Graph RAG

This example demonstrates how to implement a Retrieval-Augmented Generation (RAG) system using Mastra, OpenAI embeddings, and PGVector for vector storage.

Overview

The system implements Graph RAG using Mastra and OpenAI. Here’s what it does:

Sets up a Mastra agent with gpt-4o-mini for response generation
Creates a GraphRAG tool to manage vector store interactions and knowledge graph creation/traversal
Chunks text documents into smaller segments
Creates embeddings for these chunks
Stores them in a PostgreSQL vector database
Creates a knowledge graph of relevant chunks based on queries using GraphRAG tool
- Tool returns results from vector store and creates knowledge graph
- Traverses knowledge graph using query
Generates context-aware responses using the Mastra agent

Setup

Environment Setup

Make sure to set up your environment variables:

.env


OPENAI_API_KEY=your_openai_api_key_here
POSTGRES_CONNECTION_STRING=your_connection_string_here

Dependencies

Then, import the necessary dependencies:

index.ts


import { openai } from "@ai-sdk/openai";
import { Mastra } from "@mastra/core";
import { Agent } from "@mastra/core/agent";
import { PgVector } from "@mastra/pg";
import { MDocument, createGraphRAGTool } from "@mastra/rag";
import { embedMany } from "ai";

GraphRAG Tool Creation

Using createGraphRAGTool imported from @mastra/rag, you can create a tool that queries the vector database and converts the results into a knowledge graph:

index.ts


const graphRagTool = createGraphRAGTool({
  vectorStoreName: "pgVector",
  indexName: "embeddings",
  model: openai.embedding("text-embedding-3-small"),
  graphOptions: {
    dimension: 1536,
    threshold: 0.7,
  },
});

Agent Configuration

Set up the Mastra agent that will handle the responses:

index.ts


const ragAgent = new Agent({
  name: "GraphRAG Agent",
  instructions: `You are a helpful assistant that answers questions based on the provided context. Format your answers as follows:
 
1. DIRECT FACTS: List only the directly stated facts from the text relevant to the question (2-3 bullet points)
2. CONNECTIONS MADE: List the relationships you found between different parts of the text (2-3 bullet points)
3. CONCLUSION: One sentence summary that ties everything together
 
Keep each section brief and focus on the most important points.
 
Important: When asked to answer a question, please base your answer only on the context provided in the tool. 
If the context doesn't contain enough information to fully answer the question, please state that explicitly.`,
  model: openai("gpt-4o-mini"),
  tools: {
    graphRagTool,
  },
});

Instantiate PgVector and Mastra

Instantiate PgVector and Mastra with the components:

index.ts


const pgVector = new PgVector(process.env.POSTGRES_CONNECTION_STRING!);
 
export const mastra = new Mastra({
  agents: { ragAgent },
  vectors: { pgVector },
});
const agent = mastra.getAgent("ragAgent");

Document Processing

Create a document and process it into chunks:

index.ts


const doc = MDocument.fromText(`
# Riverdale Heights: Community Development Study
// ... text content ...
`);
 
const chunks = await doc.chunk({
  strategy: "recursive",
  size: 512,
  overlap: 50,
  separator: "\n",
});

Creating and Storing Embeddings

Generate embeddings for the chunks and store them in the vector database:

index.ts


const { embeddings } = await embedMany({
  model: openai.embedding("text-embedding-3-small"),
  values: chunks.map(chunk => chunk.text),
});
 
const vectorStore = mastra.getVector("pgVector");
await vectorStore.createIndex({
  indexName: "embeddings",
  dimension: 1536,
});
await vectorStore.upsert({
  indexName: "embeddings",
  vectors: embeddings,
  metadata: chunks?.map((chunk: any) => ({ text: chunk.text })),
});

Graph-Based Querying

Try different queries to explore relationships in the data:

index.ts


const queryOne = "What are the direct and indirect effects of early railway decisions on Riverdale Heights' current state?";
const answerOne = await ragAgent.generate(queryOne);
console.log('\nQuery:', queryOne);
console.log('Response:', answerOne.text);
 
const queryTwo = 'How have changes in transportation infrastructure affected different generations of local businesses and community spaces?';
const answerTwo = await ragAgent.generate(queryTwo);
console.log('\nQuery:', queryTwo);
console.log('Response:', answerTwo.text);
 
const queryThree = 'Compare how the Rossi family business and Thompson Steel Works responded to major infrastructure changes, and how their responses affected the community.';
const answerThree = await ragAgent.generate(queryThree);
console.log('\nQuery:', queryThree);
console.log('Response:', answerThree.text);
 
const queryFour = 'Trace how the transformation of the Thompson Steel Works site has influenced surrounding businesses and cultural spaces from 1932 to present.';
const answerFour = await ragAgent.generate(queryFour);
console.log('\nQuery:', queryFour);
console.log('Response:', answerFour.text);

View Example on GitHub