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ReferenceRAGExtractParams

ExtractParams

ExtractParams configures metadata extraction from document chunks using LLM analysis.

Example

import { MDocument } from "@mastra/rag";
 
const doc = MDocument.fromText(text);
const chunks = await doc.chunk({
  extract: {
    title: true,    // Extract titles using default settings
    summary: true,  // Generate summaries using default settings
    keywords: true  // Extract keywords using default settings
  }
});
 
// Example output:
// chunks[0].metadata = {
//   documentTitle: "AI Systems Overview",
//   sectionSummary: "Overview of artificial intelligence concepts and applications",
//   excerptKeywords: "KEYWORDS: AI, machine learning, algorithms"
// }

Parameters

The extract parameter accepts the following fields:

title?:

boolean | TitleExtractorsArgs
Enable title extraction. Set to true for default settings, or provide custom configuration.

summary?:

boolean | SummaryExtractArgs
Enable summary extraction. Set to true for default settings, or provide custom configuration.

questions?:

boolean | QuestionAnswerExtractArgs
Enable question generation. Set to true for default settings, or provide custom configuration.

keywords?:

boolean | KeywordExtractArgs
Enable keyword extraction. Set to true for default settings, or provide custom configuration.

Extractor Arguments

TitleExtractorsArgs

llm?:

LLM
Custom LLM instance to use for title extraction

nodes?:

number
Number of title nodes to extract

nodeTemplate?:

string
Custom prompt template for title node extraction. Must include {context} placeholder

combineTemplate?:

string
Custom prompt template for combining titles. Must include {context} placeholder

SummaryExtractArgs

llm?:

LLM
Custom LLM instance to use for summary extraction

summaries?:

('self' | 'prev' | 'next')[]
List of summary types to generate. Can only include 'self' (current chunk), 'prev' (previous chunk), or 'next' (next chunk)

promptTemplate?:

string
Custom prompt template for summary generation. Must include {context} placeholder

QuestionAnswerExtractArgs

llm?:

LLM
Custom LLM instance to use for question generation

questions?:

number
Number of questions to generate

promptTemplate?:

string
Custom prompt template for question generation. Must include both {context} and {numQuestions} placeholders

embeddingOnly?:

boolean
If true, only generate embeddings without actual questions

KeywordExtractArgs

llm?:

LLM
Custom LLM instance to use for keyword extraction

keywords?:

number
Number of keywords to extract

promptTemplate?:

string
Custom prompt template for keyword extraction. Must include both {context} and {maxKeywords} placeholders

Advanced Example

import { MDocument } from "@mastra/rag";
 
const doc = MDocument.fromText(text);
const chunks = await doc.chunk({
  extract: {
    // Title extraction with custom settings
    title: {
      nodes: 2,  // Extract 2 title nodes
      nodeTemplate: "Generate a title for this: {context}",
      combineTemplate: "Combine these titles: {context}"
    },
 
    // Summary extraction with custom settings
    summary: {
      summaries: ["self"],  // Generate summaries for current chunk
      promptTemplate: "Summarize this: {context}"
    },
 
    // Question generation with custom settings
    questions: {
      questions: 3,  // Generate 3 questions
      promptTemplate: "Generate {numQuestions} questions about: {context}",
      embeddingOnly: false
    },
 
    // Keyword extraction with custom settings
    keywords: {
      keywords: 5,  // Extract 5 keywords
      promptTemplate: "Extract {maxKeywords} key terms from: {context}"
    }
  }
});
 
// Example output:
// chunks[0].metadata = {
//   documentTitle: "AI in Modern Computing",
//   sectionSummary: "Overview of AI concepts and their applications in computing",
//   questionsThisExcerptCanAnswer: "1. What is machine learning?\n2. How do neural networks work?",
//   excerptKeywords: "1. Machine learning\n2. Neural networks\n3. Training data"
// }
.embed()rerank()