AI Agent Inventory Search: Implementing Product Filters

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Hey guys! So, we're diving into a cool feature today: implementing a product filter on your inventory page based on what your AI agent is doing. Imagine this: you've got an AI agent helping users search for products. When a user asks something like, "What products do you have that are…[specific criteria]?", and the agent responds with a list – say, Products A, B, and C – we want the inventory page to magically filter and display ONLY those products: A, B, and C. Let's break down how to make this happen, keeping it simple and effective.

The Goal: Dynamic Filtering Based on AI Agent Responses

The core idea is dynamic filtering. The inventory page should react in real-time to the AI agent's search results. If the agent identifies specific products as relevant, the filter kicks in, and the page updates instantly to show only those items. If the AI agent provides different sets of products based on user commands, the filter will automatically update too. We can achieve this filtering by using API calls, which can be triggered by the user command.

Let's say a user initially asks, "What items do you have that are green?" The AI agent, after consulting its knowledge base, might respond with, "We have Products A, B, and C that fit that description." Our inventory page should then filter and show only A, B, and C. Now, if the user then adds a follow-up like, "Okay, and which of those are also under $50?" and the agent replies with "Based on that, B and D are your best bets," the filter should immediately update to display just products B and D. Pretty slick, right?

This is the ideal user experience – super intuitive and helpful. This is a dynamic system, responding immediately to the AI's analysis and suggestions. It keeps users focused on the products they actually want to see. It will significantly improve the user experience by making product discovery more efficient and targeted.

Filtering Mechanism:

The filtering mechanism should avoid common pitfalls, such as incorrect filtering results, and keep data accurate.

Core Requirements and Considerations:

  • AI Agent Interaction: The system needs to understand the AI agent's responses. This involves parsing the agent's output to identify the products it has selected.
  • API Integration: The inventory page must have the ability to interact with the backend API and get data regarding the items that the AI is listing.
  • Real-time Updates: The page should update the product listings dynamically based on the AI's responses. No manual refreshes!

Exception: Cart-Related Commands

One crucial point: We don't want this filtering to happen when the user is doing something that's not an inventory search, like checking their cart. If the agent gets the user's intention, which is to see their shopping cart, we want to bypass the filtering. So, we need a way to distinguish between a request to show available products and a request to see cart contents. This will be achieved via checking if the AI is working with inventory data.

Technical Implementation: Azure AI Foundry Agent and OpenAPI 3.0

Alright, let's get into the techie stuff. This is where things get really interesting. We're going to use the Azure AI Foundry Agent, which is a powerful tool for this. Luckily, this agent can interface with the back-end APIs.

Utilizing Azure AI Foundry Agent

The AI agent is the core component of the inventory search functionality. The agent serves as an intermediary between the user's queries and the backend APIs. When a user interacts with the agent, the agent processes the user's query, understands the intent, and selects the right tools. For our scenario, the tool will be the backend API, as the agent will filter and display products accordingly.

To identify the right products, the agent uses OpenAPI 3.0.

API Call Determination

This is where the magic happens! We need to figure out if the AI agent is doing an inventory search or something else. The good news is that the Azure AI Foundry Agent might give us the data we need. We should be able to check which API the agent is calling.

So, if the agent calls the inventory search API, we know it's time to filter. If it's calling the cart API, or any other API, we skip the filtering part. The agent knows which API call to make based on OpenAPI 3.0 data. This means that the backend APIs must align with our OpenAPI 3.0 specification. The API response will then be mapped onto the front-end products.

OpenAPI 3.0 Integration

We have to configure a proper OpenAPI 3.0 specification, so the AI agent knows which back-end APIs to call. This includes:

  • Swagger.json Upload: The API definitions are provided to the Azure AI Foundry Agent by uploading a swagger.json file. This provides the agent with an understanding of the API structure.
  • API Selection: The agent is able to select the correct backend API.
  • Data Exchange: When the AI agent calls the inventory search API, it receives a filtered list. The back-end provides this information to the front-end.

Step-by-Step Implementation Guide

Here’s a simplified step-by-step guide to get you started. It outlines the basic steps from start to finish.

  1. Set up the Azure AI Foundry Agent:

    • Integrate the agent with your application's interface (web page, chatbot, etc.).
    • Upload the swagger.json for your backend inventory API into the agent. This allows the agent to understand the available API endpoints and data structures.

  2. Develop the Backend Inventory API:

    • Create an API endpoint for inventory search. This API should be able to receive search criteria (e.g., product name, color, price) and return a list of matching products.
    • The API should also support filtering based on a list of product IDs or other identifiers provided by the AI agent.

  3. Implement the Filter Logic:

    • When the user interacts with the AI agent, the agent uses the inventory search API.
    • Examine the Azure AI Foundry Agent logs or API call data to determine if the agent has initiated an inventory search. Check if the agent has called an inventory search API.
    • Parse the AI agent’s response to identify the product IDs or relevant data that the agent has listed (e.g. A, B, C).
    • Use these IDs to filter the product listings on the inventory page.

  4. Display the Filtered Results:

    • Update the inventory page to show only the products that match the criteria provided by the agent.
    • Ensure the user sees the refined results in a clear, organized way.
    • Consider visual cues (like highlighting the matched products) to make the filtered list obvious to the user.

  5. Handle Cart-Related Commands:

    • If the AI agent determines the user's query is related to their shopping cart (e.g.,