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50 lines
2.3 KiB
Plaintext
50 lines
2.3 KiB
Plaintext
---
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title: pgvector
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slug: /bundles-pgvector
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---
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import Icon from "@site/src/components/icon";
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import PartialParams from '@site/docs/_partial-hidden-params.mdx';
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import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
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import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
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import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
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<Icon name="Blocks" aria-hidden="true" /> [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
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This page describes the components that are available in the **pgvector** bundle.
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## pgvector vector store
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The **PGVector** component reads and writes to PostgreSQL vector stores using an instance of [`PGVector`](https://docs.langchain.com/oss/python/integrations/vectorstores/pgvector).
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<details>
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<summary>About vector store instances</summary>
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<PartialVectorStoreInstance />
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</details>
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<PartialVectorSearchResults />
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:::tip
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For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
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:::
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### pgvector vector store parameters
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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.
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<PartialParams />
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<PartialConditionalParams />
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For information about accepted values and functionality, see the [PGVector documentation](https://github.com/pgvector/pgvector) or inspect [component code](/concepts-components#component-code).
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| Name | Type | Description |
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| --------------- | ------------ | ----------------------------------------- |
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| pg_server_url | SecretString | Input parameter. The PostgreSQL server connection string. |
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| collection_name | String | Input parameter. The table name for the vector store. |
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| search_query | String | Input parameter. The query for similarity search. |
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| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
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| embedding | Embeddings | Input parameter. The embedding function to use. |
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| number_of_results | Integer | Input parameter. The number of results to return in search. | |