semantic.py•4.98 kB
"""
Semantic Domain for managing semantic memories.
The Semantic Domain is responsible for:
- Managing factual information and knowledge
- Organizing categorical and conceptual information
- Handling entity relationships and attributes
- Knowledge consolidation and organization
- Abstract concept representation
"""
from typing import Any, Dict, List
from loguru import logger
from memory_mcp.domains.persistence import PersistenceDomain
class SemanticDomain:
"""
Manages semantic memories (facts, knowledge, entities).
This domain handles factual information, knowledge, and
entity-relationship structures.
"""
def __init__(self, config: Dict[str, Any], persistence_domain: PersistenceDomain) -> None:
"""
Initialize the semantic domain.
Args:
config: Configuration dictionary
persistence_domain: Reference to the persistence domain
"""
self.config = config
self.persistence_domain = persistence_domain
async def initialize(self) -> None:
"""Initialize the semantic domain."""
logger.info("Initializing Semantic Domain")
# Initialization logic will be implemented here
async def process_memory(self, memory: Dict[str, Any]) -> Dict[str, Any]:
"""
Process a semantic memory.
This includes extracting key information, generating embeddings,
and enriching the memory with additional metadata.
Args:
memory: The memory to process
Returns:
Processed memory
"""
logger.debug(f"Processing semantic memory: {memory['id']}")
# Extract text representation for embedding
text_content = self._extract_text_content(memory)
# Generate embedding
embedding = await self.persistence_domain.generate_embedding(text_content)
memory["embedding"] = embedding
# Additional processing based on memory type
if memory["type"] == "entity":
memory = self._process_entity_memory(memory)
elif memory["type"] == "fact":
memory = self._process_fact_memory(memory)
return memory
def _extract_text_content(self, memory: Dict[str, Any]) -> str:
"""
Extract text content from a memory for embedding generation.
Args:
memory: The memory to extract text from
Returns:
Text representation of the memory
"""
if memory["type"] == "fact":
# For fact memories, use the fact text
if "fact" in memory["content"]:
return memory["content"]["fact"]
elif memory["type"] == "document":
# For document memories, combine title and text
title = memory["content"].get("title", "")
text = memory["content"].get("text", "")
return f"{title}\n{text}"
elif memory["type"] == "entity":
# For entity memories, combine name and attributes
name = memory["content"].get("name", "")
entity_type = memory["content"].get("entity_type", "")
# Extract attributes as text
attributes = memory["content"].get("attributes", {})
attr_text = ""
if attributes and isinstance(attributes, dict):
attr_text = "\n".join([f"{k}: {v}" for k, v in attributes.items()])
return f"{name} ({entity_type})\n{attr_text}"
# Fallback: try to convert content to string
try:
return str(memory["content"])
except:
return f"Memory {memory['id']} of type {memory['type']}"
def _process_entity_memory(self, memory: Dict[str, Any]) -> Dict[str, Any]:
"""
Process an entity memory.
Args:
memory: The entity memory to process
Returns:
Processed memory
"""
# Entity-specific processing
# This is a placeholder for future implementation
return memory
def _process_fact_memory(self, memory: Dict[str, Any]) -> Dict[str, Any]:
"""
Process a fact memory.
Args:
memory: The fact memory to process
Returns:
Processed memory
"""
# Fact-specific processing
# This is a placeholder for future implementation
return memory
async def get_stats(self) -> Dict[str, Any]:
"""
Get statistics about the semantic domain.
Returns:
Semantic domain statistics
"""
return {
"memory_types": {
"fact": 0,
"document": 0,
"entity": 0
},
"status": "initialized"
}