Deploy Langflow — Visual AI Application Builder

What Is Langflow?

Langflow is a visual framework for building AI applications. It provides a drag-and-drop interface on top of LangChain, letting you create complex AI workflows without writing code. Connect LLMs, vector databases, APIs, and data sources in a visual graph.

What Can You Build?

• RAG applications — Upload documents and ask questions about them
• AI chatbots — Custom chatbots with specific knowledge bases
• Data pipelines — Process and analyze data with AI at each step
• Content generators — Automated content creation with multiple AI steps
• AI agents — Autonomous agents that can browse the web, execute code, and use tools

Deploy on Panelica

Go to Docker → App Templates and deploy Langflow. The container starts with the web UI ready to use.

Building a RAG Pipeline

Here is a step-by-step example of building a document Q&A system:

1. Add a File Loader — Drop PDF, TXT, or DOCX files
2. Add a Text Splitter — Chunks documents into manageable pieces
3. Add an Embeddings node — Converts text chunks into vector representations
4. Add a Vector Store — Stores embeddings for fast similarity search
5. Add a Retriever — Finds relevant chunks for a given question
6. Add an LLM node — Generates answers using retrieved context
7. Add a Chat Interface — Provides a user-friendly chat window

Connect these nodes in the visual editor, and you have a working document Q&A system.

Connecting to Ollama

If you have Ollama running on the same server (see our Ollama guide), you can connect Langflow to it for completely local AI processing. Select the Ollama LLM node and point it to your Ollama instance.

Resource Requirements

Summary

Langflow democratizes AI application development. Instead of writing complex Python code, you visually connect components to build powerful AI workflows. Deploy it on Panelica alongside Ollama for a fully self-hosted AI stack with zero external dependencies.