feat(memory): hierarchical memory module

[Appointat]

🚀 Integrating Memfuse to Enhance Chat2Graph's Memory Capabilities

Hi team and community,

As Chat2Graph evolves, we're focused on significantly boosting its intelligence and contextual understanding. A critical piece of this puzzle is a robust memory module.

Current State & The Need for Advanced Memory

Currently, Chat2Graph handles short-term information via message history, workflows, and the planner. However, to achieve more advanced capabilities like:

• Cross-session information retention
• Complex environment understanding
• Insight generation
• Building an experience pool

we need a dedicated and sophisticated memory solution.

Our Desired Memory Module Features:

• Multi-level Memory Management: Support for both short-term (e.g., current conversation context) and long-term memory (e.g., user preferences, historical summaries, learned knowledge).
• Automated Memory Operations: Flexible storage, fast and accurate retrieval, timely updates, and a reasonable forgetting mechanism.
• Seamless Integration: Tight integration with core modules like Leader&Expert, Operator, and Reasoner.
• Scalability and Configurability: Easy expansion of memory types and storage structures, plus system-level configuration.

We've been envisioning this based on a DIKW (Data, Information, Knowledge, Wisdom) memory model, potentially requiring sub-modules for storage (vector, graph, traditional DBs), processing (encoding, compression, summarization, association, forgetting), and a standardized interface (API).

For more information: #178

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Introducing Memfuse: A Potential Solution?

We recently had a productive meeting (05.29) discussing Memfuse, and it appears to align well with our goals. Here are some key takeaways from Memfuse:

• Focus on Low Latency: Designed for real-time conversational flow with incremental updates, which is crucial for our interactive use cases.
• Buffer System Architecture:
  • Buffer Manager: Oversees the system.
  • Write Buffer: Handles batch writes.
  • Speculative Buffer: Caches based on predictions.
  • Query Buffer: Manages retrieval.
  • Storage units are chunks from messages.
• Emphasis on Quality > Scale ~ Performance.
• Three-Layer Algorithmic Abstraction:
  • L0 Episodic: Vector storage for raw data. (Information consolidation is a TODO for them).
  • L1 Semantic: Semantic extraction, disambiguation, incremental updates, and handling of time-series data (potential for "time travel" capabilities). Aims for real-time updates with eventual full consistency.
  • L2 Conceptual: Utilizes vector keywords for fast, asynchronous operations.
• Knowledge Adder: For incorporating fixed, correct knowledge.

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Why Memfuse for Chat2Graph?

The architecture and philosophy behind Memfuse seem promising for addressing our needs for a multi-layered, efficient, and incrementally updated memory system. Its layered approach could map well to our DIKW model aspirations.

Key Discussion Points & TODOs:

1. Integration Strategy: This is the big question. How can we best integrate Memfuse into Chat2Graph? Should Chat2Graph use Memfuse as a component, or is there a deeper integration path? This needs further exploration.
2. Impact of Long Context LLMs: An important consideration raised was: Given that current LLMs often have very long context windows, in what scenarios (e.g., varying conversation lengths) will a dedicated memory system like Memfuse provide the most significant benefits?
3. Next Steps:
   • We need to thoroughly understand Memfuse's architecture and ideology (consensus from the meeting).
   • Action Item: Formulate a plan or proposal on how to integrate Memfuse with Chat2Graph.

We believe that leveraging a system like Memfuse could significantly enhance Chat2Graph by:

• Improving long-term learning and adaptation.
• Enhancing contextual understanding and personalized interactions.
• Supporting more complex reasoning and planning.
• Increasing multi-agent collaboration efficiency.

We're excited about the potential here and would love to hear your thoughts, suggestions, and any concerns regarding this direction. Let's discuss how we can best move forward!

[pinklemon123]

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