GPT-RAG offers a robust, enterprise-grade architecture for RAG leveraging Azure OpenAI. It ensures scalability, security, and reliability for integrating LLMs into business workflows.
GPT-RAG provides an enterprise-grade reference architecture for the production deployment of LLMs using the RAG pattern on Azure OpenAI. 🏭
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🔒 Robust security - Zero trust principles for confidentiality and integrity are baked in.
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⚡️ Scalability - Built on a well-architected framework to auto-scale for fluctuating demands.
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📈 Observability - Monitoring, analytics, and logs for continuity and optimization.
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🤖 Responsible AI - Safeguards like grounding mechanisms guidance for reliability.
The goal is to streamline the integration of reasoning-capable LLMs into business workflows without compromising governance, availability, or audit needs required in enterprise settings. GPT-RAG is an accelerated path to leveraging LLMs confidently in the enterprise and deploying a next-gen search engine, analyzing documents, or building QA bots.
- 🔐 Security - Zero trust architecture ensures confidentiality for sensitive enterprise data.
- ⚙️ Customization - Tailored modules allow incremental complexity as needs evolve.
- 🚀 Productivity - Quick setup for complex LLM workflows speeds innovation cycles 10x.
- 💰 Cost - Optimized data preparation reduces unnecessary Azure OpenAI requests.
- 🎚️ Control - Guardrails like grounding mechanics and responsible AI enforce quality.
In summary, GPT-RAG simplifies secure and governable large language model deployment for enterprises with the versatility to suit complex custom needs. With accelerated leverage, engineers amplify the impact on innovation.
- 👷🏽♀️ Builders: Gonzalo Becerra, Martin Sciarrillo, Víctor Hugo Vazquez B, Paulo Lacerda
- 👩🏽💼 Builders on LinkedIn: https://www.linkedin.com/in/gonzalobecerra, https://www.linkedin.com/in/sciarrillo, https://www.linkedin.com/in/vhvb1989/, https://www.linkedin.com/in/paulolacerda
- 👩🏽🏭 Builders on X: https://twitter.com/Acatincho
- 👩🏽💻 Contributors: 9
- 💫 GitHub Stars: 134
- 🍴 Forks: 28
- 👁️ Watch: 8
- 🪪 License: MIT
- 🔗 Links: Below 👇🏽
- GitHub Repository: https://github.com/Azure/GPT-RAG/
- Official Website: https://azure.microsoft.com/en-us/products/ai-services/openai-service/
- Profile in The AI Engineer: https://github.com/theaiengineer/awesome-opensource-ai-engineering/blob/main/libraries/gpt-rag.md
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