# GraphRAG System Architecture
This document describes the architecture of the GraphRAG (Graph Retrieval Augmented Generation) system.
## Overview
GraphRAG is a hybrid system that combines knowledge graphs with large language models (LLMs) to provide accurate and contextually relevant responses to user queries.
### System Components
The system consists of several interconnected modules:
#### 1. Document Processing Pipeline
The document processing pipeline handles ingestion and analysis of input documents:
- **Text Chunking**: Splits documents into manageable segments
- **Entity Extraction**: Identifies entities using NLP techniques
- **Relationship Detection**: Discovers connections between entities
#### 2. Knowledge Graph Storage
The knowledge graph stores extracted information:
- **Entities**: Nodes representing real-world objects
- **Relationships**: Edges connecting related entities
- **Metadata**: Additional context and provenance
#### 3. Retrieval System
The retrieval system implements multiple strategies:
##### Semantic Retrieval
Uses vector embeddings to find semantically similar content. This approach captures meaning beyond exact keyword matches.
##### Keyword Retrieval (BM25)
Traditional keyword-based search using BM25 ranking algorithm. Effective for queries with specific terminology.
##### Hybrid Retrieval
Combines semantic and keyword approaches using rank fusion techniques for optimal results.
### Query Processing
When a user submits a query, the system:
1. Analyzes the query to extract intent and entities
2. Retrieves relevant subgraphs from the knowledge base
3. Ranks and filters results based on relevance
4. Generates a response using the LLM with retrieved context
## Implementation Details
### Technology Stack
- **Language**: Rust for performance and safety
- **Graph Structure**: Custom in-memory graph implementation
- **Embeddings**: Support for multiple providers (OpenAI, Voyage, local models)
- **LLM Integration**: Compatible with various language models
### Performance Characteristics
The system is optimized for:
- Low-latency retrieval (< 100ms for typical queries)
- Efficient memory usage through streaming processing
- Scalability to large document collections
## Conclusion
The GraphRAG architecture provides a robust foundation for building knowledge-intensive applications that require both structured reasoning and natural language generation capabilities.