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https://github.com/Shubhamsaboo/awesome-llm-apps.git
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34 lines
1.1 KiB
Markdown
34 lines
1.1 KiB
Markdown
## 🖇️ RAG-as-a-Service with Claude 3.5 Sonnet
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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.
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### Features
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- Production-ready RAG pipeline
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- Integration with Claude 3.5 Sonnet for response generation
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- Document upload from URLs
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- Real-time document querying
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- Support for both fast and accurate document processing modes
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### How to get Started?
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd awesome-llm-apps/rag_tutorials/rag-as-a-service
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```
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Get your Anthropic API and Ragie API Key
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- Sign up for an [Anthropic account](https://console.anthropic.com/) and get your API key
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- Sign up for an [Ragie account](https://www.ragie.ai/) and get your API key
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4. Run the Streamlit app
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```bash
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streamlit run rag_app.py
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``` |