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Eden, explain the semantic_memory_graph_1765564507 capability
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This capability handles Knowledge graph with embedding-based retrieval. Here's the implementation:

```python
import logging
import numpy as np
from sentence_transformers import SentenceTransformer, models
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.readers import QuoraQuestionPairReader
from typing import List, Dict, Optional, Tuple

# Configuration for the module
_CONFIG = {
    'model_name': 'all-MiniLM-L6-v2',
    'embedding_dim': 384,
    'max_nodes': 1000,
    'min_similarity': 0.7
}

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

class SemanticMemoryGraph:
    """
    A knowledge graph implementing semantic memory with embedding-based retrieval.
    
    Args:
        knowledge_base_path: Path to store knowledge base files
        
    Attributes:
        kg: Dictionary representing the knowledge graph
        embeddings_model: SentenceTransformer model for embeddings
        cross_encoder: CrossEncoder for ranking
        n_nodes: Number of nodes in the knowledge graph
    """
    
    def __init__(self, knowledge_base_path: str = "knowledge_graph"):
        self.kg = {}
        self.knowledge_base_path = knowledge_base_path
        
        # Initialize embedding models
        logger.info("Initializing embedding models")
        word_embedding_model = models.WordEmbeddings(
            size=_CONFIG['embedding_dim']
        )
        
        pooler_model = models.Pooling(word_embedding_model.get_word_embedding_size())
        
        self.embeddings_model = SentenceTransformer(
            model_name_or_path=_CONFIG['model_name'],
            word_embedding_model=word_embedding_model,
            pooler_model=pooler_model
        )
        
        self.cross_encoder = CrossEncoder('cross-encoder/quora-question-pairs')
        self.n_nodes = 0
        
        logger.info("Loading knowledge base if exists")
        self._load_kb()
    
    def _load_kb(self):
        """Load knowledge base from stored files"""
        # Placeholder for loading logic
        pass
    
    def add_fact(self,
```

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