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langflow/docs/versioned_docs/version-1.10.0/Components/bundles-supabase.mdx
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---
title: Supabase
slug: /bundles-supabase
---
import Icon from "@site/src/components/icon";
import PartialParams from '@site/docs/_partial-hidden-params.mdx';
import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
<Icon name="Blocks" aria-hidden="true" /> [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
This page describes the components that are available in the **Supabase** bundle.
## Supabase vector store
The **Supabase** component reads and writes to Supabase vector stores using an instance of `SupabaseVectorStore`.
<details>
<summary>About vector store instances</summary>
<PartialVectorStoreInstance />
</details>
<PartialVectorSearchResults />
:::tip
For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
:::
### Supabase vector store parameters
You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
<PartialParams />
<PartialConditionalParams />
For information about accepted values and functionality, see the [Supabase documentation](https://supabase.com/docs/guides/ai) or inspect [component code](/concepts-components#component-code).
| Name | Type | Description |
| ------------------- | ------------ | ----------------------------------------- |
| supabase_url | String | Input parameter. The URL of the Supabase instance. |
| supabase_service_key| SecretString | Input parameter. The service key for Supabase authentication. |
| table_name | String | Input parameter. The name of the table in Supabase. |
| query_name | String | Input parameter. The name of the query to use. |
| search_query | String | Input parameter. The query for similarity search. |
| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
| embedding | Embeddings | Input parameter. The embedding function to use. |
| number_of_results | Integer | Input parameter. The number of results to return in search. |