forked from Archives/langchain
956416c150
update links to be relative Co-authored-by: Marc Green <marcgreen@users.noreply.github.com>
21 lines
1.1 KiB
Markdown
21 lines
1.1 KiB
Markdown
# Summarization
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Summarization involves creating a smaller summary of multiple longer documents.
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This can be useful for distilling long documents into the core pieces of information.
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The recommended way to get started using a summarization chain is:
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```python
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from langchain.chains.summarize import load_summarize_chain
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chain = load_summarize_chain(llm, chain_type="map_reduce")
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chain.run(docs)
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```
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The following resources exist:
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- [Summarization Notebook](../modules/chains/combine_docs_examples/summarize.ipynb): A notebook walking through how to accomplish this task.
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Additional related resources include:
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- [Utilities for working with Documents](../modules/utils/how_to_guides.rst): Guides on how to use several of the utilities which will prove helpful for this task, including Text Splitters (for splitting up long documents).
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- [CombineDocuments Chains](../modules/chains/combine_docs.md): A conceptual overview of specific types of chains by which you can accomplish this task.
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- [Data Augmented Generation](./combine_docs.md): An overview of data augmented generation, which is the general concept of combining external data with LLMs (of which this is a subset).
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