--- title: Milvus Vector Store node documentation description: Learn how to use the Milvus Vector Store node in n8n. Follow technical documentation to integrate Milvus Vector Store node into your workflows. contentType: [integration, reference] priority: medium --- # Milvus Vector Store node Use the Milvus node to interact with your Milvus database as [vector store](/glossary.md#ai-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](/glossary.md#ai-chain), or connect directly to an [agent](/glossary.md#ai-agent) as a [tool](/glossary.md#ai-tool). On this page, you'll find the node parameters for the Milvus node, and links to more resources. /// note | Credentials You can find authentication information for this node [here](/integrations/builtin/credentials/milvus.md). /// --8<-- "_snippets/integrations/builtin/cluster-nodes/sub-node-expression-resolution.md" ## Node usage patterns You can use the Milvus Vector Store node 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](https://n8n.io/workflows/3573-create-a-rag-system-with-paul-essays-milvus-and-openai-for-cited-answers/) 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](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/index.md) 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](https://n8n.io/workflows/3576-paul-graham-essay-search-and-chat-with-milvus-vector-database/) 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](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.retrievervectorstore.md) 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](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa/index.md) 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](https://n8n.io/workflows/3574-create-a-paul-graham-essay-qanda-system-with-openai-and-milvus-vector-database/) 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](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolvectorstore.md) 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 parameters --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md" ### Rerank Results --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md" ### Get Many parameters * **Milvus Collection**: Select or enter the Milvus Collection to use. * **Prompt**: Enter your search query. * **Limit**: Enter how many results to retrieve from the vector store. For example, set this to `10` to get the ten best results. ### Insert Documents parameters * **Milvus Collection**: Select or enter the Milvus Collection to use. * **Clear Collection**: Specify whether to clear the collection before inserting new documents. ### Retrieve Documents (As Vector Store for Chain/Tool) parameters * **Milvus collection**: Select or enter the Milvus Collection to use. ### Retrieve Documents (As Tool for AI Agent) parameters * **Name**: The name of the vector store. * **Description**: Explain to the LLM what this tool does. A good, specific description allows LLMs to produce expected results more often. * **Milvus Collection**: Select or enter the Milvus Collection to use. * **Limit**: Enter how many results to retrieve from the vector store. For example, set this to `10` to get the ten best results. ## Node options ### Metadata Filter --8<-- "_snippets/integrations/builtin/cluster-nodes/langchain-root-nodes/vector-store-metadata-filter.md" ### Clear Collection Available in **Insert Documents** mode. Deletes all data from the collection before inserting the new data. ## Related resources Refer to [LangChain's Milvus documentation](https://js.langchain.com/docs/integrations/vectorstores/milvus/) for more information about the service. --8<-- "_snippets/integrations/builtin/cluster-nodes/langchain-overview-link.md"