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Edited mainly the `Concepts` section in the LangChain documentation. Overview: * Updated some explanations to make the point more clear / Add missing words for some documentations. * Rephrased some sentences to make it shorter and more concise. --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
46 lines
1.5 KiB
Plaintext
46 lines
1.5 KiB
Plaintext
# Document loaders
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<span data-heading-keywords="document loader,document loaders"></span>
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:::info[Prerequisites]
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* [Document loaders API reference](/docs/how_to/#document-loaders)
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:::
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Document loaders are designed to load document objects. LangChain has hundreds of integrations with various data sources to load data from: Slack, Notion, Google Drive, etc.
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## Integrations
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You can find available integrations on the [Document loaders integrations page](/docs/integrations/document_loaders/).
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## Interface
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Documents loaders implement the [BaseLoader interface](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.base.BaseLoader.html).
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Each DocumentLoader has its own specific parameters, but they can all be invoked in the same way with the `.load` method or `.lazy_load`.
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Here's a simple example:
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```python
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from langchain_community.document_loaders.csv_loader import CSVLoader
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loader = CSVLoader(
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... # <-- Integration specific parameters here
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)
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data = loader.load()
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```
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When working with large datasets, you can use the `.lazy_load` method:
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```python
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for document in loader.lazy_load():
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print(document)
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```
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## Related resources
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Please see the following resources for more information:
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* [How-to guides for document loaders](/docs/how_to/#document-loaders)
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* [Document API reference](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)
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* [Document loaders integrations](/docs/integrations/document_loaders/)
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