In the era of data-driven decision-making, accessing and understanding complex databases efficiently is more crucial than ever. This unique blend allows you to "talk" to your databases in natural language, simplifying data interaction. It combines the Retrieve, Answer, Generate (RAG) pattern with Azure AI's search, powered by the advanced reasoning of AOAI GPT-4 models.
The RAG Pattern Project is engineered as a quick-start template to accelerate the development of AI-driven database querying systems. It harnesses the power of Azure AI Search to significantly enhance the relevance of search results across databases. Simultaneously, the GPT-4 Turing model acts as a sophisticated reasoning engine, capable of understanding complex queries, generating accurate SQL commands, and providing insightful answers drawn directly from your data stores. 💡
- Intelligent Query Generation: Automatically translate natural language queries into precise SQL commands using GPT-4 Turing's generative AI capabilities.
- Enhanced Search Relevance: Leverage Azure AI Search to sift through databases, ensuring that search results are as relevant and accurate as possible.
- Cross-Domain Applicability: Whether you're analyzing financial records, managing inventory, or conducting academic research, our solution is versatile enough to cater to a wide range of domains and applications.
Please make sure you have met all the prerequisites for this project. A detailed guide on how to set up your environment and get ready to run all the notebooks and code in this repository can be found in the REQUIREMENTS.md file. Please follow the instructions there to ensure a smooth exprience.
This project leverages GitHub Actions for automating our DevOps lifecycle. More #TODO
You can view the configuration and status of our GitHub Actions workflows in the .github/workflows directory and the "Actions" tab of our GitHub repository, respectively.
Eager to make significant contributions? Our CONTRIBUTING guide is your essential resource! It lays out a clear path.