# RAG As A Service

> 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.

- Source: https://doutorai.pt/en/apps/rag-as-a-service
- Category: RAG and Knowledge
- Difficulty: Intermediate
- Models: Claude
- Frameworks: -
- Runs locally?: Needs cloud APIs
- Required keys: None
- License: Apache-2.0
- Site: Doutor AI (https://doutorai.pt/en) - AI tools for small businesses

## 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
```bash
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/rag_tutorials/rag-as-a-service
```

2. Install the required dependencies:

```bash
pip install -r requirements.txt
```

3. Get your Anthropic API and Ragie API Key

- Sign up for an [Anthropic account](https://console.anthropic.com/) and get your API key
- Sign up for an [Ragie account](https://www.ragie.ai/) and get your API key

4. Run the Streamlit app
```bash
streamlit run rag_app.py
```

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Content by Doutor AI (https://doutorai.pt/en). When quoting, please cite the source: https://doutorai.pt/en/apps/rag-as-a-service