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

使用 Qdrant 节点与你的 Qdrant 集合作为 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文档，将文档检索出来以提供给连接到 链 的检索器，或直接将其连接到 代理 作为 工具 使用。

🌐 Use the Qdrant node to interact with your Qdrant collection as a 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.

本页提供 Qdrant 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 Qdrant node, and links to more resources.

凭证

你可以在这里找到该节点的认证信息。

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.

节点使用模式(Node usage patterns)#

你可以按照以下模式使用 Qdrant 向量存储节点。

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

作为常规应用使用用于插入和检索文档的节点(Use as a regular node to insert and retrieve documents)#

你可以将 Qdrant 向量存储作为常规节点来插入或获取文档。这种模式将 Qdrant 向量存储放在常规连接流程中，而不使用代理。

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

您可以在此模板的第一部分看到一个例子。

🌐 You can see an example of this in the first part 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 Qdrant 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 Qdrant 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）-> Qdrant 向量存储节点。

🌐 Here, the connection would be: AI agent (tools connector) -> Qdrant 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 Qdrant 向量存储节点一起使用，从 Qdrant 向量存储节点中获取文档。这通常与 Question and Answer Chain 节点一起使用，以获取与给定聊天输入匹配的向量存储文档。

🌐 You can use the Vector Store Retriever node with the Qdrant Vector Store node to fetch documents from the Qdrant 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.

一个连接流程示例可能是：问答链（检索器连接器）-> 向量存储检索器（向量存储连接器）-> Qdrant 向量存储。

🌐 An example of the connection flow would be: Question and Answer Chain (Retriever connector) -> Vector Store Retriever (Vector Store connector) -> Qdrant 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 来汇总结果并回答来自 Qdrant 向量存储节点的问题。与直接将 Qdrant 向量存储作为工具连接不同，这种模式使用专门设计的工具来汇总向量存储中的数据。

🌐 Another pattern uses the Vector Store Question Answer Tool to summarize results and answer questions from the Qdrant Vector Store node. Rather than connecting the Qdrant 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）-> Qdrant 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) -> Qdrant 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.

获取多个参数(Get Many parameters)#

  • Qdrant 集合名称：输入要使用的 Qdrant 集合名称。
  • 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。

此操作模式包括一个 节点选项，元数据过滤器。

🌐 This Operation Mode includes one Node option, the Metadata Filter.

插入文档参数(Insert Documents parameters)#

  • Qdrant 集合名称：输入要使用的 Qdrant 集合名称。

此操作模式包含一个 节点选项：

🌐 This Operation Mode includes one Node option:

  • 集合配置：输入用于创建 Qdrant 集合的 JSON 选项配置。更多信息请参考 Qdrant 集合 文档。

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)#

  • Qdrant 集合：输入要使用的 Qdrant 集合的名称。

此操作模式包括一个 节点选项，元数据过滤器。

🌐 This Operation Mode includes one Node option, the Metadata Filter.

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。
  • Qdrant 集合：输入要使用的 Qdrant 集合的名称。
  • 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)#

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 Qdrant 文档。

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

View n8n's Advanced AI documentation.

Self-hosted AI Starter Kit#

New to working with AI and using self-hosted n8n? Try n8n's self-hosted AI Starter Kit to get started with a proof-of-concept or demo playground using Ollama, Qdrant, and PostgreSQL.