as interconnected data
+- **Vector Search**: You found relevant information using semantic similarity
+- **Graph RAG**: You asked questions and received contextually-aware answers
 
-### Storage Layer
-Coming soon - storage layer explanation
+## Essential Terminology
 
-## Terminology
+**Knowledge Graph**: Network of interconnected entities and relationships
+**GraphRAG**: Graph-enhanced retrieval and generation for AI responses
+**Knowledge Package**: Combined graph and vector representation of knowledge
+**Entity**: A person, place, concept, or object in your data
+**Relationship**: A connection between two entities
+**Vector Embedding**: Mathematical representation of text for similarity search
+**Agent Intelligence**: AI that understands context and relationships
+**N-Triples**: Standard format for representing graph data as subject-predicate-object statements
 
-For a complete list of terms, see our [Glossary](../reference/glossary.md).
+## Next Steps
 
-Coming soon - comprehensive concept explanations!
+Now that you understand TrustGraph's core concepts:
+- Explore [Deployment Options](../deployment/) for production use
+- Learn about [API Integration](../reference/) for custom applications
+- Review [How-to Guides](../guides/) for specific use cases

getting-started/installation.md

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 title: Installation
 layout: default
-nav_order: 1
+nav_order: 2
 parent: Getting Started
 grand_parent: TrustGraph Documentation
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