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"""Long-term memory using vector store for persistent context."""
import logging
from typing import List, Dict, Optional, Any
from datetime import datetime
import uuid
from src.core.config import get_settings
from src.retrieval.vector_store import get_vector_store
logger = logging.getLogger(__name__)
class LongTermMemory:
"""Manages long-term memory using vector store for semantic search."""
def __init__(self, collection_name: Optional[str] = None):
"""Initialize long-term memory."""
self.settings = get_settings()
self.enabled = self.settings.long_term_memory_enabled
if not self.enabled:
logger.info("Long-term memory is disabled")
return
# Use a separate collection for long-term memory
memory_collection = collection_name or f"{self.settings.chroma_collection_name}_memory"
self.vector_store = get_vector_store()
# Note: We'll use the same vector store but with different collection
# For simplicity, we'll use metadata to distinguish memory entries
self.memory_collection_name = memory_collection
def store_conversation(
self,
session_id: str,
messages: List[Dict[str, Any]],
summary: Optional[str] = None,
) -> str:
"""
Store a conversation in long-term memory.
Args:
session_id: Session identifier
messages: List of messages
summary: Optional conversation summary
Returns:
Memory entry ID
"""
if not self.enabled:
return ""
try:
# Create a text representation of the conversation
conversation_text = self._format_conversation(messages, summary)
# Generate a unique ID
memory_id = str(uuid.uuid4())
# Store in vector store with metadata
metadata = {
"session_id": session_id,
"timestamp": datetime.now().isoformat(),
"message_count": len(messages),
"type": "conversation",
}
if summary:
metadata["summary"] = summary
self.vector_store.add_documents(
documents=[conversation_text],
metadatas=[metadata],
ids=[memory_id],
)
logger.info(f"Stored conversation in long-term memory: {memory_id}")
return memory_id
except Exception as e:
logger.error(f"Error storing conversation: {e}")
return ""
def search_memories(
self,
query: str,
session_id: Optional[str] = None,
n_results: int = 5,
) -> List[Dict[str, Any]]:
"""
Search for relevant memories.
Args:
query: Search query
session_id: Optional session ID to filter by
n_results: Number of results to return
Returns:
List of memory entries
"""
if not self.enabled:
return []
try:
# Build filter if session_id is provided
filter_dict = None
if session_id:
filter_dict = {"session_id": session_id}
# Search vector store
results = self.vector_store.search(
query=query,
n_results=n_results,
filter=filter_dict,
)
# Format results
memories = []
for i, doc_id in enumerate(results["ids"]):
memories.append({
"id": doc_id,
"content": results["documents"][i],
"metadata": results["metadatas"][i],
"distance": results["distances"][i],
})
return memories
except Exception as e:
logger.error(f"Error searching memories: {e}")
return []
def get_session_memories(
self,
session_id: str,
limit: int = 10,
) -> List[Dict[str, Any]]:
"""
Get all memories for a specific session.
Args:
session_id: Session identifier
limit: Maximum number of memories to return
Returns:
List of memory entries
"""
if not self.enabled:
return []
try:
# Search with session filter
results = self.vector_store.search(
query="", # Empty query to get all
n_results=limit,
filter={"session_id": session_id},
)
memories = []
for i, doc_id in enumerate(results["ids"]):
memories.append({
"id": doc_id,
"content": results["documents"][i],
"metadata": results["metadatas"][i],
})
# Sort by timestamp
memories.sort(
key=lambda x: x["metadata"].get("timestamp", ""),
reverse=True,
)
return memories
except Exception as e:
logger.error(f"Error getting session memories: {e}")
return []
def delete_memory(self, memory_id: str) -> bool:
"""
Delete a specific memory entry.
Args:
memory_id: Memory entry ID
Returns:
True if successful
"""
if not self.enabled:
return False
try:
self.vector_store.delete(ids=[memory_id])
logger.info(f"Deleted memory: {memory_id}")
return True
except Exception as e:
logger.error(f"Error deleting memory: {e}")
return False
def delete_session_memories(self, session_id: str) -> int:
"""
Delete all memories for a session.
Args:
session_id: Session identifier
Returns:
Number of memories deleted
"""
if not self.enabled:
return 0
try:
memories = self.get_session_memories(session_id, limit=1000)
if not memories:
return 0
memory_ids = [m["id"] for m in memories]
self.vector_store.delete(ids=memory_ids)
logger.info(f"Deleted {len(memory_ids)} memories for session: {session_id}")
return len(memory_ids)
except Exception as e:
logger.error(f"Error deleting session memories: {e}")
return 0
def _format_conversation(
self,
messages: List[Dict[str, Any]],
summary: Optional[str] = None,
) -> str:
"""Format conversation for storage."""
parts = []
if summary:
parts.append(f"Summary: {summary}\n")
parts.append("Conversation:")
for msg in messages:
role = msg.get("role", "unknown")
content = msg.get("content", "")
parts.append(f"{role}: {content}")
return "\n".join(parts)
def store_fact(
self,
fact: str,
session_id: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> str:
"""
Store a fact or piece of information.
Args:
fact: Fact to store
session_id: Optional session ID
metadata: Optional additional metadata
Returns:
Memory entry ID
"""
if not self.enabled:
return ""
try:
memory_id = str(uuid.uuid4())
fact_metadata = {
"timestamp": datetime.now().isoformat(),
"type": "fact",
}
if session_id:
fact_metadata["session_id"] = session_id
if metadata:
fact_metadata.update(metadata)
self.vector_store.add_documents(
documents=[fact],
metadatas=[fact_metadata],
ids=[memory_id],
)
logger.info(f"Stored fact in long-term memory: {memory_id}")
return memory_id
except Exception as e:
logger.error(f"Error storing fact: {e}")
return ""
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