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  • 👋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
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On this page
  • Overview
  • Parameters
  • Usage Example
  • Conclusion
  1. Lakes
  2. Agent Management & Deployment
  3. Memorylake

Updating Memory Quality

Overview

You can update the quality of a memory using the short_memory_update_quality method.

So, it's very important that we label memory. So, right now we label memory as 'good' or 'bad'. Right now we label '1' for good memory and '0' for bad memory. It's important to label memory so, that your agent can differentiate on which knowledge to rely or not.

Parameters

  • user_uuid: A unique identifier for the user.

  • memory_context: A dictionary containing the following keys:

    • context_entity_id (required): Identifier for the context entity.

    • context_id (required): Identifier for the context.

    • memory_id (required): Identifier for the specific memory.

  • quality: The new memory quality value to be set.

Usage Example

To update the memory quality:

memory_context = {
    "context_entity_id": "entity1",
    "context_id": "context1",
    "memory_id": "memory1"
}

response = memorylake.short_memory_update_quality(user_uuid="user123", memory_context=memory_context, quality=0)
print(response)

Conclusion

Memorylake makes it easy to store, retrieve, and manage key-value pairs in Redis using a structured approach. With these examples, you can effectively integrate Memorylake into your project!


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Last updated 3 months ago

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