mastra-rag

name	mastra-rag
description	Mastra RAG pipeline development - document processing, chunking, embedding, vector databases, retrieval, GraphRAG, and agent integration tools

Mastra RAG Pipeline Development

Comprehensive guide for building retrieval-augmented generation pipelines with Mastra. Covers document processing (MDocument), chunking strategies, embedding with AI SDK, 17+ vector database providers, retrieval patterns, reranking, GraphRAG, and agent integration tools.

Usage

/mastra-rag

Provides context for:

• MDocument class (fromText, fromMarkdown, fromHTML)
• Chunking with .chunk({ strategy, maxSize, overlap })
• Embedding via AI SDK embed() from 'ai'
• Vector store setup (PgVector, Pinecone, Qdrant, Chroma, etc.)
• createVectorQueryTool(), createDocumentChunkerTool(), createGraphRAGTool()
• Reranking with rerank() and rerankWithScorer()
• Metadata filters