Memo Chat Memory
The Memo Chat Memory component provides an advanced memory system for chat applications with sophisticated configuration options. It enables AI assistants to maintain rich conversational context with customizable memory management, search capabilities, and integration with LLM systems.
Memo Chat Memory interface
Component Inputs
- MemoⓂ️ Configuration: Configuration settings for the Memo memory system
Access advanced configuration options through the embedded editor
- Message to Ingest: Message content to store in memory
The text content to be added to the memory system
- Existing Memory Instance: Reference to an existing memory instance
Connect to previously created memory instances
- User ID: Identifier for the user in the conversation
Used to associate memories with specific users
- Search Query: Query to search within stored memories
Retrieve relevant memories using semantic search
- MemoⓂ️ API Key: Authentication key for the Memo service
Required for accessing Memo's advanced memory features
- Metadata: Additional data to associate with stored memories
Key-value pairs for enhanced memory organization and retrieval
- OpenAI API Key: Authentication key for OpenAI services
Used for embedding generation and semantic search
Component Outputs
- MemoⓂ️ Memory: Retrieved or stored memory data
- Search Results: Results from memory queries
Use Cases
- Advanced Chatbots: Create chatbots with sophisticated memory capabilities
- Personalized AI Assistants: Build assistants that remember user preferences and past interactions
- Knowledge Management: Store and retrieve conversational knowledge
- Context-Aware Applications: Develop applications that maintain rich context
- Customer Support Systems: Remember customer issues and resolutions
- Long-term Relationships: Enable AI systems to build ongoing relationships with users
Best Practices
- Design appropriate memory schemas for your specific use case
- Store sensitive API keys securely using environment variables
- Structure metadata to enhance search and retrieval capabilities
- Implement memory filtering to reduce irrelevant information
- Consider memory length limitations to optimize performance
- Use vector search for semantic memory retrieval
- Implement proper user identification for multi-user systems
- Test memory performance with various conversation lengths
- Consider privacy implications when storing user conversations