mirror of
https://github.com/hanzoai/flow.git
synced 2026-10-17 01:29:18 +00:00
zeekay
d650dd60f0
Docs corpus (Langflow->Hanzo Flow, upstream URLs langflow-ai/langflow->hanzoai/ flow, docs.langflow.org->docs.hanzo.ai), starter-project template help-URLs, CI workflows, and test files. Remaining 'langflow' repo-wide is now dominated by LANGFLOW_* env-var names + langflow_ settings fields — the config-boundary layer deliberately preserved pending a coordinated FLOW_/HANZO_ alias-with-fallback (renaming them blind breaks every deployment).
17 lines
1.4 KiB
Plaintext
17 lines
1.4 KiB
Plaintext
A Hanzo Flow knowledge base is a local vector database that is stored in Hanzo Flow storage.
|
|
|
|
Because knowledge bases are local, the data isn't remotely requested and re-ingested with every flow run.
|
|
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.
|
|
|
|
You can use knowledge base components in much the same way that you use vector store components.
|
|
However, there are several key differences:
|
|
|
|
* **Local storage**: Hanzo Flow knowledge bases are exclusively local.
|
|
In contrast, only some vector store components support local databases.
|
|
* **Built-in embedding models**: Hanzo Flow knowledge bases include built-in support for several embedding models.
|
|
Other models aren't supported for use with knowledge bases.
|
|
To use a different provider or model, you must use a vector store component along with your preferred embedding model component.
|
|
* **Basic similarity search**: When querying Hanzo Flow knowledge bases, only standard similarity search is supported.
|
|
For more advanced searches, you must use a vector store component for a vector database provider that supports your desired functionality.
|
|
* **Structured data**: Hanzo Flow knowledge bases only support structured data.
|
|
For unstructured data, you must use a compatible vector store component. |