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 (
    TitleExtractor,
    QuestionsAnsweredExtractor,
)
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 = pipeline.run(
    documents=documents,
    in_place=True,
    show_progress=True,
)

or insert into an index:

from llama_index.core import VectorStoreIndex

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

Resources

• SEC Documents Metadata Extraction
• LLM Survey Extraction
• Entity Extraction
• Marvin Metadata Extraction
• Pydantic Metadata Extraction