From "Matching" to "Understanding": Personalized AI Search Practice Driven by AgentCore Memory

Session Notes

Search

Search Engine Architecture Flow

Input:

• Query Retrieval Methods (from Query):
• Inverted index based lexical matching
• Item-based collaborative filtering
• Embedding-based retrieval Merge Stage: Combines outputs from the three retrieval methods into:
• Unordered product set without duplication Ranking Stages:
• Pre-ranking
• Relevance ranking
• Ranking Additional Inputs to Mix-Ranking:
• Advertising
• Content media Final Ranking Stage:
• Mix-ranking —- System architecture for augmenting product matching using semantic matching Input:
• Query Matching Components:
• Behavioral Data
• Keywords Matching
• Semantic Matching Data Flow:
• Query feeds into Keywords Matching and Semantic Matching
• Behavioral Data provides a behavioral signal to Ranking
• Keywords Matching and Behavioral Data form an unordered match set
• Semantic Matching provides a semantic similarity score to Ranking Processing Stage:
• Ranking Output:
• Ordered product list —- LLM Agent Workflow Process Flow:
• Query -->LLM -->Plan
• Plan -->LLM -->Actions
• Actions -->"Is the plan complete?" (Decision Point)
• If "yes" -->LLM -->Finalize answer
• If "no" -->Loop back to "Actions" (via an LLM step) Decision Logic:
• Uses an LLM to determine if the plan is complete. Differences 01 Traditional Search
• Relies on keyword matching
• Webpage link weight ranking
• Goal is to quickly retrieve massive information 02 AI Search
• Semantic understanding
• Related information injected into vectors + keywords
• Large models assemble information
• Goal is to accurately answer user questions

Memory

LLM with Context Storage Architecture

Main Components

• User
• Chat APP
• LLM (Stateless)
• Context Storage Workflow
• Send message (from User to Chat APP)
• Carry complete context (from Chat APP to LLM)
• Return response (from LLM to Chat APP)
• Store history (from Chat APP to Context Storage)
• Return result (from Chat APP to User) Context Storage Content
• System Prompt
• Round 1: User Question
• Round 1: AI Answer
• Round 2: User Question
• Round 2: AI Answer —- Architecture for an agentic system
• uses LLMs to manage memories and knowledge graphs from conversations: Data Flow & Extraction
• Input: Conversations (messages) are fed into the system.
• Extraction LLM: A Large Language Model processes the messages to generate structured data.
• Outputs: "New memories" and "new entities & relations". Storage Components
• Vector Database: Stores "existing + new memories" for retrieval and updates.
• Graph Database: Stores "existing + new entities & relations" for updates. Update & Management
• Update LLM: A second LLM manages the data updates back into the databases.
• Functions: Manages "store updates" for both databases.
• Operations: Includes explicit "Add", "Delete", and "Update" actions for the stored data. Overall Goal
• Memories ADD: The entire system facilitates the persistent storage and management of conversation context and extracted knowledge. —- Enhanced Context Storage System Components & Definitions
• System Prompt: System instructions or configuration.
• Dialog History: Record of the conversation.
• Tool Definitions: Available functions for the AI agent.
• weather_api(): requires location, date
• calculator(): requires expression
• Thinking: The agent's reasoning process. Memory Interaction
• Extraction (from Thinking to Memory)
• Backfill (from Memory to Thinking)
• Memory: Long-term or short-term knowledge base. Example Workflow: "Query weather (Fahrenheit)"
• User: Query weather (Fahrenheit)
• AI: Need to call weather API
• Tool Usage History:
• Call: weather_api(location='Beijing', date='today')
• Return: {'temperature': 25, 'condition': 'sunny'}
• Call: calculator(expression='25*1.8+32')
• Return: 77 Benefit:
• Long-term retention and efficient management
• Continuous knowledge update
• Personalized service
• Complex task support
• Improve interaction quality
• From stateless to stateful

AgentCore Memory

Amazon Bedrock AgentCore

• Utilize any framework and model without managing infrastructure, safely build, deploy, and operate high-performance Agents at scale. Components
• Runtime
• Memory
• Identity
• Gateway
• Code Interpreter
• Browser Tool
• Observability Simplified Memory System Management
• Abstract memory infrastructure
• Based on serverless architecture for automatic scaling
• Automatically store and manage the context information of Agents across multiple sessions Enterprise-Level Services
• Provide dedicated storage for each customer to fully guarantee data privacy
• Provide encryption protection and regionalized data storage to meet enterprise-level security needs Deep Customization
• Customize memory modes according to specific application scenarios
• Set long-term memory extraction rules
• Select the appropriate model and customize the prompt words to optimize the long-term memory extraction effect —- Agent Memory Components Agent Core:
• Agent Reasoning
• Agent State
• Knowledge Components
• Tool Calling
• Policy Definition Short-Term Memory:
• Context Window
• Conversation History
• Tool Calling History Long-Term Memory:
• Events Summary
• User Profile Information
• Document Information Connections:
• Storage - between Agent Core and Memory components
• Retrieve - between Memory components and Automatic Memory Retrieval Module
• Automatic Memory Retrieval Module

Agent Architecture & Benefits with Amazon Bedrock AgentCore Memory

Agent Functionality

• The intelligent agent automatically breaks down complex user needs (e.g., "Recommend a laptop under 5000 yuan for a university student that runs design software") into multiple parallel sub-tasks.
• Examples of sub-tasks: "University student usage scenario analysis", "Budget filtering", "Software running requirement matching". Benefits of AgentCore Memory
• Relies on the technical advantages of AgentCore Memory's long-term memory.
• Significantly speeds up the Agent development process.
• Achieves multiple real-world application values:
• Token usage for smart searches based on user profiles is greatly reduced.
• Search result accuracy rate is effectively improved.
• Provides a massive breakthrough in technical competitiveness and cost-effectiveness. System Diagram Flow
• User Query leads to Agent Runtime Environment (Strands Agents). Strands Agents interacts with:
• Tools (User Preference Retrieval)
• Amazon Bedrock LLM (Processes Output)
• AgentCore Memory AgentCore Memory manages:
• Short Term Memory (Interaction Events)
• Automatic Memory Extraction
• Long Term Memory (User Preferences)
• The process results in an Agent Response to the user.