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Supabase 向量存储节点(Supabase Vector Store node)#

使用 Supabase 向量存储将你的 Supabase 数据库作为向量存储进行交互。你可以将文档插入向量数据库，从向量数据库中获取多个文档，并检索文档以提供给连接到链的检索器。

🌐 Use the Supabase Vector Store to interact with your Supabase database as vector store. You can insert documents into a vector database, get many documents from a vector database, and retrieve documents to provide them to a retriever connected to a chain.

使用 Supabase 向量存储以 向量存储 的形式与你的 Supabase 数据库进行交互。你可以将文档插入向量数据库，从向量数据库获取文档，检索文档以提供给连接到 链 的检索器，或者直接将其连接到 代理 以用作 工具。你还可以通过 ID 更新向量存储中的某一项。

🌐 Use the Supabase Vector Store to interact with your Supabase database as vector store. You can insert documents into a vector database, get documents from a vector database, retrieve documents to provide them to a retriever connected to a chain, or connect it directly to an agent to use as a tool. You can also update an item in a vector store by its ID.

本页包含 Supabase 节点的节点参数以及更多资源的链接。

🌐 On this page, you'll find the node parameters for the Supabase node, and links to more resources.

凭证

你可以在此处找到此节点的认证信息。

Parameter resolution in sub-nodes

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.

Supabase 提供了一个向量存储快速入门指南。如果你在快速入门中使用了除默认值之外的设置，可能会影响 n8n 中的参数设置。请确保你了解自己在做什么。

🌐 Supabase provides a quickstart for setting up your vector store. If you use settings other than the defaults in the quickstart, this may affect parameter settings in n8n. Make sure you understand what you're doing.

节点使用模式(Node usage patterns)#

你可以在以下模式下使用 Supabase 向量存储节点。

🌐 You can use the Supabase Vector Store node in the following patterns.

用作常规节点，用于插入、更新和检索文档(Use as a regular node to insert, update, and retrieve documents)#

您可以将 Supabase 向量存储作为常规节点来插入、更新或获取文档。这种模式将 Supabase 向量存储放在常规连接流程中，而无需使用代理。

🌐 You can use the Supabase Vector Store as a regular node to insert, update, or get documents. This pattern places the Supabase Vector Store in the regular connection flow without using an agent.

您可以在此模板的场景1中看到一个示例。

🌐 You can see an example of this in scenario 1 of this template.

直接连接到 AI 代理作为工具(Connect directly to an AI agent as a tool)#

你可以将 Supabase 向量存储节点直接连接到 AI 代理 的工具连接器，以在回答查询时将向量存储作为资源使用。

🌐 You can connect the Supabase Vector Store node directly to the tool connector of an AI agent to use a vector store as a resource when answering queries.

这里，连接将是：AI 代理（工具连接器）-> Supabase 向量存储节点。

🌐 Here, the connection would be: AI agent (tools connector) -> Supabase Vector Store node.

使用检索器获取文档(Use a retriever to fetch documents)#

你可以将 Vector Store Retriever 节点与 Supabase 向量存储节点一起使用，从 Supabase 向量存储节点获取文档。这通常与 Question and Answer Chain 节点一起使用，从向量存储中获取与给定聊天输入匹配的文档。

🌐 You can use the Vector Store Retriever node with the Supabase Vector Store node to fetch documents from the Supabase Vector Store node. This is often used with the Question and Answer Chain node to fetch documents from the vector store that match the given chat input.

一个连接流程示例（示例使用了 Pinecone，但模式相同）可以是：问答链（检索器连接器）-> 向量存储检索器（向量存储连接器）-> Supabase 向量存储。

🌐 An example of the connection flow (the example uses Pinecone, but the pattern in the same) would be: Question and Answer Chain (Retriever connector) -> Vector Store Retriever (Vector Store connector) -> Supabase Vector Store.

使用 Vector Store 问答工具回答问题(Use the Vector Store Question Answer Tool to answer questions)#

另一种模式使用 Vector Store Question Answer Tool 来总结结果并回答来自 Supabase 向量存储节点的问题。与直接将 Supabase 向量存储作为工具连接不同，这种模式使用了一个专门设计用于总结向量存储中数据的工具。

🌐 Another pattern uses the Vector Store Question Answer Tool to summarize results and answer questions from the Supabase Vector Store node. Rather than connecting the Supabase Vector Store directly as a tool, this pattern uses a tool specifically designed to summarizes data in the vector store.

在这种情况下，连接流程 看起来如下：AI 代理（工具连接器）-> 向量存储问答工具（向量存储连接器）-> Supabase 向量存储。

🌐 The connections flow in this case would look like this: AI agent (tools connector) -> Vector Store Question Answer Tool (Vector Store connector) -> Supabase Vector store.

节点参数(Node parameters)#

操作模式(Operation Mode)#

This Vector Store node has five modes: Get Many, Insert Documents, Retrieve Documents (As Vector Store for Chain/Tool), Retrieve Documents (As Tool for AI Agent), and Update Documents. The mode you select determines the operations you can perform with the node and what inputs and outputs are available.

Get Many#

In this mode, you can retrieve multiple documents from your vector database by providing a prompt. The prompt will be embedded and used for similarity search. The node will return the documents that are most similar to the prompt with their similarity score. This is useful if you want to retrieve a list of similar documents and pass them to an agent as additional context.

Insert Documents#

Use Insert Documents mode to insert new documents into your vector database.

Retrieve Documents (As Vector Store for Chain/Tool)#

Use Retrieve Documents (As Vector Store for Chain/Tool) mode with a vector-store retriever to retrieve documents from a vector database and provide them to the retriever connected to a chain. In this mode you must connect the node to a retriever node or root node.

Retrieve Documents (As Tool for AI Agent)#

Use Retrieve Documents (As Tool for AI Agent) mode to use the vector store as a tool resource when answering queries. When formulating responses, the agent uses the vector store when the vector store name and description match the question details.

Update Documents#

Use Update Documents mode to update documents in a vector database by ID. Fill in the ID with the ID of the embedding entry to update.

重新排序结果(Rerank Results)#

Enables reranking. If you enable this option, you must connect a reranking node to the vector store. That node will then rerank the results for queries. You can use this option with the Get Many, Retrieve Documents (As Vector Store for Chain/Tool) and Retrieve Documents (As Tool for AI Agent) modes.

获取多个参数(Get Many parameters)#

  • 表名：输入要使用的 Supabase 表。
  • 提示：请输入搜索查询。
  • 限制：输入要从向量存储中获取的结果数量。例如，将其设置为 10 可获取十个最佳结果。

插入文档参数(Insert Documents parameters)#

  • 表名：输入要使用的 Supabase 表。

检索文档（作为链/工具的向量存储）参数(Retrieve Documents (As Vector Store for Chain/Tool) parameters)#

  • 表名：输入要使用的 Supabase 表。

检索文档（作为工具） （适用于 AI 代理）参数(Retrieve Documents (As Tool for AI Agent) parameters)#

  • 名称：向量存储的名称。
  • 描述：向大型语言模型（LLM）解释此工具的功能。具体而清晰的描述可以让大型语言模型更频繁地产生预期的结果。
  • 表名：输入要使用的 Supabase 表。
  • 限制：输入要从向量存储中获取的结果数量。例如，将其设置为 10 可获取十个最佳结果。

更新文档(Update Documents)#

  • 表名：输入要使用的 Supabase 表。
  • ID：嵌入条目的ID。

更新文档的参数

🌐 Parameters for Update Documents

  • 身份证

节点选项(Node options)#

查询名称(Query Name)#

你在 Supabase 中设置的匹配函数的名称。如果你按照 Supabase 快速入门 操作，这将是 match_documents。

🌐 The name of the matching function you set up in Supabase. If you follow the Supabase quickstart, this will be match_documents.

元数据筛选器(Metadata Filter)#

Available in Get Many mode. When searching for data, use this to match with metadata associated with the document.

This is an AND query. If you specify more than one metadata filter field, all of them must match.

When inserting data, the metadata is set using the document loader. Refer to Default Data Loader for more information on loading documents.

模板和示例(Templates and examples)#

Template widget placeholder.

有关该服务的更多信息，请参阅 LangChain 的 Supabase 文档。

🌐 Refer to LangChain's Supabase documentation for more information about the service.

View n8n's Advanced AI documentation.