# GraphRAG

The `GraphRAG` class implements a graph-based approach to retrieval augmented generation. It creates a knowledge graph from document chunks where nodes represent documents and edges represent semantic relationships, enabling both direct similarity matching and discovery of related content through graph traversal.

## Basic Usage

```typescript
import { GraphRAG } from "@mastra/rag";

const graphRag = new GraphRAG({
  dimension: 1536,
  threshold: 0.7,
});

// Create the graph from chunks and embeddings
graphRag.createGraph(documentChunks, embeddings);

// Query the graph with embedding
const results = await graphRag.query({
  query: queryEmbedding,
  topK: 10,
  randomWalkSteps: 100,
  restartProb: 0.15,
});
```

## Constructor Parameters

### dimension?:

number

Dimension of the embedding vectors

### threshold?:

number

Similarity threshold for creating edges between nodes (0-1)

## Methods

### createGraph

Creates a knowledge graph from document chunks and their embeddings.

```typescript
createGraph(chunks: GraphChunk[], embeddings: GraphEmbedding[]): void
```

#### Parameters

### chunks:

GraphChunk\[]

Array of document chunks with text and metadata

### embeddings:

GraphEmbedding\[]

Array of embeddings corresponding to chunks

### query

Performs a graph-based search combining vector similarity and graph traversal.

```typescript
query({
  query,
  topK = 10,
  randomWalkSteps = 100,
  restartProb = 0.15
}: {
  query: number[];
  topK?: number;
  randomWalkSteps?: number;
  restartProb?: number;
}): RankedNode[]
```

#### Parameters

### query:

number\[]

Query embedding vector

### topK?:

number

Number of results to return

### randomWalkSteps?:

number

Number of steps in random walk

### restartProb?:

number

Probability of restarting walk from query node

#### Returns

Returns an array of `RankedNode` objects, where each node contains:

### id:

string

Unique identifier for the node

### content:

string

Text content of the document chunk

### metadata:

Record\<string, any>

Additional metadata associated with the chunk

### score:

number

Combined relevance score from graph traversal

## Advanced Example

```typescript
const graphRag = new GraphRAG({
  dimension: 1536,
  threshold: 0.8, // Stricter similarity threshold
});

// Create graph from chunks and embeddings
graphRag.createGraph(documentChunks, embeddings);

// Query with custom parameters
const results = await graphRag.query({
  query: queryEmbedding,
  topK: 5,
  randomWalkSteps: 200,
  restartProb: 0.2,
});
```

## Related

- [createGraphRAGTool](https://mastra.ai/reference/tools/graph-rag-tool/llms.txt)