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* docs-add-opensearch-provider-and-adjust-kb-docs * docs-combine-kb-config-sections-and-update-partial * add-release-note * Apply suggestions from code review Co-authored-by: Mendon Kissling <59585235+mendonk@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com> * docs-clarify-embedding-model-step --------- Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com>
19 lines
1.6 KiB
Plaintext
19 lines
1.6 KiB
Plaintext
A Langflow knowledge base is a vector database that stores embeddings for use in your flows.
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By default, knowledge bases use Chroma as a local vector store, but you can configure an external vector database provider such as OpenSearch.
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For more information, see [Configure vector database providers](/knowledge#configure-vector-database-providers).
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Because knowledge bases don't re-ingest data with every flow run, they can be more efficient than using a remote vector database.
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They are 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 by default**: Langflow knowledge bases use Chroma local storage by default.
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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. |