2023-03-22 22:00:44 +00:00
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# Extraction
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2023-03-27 04:43:51 +00:00
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> [Conceptual Guide](https://docs.langchain.com/docs/use-cases/extraction)
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2023-03-27 02:49:46 +00:00
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2023-03-22 22:00:44 +00:00
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Most APIs and databases still deal with structured information.
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Therefore, in order to better work with those, it can be useful to extract structured information from text.
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Examples of this include:
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- Extracting a structured row to insert into a database from a sentence
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- Extracting multiple rows to insert into a database from a long document
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- Extracting the correct API parameters from a user query
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2023-03-27 02:49:46 +00:00
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This work is extremely related to [output parsing](../modules/prompts/output_parsers.rst).
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2023-03-22 22:00:44 +00:00
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Output parsers are responsible for instructing the LLM to respond in a specific format.
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In this case, the output parsers specify the format of the data you would like to extract from the document.
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Then, in addition to the output format instructions, the prompt should also contain the data you would like to extract information from.
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While normal output parsers are good enough for basic structuring of response data,
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when doing extraction you often want to extract more complicated or nested structures.
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For a deep dive on extraction, we recommend checking out [`kor`](https://eyurtsev.github.io/kor/),
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a library that uses the existing LangChain chain and OutputParser abstractions
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but deep dives on allowing extraction of more complicated schemas.
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