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Zentry provides two core operations for managing memories in AI applications: adding new memories and searching existing ones. This guide covers how these operations work and how to use them effectively in your application.

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Core Operations

Zentry exposes two main endpoints for interacting with memories:
  • The add endpoint for ingesting conversations and storing them as memories
  • The search endpoint for retrieving relevant memories based on queries

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Adding Memories

Architecture diagram illustrating the process of adding memories.

The add operation processes conversations through several steps:
  1. Information Extraction
    • An LLM extracts relevant memories from the conversation
    • It identifies important entities and their relationships
  2. Conflict Resolution
    • The system compares new information with existing data
    • It identifies and resolves any contradictions
  3. Memory Storage
    • Vector database stores the actual memories
    • Graph database maintains relationship information
    • Information is continuously updated with each interaction

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Searching Memories

Architecture diagram illustrating the memory search process.

The search operation retrieves memories through a multi-step process:
  1. Query Processing
    • LLM processes and optimizes the search query
    • System prepares filters for targeted search
  2. Vector Search
    • Performs semantic search using the optimized query
    • Ranks results by relevance to the query
    • Applies specified filters (user, agent, metadata, etc.)
  3. Result Processing
    • Combines and ranks the search results
    • Returns memories with relevance scores
    • Includes associated metadata and timestamps
This semantic search approach ensures accurate memory retrieval, whether you’re looking for specific information or exploring related concepts.