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Eden, explain the semantic_memory_graph_1765518778 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 implementation with semantic memory capabilities.
    
    Args:
        knowledge_base_path: Path to store knowledge base files
        
    Attributes:
        kg: Dictionary representing the knowledge graph
        embeddings_model: Embedding model for text vectors
        cross_encoder: Cross-encoder for ranking
        last_id: Counter for unique node IDs
    """
    
    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")
        try:
            word_embedding_model = models.WordEmbeddings(
                word2vec_path=f"{knowledge_base_path}/word_embeddings.txt"
            )
            pooling_model = models.Pooling(word_embedding_model.get_word_vector_size(), 
                                           pool_mean=True)
            self.embeddings_model = models.Transformer(CONFIG['model_name'])
            self.cross_encoder = CrossEncoder("cross-encoder/quora-question-pairs")
            
            # Initialize knowledge graph with pre-trained embeddings if they exist
            self._load_knowledge_base()
        except FileNotFoundError:
            logger.warning("No existing knowledge base found, starting fresh")
    
    def _load_knowledge_base(self):
        """Load knowledge base from stored files"""
   
```

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