episodic.py•3.94 kB
"""
Episodic Domain for managing episodic memories.
The Episodic Domain is responsible for:
- Recording and retrieving conversation histories
- Managing session-based interactions
- Contextualizing memories with temporal and situational details
- Narrative memory construction
- Recording agent reflections and observations
"""
from typing import Any, Dict, List
from loguru import logger
from memory_mcp.domains.persistence import PersistenceDomain
class EpisodicDomain:
"""
Manages episodic memories (conversations, experiences, reflections).
This domain handles memories that are experiential in nature,
including conversation histories, reflections, and interactions.
"""
def __init__(self, config: Dict[str, Any], persistence_domain: PersistenceDomain) -> None:
"""
Initialize the episodic 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 episodic domain."""
logger.info("Initializing Episodic Domain")
# Initialization logic will be implemented here
async def process_memory(self, memory: Dict[str, Any]) -> Dict[str, Any]:
"""
Process an episodic 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 episodic 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 will be implemented here
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"] == "conversation":
# For conversation memories, extract from the message content
if "role" in memory["content"] and "message" in memory["content"]:
return f"{memory['content']['role']}: {memory['content']['message']}"
# Handle conversation arrays
if "messages" in memory["content"]:
messages = memory["content"]["messages"]
if isinstance(messages, list):
return "\n".join([f"{m.get('role', 'unknown')}: {m.get('content', '')}" for m in messages])
elif memory["type"] == "reflection":
# For reflection memories, combine subject and reflection
if "subject" in memory["content"] and "reflection" in memory["content"]:
return f"{memory['content']['subject']}: {memory['content']['reflection']}"
# Fallback: try to convert content to string
try:
return str(memory["content"])
except:
return f"Memory {memory['id']} of type {memory['type']}"
async def get_stats(self) -> Dict[str, Any]:
"""
Get statistics about the episodic domain.
Returns:
Episodic domain statistics
"""
return {
"memory_types": {
"conversation": 0,
"reflection": 0
},
"status": "initialized"
}