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MongoDB Atlas 向量存储节点(MongoDB Atlas Vector Store node)#

MongoDB Atlas 向量搜索是 MongoDB Atlas 的一项功能，允许用户存储和查询向量嵌入。使用此节点可以与 MongoDB Atlas 集合中的向量搜索索引进行交互。你可以插入文档、检索文档，并在链中或作为代理工具使用向量存储。

🌐 MongoDB Atlas Vector Search is a feature of MongoDB Atlas that enables users to store and query vector embeddings. Use this node to interact with Vector Search indexes in your MongoDB Atlas collections. You can insert documents, retrieve documents, and use the vector store in chains or as a tool for agents.

本页包含 MongoDB Atlas 向量存储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 MongoDB Atlas Vector Store 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.

先决条件(Prerequisites)#

在使用此节点之前，请在你的 MongoDB Atlas 集合中创建一个 向量搜索索引。按照以下步骤创建：

🌐 Before using this node, create a Vector Search index in your MongoDB Atlas collection. Follow these steps to create one:

  1. 登录 MongoDB Atlas 仪表板。
  2. 选择你的组织和项目。
  3. 找到“搜索与向量搜索”部分。
  4. 选择你的集群，然后点击“前往搜索”。
  5. 点击“创建搜索索引”。
  6. 选择“向量搜索”模式，并使用可视化或 JSON 编辑器。例如：
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{
  "fields": [
    {
      "type": "vector",
      "path": "<field-name>",
      "numDimensions": 1536, // any other value
      "similarity": "<similarity-function>"
    }
  ]
}
  1. 根据你的嵌入模型调整“dimensions”的值（例如，OpenAI 的 text-embedding-small-3 对应 1536）。10. 为你的索引命名并创建它。

配置节点时，请务必注意以下必填值：

🌐 Make sure to note the following values which are required when configuring the node:

  • 收藏名称
  • 向量索引名
  • 嵌入和元数据的字段名称。

节点使用模式(Node usage patterns)#

你可以按照以下模式使用 MongoDB Atlas 向量存储节点：

🌐 You can use the MongoDB Atlas 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 MongoDB Atlas 向量存储作为常规节点来插入或获取文档。这种模式将 MongoDB Atlas 向量存储放在常规连接流程中，而不使用代理。

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

你可以在此模板的场景1中看到一个例子（该模板使用 Supabase 向量存储，但模式是相同的）。

🌐 You can see an example of this in scenario 1 of this template (the template uses the Supabase Vector Store, 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 MongoDB Atlas 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 MongoDB Atlas Vector Store node directly to the tool connector of an AI agent to use the vector store as a resource when answering queries.

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

🌐 Here, the connection would be: AI agent (tools connector) -> MongoDB Atlas 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 MongoDB Atlas Vector Store 节点结合使用，从 MongoDB Atlas 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 MongoDB Atlas Vector Store node to fetch documents from the MongoDB Atlas 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.

一个连接流程的示例（链接的示例使用了 Pinecone，但模式是相同的）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）-> MongoDB Atlas 向量存储。

🌐 An example of the connection flow (the linked example uses Pinecone, but the pattern is the same) would be: Question and Answer Chain (Retriever connector) -> Vector Store Retriever (Vector Store connector) -> MongoDB Atlas 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 向量存储问答工具 来总结结果并回答来自 MongoDB Atlas 向量存储节点的问题。这种模式不是将 MongoDB Atlas 向量存储直接作为工具连接，而是使用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 MongoDB Atlas Vector Store node. Rather than connecting the MongoDB Atlas Vector Store directly as a tool, this pattern uses a tool specifically designed to summarize data in the vector store.

在这种情况下，连接流程（示例中使用的是内存向量存储，但模式相同）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）-> 内存向量存储。

🌐 The connections flow (the linked example uses the In-Memory Vector Store, 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) -> In-Memory 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)#

  • Mongo 集合：输入要使用的 MongoDB 集合名称。
  • 向量索引名称：输入你在 MongoDB Atlas 集合中向量搜索索引的名称。
  • 嵌入字段：输入文档中包含向量嵌入的字段名称。
  • 元数据字段：输入文档中包含文本元数据的字段名称。

插入文档参数(Insert Documents parameters)#

  • Mongo 集合：输入要使用的 MongoDB 集合名称。
  • 向量索引名称：输入你在 MongoDB Atlas 集合中向量搜索索引的名称。
  • 嵌入字段：输入文档中包含向量嵌入的字段名称。
  • 元数据字段：输入文档中包含文本元数据的字段名称。

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

  • Mongo 集合：输入要使用的 MongoDB 集合名称。
  • 向量索引名称：输入你在 MongoDB Atlas 集合中向量搜索索引的名称。
  • 嵌入字段：输入文档中包含向量嵌入的字段名称。
  • 元数据字段：输入文档中包含文本元数据的字段名称。

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。
  • Mongo 集合：输入要使用的 MongoDB 集合名称。
  • 向量索引名称：输入你在 MongoDB Atlas 集合中向量搜索索引的名称。
  • 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)#

选项(Options)#

  • 元数据过滤器：根据元数据筛选结果。

模板和示例(Templates and examples)#

Template widget placeholder.

参考：

🌐 Refer to:

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.