Building Production-Ready RAG Systems with Azure: From Basics to Advanced Techniques

Retrieval-Augmented Generation (RAG) is a technique that enhances the performance of generative AI models by integrating real-time factual information from external databases or knowledge sources. In this approach, the model first retrieves relevant documents or data based on a query and then uses that information to generate more accurate and contextually grounded responses. This method improves the model’s reliability, as it reduces hallucinations and ensures that the output is aligned with up-to-date, factual content.

Building a RAG (Retrieval Augmented Generation) system to "chat with your data" might seem straightforward at first glance. With popular LLM orchestrators like "LangChain" or "LlamaIndex", and Azure's powerful cloud services, you might think it's just a matter of vectorizing your data, indexing it in a vector database, and setting up a pipeline with a default prompt. However, the reality is more complex. Vanilla RAG implementations, while great for quick demos, often fall short in real business scenarios.

This post will explore the business imperatives and technical challenges of building a production-ready RAG system, with a focus on leveraging Azure services throughout the process.

1. Clarify the Business Value

Before diving into implementation, it's crucial to understand the business context and requirements so here are the high level bullet points to think about.

• Clarify the context: Understand your users and their primary business issues and define how the success would look like.
• Educate non-technical users: Use Azure AI Studio to create demos and explanations of AI capabilities. Get feedback on your success criteria and refine it based on this early feedback
• Understand the user journey: Map out how the RAG system will integrate into existing workflows. What and where the value would be added for the existing usecases.
• What kind of data will be Indexed: Use Azure Data Catalog to inventory and qualify your data sources and anticipate what kind of data will be indexed.

2. Understand What You're Indexing

Each modality requires distinct processing techniques to convert the data into vectors for retrieval. Here’s a common approach for combining multimodal data (text, tables, and images) with Azure services:

• Text data is chunked and embedded using Azure's Cognitive Search or Azure OpenAI embedding models. These embeddings are then stored in Azure Cognitive Search or Azure Cosmos DB for fast retrieval.
• Tables are summarized with Azure OpenAI's GPT-3.5/4 models, and the descriptions are embedded for indexing. When retrieved, tables can be presented in their raw tabular format, stored in Azure SQL Database or Azure Table Storage, depending on the structure.
• Code snippets are chunked carefully and embedded using Azure OpenAI embeddings. They can be stored and retrieved via Azure Cognitive Search or other vector databases like Azure Cosmos DB.
• Images are processed into embeddings using Azure Cognitive Services or multi-modal models like CLIP (Contrastive Language–Image Pretraining) via Azure OpenAI Vision models. The images and embeddings can be stored in Azure Blob Storage and indexed for retrieval.

3. Improve Chunk Quality

• Adjust Chunk Size Based on Content: There’s no universal rule for chunk size. If your documents are long and express a single idea in lengthy paragraphs, the chunk size should be larger. In contrast, documents written in bullet points require smaller chunks.
• No Chunking Needed for Short Data: Some data, like support tickets, are short and self-contained. In such cases, chunking isn’t necessary.
• Semantic Chunking: This method generates chunks based on semantic relevance, making the chunks more meaningful. While this approach is time-consuming, as it relies on embedding models, it often produces better results.

4. Improve Pre-retrieval

Enhance your query processing with these Azure-powered techniques:

• Query Rewriting: Use Azure OpenAI Service to rephrase and expand user queries for better clarity and precision. By rewriting ambiguous or incomplete queries, you can ensure that they retrieve more relevant data.
• Query Augmentation: Leverage Azure Logic Apps to build automated workflows that combine original queries with preliminary outputs. This can involve enriching queries with additional context or data, such as user history or document metadata, to refine the results before submission to the LLM.

These Azure services can significantly enhance query processing, making it more dynamic, contextually rich, and optimized for better retrieval in your RAG system.

5. Improve Retrieval

Optimize your retrieval process with these Azure-specific enhancements:

• Hybrid Search: Utilize Azure Cognitive Search's built-in hybrid search capabilities. This approach allows you to blend traditional keyword-based indexing with advanced vector-based retrieval powered by Azure OpenAI embeddings. By using both methods, hybrid search enhances the precision of results by balancing relevance from keywords with deeper semantic meaning, offering more comprehensive search results.
• Filter on metadata: Use Azure Cosmos DB for flexible and scalable metadata storage and querying. Store document metadata (like tags, authors, dates, and categories) in Azure Cosmos DB, which allows for flexible indexing and querying. Cosmos DB supports rich query capabilities with filters on attributes, making it easy to isolate specific documents based on metadata properties.

Next Steps

Building a production-ready RAG system is an ongoing process. Once your Azure-powered RAG system is deployed:

• Serve it through Azure API Management or Azure App Service.
• Monitor performance and costs using Azure Monitor and Azure Cost Management.
• Set up regular updates using Azure Data Factory for data ingestion and Azure DevOps for CI/CD pipelines.

By leveraging Azure's comprehensive suite of AI and cloud services, you can build, deploy, and maintain a robust RAG system that delivers real business value.