LogoLogo
  • 👋Welcome to Groclake
  • ⏩Jump right in
  • 🗣️Introduction to Groclake
  • 🧠High level Concepts
    • Agent Discovery
    • Agent Registry
    • Agent Communication
      • Agent Text Transfer Protocol - ATTP
    • Agent Security
      • Agent Private Cloud - APC
      • Authentication & Encryption
      • Zero Trust Policy
  • 💽Installation & Guide
  • 🏗️Groclake Use Cases
  • 📰Groclake Records
  • Example Codes
  • GrocAgent
    • What is GrocAgent?
    • Example Chat Agent
    • Reflections in GrocAgent
      • Workflow of Reflection Handler
  • Lakes
    • 💾Data & Model Management
      • Datalake
        • Create Datalake
        • Retrieve Document
        • Upload Documents
        • Datalake Connections
          • Snowflake integration
      • Vectorlake
        • Creating vector
        • Generating Vector
        • Pushing Vector
        • Retrieve Document
        • Searching Vector
      • Modellake
        • Create Modellake
        • Language Translation
        • Conversation AI
        • Text to Speech
        • Chat Completion
      • Knowledgelake
        • Create Knowledge Base
        • Push Documents from a URL
        • Push Documents from Local Storage
        • Searching for Information
    • ⚒️Tool Management & Gateway
      • Toollake
        • Tools
        • Salesforce CRM Integration
        • Slack Communication Module
        • New Relic Integration
        • Google Calendar Integration
          • Check Slot Availability
          • Get Available Slots
          • Delete Event
          • Create new event
          • Create new calendar event
    • 🤖Agent Management & Deployment
      • Agentlake
        • Register your agent
        • Fetch agent details & categories
        • Create Agent Private Cloud (APC)
        • Assign Agent Private Cloud (APC) to an Agent
      • Promptlake
        • Setting Connection & Initializing
        • Storing a Prompt
        • Fetching a Prompt
        • Example API Calls
      • Memorylake
        • Context Component Examples
        • Value Structure
        • Setup & Guide
        • Storing & Retrieving Memory
        • Wildcard Search
        • Updating Memory Quality
    • 🗃️Index Stores
      • Cataloglake
        • Create catalog
        • Generate Product Data
        • Fetch Catalog Data
        • Push Product Data
        • Optimize Data Retrieval with Catalog Caching
        • Search for Products
        • Filter Product Search
        • Update Product Data
        • Recommend Products Based on Product Name
        • Update Inventory in Catalog
        • Fetch Inventory Details from Catalog
        • Fetch Product Price
        • Update Product Price in Catalog
        • Cache Image in Catalog
        • Sync Your Catalog with external ecomm platforms
        • Deleting items
        • Address Parsing and Intent Extraction
        • Creating Mapper
        • Convert Mapper's Metadata
        • Fetching Mapper
        • Updating Mapper
        • Example use case of Cataloglake
      • Joblake
        • Joblake Mapping
        • Creating a Joblake
      • Resumelake
        • Resumelake Mapping
        • Creating a Resumelake
Powered by GitBook
On this page
  • Default Value Structure
  • Example of the Value Structure
  1. Lakes
  2. Agent Management & Deployment
  3. Memorylake

Value Structure

Each stored value consists of the following fields:

  • query_text: The user's input/query.

  • response_text: The system's response.

  • entities: A list containing metadata:

    • time: Timestamp when the memory was stored.

    • memory_id: Matches the memory ID in the key.

    • context_id: Matches the context ID in the key.

    • memory_quality: A rating representing the quality of the memory. Memory quality is stored as 1 by default, which means a "good" memory, while 0 indicates a "bad" memory.

  • metadata: Additional information:

    • context_entity_id: Matches the context entity ID in the key.

    • user_uuid: Matches the user UUID in the key.

Default Value Structure

value = {
    "query_text": memory['query_text'],
    "response_text": memory['response_text'],
    "entities": [
        {
            "time": memory['time'],
            "memory_id": memory_context['memory_id'],
            "context_id": memory_context['context_id'],
            "memory_quality": 1  # Default memory quality
        }
    ],
    "metadata": {"context_entity_id": memory_context['context_entity_id'], "user_uuid": user_uuid}
}

Example of the Value Structure

memory = {
    "query_text": "Hello!",
    "response_text": "Hi there! How can I help you?",
    "time": "2025-02-05T12:00:00Z",
    "cache_ttl": 3600,  # Time-to-live in seconds
    "entities": [
        {
            "time": "2025-02-05T12:00:00Z",
            "memory_id": "msg001",
            "context_id": "session1",
            "memory_quality": 1,
        }
    ],
    "metadata": {
        "context_entity_id": "chatbot",
        "user_uuid": "user123",
    }
}
PreviousContext Component ExamplesNextSetup & Guide

Last updated 3 months ago

🤖