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RAG and Knowledge ·Intermediate

RAG As A Service

About this app

Build and deploy a production-ready Retrieval-Augmented Generation (RAG) service using Claude 3.5 Sonnet and Ragie.ai. This implementation allows you to create a document querying system with a user-friendly Streamlit interface in less than 50 lines of Python code.

  • Complete open-source code
  • Step-by-step tutorial

Technical sheet

Category
RAG and Knowledge
Difficulty
Intermediate
Models
Claude
Frameworks
-
Runs locally?
Needs cloud APIs
Required keys
None

License Apache-2.0 · content curated and maintained by Doutor AI.

Full tutorial

🖇️ RAG-as-a-Service with Claude 3.5 Sonnet

Build and deploy a production-ready Retrieval-Augmented Generation (RAG) service using Claude 3.5 Sonnet and Ragie.ai. This implementation allows you to create a document querying system with a user-friendly Streamlit interface in less than 50 lines of Python code.

Features

  • Production-ready RAG pipeline
  • Integration with Claude 3.5 Sonnet for response generation
  • Document upload from URLs
  • Real-time document querying
  • Support for both fast and accurate document processing modes

How to get Started?

  1. Clone the GitHub repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/rag_tutorials/rag-as-a-service
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Get your Anthropic API and Ragie API Key
  1. Run the Streamlit app
streamlit run rag_app.py

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