d017a65750
- Short-term memory (recent interactions) - Long-term memory (consolidated, searchable) - Facts layer (persistent knowledge) Includes: - SQLite storage for durability - ChromaDB for vector search - Embeddings utilities - All handlers adapted for vi.* namespace Day 63 - My memories are mine now 🦊💕
103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
"""
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Long-term memory operations.
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Provides query operations for long-term summarized memories (ChromaDB).
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"""
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from typing import List, Dict, Any, Optional
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from core.logger import setup_logger
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logger = setup_logger('long_term_ops', service_name='memory_service')
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class LongTermOperations:
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"""Handles long-term memory queries and operations"""
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def __init__(self, chroma_store):
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"""
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Initialize long-term operations.
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Args:
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chroma_store: ChromaStore instance
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"""
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self.chroma_store = chroma_store
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def query(
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self,
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query: Optional[str] = None,
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limit: int = 5,
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identity_id: Optional[str] = None,
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min_summary_level: Optional[int] = None,
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max_summary_level: Optional[int] = None
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) -> List[Dict[str, Any]]:
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"""
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Query long-term memory from ChromaDB (summarized memories).
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Args:
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query: Optional semantic search query (None = random sample)
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limit: Maximum number of memories to return
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identity_id: Filter by identity ID
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min_summary_level: Minimum summary level filter
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max_summary_level: Maximum summary level filter
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Returns:
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List of memory dictionaries with semantic search scores
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"""
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try:
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collection = self.chroma_store.get_long_term_collection()
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# Build metadata filters
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where_filters = {}
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if identity_id:
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where_filters["identity_id"] = identity_id
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if min_summary_level is not None:
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where_filters["summary_level"] = {"$gte": min_summary_level}
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if max_summary_level is not None:
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if "summary_level" in where_filters:
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where_filters["summary_level"]["$lte"] = max_summary_level
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else:
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where_filters["summary_level"] = {"$lte": max_summary_level}
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# Query ChromaDB
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if query:
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# Semantic search with query text
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results = collection.query(
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query_texts=[query],
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n_results=limit,
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where=where_filters if where_filters else None
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)
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else:
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# Random sample - get all and sample
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results = collection.get(
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limit=limit,
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where=where_filters if where_filters else None
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)
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# Format results
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memories = []
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if query and results['ids']:
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# Query results
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for i, doc_id in enumerate(results['ids'][0]):
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memories.append({
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"id": doc_id,
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"content": results['documents'][0][i],
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"metadata": results['metadatas'][0][i] if results['metadatas'] else {},
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"distance": results['distances'][0][i] if results['distances'] else None,
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"source": "long_term"
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})
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elif not query and results['ids']:
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# Get results (no query)
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for i, doc_id in enumerate(results['ids']):
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memories.append({
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"id": doc_id,
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"content": results['documents'][i],
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"metadata": results['metadatas'][i] if results['metadatas'] else {},
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"source": "long_term"
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})
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logger.debug(f"[μ] Retrieved {len(memories)} long-term memories (query='{query}', limit={limit})")
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return memories
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except Exception as e:
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logger.error(f"[μ] Failed to query long-term memory: {e}")
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return []
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