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Add generative AI capabilities to your web apps, leveraging vector databases and caching

Science & Technology

Introduction

In today's digital landscape, enhancing web applications with generative AI capabilities opens up new dimensions for user interaction and data handling. In this article, we'll explore how to integrate a chat assistant powered by Azure OpenAI APIs into a simple shopping cart application, while leveraging vector databases and caching mechanisms to boost performance.

Application Architecture Overview

Our application under review is a traditional shopping cart application, referred to as the "devshop." The primary goal is to create a chat assistant to provide users with helpful information about products. The integration of Azure's chat completion API is essential for this project, and we have a couple of enhancements planned:

  1. Caching Responses: We will store both questions and answers in a vector database running alongside our main application. This "vector cache" enables quicker responses to frequently asked questions, reducing the need to call the OpenAI service repeatedly.

  2. SQL Integration: The chat assistant will fetch data solely from our SQL database. To achieve this, we have created a SQL index and utilized Azure AI Search, which allows the assistant to retrieve product information effectively.

Demo Walkthrough

To illustrate the implementation, let's examine the functioning of our chat assistant. When a user selects a product, they can click a button to chat with the AI assistant. Users can ask questions, for instance, "Tell me more about the formal player."

Two outcomes are possible:

  • If the assistant locates the answer in the vector cache, it retrieves the response swiftly.
  • If not, it queries the Azure AI Search for the needed information.

In the demo, we have pre-populated the vector cache with responses, so questions such as "How many pockets does it have?" will return quick results from the cache.

Behind the Scenes: Code Implementation

The core functionality resides in the get response method, where several configurations and initializations are performed:

  1. Azure AI Search Configuration: The appropriate endpoint and SQL index settings are initialized.

  2. Query Execution: Upon receiving a user message, the system queries the SQL index using Azure AI Search to retrieve relevant product details (name, ID, description, etc.).

  3. Vector Database Setup: A vector database, using Quadrant, serves as a cache. Notably, Quadrant runs in a "sidecar" alongside our main application, allowing direct communication via localhost.

  4. Semantic Kernel Integration: We use the open-source semantic kernel SDK, which connects to various large language models (LLMs) and handles embedding generation for the data before it is stored in the vector database.

  5. Session State Management: Chat history is stored in the session state, allowing for a seamless user experience. In cases of load balancing across multiple instances, a distributed cache such as Azure Redis Cache would be ideal.

  6. Prompt Template Creation: A prompt template is prepared to send to the chat completion API, which includes model specifications (in this case, the GPT-3.5 Turbo model).

  7. Response Generation: The user input is processed to first check the vector cache and, if required, fall back to Azure AI Search, thus ensuring that the response is either cached or freshly queried.

  8. Result Presentation: Finally, the response is displayed on the web page while also updating the session state and storing it back in the vector database for future queries.

Conclusion

Integrating generative AI capabilities into web applications can significantly enhance user interactions by providing intelligent, context-aware responses. By leveraging vector databases and caching strategies, such integrations can operate efficiently, providing users with a seamless experience while optimizing resource usage.

Keyword

  • Generative AI
  • Web Applications
  • Azure OpenAI APIs
  • Chat Assistant
  • Vector Database
  • Caching Mechanism
  • SQL Integration
  • Semantic Kernel
  • AI Search
  • Performance Optimization

FAQ

Q1: What is the primary purpose of integrating a chat assistant into a web application?
A1: The chat assistant enhances user interaction by providing immediate, context-aware responses to product inquiries, thus improving the overall shopping experience.

Q2: How does the vector database improve performance?
A2: The vector database caches previous questions and answers, enabling faster retrieval of information for commonly asked questions, thus reducing the need to query the OpenAI API for each interaction.

Q3: What technology is used for searching product details in this application?
A3: The application utilizes Azure AI Search with a SQL index to efficiently retrieve product information based on user queries.

Q4: Can chat history be maintained for user sessions?
A4: Yes, chat history is maintained in session state, thereby allowing users to review their previous interactions seamlessly.

Q5: How does the semantic kernel facilitate the integration of different large language models?
A5: The semantic kernel is an open-source SDK that allows for flexible connections to different LLMs, enabling easy switching between models without extensive code changes.