Qdrant 向量存储节点#

¥Qdrant Vector Store node

使用 Qdrant 节点以矢量商店的形式与你的 Qdrant 集合进行交互。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 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.

Credentials

你可以在此处中找到此节点的身份验证信息。

¥You can find authentication information for this node here.

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 向量存储用作常规节点来插入或获取文档。此模式将 Qdrant 向量存储置于常规连接流程中，无需使用代理。

¥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.

你可以在此模板的第一部分中找到示例。

¥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，以便在响应查询时使用向量存储作为资源。

¥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.

在这里，连接方式如下：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 检索器节点与 Qdrant 向量存储节点配合使用，从 Qdrant 向量存储节点获取文档。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 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.

连接流程示例可以是：问答链（检索器连接器）-> 向量存储检索器（向量存储连接器）-> 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 问答工具来汇总来自 Qdrant 向量存储节点的结果并回答问题。此模式并非直接连接 Qdrant 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 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）-> 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 集合的名称。

¥Qdrant collection name: Enter the name of the Qdrant collection to use.

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.

此操作模式包含一个节点选项，即元数据筛选器。

¥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 集合的名称。

¥Qdrant collection name: Enter the name of the Qdrant collection to use.

此操作模式包含一个节点选项：

¥This Operation Mode includes one Node option:

收藏配置：输入用于创建 Qdrant 集合的 JSON 选项。请参阅 Qdrant 组合所有可能的组合文档了解更多信息。

¥Collection Config: Enter JSON options for creating a Qdrant collection creation configuration. Refer to the Qdrant Collections documentation for more information.

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。

¥Qdrant Collection: Enter the name of the Qdrant collection to use.

此操作模式包含一个节点选项，即元数据筛选器。

¥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。

¥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.

Qdrant 集合：输入要使用的 Qdrant 集合的名称。

¥Qdrant Collection: Enter the name of the Qdrant collection to use.

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

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

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.