Zep Vector Store 节点(Zep Vector Store node)#
已弃用
该节点已弃用，将在未来版本中移除。
使用 Zep 向量存储与 Zep 向量数据库进行交互。你可以将文档插入向量数据库，从向量数据库获取文档，将文档检索出来提供给连接到 chain 的检索器，或直接将其连接到 agent 用作 tool。
🌐 Use the Zep Vector Store to interact with Zep vector databases. 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.
在此页面，你可以找到 Zep Vector Store 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 Zep Vector Store node, and links to more resources.
凭证
您可以在此处找到此节点的认证信息。
示例和模板
有关使用示例和模板以帮助你入门，请参阅 n8n 的 Zep 向量存储集成 页面。
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.
节点使用模式(Node usage patterns)#
你可以在以下模式下使用 Zep Vector Store 节点。
🌐 You can use the Zep 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)#
你可以将 Zep 向量存储作为普通节点来插入或获取文档。这种模式将 Zep 向量存储放在常规连接流程中，而无需使用代理。
🌐 You can use the Zep Vector Store as a regular node to insert or get documents. This pattern places the Zep Vector Store in the regular connection flow without using an agent.
你可以在 这个模板 的场景 1 中看到一个例子（例子使用的是 Supabase，但模式是相同的）。
🌐 You can see an example of this in scenario 1 of this template (the example uses Supabase, but the pattern is the same).
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 Zep Vector Store 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用作资源。
🌐 You can connect the Zep 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）-> Zep 向量存储节点。
🌐 Here, the connection would be: AI agent (tools connector) -> Zep 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 Zep Vector Store 节点结合使用，从 Zep Vector Store 节点中获取文档。这通常与 Question and Answer Chain 节点一起使用，以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 Zep Vector Store node to fetch documents from the Zep 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）-> Zep 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) -> Zep 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 Zep 向量存储节点的问题。这个模式不是直接将 Zep 向量存储作为工具连接，而是使用专门设计的工具来总结向量存储中的数据。
🌐 Another pattern uses the Vector Store Question Answer Tool to summarize results and answer questions from the Zep Vector Store node. Rather than connecting the Zep 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（此示例使用 Supabase，但模式相同）如下所示：AI 代理（工具连接器）-> 向量存储问答工具（向量存储连接器）-> Zep 向量存储。
🌐 The connections flow (this example uses Supabase, but the pattern is the same) in this case would look like this: AI agent (tools connector) -> Vector Store Question Answer Tool (Vector Store connector) -> Zep Vector store.
节点参数(Node parameters)#
Operation Mode#
This Vector Store node has four modes: Get Many, Insert Documents, Retrieve Documents (As Vector Store for Chain/Tool), and Retrieve Documents (As Tool for AI Agent). 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 is embedded and used for similarity search. The node returns 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.
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.
插入文档参数(Insert Documents parameters)#
- 集合名称：输入要存储数据的集合名称。
获取多个参数(Get Many parameters)#
- 集合名称：输入集合名称以检索数据。
- 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。
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)#
- 集合名称：输入集合名称以检索数据。
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。
- 集合名称：输入集合名称以检索数据。
- 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。
节点选项(Node options)#
嵌入维度(Embedding Dimensions)#
嵌入数据和查询数据时必须相同。
🌐 Must be the same when embedding the data and when querying it.
此设置用于设置用于表示文本文档语义的浮点数数组的大小。
🌐 This sets the size of the array of floats used to represent the semantic meaning of a text document.
是否支持自动嵌入？(Is Auto Embedded)#
在 插入文档 操作模式下可用，默认启用。
🌐 Available in the Insert Documents Operation Mode, enabled by default.
禁用此选项可在 Zep 而非 n8n 中配置嵌入内容。
🌐 Disable this to configure your embeddings in Zep instead of in n8n.
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)#
相关资源(Related resources)#
有关该服务的更多信息，请参阅 LangChain 的 Zep 文档。
🌐 Refer to LangChain's Zep documentation for more information about the service.
View n8n's Advanced AI documentation.