Implement Clustering for Improved Memory Management

[Lucid]

The current implementation of the memory management system lacks the ability to group similar memories together, which can be beneficial for improving the efficiency and relevance of memory retrieval.

We should consider integrating clustering functionality into the system. This could be achieved by using clustering algorithms such as K-Means or DBSCAN. These algorithms can help identify groups of memories that are similar to each other and separate from other groups.

In addition to the clustering, we should consider adding a feature to filter or segment memories based on a distance threshold. This will allow for finer control over the size and number of clusters.

Tasks:

• Research K-Means and DBSCAN algorithms.
• Implement selected clustering algorithm(s).
• Add distance threshold-based filtering for memories.
• Test the implementation with different collections and thresholds.

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Implemented DBScan in v0.3.4, please test it. Closing this.