adrianfz / GPT-RAG-2023

Sharing the learning along the way we been gathering to enable Azure OpenAI at scale in a secure manner. GPT-RAG core is a Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.

The RAG pattern enables businesses to use the reasoning capabilities of LLMs, using their existing models to process and generate responses based on new data. RAG facilitates periodic data updates without the need for fine-tuning, thereby streamlining the integration of LLMs into businesses.

The Enterprise RAG Solution Accelerator (GPT-RAG) offers a robust architecture tailored for enterprise-grade deployment of the RAG pattern. It ensures grounded responses and is built on Zero-trust security and Responsible AI, ensuring availability, scalability, and auditability. Ideal for organizations transitioning from exploration and PoC stages to full-scale production and MVPs.

Components

• Data ingestion Optimizes data preparation for Azure OpenAI.

• Orchestrator The system's dynamic backbone ensuring scalability and a consistent user experience.

• App Front-End Built with Azure App Services and the Backend for Front-End pattern, offers a smooth and scalable user interface.

Concepts & Architecture

• Enterprise RAG +Prompt Engineering+Finetuning+Train (Video in Spanish)

• RAG Pattern: What and Why?

• Solution Architecture Overview

Deployment Instructions

To deploy Enterprise RAG and have your solution up and running you just need to execute the next four steps using Azure Developer CLI (azd) in a terminal:

1 Download the Repository:

azd init -t azure/gpt-rag

2 Login to Azure:

azd auth login

3 Start Building the infrastructure and components deployment:

azd up

4 Add source documents to object storage

Upload your documents to the documents folder in the storage account which name starts with strag.

Notes:

• For a rapid deployment using the default settings, just adhere to the previously outlined instructions.

• For a customized deployment, refer to the Custom Deployment section to understand the customization options before executing the previously mentioned steps.

Additional Resources

Pricing Estimation

• Pricing Model

Governance

• Governance Model

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.