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Metadata Extraction Usage Pattern#

You can use LLMs to automate metadata extraction with our Metadata Extractor modules.

Our metadata extractor modules include the following "feature extractors":

  • SummaryExtractor - automatically extracts a summary over a set of Nodes
  • QuestionsAnsweredExtractor - extracts a set of questions that each Node can answer
  • TitleExtractor - extracts a title over the context of each Node
  • EntityExtractor - extracts entities (i.e. names of places, people, things) mentioned in the content of each Node

Then you can chain the Metadata Extractors with our node parser:

from llama_index.core.extractors import (
from llama_index.core.node_parser import TokenTextSplitter

text_splitter = TokenTextSplitter(
    separator=" ", chunk_size=512, chunk_overlap=128
title_extractor = TitleExtractor(nodes=5)
qa_extractor = QuestionsAnsweredExtractor(questions=3)

# assume documents are defined -> extract nodes
from llama_index.core.ingestion import IngestionPipeline

pipeline = IngestionPipeline(
    transformations=[text_splitter, title_extractor, qa_extractor]

nodes =

or insert into an index:

from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(
    documents, transformations=[text_splitter, title_extractor, qa_extractor]