Zep Vector Store 节点#
¥Zep Vector Store node
Deprecated
此节点已弃用，将在未来的版本中移除。
¥This node is deprecated, and will be removed in a future version.
使用 Zep Vector Store 与 Zep 矢量数据库进行交互。你可以将文档插入向量数据库，从向量数据库获取文档，检索文档并将其提供给连接到 chain 的检索器，或者直接将其连接到 agent 以用作 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.
Examples and templates
有关使用示例和模板以帮助你入门，请参阅 n8n 的 Zep Vector Store 集成 页面。
¥For usage examples and templates to help you get started, refer to n8n's Zep Vector Store integrations page.
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 Vector Store 用作常规节点来插入或获取文档。此模式将 Zep 向量存储置于常规连接流程中，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 此模板 的场景 1 中找到示例（该示例使用 Supabase，但模式相同）。
¥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 矢量存储节点直接连接到 AI 代理 的工具连接器，以便在回答查询时使用矢量存储作为资源。
¥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.
在这里，连接方式如下：AI 代理（工具连接器）-> Zep Vector Store 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 检索器 节点与 Zep Vector Store 节点配合使用，从 Zep Vector Store 节点获取文档。这通常与 Question and Answer 链 节点一起使用，用于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.
连接流程示例（示例使用 Pinecone，但模式相同）如下所示：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 问答工具 来汇总结果并回答来自 Zep Vector Store 节点的问题。此模式并非直接连接 Zep 向量存储作为工具，而是使用专门用于汇总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 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，但格式相同）如下所示：AI 代理（工具连接器）-> Vector Store 问答工具（Vector Store 连接器）-> Zep Vector Store。
¥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
- 收藏名称：输入要存储数据的集合名称。
¥Collection Name: Enter the collection name to store the data in.
获取多个参数#
¥Get Many parameters
- 收藏名称：输入要从中检索数据的集合名称。
¥Collection Name: Enter the collection name to retrieve the data from.
- 提示：输入搜索查询。
¥Prompt: Enter the search query.
- 限制：输入要从向量存储中检索的结果数量。例如，将其设置为
10以获取十个最佳结果。
¥Limit: Enter how many results to retrieve from the vector store. For example, set this to 10 to get the ten best results.
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
- 收藏名称：输入要从中检索数据的集合名称。
¥Collection Name: Enter the collection name to retrieve the data from.
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。
¥Name: The name of the vector store.
- 描述：向 LLM 解释此工具的功能。良好的、具体的描述有助于 LLM 更频繁地生成预期结果。
¥Description: Explain to the LLM what this tool does. A good, specific description allows LLMs to produce expected results more often.
- 收藏名称：输入要从中检索数据的集合名称。
¥Collection Name: Enter the collection name to retrieve the data from.
- 限制：输入要从向量存储中检索的结果数量。例如，将其设置为
10以获取十个最佳结果。
¥Limit: Enter how many results to retrieve from the vector store. For example, set this to 10 to get the ten best results.
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.