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Add docs for new PGVector Vector store node (n8n-io#2442)
Co-authored-by: freakwriter <[email protected]>
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_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md

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### Operation Mode
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Vector Store nodes in n8n have three modes: **Get Many**, **Insert Documents** and **Retrieve Documents**. The mode you select determines the operations you can perform with the node and what inputs and outputs are available.
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Vector Store nodes in n8n have three modes: **Get Many**, **Insert Documents**, and **Retrieve Documents**. The mode you select determines the operations you can perform with the node and what inputs and outputs are available.
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#### Get Many
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#### Insert Documents
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Use insert documents mode to insert new documents into your vector database.
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Use Insert Documents mode to insert new documents into your vector database.
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#### Retrieve Documents (For Agent/Chain)
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docs/advanced-ai/langchain/langchain-n8n.md

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Vector stores store embedded data, and perform vector searches on it.
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* [In Memory Vector Store](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreinmemory/)
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* [PGVector Vector Store](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorepgvector/)
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* [Pinecone Vector Store](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorepinecone/)
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* [Qdrant Vector Store](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant/)
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* [Supabase Vector Store](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoresupabase/)
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---
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#https://www.notion.so/n8n/Frontmatter-432c2b8dff1f43d4b1c8d20075510fe4
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title: PGVector Vector Store
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description: Documentation for the PGVector node in n8n, a workflow automation platform. Includes details of operations and configuration, and links to examples and credentials information.
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priority: medium
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---
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# PGVector Vector Store
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PGVector is an extension of Postgresql. Use this node to interact with the PGVector tables in your Postgresql database. You can insert documents into a vector table, get documents from a vector table, and retrieve documents to provide them to a retriever connected to a chain.
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On this page, you'll find the node parameters for the PGVector node, and links to more resources.
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/// note | Credentials
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You can find authentication information for this node [here](/integrations/builtin/credentials/postgres/).
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///
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## Node parameters
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### Get Many parameters
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* **Table name**: Enter the name of the table you want to query.
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* **Prompt**: Enter your search query.
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* **Limit**: Enter a number to set how many results to retrieve from the vector store. For example, set this to `10` to get the ten best results.
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### Insert Documents parameters
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* **Table name**: Enter the name of the table you want to query.
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### Retrieve Documents parameters (for Agent/Chain)
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* **Table name**: Enter the name of the table you want to query.
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## Node options
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### Collection
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A way to separate datasets in PGVector. This creates a separate table and column to keep track of which collection a vector belongs to.
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* **Use Collection**: Select whether to use a collection (turned on) or not (turned off).
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* **Collection Name**: Enter the name of the collection you want to use.
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* **Collection Table Name**: Enter the name of the table to store collection information in.
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### Column Names
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The following options specify the names of the columns to store the vectors and corresponding information in:
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* **ID Column Name**
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* **Vector Column Name**
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* **Content Column Name**
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* **Metadata Column Name**
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### Metadata Filter
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## Templates and examples
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<!-- see https://www.notion.so/n8n/Pull-in-templates-for-the-integrations-pages-37c716837b804d30a33b47475f6e3780 -->
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[[ templatesWidget(title, 'pgvector-vector-store') ]]
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## Related resources
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Refer to [LangChain's PGVector documentation](https://js.langchain.com/docs/integrations/vectorstores/pgvector){:target=_blank .external-link} for more information about the service.
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docs/integrations/builtin/credentials/postgres.md

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- [Postgres](/integrations/builtin/app-nodes/n8n-nodes-base.postgres/)
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- [Agent](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent)
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- [Postgres Chat Memory](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.memorypostgreschat/)
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- [PGVector Vector Store](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorepgvector/)
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/// note | Agent node users
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The Agent node doesn't support SSH tunnels.

mkdocs.yml

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- integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.sentimentanalysis.md
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- integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.code.md
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- integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreinmemory.md
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- integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorepgvector.md
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- integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorepinecone.md
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- integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant.md
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- integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoresupabase.md

styles/config/vocabularies/default/accept.txt

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Oura
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PDFs
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Peekalink
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PGVector
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PhantomBuster
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Pinterest
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Pipedrive

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