Generative AI Technical Patterns: Chat with Your Data

Generative AI Technical Patterns: Chat with Your Data

The "Chat with Your Data" reference architecture is a modern, transformative solution designed for businesses that need to streamline their knowledge retrieval and synthesis. It empowers users to engage with their data repositories in an intuitive conversational manner, leveraging the latest in AI-driven insights. This architecture is ideal for companies looking to enhance their customer service, decision-making, and overall data accessibility. With a system built on the Microsoft Azure platform, organizations can confidently offer a sophisticated, AI-enhanced experience that allows end-users to ask questions and receive information as if they were chatting with a knowledgeable human assistant.

Microsoft's CSA CTO Office actively maintains a one click deploy solution accelerator for this reference architecture. 
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Data Integration and Management
Data Layer: The documents for topics are stored in scalable and secure cloud storage. Data may be stored in a variety of data services based on the data type, scale, and expected access patterns. These data services include Azure Database for PostgreSQLAzure CosmosDBAzure SQL Database, and Storage Accounts.  A guide to selecting between these services are covered in this blog.
Admin UI Streamlit (Python): A user-friendly admin interface is crucial. This component allows administrators to easily manage document uploads and configure settings without deep technical knowledge, and users to interact with the service. facilitating a smoother operation.
Function Apps: These are the workhorses that process the uploaded documents. They read files, process them into chunks, create embeddings for machine learning models, and store the results. This ensures that data is not only stored but also prepared for efficient retrieval and analysis.
Azure AI Document Intelligence: This service uses AI to understand and extract information from documents. It improves over time, ensuring that businesses can extract layout and content from their documents with increasing accuracy.
Answering/End User
Chat Frontend (JS) + Backend (Python): The user interface is designed for simplicity and ease of use, with a responsive design that works across various devices. This ensures that end-users can access the chat service conveniently.
Speech Service: By incorporating speech-to-text services, the architecture breaks down barriers for users who prefer to communicate verbally, enhancing accessibility and user satisfaction.
Azure AI Search: This is where the magic happens. Once the documents are processed, they are stored here with their embeddings, allowing for quick and intelligent retrieval based on user queries.
Azure Open AI Service: Finally, the reasoning engine. It takes chunks of text processed by Azure AI Search and applies advanced language models to generate human-like responses, completing the conversational experience.

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