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Eden, explain the semantic_memory_graph_1765536361 capability
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This capability handles Knowledge graph with embedding-based retrieval. Here's the implementation:

```python
import logging
from typing import List, Dict, Optional, Any
from sentence_transformers import SentenceTransformer
import networkx as nx


class SemanticMemoryGraph:
    """
    Knowledge graph that stores concepts and retrieves by semantic meaning
    
    Attributes:
        model_name: Name of embedding model
        num_nodes: Number of concepts stored
        avg_edges_per_node: Average edges per node
        embeddings_model: Transformer model for creating embeddings
        graph: NetworkX multi-graph structure
        
    Methods:
        add_concept: Add a concept with related terms
        retrieve_similar: Find similar concepts by query
        get_recommendations: Get personalized recommendations
        save_graph: Serialize graph to file
        load_graph: Deserialize graph from file
    """
    
    def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
        """
        Initialize semantic memory graph
        
        Args:
            model_name: Embedding model name (default: all-MiniLM-L6-v2)
        """
        self.model_name = model_name
        self.num_nodes = 0
        self.avg_edges_per_node = 0.0
        self.embeddings_model = SentenceTransformer(model_name)
        self.graph = nx.MultiGraph()
        
    def add_concept(self, 
                    concept: str, 
                    related_terms: List[str],
                    metadata: Optional[Dict] = None) -> bool:
        """
        Add a concept with related terms to the graph
        
        Args:
            concept: Main concept term
            related_terms: List of related terms
            metadata: Optional dictionary of additional info
            
        Returns:
            True if added successfully, False on error
        """
        try:
            # Ensure metadata is empty dict if None
            if metadata is None:
                metadata = {}
            
            # Add main node with concept and metadata
            if not self.graph.has_node(con
```

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