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Redis 向量存储节点#

¥Redis Vector Store node

使用 Redis Vector Store 节点以 矢量商店 身份与 Redis 数据库进行交互。你可以将文档插入向量数据库，从中获取文档，使用连接到 chain 的检索器检索文档，或者直接连接到 agent 以用作 tool。

¥Use the Redis Vector Store node to interact with your Redis database as a vector store. You can insert documents into the vector database, get documents from the vector database, retrieve documents using a retriever connected to a chain, or connect it directly to an agent to use as a tool.

本页提供 Redis 向量存储节点的参数以及更多资源的链接。

¥On this page, you'll find the node parameters for the Redis Vector Store 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.

先决条件#

¥Prerequisites

在使用此节点之前，你需要一个已启用 Redis 查询引擎 的 Redis 数据库。使用以下格式之一：

¥Before using this node, you need a Redis database with the Redis Query Engine enabled. Use one of the following:

  • Redis 开源版（v8.0 及更高版本） - 默认包含 Redis 查询引擎

¥Redis Open Source (v8.0 and later) - includes the Redis Query Engine by default

¥Redis Cloud - fully managed service

¥Redis Software - self-managed deployment

A new index will be created if you don't have one.

只有当你想使用自定义索引架构或重用现有索引时，才需要预先创建自己的索引。否则，你可以跳过此步骤，让节点根据你指定的选项为你创建一个新索引。

¥Creating your own indices in advance is only necessary if you want to use a custom index schema or reuse an existing index. Otherwise, you can skip this step and let the node create a new index for you based on the options you specify.

节点使用模式#

¥Node usage patterns

你可以在以下模式下使用 Redis 向量存储节点：

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

¥You can use the Redis Vector Store as a regular node to insert or get documents. This pattern places the Redis 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（该模板使用 Supabase Vector Store，但模式相同）。

¥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 Redis 向量存储节点直接连接到 AI 代理 的 tool 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 Redis 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）-> Redis 向量存储节点。

¥Here, the connection would be: AI agent (tools connector) -> Redis 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 Redis 向量存储节点配合使用，从 Redis 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 Redis Vector Store node to fetch documents from the Redis 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（链接的示例使用 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）-> Redis 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) -> Redis 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 Redis 向量存储节点的结果并回答问题。此模式并非直接连接 Redis 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 Redis Vector Store node. Rather than connecting the Redis 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（链接示例使用 Qdrant，但格式相同）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）-> Redis 向量存储。

¥The connections flow (the linked example uses Qdrant, 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) -> Redis 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

  • Redis 索引：输入要使用的 Redis 向量搜索索引的名称。可从列表中选择现有前缀。

¥Redis Index: Enter the name of the Redis vector search index to use. Optionally choose an existing one from the list.

  • 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

  • Redis 索引：输入要使用的 Redis 向量搜索索引的名称。可从列表中选择现有前缀。

¥Redis Index: Enter the name of the Redis vector search index to use. Optionally choose an existing one from the list.

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

  • Redis 索引：输入要使用的 Redis 向量搜索索引的名称。

¥Redis Index: Enter the name of the Redis vector search index to use.

此操作模式包含一个节点选项，即 元数据筛选器。可从列表中选择现有前缀。

¥This Operation Mode includes one Node option, the Metadata Filter. Optionally choose an existing one from the list.

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.

  • Redis 索引：输入要使用的 Redis 向量搜索索引的名称。可从列表中选择现有前缀。

¥Redis Index: Enter the name of the Redis vector search index to use. Optionally choose an existing one from the list.

  • 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.

包含元数据#

¥Include Metadata

是否包含文档元数据。

¥Whether to include document metadata.

此功能可用于 获取多个 和 检索文档（作为 AI 代理的工具） 模式。

¥You can use this with the Get Many and Retrieve Documents (As Tool for AI Agent) modes.

节点选项#

¥Node options

元数据筛选器#

¥Metadata Filter

元数据过滤器适用于 获取多个、检索文档（作为链/工具的向量存储） 和 检索文档（作为 AI 代理的工具） 操作模式。这是一个 OR 查询。如果你指定了多个元数据筛选字段，则至少要有一个字段匹配。插入数据时，元数据通过文档加载器设置。有关加载文档的更多信息，请参阅 默认数据加载器。

¥Metadata filters are available for the Get Many, Retrieve Documents (As Vector Store for Chain/Tool), and Retrieve Documents (As Tool for AI Agent) operation modes. This is an OR query. If you specify more than one metadata filter field, at least one 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.

Redis 配置选项#

¥Redis Configuration Options

适用于所有操作模式：

¥Available for all operation modes:

  • 元数据键：输入 Redis 哈希表中元数据字段的键（默认值：metadata）。

¥Metadata Key: Enter the key for the metadata field in the Redis hash (default: metadata).

  • 密钥前缀：输入用于存储文档的键前缀（默认值：doc:）。

¥Key Prefix: Enter the key prefix for storing documents (default: doc:).

  • 内容密钥：输入 Redis 哈希表中内容字段的键（默认值：content）。

¥Content Key: Enter the key for the content field in the Redis hash (default: content).

  • 嵌入键：输入 Redis 哈希表中嵌入字段的键（默认值：embedding）。

¥Embedding Key: Enter the key for the embedding field in the Redis hash (default: embedding).

插入选项#

¥Insert Options

适用于 插入文档 操作模式：

¥Available for the Insert Documents operation mode:

  • 覆盖文档：选择是否覆盖现有文档（已启用）或不覆盖（已禁用）。同时删除索引。

¥Overwrite Documents: Select whether to overwrite existing documents (turned on) or not (turned off). Also deletes the index.

  • 生存时间：输入文档的生存时间（以秒为单位）。不会使索引过期。

¥Time-to-Live: Enter the time-to-live for documents in seconds. Does not expire the index.

模板和示例#

¥Templates and examples

Template widget placeholder.

相关资源#

¥Related resources

参考：

¥Refer to:

¥Redis Vector Search documentation for more information about Redis vector capabilities.

¥RediSearch documentation for more information about RediSearch.

¥LangChain's Redis Vector Store 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.