RagUI

RagUI is a Python framework that transforms your RAG (Retrieval Augmented Generation) pipelines into production-ready web applications. It's framework-agnostic and works seamlessly with popular LLM frameworks like LangChain and LlamaIndex.

⚠️ Development Status

WARNING: RagUI is undergoing rapid development and frequent changes. For the latest features and fixes, we recommend installing directly from the GitHub repository rather than PyPI.

Features

• Instant Web Interface: Get a polished chat UI for your RAG pipeline with zero frontend code
• Production Ready: Built on FastAPI and async Python for high performance
• Framework Agnostic: Works with LangChain, LlamaIndex, or your custom LLM implementation
• Fast Onboarding: Adapt any existing RAG pipeline with minimal changes

Coming Soon:

• 💾 Conversation History & Analytics
• 🔄 Feedback Collection & Fine-tuning
• 🔒 Authentication & User Management
• 📊 Admin Dashboard
• 💰 Subscription Management

Alternatives

Feature	RagUI	Streamlit	Gradio
Production Focus	✅ Built on FastAPI	⚠️ Possible but manual setup	⚠️ Commonly used for demos
RAG-Centric	✅ Purpose-built	❌ General-purpose	❌ General-purpose
Multiple Pipelines	✅ Out of the box	❌ Must wire manually	❌ Must wire manually
Conversation Persistence	🚧 On roadmap	❌ Manual DB integration	❌ Manual DB integration
Feedback Collection	🚧 On roadmap	❌ Custom coding	❌ Custom coding
User Authentication	🚧 On roadmap	❌ No built-in	❌ No built-in
Usage Metrics	🚧 On roadmap	❌ Need external services	❌ Need external services
Admin Dashboard	🚧 On roadmap	❌ Custom or third-party	❌ Custom or third-party

Quick Start

Install from GitHub repository (recommended for latest features during initial development):

pip install git+https://github.com/spyrosavl/ragui.git

Create a pipeline (main.py):

from ragui import RagUI
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

ragui = RagUI()

# Define your LangChain components
prompt = ChatPromptTemplate.from_messages([
    ("user", "Context: {context}\nQuestion: {user_message}")
])
chain = prompt | ChatOpenAI() | StrOutputParser()

@ragui.pipeline(
    title="RAG Pipeline",
    disclaimer=None,
    info=None,
    sample_questions=["What is RAG?", "How does this work?"],
    theme="light"
)
async def rag_pipeline(query, message_history, user_context):
    async for chunk in chain.astream({
        "context": "Your retrieved context here",
        "user_message": query
    }):
        yield chunk

Launch your application:

ragui main.py

Visit http://localhost:8000 to interact with your pipeline.

For a more sophisticated example with multiple themed pipelines, check out our example implementation.

Why RagUI?

• Save Development Time: Focus on your RAG logic instead of building UI/infrastructure
• Production Ready: Built with production use-cases in mind
• Future Proof: As we add features, your application automatically gets upgrades
• Full Control: Customize and extend any aspect of the framework

Roadmap

Phase 1: Core Functionality (MVP)

• Simple UI for text input/output

  • The essential starting point: a user-friendly interface for sending and receiving messages.

• Support for Multiple Pipelines

  • Allow different RAG flows or LLM backends within a single deployment.

• Messages Persistence (Database)

  • Store chat data for future reference, analytics, and advanced features.

• Conversation History

  • Enable users to review previous messages and maintain context across sessions.

• Feedback Collection

  • Gather user feedback for iterative improvements and model fine-tuning.

Phase 2: Feature Expansion

• Support for File Input

  • Accept documents, PDFs, or other file types as context sources for RAG.

• Support for Audio Input

  • Expand interaction possibilities by handling voice-based queries.

Phase 3: Production Hardening

• User Authentication

  • Secure access control, user profiles, and personalized interactions.

• Usage Metrics

  • Track requests, errors, response times, and user engagement to guide scaling.

• Admin Dashboard

  • Centralize management of pipelines, users, and overall system health.

Phase 4: Monetization & Advanced Integrations

• Subscription Management

  • Implement billing, tiered plans, and payment handling for monetized deployments.

• Add to Website Support (Chat Bubble)

  • Easily embed RagUI as a widget on external websites for broader audience reach.