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ReferenceToolscreateGraphRAGTool()

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

import { openai } from "@ai-sdk/openai";
import { createGraphRAGTool } from "@mastra/rag";
 
const graphTool = createGraphRAGTool({
  vectorStoreName: "pinecone",
  indexName: "docs",
  model: openai.embedding('text-embedding-3-small'),
  graphOptions: {
    dimension: 1536,
    threshold: 0.7,
    randomWalkSteps: 100,
    restartProb: 0.15
  }
});

Parameters

vectorStoreName:

string
Name of the vector store to query

indexName:

string
Name of the index within the vector store

model:

EmbeddingModel
Embedding model to use for vector search

graphOptions?:

GraphOptions
= Default graph options
Configuration for the graph-based retrieval

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'

GraphOptions

dimension?:

number
= 1536
Dimension of the embedding vectors

threshold?:

number
= 0.7
Similarity threshold for creating edges between nodes (0-1)

randomWalkSteps?:

number
= 100
Number of steps in random walk for graph traversal

restartProb?:

number
= 0.15
Probability of restarting random walk from query node

Returns

The tool returns an object with:

relevantContext:

string
Combined text from the most relevant document chunks, retrieved using graph-based ranking

Default Tool Description

The default description focuses on:

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

Advanced Example

const graphTool = createGraphRAGTool({
  vectorStoreName: "pinecone",
  indexName: "docs",
  model: openai.embedding('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

const graphTool = createGraphRAGTool({
  vectorStoreName: "pinecone",
  indexName: "docs",
  model: openai.embedding('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.

createDocumentChunkerTool()createVectorQueryTool()