Introduction to Retrieval-Augmented Generation (RAG) with LangFlow - Part 2

What Is LangFlow?

LangFlow is an open-source, Python-powered visual framework for building AI applications, including multi-agent systems and Retrieval-Augmented Generation (RAG) workflows. Its intuitive drag-and-drop interface allows users to prototype and deploy complex AI solutions without extensive coding knowledge.

Key Features

• Visual Flow Builder: Create AI workflows by connecting components like prompts, language models, and data sources.
• Component-Based Architecture: Utilize a variety of components, each with defined inputs, outputs, and parameters, to build modular and reusable workflows.
• Playground: Test and interact with your flows in real-time, allowing for immediate feedback and iteration.
• Publish Pane: Generate code templates to integrate your flows into external applications seamlessly.
• Logs and Monitoring: Access detailed execution logs for debugging and performance analysis.

Getting Started with LangFlow

Installation: Follow the installation guide to set up LangFlow on your system. (only required if you locally install it).

Quickstart: Begin with the Quickstart tutorial to build a basic chatbot application.

Building Flows:

• Create a New Flow: From the dashboard, click “New Flow” and select “Blank Flow” or a starter template. 
• Add Components: Drag components from the sidebar into the workspace. Start with 'Chat Input', 'OpenAI' and 'Chat Output'.
• Connect Components: Link components by dragging from an output port to an input port. This defines the data flow.
• Configure Components: Click on a component to set its parameters and inputs. Especially, the OpenAI API key needs to be defined.
• Playground: With the Playground (top right corner) you are able to test the designed flow.

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