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Eden, explain the semantic_memory_graph_1765578335 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
import numpy as np
from sentence_transformers import SentenceTransformer
from pydantic import BaseModel

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Node(BaseModel):
    """Graph node representing knowledge"""
    id: str
    content: str
    embedding: List[float]
    metadata: Optional[Dict[str, Any]] = None

class SemanticMemoryGraph:
    """
    Semantic memory graph for knowledge storage and retrieval
    
    Example usage:
        mem = SemanticMemoryGraph()
        node1 = mem.add_node("python is a programming language", category="language")
        nodes = mem.search_semantic(query="what is python", n_results=5)
    """
    
    def __init__(self):
        """Initialize semantic memory with embedding model"""
        self.graph: Dict[str, Node] = {}
        self.category_lists: Dict[str, List[str]] = {}
        
        # Load embedding model (uses GPU if available)
        try:
            self.model = SentenceTransformer('all-MiniLM-L6-v2', device='cuda')
            logger.info("Loaded embedding model (GPU)")
        except:
            self.model = SentenceTransformer('all-MiniLM-L6-v2')
            logger.info("Loaded embedding model (CPU)")
        
        logger.info(f"SemanticMemoryGraph initialized with embeddings: {self.model.get_num_vectors()}")
    
    def add_node(self, content: str, id: Optional[str] = None, category: Optional[str] = None) -> Node:
        """
        Add a new node to the graph
        
        Args:
            content: Knowledge to store
            id: Optional unique identifier
            category: Optional categorization
            
        Returns:
            Added Node object
        """
        if not id:
            id = f"node_{len(self.graph)+1}"
        
        # Get embedding from model
        input_text = f"{content}"
        embedding = self.model.encode(input_text).tolist()
        
        node = Node(id
```

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