Langflow and Zilliz Cloud Integration
Langflow and Zilliz Cloud integrate to build production-ready RAG pipelines with visual workflow design and high-performance vector search, enabling teams to prototype, test, and deploy AI applications through a drag-and-drop interface backed by scalable vector storage.
Use this integration for FreeWhat is Langflow
Langflow is an open-source visual framework for building LLM-powered workflows using a drag-and-drop interface. It simplifies the creation of complex AI pipelines by providing visual components for LangChain flows, allowing users to prototype, test, and deploy AI applications without writing extensive code.
By integrating with Zilliz Cloud (fully managed Milvus), Langflow gains access to a fully managed vector database layer that handles high-performance storage and retrieval of vector embeddings, making it easy to build context-aware applications like semantic search, document Q&A, and recommendation systems at scale.
Benefits of the Langflow + Zilliz Cloud Integration
- Visual pipeline building with powerful vector search: Langflow's drag-and-drop interface lets you design complex RAG workflows visually, while Zilliz Cloud provides the high-performance vector storage and similarity search behind the scenes.
- Rapid prototyping to production: Teams can quickly prototype AI applications in Langflow's visual canvas and deploy them with Zilliz Cloud's production-grade infrastructure, reducing time from concept to deployment.
- Seamless embedding management: The integration handles the full lifecycle of vector embeddings — from generation and storage to retrieval — without requiring manual pipeline code.
- Scalable retrieval for contextual AI: Zilliz Cloud's distributed architecture ensures fast, accurate similarity search even as your knowledge base grows, keeping your Langflow-based applications responsive.
- Low-code accessibility: Developers and non-developers alike can build sophisticated RAG pipelines without writing extensive code, while still leveraging enterprise-grade vector database capabilities.
How the Integration Works
Langflow provides a visual development environment where you can design LLM workflows by connecting modular components on a canvas. It supports various data processing, embedding, and generation nodes, making it straightforward to build end-to-end AI pipelines through its intuitive drag-and-drop interface.
Zilliz Cloud serves as the vector database layer in the pipeline, storing and indexing vector embeddings generated from your documents. It provides high-performance similarity search with low latency, enabling your application to retrieve the most relevant context from large knowledge bases efficiently.
Together, Langflow and Zilliz Cloud create a complete RAG solution where Langflow orchestrates the visual workflow — from document ingestion and chunking to query processing and response generation — while Zilliz Cloud handles the critical vector storage and retrieval step. This combination allows teams to build, test, and deploy context-aware AI applications with minimal code and maximum scalability.
Step-by-Step Guide
1. Install Langflow and Start the Dashboard
Install Langflow using pip:
$ python -m pip install langflow -UOnce installed, start the Langflow dashboard:
$ python -m langflow runA dashboard will open in your browser where you can begin building your RAG workflow.
2. Create a New Vector Store RAG Project
From the Langflow dashboard, click the New Project button. In the panel that appears, select the Vector Store RAG template. This creates a pre-configured RAG pipeline with default components that you will customize for Milvus.
3. Replace the Default Vector Store with Milvus
The default template uses AstraDB as the vector store. To switch to Milvus:
- Remove the two existing AstraDB cards by clicking on them and pressing Backspace.
- Click the Vector Store option in the sidebar, select Milvus, and drag it onto the canvas. Repeat this to create two Milvus cards — one for the file ingestion workflow and one for the search workflow.
- Connect the Milvus modules to the rest of the components in the pipeline.
4. Configure Milvus Credentials
Configure the Milvus credentials for both Milvus modules. The simplest way is to use Milvus Lite by setting the Connection URI to
milvus_demo.db. If you have a self-deployed Milvus server or on Zilliz Cloud, set the Connection URI to the server endpoint and the Connection Password to the token (for Milvus, the token is concatenated<username>:<password>; for Zilliz Cloud, it is the API Key).5. Upload Knowledge and Run the Ingestion Workflow
- Upload a document through the File module on the bottom left of the canvas. This serves as the knowledge base for your RAG system.
- Press the Run button on the Milvus module at the bottom right to execute the ingestion workflow. This processes the document, generates embeddings, and stores them in the Milvus vector store.
6. Query Your RAG System
Open the Playground in Langflow and ask questions related to the document you uploaded. The system will retrieve relevant context from the Milvus vector store and generate informed responses using the LLM.
Learn More
- Building a RAG System Using Langflow with Milvus — Official Milvus tutorial for building a RAG pipeline with Langflow
- Drag, Drop, and Deploy RAG Workflows with Langflow & Milvus — Milvus blog post on building advanced RAG workflows with Langflow
- Build Your Custom RAG Pipelines with Hands-on Tutorials — Zilliz collection of RAG tutorials and hands-on guides
- Langflow Documentation — Official Langflow documentation
- Langflow GitHub Repository — Langflow source code and community resources