---
title: pgvector
slug: /bundles-pgvector
---

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 **pgvector** bundle.

## pgvector vector store

The **PGVector** component reads and writes to PostgreSQL vector stores using an instance of [`PGVector`](https://docs.langchain.com/oss/python/integrations/vectorstores/pgvector).

<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).
:::

### pgvector 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 [PGVector documentation](https://github.com/pgvector/pgvector) or inspect [component code](/concepts-components#component-code).

| Name            | Type         | Description                               |
| --------------- | ------------ | ----------------------------------------- |
| pg_server_url   | SecretString | Input parameter. The PostgreSQL server connection string.       |
| collection_name | String       | Input parameter. The table name for the vector store.           |
| 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.     |