publish a new blog: langflow+milvus (#479)

* publish a new blog: langflow+milvus

* update serial number

* Update drag-drop-and-deploy-how-to-build-rag-workflows-with-langflow-and-milvus.md

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Co-authored-by: Fendy <[email protected]>

Author: ChelseyZ

blog/en/drag-drop-and-deploy-how-to-build-rag-workflows-with-langflow-and-milvus.md

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+---
+id: drag-drop-and-deploy-how-to-build-rag-workflows-with-langflow-and-milvus.md
+title: >
+ Drag, Drop, and Deploy: How to Build RAG Workflows with Langflow and Milvus
+author: Min Yin
+date: 2025-10-30
+cover: assets.zilliz.com/langflow_milvus_cover_9f75a11f90.png
+recommend: false
+publishToMedium: true
+tags: Milvus, vector database
+meta_keywords: Langflow, Milvus, RAG, AI workflow
+meta_title: Drag, Drop, and Deploy RAG Workflows with Langflow & Milvus
+desc: Learn how to build visual RAG workflows using Langflow and Milvus. Drag, drop, and deploy context-aware AI apps in minutes—no coding required.
+origin: https://milvus.io/blog/drag-drop-and-deploy-how-to-build-rag-workflows-with-langflow-and-milvus.md
+---
+
+Building an AI workflow often feels harder than it should. Between writing glue code, debugging API calls, and managing data pipelines, the process can eat up hours before you even see results. [**Langflow**](https://www.langflow.org/) and [**Milvus**](https://milvus.io/) simplify this dramatically — giving you a code-light way to design, test, and deploy retrieval-augmented generation (RAG) workflows in minutes, not days.
+
+**Langflow** offers a clean, drag-and-drop interface that feels more like sketching ideas on a whiteboard than coding. You can visually connect language models, data sources, and external tools to define your workflow logic — all without touching a line of boilerplate code.
+
+Paired with **Milvus**, the open-source vector database that gives LLMs long-term memory and contextual understanding, the two form a complete environment for production-grade RAG. Milvus efficiently stores and retrieves embeddings from your enterprise or domain-specific data, allowing LLMs to generate answers that are grounded, accurate, and context-aware.
+
+In this guide, we’ll walk through how to combine Langflow and Milvus to build an advanced RAG workflow — all through a few drags, drops, and clicks.
+
+
+## What is Langflow? 
+
+Before going through the RAG demo, let’s learn what Langflow is and what it can do. 
+
+Langflow is an open-source, Python-based framework that makes it easier to build and experiment with AI applications. It supports key AI capabilities such as agents and the Model Context Protocol (MCP), giving developers and non-developers alike a flexible foundation for creating intelligent systems. 
+
+At its core, Langflow provides a visual editor. You can drag, drop, and connect different resources to design complete applications that combine models, tools, and data sources. When you export a workflow, Langflow automatically generates a file named `FLOW_NAME.json` on your local machine. This file records all the nodes, edges, and metadata that describe your flow, allowing you to version-control, share, and reproduce projects easily across teams.
+
+![](https://assets.zilliz.com/Langflow_s_visual_editor_cd553ad4ad.png)
+
+Behind the scenes, a Python-based runtime engine executes the flow. It orchestrates LLMs, tools, retrieval modules, and routing logic — managing data flow, state, and error handling to ensure smooth execution from start to finish.
+
+Langflow also includes a rich component library with prebuilt adapters for popular LLMs and vector databases — including [Milvus](https://milvus.io/). You can extend this further by creating custom Python components for specialized use cases. For testing and optimization, Langflow offers step-by-step execution, a Playground for rapid testing, and integrations with LangSmith and Langfuse for monitoring, debugging, and replaying workflows end-to-end.
+
+
+## Hands-on Demo: How to Build a RAG Workflow with Langflow and Milvus
+
+Building on Langflow’s architecture, Milvus can serve as the vector database that manages embeddings and retrieves private enterprise data or domain-specific knowledge.
+
+In this demo, we’ll use Langflow’s Vector Store RAG template to demonstrate how to integrate Milvus and build a vector index from local data, enabling efficient, context-enhanced question answering.
+
+![](https://assets.zilliz.com/data_processing_flow_289a9376c9.webp)
+
+### Prerequisites：
+
+1.Python 3.11 (or Conda)
+
+2.uv
+
+3.Docker & Docker Compose
+
+4.OpenAI key
+
+
+### Step 1. Deploy Milvus Vector Database
+
+Download the deployment files.
+```
+wget <https://github.com/Milvus-io/Milvus/releases/download/v2.5.12/Milvus-standalone-docker-compose.yml> -O docker-compose.yml
+```
+Start the Milvus service.
+```
+docker-compose up -d
+```
+```
+docker-compose ps -a
+```
+
+![](https://assets.zilliz.com/start_milvus_service_860353ed55.webp)
+
+### Step 2. Create a Python Virtual Environment
+```
+conda create -n langflow
+# activate langflow and launch it
+conda activate langflow
+```
+### Step 3. Install the Latest Packages
+```
+pip install langflow -U
+```
+### Step 4. Launch Langflow
+```
+uv run langflow run
+```
+Visit Langflow.
+```
+<http://127.0.0.1:7860>
+```
+### Step 5. Configure the RAG Template
+Select the Vector Store RAG template in Langflow.
+
+![](https://assets.zilliz.com/rag1_fcb0d1c3c5.webp)
+
+![](https://assets.zilliz.com/rag2_f750e10a41.webp)
+
+Choose Milvus as your default vector database.
+
+![](https://assets.zilliz.com/vdb_milvus_925c6ce846.webp)
+
+In the left panel, search for “Milvus” and add it to your flow.
+
+![](https://assets.zilliz.com/add_milvus1_862d14d0d0.webp)
+
+![](https://assets.zilliz.com/add_milvus2_4e3d6aacda.webp)
+
+Configure Milvus connection details. Leave other options as the default for now.
+
+![](https://assets.zilliz.com/connect1_a27d3e4f43.webp)
+
+![](https://assets.zilliz.com/connect2_d8421c1525.webp)
+
+Add your OpenAI API key to the relevant node.
+
+![](https://assets.zilliz.com/openai_key_7a6596868c.webp)
+
+![](https://assets.zilliz.com/openai_key2_4753bfb4d0.webp)
+
+### Step 6. Prepare Test Data
+Note: Use the official FAQ for Milvus 2.6 as the test data.
+```
+https://github.com/milvus-io/milvus-docs/blob/v2.6.x/site/en/faq/product_faq.md
+```
+### Step 7. Phase One Testing
+Upload your dataset and ingest it into Milvus.
+Note: Langflow then converts your text into vector representations. You must upload at least two datasets, or the embedding process will fail. This is a known bug in Langflow’s current node implementation.
+
+![](https://assets.zilliz.com/ingest_7b804d870a.webp)
+
+![](https://assets.zilliz.com/ingest2_fc7f1e4d9a.webp)
+
+Check the status of your nodes.
+
+![](https://assets.zilliz.com/test_48e02d48ca.webp)
+
+### Step 8. Phase Two Testing
+
+![](https://assets.zilliz.com/ingest_7b804d870a.webp)
+
+### Step 9. Run the Full RAG Workflow
+
+![](https://assets.zilliz.com/full_flow1_5b4f4962f5.webp)
+
+![](https://assets.zilliz.com/full_flow2_535c722a3d.webp)
+
+## Conclusion
+
+Building AI workflows doesn’t have to be complicated. Langflow + Milvus makes it fast, visual, and code-light — a simple way to enhance RAG without heavy engineering effort.
+
+Langflow’s drag-and-drop interface makes it a suitable choice for teaching, workshops, or live demos, where you need to demonstrate how AI systems work in a clear and interactive manner. For teams seeking to integrate intuitive workflow design with enterprise-grade vector retrieval, combining Langflow’s simplicity with Milvus’s high-performance search delivers both flexibility and power.
+
+👉 Start building smarter RAG workflows with [Milvus](https://milvus.io/) today.
+
+Have questions or want a deep dive on any feature? Join our[ Discord channel](https://discord.com/invite/8uyFbECzPX) or file issues on[ GitHub](https://github.com/milvus-io/milvus). You can also book a 20-minute one-on-one session to get insights, guidance, and answers to your questions through[ Milvus Office Hours](https://milvus.io/blog/join-milvus-office-hours-to-get-support-from-vectordb-experts.md).