---
title: Metadata Extraction Usage Pattern | LlamaIndex OSS Documentation
---

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 Extractor`s 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](/python/examples/metadata_extraction/metadataextractionsec/index.md)
- [LLM Survey Extraction](/python/examples/metadata_extraction/metadataextraction_llmsurvey/index.md)
- [Entity Extraction](/python/examples/metadata_extraction/entityextractionclimate/index.md)
- [Marvin Metadata Extraction](/python/examples/metadata_extraction/marvinmetadataextractordemo/index.md)
- [Pydantic Metadata Extraction](/python/examples/metadata_extraction/pydanticextractor/index.md)