RAG using Azure Search
This post explain the RAG pattern and its implementation using the Azure SEarch as a vector store. It also shows how to leverage the different search modes in Azure Search
RAG using Azure Search
What is RAG
RAG stands for Retrieve, Answer and Generate. It is a pattern used in AI to generate responses to user queries. The RAG model is a powerful tool that can be used to generate responses to user queries by retrieving relevant information from a knowledge base, answering the query based on the retrieved information, and generating a response that is both informative and contextually relevant.
What is Text Generation
About Azure Search
Azure Search is a cloud-based search service that provides powerful indexing and querying capabilities. It is essential for:
- Efficient Data Retrieval: Azure Search enables fast and efficient retrieval of data from large datasets.
- Advanced Querying: With support for complex queries, Azure Search allows users to perform sophisticated searches and filter results based on various criteria.
- Integration with Other Azure Services: Azure Search seamlessly integrates with other Azure services, such as Azure Cognitive Services and Azure Machine Learning, to provide a comprehensive data processing solution.
- Skill Set: Azure Search is a powerful tool that allows you customized the retrival, extraction, transformation of content by using skill pipelines.
This post is licensed under CC BY 4.0 by the author.