Getting Started with Milvus and n8n
Introduction to n8n and the Milvus Vector Store Node
n8n is a powerful open-source workflow automation platform that allows you to connect various applications, services, and APIs together to create automated workflows without coding. With its node-based visual interface, n8n enables users to build complex automation processes by simply connecting nodes that represent different services or actions. It is self-hostable, highly extensible, and supports both fair-code and enterprise licensing.
The Milvus Vector Store node in n8n integrates Milvus into your automation workflows. This allows you to perform semantic search, power retrieval-augmented generation (RAG) systems, and build intelligent AI applications—all within the n8n ecosystem.
This documentation is primarily based on the official n8n Milvus Vector Store documentation. If you find any outdated or inconsistent content, please prioritize the official documentation and feel free to raise an issue for us.
Key Features
With the Milvus Vector Store node in n8n, you can:
- Interact with your Milvus database as a vector store
- Insert documents into Milvus
- Get documents from Milvus
- Retrieve documents to provide them to a retriever connected to a chain
- Connect directly to an agent as a tool
- Filter documents based on metadata
Node Usage Patterns
You can use the Milvus Vector Store node in n8n in the following patterns.
Use as a regular node to insert and retrieve documents
You can use the Milvus Vector Store as a regular node to insert, or get documents. This pattern places the Milvus Vector Store in the regular connection flow without using an agent.
See this example template for how to build a system that stores documents in Milvus and retrieves them to support cited, chat-based answers.
Connect directly to an AI agent as a tool
You can connect the Milvus Vector Store node directly to the tool connector of an AI agent to use a vector store as a resource when answering queries.
Here, the connection would be: AI agent (tools connector) -> Milvus Vector Store node. See this example template where data is embedded and indexed in Milvus, and the AI Agent uses the vector store as a knowledge tool for question-answering.
Use a retriever to fetch documents
You can use the Vector Store Retriever node with the Milvus Vector Store node to fetch documents from the Milvus 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.
A typical node connection flow looks like this: Question and Answer Chain (Retriever connector) -> Vector Store Retriever (Vector Store connector) -> Milvus Vector Store.
Check out this workflow example to see how to ingest external data into Milvus and build a chat-based semantic Q&A system.
Use the Vector Store Question Answer Tool to answer questions
Another pattern uses the Vector Store Question Answer Tool to summarize results and answer questions from the Milvus Vector Store node. Rather than connecting the Milvus Vector Store directly as a tool, this pattern uses a tool specifically designed to summarizes data in the vector store.
The connections flow would look like this: AI agent (tools connector) -> Vector Store Question Answer Tool (Vector Store connector) -> Milvus Vector store.
Node Operation Modes
The Milvus Vector Store node supports multiple operation modes, each tailored for different workflow use cases. Understanding these modes helps design more effective workflows.
We will provide a high-level overview of the available operation modes and options below. For a complete list of input parameters and configuration options for each mode, please refer to the official documentation.
Operation Modes Overview
The Milvus Vector Store node supports four distinct modes:
- Get Many: Retrieve multiple documents based on semantic similarity to a prompt.
- Insert Documents: Insert new documents into your Milvus collection.
- Retrieve Documents (As Vector Store for Chain/Tool): Use the node as a retriever within a chain-based system.
- Retrieve Documents (As Tool for AI Agent): Use the node as a tool resource for an AI agent during question-answering tasks.
Additional Node Options
- Metadata Filter (Get Many mode only): Filter results based on custom metadata keys. Multiple fields apply an AND condition.
- Clear Collection (Insert Documents mode only): Remove existing documents from the collection prior to inserting new ones.