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 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.

<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. :::

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 or inspect 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.