@mastra/rag/dist/docs/references/reference-tools-graph-rag-tool.md

The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.

# createGraphRAGTool()

The `createGraphRAGTool()` creates a tool that enhances RAG by building a graph of semantic relationships between documents. It uses the `GraphRAG` system under the hood to provide graph-based retrieval, finding relevant content through both direct similarity and connected relationships.

## Usage example

```typescript
import { createGraphRAGTool } from '@mastra/rag'
import { ModelRouterEmbeddingModel } from '@mastra/core/llm'

const graphTool = createGraphRAGTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  graphOptions: {
    dimension: 1536,
    threshold: 0.7,
    randomWalkSteps: 100,
    restartProb: 0.15,
  },
})
```

## Parameters

> **Note:** **Parameter Requirements:** Most fields can be set at creation as defaults. Some fields can be overridden at runtime via the request context or input. If a required field is missing from both creation and runtime, an error will be thrown. Note that `model`, `id`, and `description` can only be set at creation time.

**id** (`string`): Custom ID for the tool. By default: 'GraphRAG {vectorStoreName} {indexName} Tool'. (Set at creation only.)

**description** (`string`): Custom description for the tool. By default: 'Access and analyze relationships between information in the knowledge base to answer complex questions about connections and patterns.' (Set at creation only.)

**vectorStoreName** (`string`): Name of the vector store to query. (Can be set at creation or overridden at runtime.)

**indexName** (`string`): Name of the index within the vector store. (Can be set at creation or overridden at runtime.)

**model** (`EmbeddingModel`): Embedding model to use for vector search. (Set at creation only.)

**enableFilter** (`boolean`): Enable filtering of results based on metadata. (Set at creation only, but will be automatically enabled if a filter is provided in the request context.) (Default: `false`)

**includeSources** (`boolean`): Include the full retrieval objects in the results. (Can be set at creation or overridden at runtime.) (Default: `true`)

**graphOptions** (`GraphOptions`): Configuration for the graph-based retrieval (Default: `Default graph options`)

**graphOptions.dimension** (`number`): Dimension of the embedding vectors

**graphOptions.threshold** (`number`): Similarity threshold for creating edges between nodes (0-1)

**graphOptions.randomWalkSteps** (`number`): Number of steps in random walk for graph traversal. (Can be set at creation or overridden at runtime.)

**graphOptions.restartProb** (`number`): Probability of restarting random walk from query node. (Can be set at creation or overridden at runtime.)

**providerOptions** (`Record<string, Record<string, any>>`): Provider-specific options for the embedding model (e.g., outputDimensionality). \*\*Important\*\*: Only works with AI SDK EmbeddingModelV2 models. For V1 models, configure options when creating the model itself.

**vectorStore** (`MastraVector | VectorStoreResolver`): Direct vector store instance or a resolver function for dynamic selection. Use a function for multi-tenant applications where the vector store is selected based on request context. When provided, \`vectorStoreName\` becomes optional.

## Returns

The tool returns an object with:

**relevantContext** (`string`): Combined text from the most relevant document chunks, retrieved using graph-based ranking

**sources** (`QueryResult[]`): Array of full retrieval result objects. Each object contains all information needed to reference the original document, chunk, and similarity score.

### `QueryResult` object structure

```typescript
{
  id: string;         // Unique chunk/document identifier
  metadata: any;      // All metadata fields (document ID, etc.)
  vector: number[];   // Embedding vector (if available)
  score: number;      // Similarity score for this retrieval
  document: string;   // Full chunk/document text (if available)
}
```

## Default tool description

The default description focuses on:

- Analyzing relationships between documents
- Finding patterns and connections
- Answering complex queries

## Advanced example

```typescript
const graphTool = createGraphRAGTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  graphOptions: {
    dimension: 1536,
    threshold: 0.8, // Higher similarity threshold
    randomWalkSteps: 200, // More exploration steps
    restartProb: 0.2, // Higher restart probability
  },
})
```

## Example with custom description

```typescript
const graphTool = createGraphRAGTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: 'openai/text-embedding-3-small	',
  description:
    "Analyze document relationships to find complex patterns and connections in our company's historical data",
})
```

This example shows how to customize the tool description for a specific use case while maintaining its core purpose of relationship analysis.

## Example: Using request context

```typescript
const graphTool = createGraphRAGTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: 'openai/text-embedding-3-small	',
})
```

When using request context, provide required parameters at execution time via the request context:

```typescript
const requestContext = new RequestContext<{
  vectorStoreName: string
  indexName: string
  topK: number
  filter: any
}>()
requestContext.set('vectorStoreName', 'my-store')
requestContext.set('indexName', 'my-index')
requestContext.set('topK', 5)
requestContext.set('filter', { category: 'docs' })
requestContext.set('randomWalkSteps', 100)
requestContext.set('restartProb', 0.15)

const response = await agent.generate('Find documentation from the knowledge base.', {
  requestContext,
})
```

For more information on request context, please see:

- [Agent Request Context](https://mastra.ai/docs/server/request-context)
- [Request Context](https://mastra.ai/docs/server/request-context)

## Dynamic vector store for multi-tenant applications

For multi-tenant applications where each tenant has isolated data, you can pass a resolver function instead of a static vector store:

```typescript
import { createGraphRAGTool, VectorStoreResolver } from '@mastra/rag'
import { PgVector } from '@mastra/pg'

const vectorStoreResolver: VectorStoreResolver = async ({ requestContext }) => {
  const tenantId = requestContext?.get('tenantId')

  return new PgVector({
    id: `pg-vector-${tenantId}`,
    connectionString: process.env.POSTGRES_CONNECTION_STRING!,
    schemaName: `tenant_${tenantId}`,
  })
}

const graphTool = createGraphRAGTool({
  indexName: 'embeddings',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  vectorStore: vectorStoreResolver,
})
```

See [createVectorQueryTool - Dynamic Vector Store](https://mastra.ai/reference/tools/vector-query-tool) for more details.

## Related

- [createVectorQueryTool](https://mastra.ai/reference/tools/vector-query-tool)
- [GraphRAG](https://mastra.ai/reference/rag/graph-rag)