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* fix: nightly now properly gets 1.9.0 branch (#12215) before it was attempting to pull release-notes as letters are alphanumerically after numbers when we sort -V then grab tail now we only look at branch names that follow the pattern '^release-[0-9]+\.[0-9]+\.[0-9]+$' * docs: add search icon (#12216) add-back-svg * initial-content * cut-1.8-release-and-include-next-version * stage-1.8.0-and-next --------- Co-authored-by: Adam-Aghili <149833988+Adam-Aghili@users.noreply.github.com>
17 lines
1.3 KiB
Plaintext
17 lines
1.3 KiB
Plaintext
A Langflow knowledge base is a local vector database that is stored in Langflow storage.
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Because knowledge bases are local, the data isn't remotely requested and re-ingested with every flow run.
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This can be more efficient than using a remote vector database, and it is a good choice for flows that use custom, domain-specific datasets, like slices of customer and product data.
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You can use knowledge base components in much the same way that you use vector store components.
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However, there are several key differences:
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* **Local storage**: Langflow knowledge bases are exclusively local.
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In contrast, only some vector store components support local databases.
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* **Built-in embedding models**: Langflow knowledge bases include built-in support for several embedding models.
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Other models aren't supported for use with knowledge bases.
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To use a different provider or model, you must use a vector store component along with your preferred embedding model component.
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* **Basic similarity search**: When querying Langflow knowledge bases, only standard similarity search is supported.
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For more advanced searches, you must use a vector store component for a vector database provider that supports your desired functionality.
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* **Structured data**: Langflow knowledge bases only support structured data.
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For unstructured data, you must use a compatible vector store component. |