1319 lines
51 KiB
Python
1319 lines
51 KiB
Python
"""
|
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Temporal + Semantic + Entity Memory System for AI Agents.
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This implements a sophisticated memory architecture that combines:
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1. Temporal links: Memories connected by time proximity
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2. Semantic links: Memories connected by meaning/similarity
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3. Entity links: Memories connected by shared entities (PERSON, ORG, etc.)
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4. Spreading activation: Search through the graph with activation decay
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5. Dynamic weighting: Recency and frequency-based importance
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"""
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import os
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from datetime import datetime, timedelta, timezone
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from typing import Any, Dict, List, Optional, Tuple
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import psycopg2
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from psycopg2.extras import RealDictCursor, execute_values
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from pgvector.psycopg2 import register_vector
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from sentence_transformers import SentenceTransformer
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from dotenv import load_dotenv
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import asyncio
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import time
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from concurrent.futures import ProcessPoolExecutor
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import numpy as np
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from .utils import (
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extract_facts,
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calculate_recency_weight,
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calculate_frequency_weight,
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)
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from .entity_resolver import EntityResolver
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def utcnow():
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"""Get current UTC time with timezone info."""
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return datetime.now(timezone.utc)
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# Global process pool for parallel embedding generation
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# Each process loads its own copy of the embedding model
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# This provides TRUE parallelism for CPU-bound embedding operations
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_PROCESS_POOL = None
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_EMBEDDING_MODEL_NAME = "BAAI/bge-small-en-v1.5"
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# Process-local model cache (one per worker process)
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_worker_model = None
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def _get_worker_model():
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"""Get or load the embedding model in worker process."""
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global _worker_model
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if _worker_model is None:
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_worker_model = SentenceTransformer(_EMBEDDING_MODEL_NAME)
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return _worker_model
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def _encode_batch_worker(texts: List[str]) -> List[List[float]]:
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"""
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Worker function for process pool - encodes texts to embeddings.
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This function runs in a separate process and loads its own model.
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"""
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model = _get_worker_model()
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embeddings = model.encode(texts, convert_to_numpy=True, show_progress_bar=False)
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return [emb.tolist() for emb in embeddings]
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def _get_process_pool():
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"""Get or create the global process pool."""
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global _PROCESS_POOL
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if _PROCESS_POOL is None:
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# Use 4 worker processes for true parallelism
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# Adjust based on your CPU cores (each process loads ~500MB model)
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_PROCESS_POOL = ProcessPoolExecutor(max_workers=4)
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return _PROCESS_POOL
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class TemporalSemanticMemory:
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"""
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Advanced memory system using temporal and semantic linking with PostgreSQL.
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"""
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def __init__(
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self,
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db_url: Optional[str] = None,
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embedding_model: str = "BAAI/bge-small-en-v1.5",
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):
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"""
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Initialize the temporal + semantic memory system.
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Args:
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db_url: PostgreSQL connection URL (postgresql://user:pass@host:port/dbname)
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embedding_model: Name of the SentenceTransformer model to use
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"""
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load_dotenv()
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# Initialize PostgreSQL connection
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self.db_url = db_url or os.getenv("DATABASE_URL")
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if not self.db_url:
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raise ValueError(
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"Database URL not found. "
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"Set DATABASE_URL environment variable."
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)
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self.conn = psycopg2.connect(self.db_url)
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register_vector(self.conn)
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# Initialize entity resolver
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self.entity_resolver = EntityResolver(self.conn)
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# Initialize local embedding model (384 dimensions)
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print(f"Loading embedding model: {embedding_model}...")
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self.embedding_model = SentenceTransformer(embedding_model)
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print(f"✓ Model loaded (embedding dim: {self.embedding_model.get_sentence_embedding_dimension()})")
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def __del__(self):
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"""Clean up database connection."""
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if hasattr(self, 'conn') and self.conn:
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self.conn.close()
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def _generate_embedding(self, text: str) -> List[float]:
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"""
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Generate embedding for text using local SentenceTransformer model.
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Args:
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text: Text to embed
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Returns:
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384-dimensional embedding vector (bge-small-en-v1.5)
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"""
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try:
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embedding = self.embedding_model.encode(text, convert_to_numpy=True, show_progress_bar=False)
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return embedding.tolist()
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except Exception as e:
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raise Exception(f"Failed to generate embedding: {str(e)}")
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async def _generate_embeddings_batch(self, texts: List[str]) -> List[List[float]]:
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"""
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Generate embeddings for multiple texts using local model in parallel.
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Uses a ProcessPoolExecutor to achieve TRUE parallelism for CPU-bound
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embedding generation. Each worker process loads its own model copy.
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When multiple put_async calls run in parallel, each can generate
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embeddings concurrently in separate processes (no GIL contention).
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Args:
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texts: List of texts to embed
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Returns:
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List of 384-dimensional embeddings in same order as input texts
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"""
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try:
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# Run in process pool for true parallelism
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loop = asyncio.get_event_loop()
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pool = _get_process_pool()
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embeddings = await loop.run_in_executor(
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pool,
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_encode_batch_worker,
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texts
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)
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return embeddings
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except Exception as e:
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raise Exception(f"Failed to generate batch embeddings: {str(e)}")
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def _find_duplicate_facts_batch(
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self,
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cursor,
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agent_id: str,
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texts: List[str],
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embeddings: List[List[float]],
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event_date: datetime,
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time_window_hours: int = 24,
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similarity_threshold: float = 0.95
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) -> List[bool]:
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"""
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Check which facts are duplicates using semantic similarity + temporal window.
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For each new fact, checks if a semantically similar fact already exists
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within the time window. Uses pgvector cosine similarity for efficiency.
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Args:
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cursor: Database cursor
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agent_id: Agent identifier
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texts: List of fact texts to check
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embeddings: Corresponding embeddings
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event_date: Event date for temporal filtering
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time_window_hours: Hours before/after event_date to search (default: 24)
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similarity_threshold: Minimum cosine similarity to consider duplicate (default: 0.95)
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Returns:
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List of booleans - True if fact is a duplicate (should skip), False if new
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"""
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is_duplicate = []
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time_lower = event_date - timedelta(hours=time_window_hours)
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time_upper = event_date + timedelta(hours=time_window_hours)
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for text, embedding in zip(texts, embeddings):
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# Query for similar facts within time window
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cursor.execute(
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"""
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SELECT id, text, 1 - (embedding <=> %s::vector) AS similarity
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FROM memory_units
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WHERE agent_id = %s
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AND event_date BETWEEN %s AND %s
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AND 1 - (embedding <=> %s::vector) > %s
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ORDER BY similarity DESC
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LIMIT 1
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""",
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(embedding, agent_id, time_lower, time_upper, embedding, similarity_threshold)
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)
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result = cursor.fetchone()
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if result:
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is_duplicate.append(True)
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else:
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is_duplicate.append(False)
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return is_duplicate
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def put(
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self,
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agent_id: str,
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content: str,
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context: str = "",
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event_date: Optional[datetime] = None,
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) -> List[str]:
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"""
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Store content as memory units (synchronous wrapper).
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This is a synchronous wrapper around put_async() for convenience.
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For best performance, use put_async() directly.
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Args:
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agent_id: Unique identifier for the agent
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content: Text content to store
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context: Context about when/why this memory was formed
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event_date: When the event occurred (defaults to now)
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Returns:
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List of created unit IDs
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"""
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# Run async version synchronously
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return asyncio.run(self.put_async(agent_id, content, context, event_date))
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async def put_async(
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self,
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agent_id: str,
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content: str,
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context: str = "",
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event_date: Optional[datetime] = None,
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) -> List[str]:
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"""
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Store content as memory units with temporal and semantic links (ASYNC version).
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This is a convenience wrapper around put_batch_async for a single content item.
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Args:
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agent_id: Unique identifier for the agent
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content: Text content to store
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context: Context about when/why this memory was formed
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event_date: When the event occurred (defaults to now)
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Returns:
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List of created unit IDs
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"""
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# Use put_batch_async with a single item (avoids code duplication)
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result = await self.put_batch_async(
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agent_id=agent_id,
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contents=[{
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"content": content,
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"context": context,
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"event_date": event_date
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}]
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)
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# Return the first (and only) list of unit IDs
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return result[0] if result else []
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async def put_batch_async(
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self,
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agent_id: str,
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contents: List[Dict[str, Any]],
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) -> List[List[str]]:
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"""
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Store multiple content items as memory units in ONE batch operation.
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This is MUCH more efficient than calling put_async multiple times:
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- Extracts facts from all contents in parallel
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- Generates ALL embeddings in ONE batch
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- Does ALL database operations in ONE transaction
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Args:
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agent_id: Unique identifier for the agent
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contents: List of dicts with keys:
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- "content" (required): Text content to store
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- "context" (optional): Context about the memory
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- "event_date" (optional): When the event occurred
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Returns:
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List of lists of unit IDs (one list per content item)
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Example:
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unit_ids = await memory.put_batch_async(
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agent_id="user123",
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contents=[
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{"content": "Alice works at Google", "context": "conversation"},
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{"content": "Bob loves Python", "context": "conversation"},
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]
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)
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# Returns: [["unit-id-1"], ["unit-id-2"]]
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"""
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start_time = time.time()
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print(f"\n{'='*60}")
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print(f"PUT_BATCH_ASYNC START: {agent_id}")
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print(f"Batch size: {len(contents)} content items")
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print(f"{'='*60}")
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if not contents:
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return []
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# Step 1: Extract facts from ALL contents in parallel
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step_start = time.time()
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# Create tasks for parallel fact extraction
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fact_extraction_tasks = []
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for item in contents:
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content = item["content"]
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context = item.get("context", "")
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event_date = item.get("event_date") or utcnow()
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task = extract_facts(content, event_date, context)
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fact_extraction_tasks.append((task, event_date, context))
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# Wait for all fact extractions to complete
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all_fact_results = await asyncio.gather(*[task for task, _, _ in fact_extraction_tasks])
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|
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# Flatten and track which facts belong to which content
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all_fact_texts = []
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all_fact_dates = []
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all_contexts = []
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content_boundaries = [] # [(start_idx, end_idx), ...]
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|
|
current_idx = 0
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for i, ((_, event_date, context), fact_dicts) in enumerate(zip(fact_extraction_tasks, all_fact_results)):
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start_idx = current_idx
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for fact_dict in fact_dicts:
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all_fact_texts.append(fact_dict['fact'])
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try:
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from dateutil import parser as date_parser
|
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fact_date = date_parser.isoparse(fact_dict['date'])
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all_fact_dates.append(fact_date)
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except Exception:
|
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all_fact_dates.append(event_date)
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all_contexts.append(context)
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end_idx = current_idx + len(fact_dicts)
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content_boundaries.append((start_idx, end_idx))
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current_idx = end_idx
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total_facts = len(all_fact_texts)
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|
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if total_facts == 0:
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return [[] for _ in contents]
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|
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# Step 2: Generate ALL embeddings in ONE batch (HUGE speedup!)
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step_start = time.time()
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all_embeddings = await self._generate_embeddings_batch(all_fact_texts)
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print(f"[2] Generate embeddings (parallel): {len(all_embeddings)} embeddings in {time.time() - step_start:.3f}s")
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# Step 3: Process everything in ONE database transaction
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cursor = self.conn.cursor()
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try:
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# Deduplication check for all facts
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|
step_start = time.time()
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all_is_duplicate = []
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for sentence, embedding, fact_date in zip(all_fact_texts, all_embeddings, all_fact_dates):
|
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dup_flags = self._find_duplicate_facts_batch(
|
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cursor, agent_id, [sentence], [embedding], fact_date
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)
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all_is_duplicate.extend(dup_flags)
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duplicates_filtered = sum(all_is_duplicate)
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new_facts = total_facts - duplicates_filtered
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print(f"[3] Deduplication check: {duplicates_filtered} duplicates filtered, {new_facts} new facts in {time.time() - step_start:.3f}s")
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# Filter out duplicates
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filtered_sentences = [s for s, is_dup in zip(all_fact_texts, all_is_duplicate) if not is_dup]
|
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filtered_embeddings = [e for e, is_dup in zip(all_embeddings, all_is_duplicate) if not is_dup]
|
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filtered_dates = [d for d, is_dup in zip(all_fact_dates, all_is_duplicate) if not is_dup]
|
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filtered_contexts = [c for c, is_dup in zip(all_contexts, all_is_duplicate) if not is_dup]
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|
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if not filtered_sentences:
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print(f"[PUT_BATCH_ASYNC] All facts were duplicates, returning empty")
|
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return [[] for _ in contents]
|
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|
|
# Batch insert ALL units
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step_start = time.time()
|
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from psycopg2.extras import execute_values
|
|
unit_data = [
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(agent_id, sentence, context, embedding, date, 0) # access_count starts at 0
|
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for sentence, context, embedding, date in zip(
|
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filtered_sentences, filtered_contexts, filtered_embeddings, filtered_dates
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)
|
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]
|
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|
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results = execute_values(
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cursor,
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"""
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INSERT INTO memory_units (agent_id, text, context, embedding, event_date, access_count)
|
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VALUES %s
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RETURNING id
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""",
|
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unit_data,
|
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fetch=True
|
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)
|
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|
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created_unit_ids = [str(row[0]) for row in results]
|
|
print(f"[5] Batch insert units: {len(created_unit_ids)} units in {time.time() - step_start:.3f}s")
|
|
|
|
# Process entities for ALL units
|
|
step_start = time.time()
|
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all_entity_links = self._extract_entities_batch_optimized(
|
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cursor, agent_id, created_unit_ids, filtered_sentences, "", filtered_dates
|
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)
|
|
print(f"[6] Extract entities (batched): {time.time() - step_start:.3f}s")
|
|
|
|
# Create temporal links
|
|
step_start = time.time()
|
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self._create_temporal_links_batch_per_fact(cursor, agent_id, created_unit_ids)
|
|
print(f"[7] Batch create temporal links: {time.time() - step_start:.3f}s")
|
|
|
|
# Create semantic links
|
|
step_start = time.time()
|
|
self._create_semantic_links_batch(cursor, agent_id, created_unit_ids, filtered_embeddings)
|
|
print(f"[8] Batch create semantic links: {time.time() - step_start:.3f}s")
|
|
|
|
# Insert entity links
|
|
step_start = time.time()
|
|
if all_entity_links:
|
|
self._insert_entity_links_batch(cursor, all_entity_links)
|
|
print(f"[9] Batch insert entity links: {time.time() - step_start:.3f}s")
|
|
|
|
# Commit everything
|
|
commit_start = time.time()
|
|
self.conn.commit()
|
|
print(f"[10] Commit: {time.time() - commit_start:.3f}s")
|
|
|
|
# Map created unit IDs back to original content items
|
|
# Account for duplicates when mapping back
|
|
result_unit_ids = []
|
|
filtered_idx = 0
|
|
|
|
for start_idx, end_idx in content_boundaries:
|
|
content_unit_ids = []
|
|
for i in range(start_idx, end_idx):
|
|
if not all_is_duplicate[i]:
|
|
content_unit_ids.append(created_unit_ids[filtered_idx])
|
|
filtered_idx += 1
|
|
result_unit_ids.append(content_unit_ids)
|
|
|
|
total_time = time.time() - start_time
|
|
print(f"\n{'='*60}")
|
|
print(f"PUT_BATCH_ASYNC COMPLETE: {len(created_unit_ids)} units from {len(contents)} contents in {total_time:.3f}s")
|
|
print(f"{'='*60}\n")
|
|
|
|
return result_unit_ids
|
|
|
|
except Exception as e:
|
|
self.conn.rollback()
|
|
raise Exception(f"Failed to store batch memory: {str(e)}")
|
|
finally:
|
|
cursor.close()
|
|
|
|
def _create_temporal_links(
|
|
self,
|
|
cursor,
|
|
agent_id: str,
|
|
unit_id: str,
|
|
event_date: datetime,
|
|
time_window_hours: int = 24,
|
|
):
|
|
"""
|
|
Create temporal links to recent memories.
|
|
|
|
Links this unit to other units that occurred within a time window.
|
|
|
|
Args:
|
|
cursor: Database cursor
|
|
agent_id: Agent ID
|
|
unit_id: ID of the current unit
|
|
event_date: When this event occurred
|
|
time_window_hours: Size of the temporal window
|
|
"""
|
|
try:
|
|
# Get recent units within time window
|
|
cursor.execute(
|
|
"""
|
|
SELECT id, event_date
|
|
FROM memory_units
|
|
WHERE agent_id = %s
|
|
AND id != %s
|
|
AND event_date >= %s
|
|
ORDER BY event_date DESC
|
|
LIMIT 10
|
|
""",
|
|
(agent_id, unit_id, event_date - timedelta(hours=time_window_hours))
|
|
)
|
|
|
|
recent_units = cursor.fetchall()
|
|
|
|
# Create links to recent units
|
|
links = []
|
|
for recent_id, recent_event_date in recent_units:
|
|
# Calculate temporal proximity weight
|
|
time_diff_hours = abs((event_date - recent_event_date).total_seconds() / 3600)
|
|
weight = max(0.3, 1.0 - (time_diff_hours / time_window_hours))
|
|
|
|
links.append((unit_id, recent_id, 'temporal', weight, None))
|
|
|
|
if links:
|
|
execute_values(
|
|
cursor,
|
|
"""
|
|
INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
|
|
VALUES %s
|
|
ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
|
|
""",
|
|
links
|
|
)
|
|
|
|
except Exception as e:
|
|
print(f"ERROR: Failed to create temporal links: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
# Re-raise to trigger rollback at put_async level
|
|
raise
|
|
|
|
def _create_semantic_links(
|
|
self,
|
|
cursor,
|
|
agent_id: str,
|
|
unit_id: str,
|
|
embedding: List[float],
|
|
top_k: int = 5,
|
|
threshold: float = 0.7,
|
|
):
|
|
"""
|
|
Create semantic links to similar memories.
|
|
|
|
Links this unit to other units with similar meaning.
|
|
|
|
Args:
|
|
cursor: Database cursor
|
|
agent_id: Agent ID
|
|
unit_id: ID of the current unit
|
|
embedding: Embedding of the current unit
|
|
top_k: Number of similar units to link to
|
|
threshold: Minimum similarity threshold
|
|
"""
|
|
try:
|
|
# Find similar units using vector similarity
|
|
cursor.execute(
|
|
"""
|
|
SELECT id, 1 - (embedding <=> %s::vector) AS similarity
|
|
FROM memory_units
|
|
WHERE agent_id = %s
|
|
AND id != %s
|
|
AND embedding IS NOT NULL
|
|
AND (1 - (embedding <=> %s::vector)) >= %s
|
|
ORDER BY embedding <=> %s::vector
|
|
LIMIT %s
|
|
""",
|
|
(embedding, agent_id, unit_id, embedding, threshold, embedding, top_k)
|
|
)
|
|
|
|
similar_units = cursor.fetchall()
|
|
|
|
# Create links to similar units
|
|
links = []
|
|
for similar_id, similarity in similar_units:
|
|
links.append((unit_id, similar_id, 'semantic', float(similarity), None))
|
|
|
|
if links:
|
|
execute_values(
|
|
cursor,
|
|
"""
|
|
INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
|
|
VALUES %s
|
|
ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
|
|
""",
|
|
links
|
|
)
|
|
|
|
except Exception as e:
|
|
print(f"ERROR: Failed to create semantic links: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
# Re-raise to trigger rollback at put_async level
|
|
raise
|
|
|
|
def search(
|
|
self,
|
|
agent_id: str,
|
|
query: str,
|
|
thinking_budget: int = 50,
|
|
top_k: int = 10,
|
|
live_tracer=None,
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Search memories using spreading activation (synchronous wrapper).
|
|
|
|
This is a synchronous wrapper around search_async() for convenience.
|
|
For best performance, use search_async() directly.
|
|
|
|
Args:
|
|
agent_id: Agent ID to search for
|
|
query: Search query
|
|
thinking_budget: How many units to explore (computational budget)
|
|
top_k: Number of results to return
|
|
live_tracer: Optional LiveSearchTracer for visualization
|
|
|
|
Returns:
|
|
List of memory units with their weights, sorted by relevance
|
|
"""
|
|
# Run async version synchronously
|
|
return asyncio.run(self.search_async(agent_id, query, thinking_budget, top_k, live_tracer))
|
|
|
|
async def search_async(
|
|
self,
|
|
agent_id: str,
|
|
query: str,
|
|
thinking_budget: int = 50,
|
|
top_k: int = 10,
|
|
live_tracer=None,
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Search memories using spreading activation (ASYNC version).
|
|
|
|
This implements the core SEARCH operation:
|
|
1. Find entry points (most relevant units via vector search)
|
|
2. Spread activation through the graph
|
|
3. Weight results by activation + recency + frequency
|
|
4. Return top results
|
|
|
|
Args:
|
|
agent_id: Agent ID to search for
|
|
query: Search query
|
|
thinking_budget: How many units to explore (computational budget)
|
|
top_k: Number of results to return
|
|
live_tracer: Optional LiveSearchTracer for visualization
|
|
|
|
Returns:
|
|
List of memory units with their weights, sorted by relevance
|
|
"""
|
|
cursor = self.conn.cursor(cursor_factory=RealDictCursor)
|
|
|
|
search_start = time.time()
|
|
print(f"\n[SEARCH] Starting search for query: '{query[:50]}...' (thinking_budget={thinking_budget}, top_k={top_k})")
|
|
|
|
try:
|
|
# Step 1: Generate query embedding
|
|
step_start = time.time()
|
|
query_embedding = self._generate_embedding(query)
|
|
print(f" [1] Generate query embedding: {time.time() - step_start:.3f}s")
|
|
|
|
# Step 2: Find entry points
|
|
step_start = time.time()
|
|
cursor.execute(
|
|
"""
|
|
SELECT id, text, context, event_date, access_count, embedding,
|
|
1 - (embedding <=> %s::vector) AS similarity
|
|
FROM memory_units
|
|
WHERE agent_id = %s
|
|
AND embedding IS NOT NULL
|
|
AND (1 - (embedding <=> %s::vector)) >= 0.5
|
|
ORDER BY embedding <=> %s::vector
|
|
LIMIT 3
|
|
""",
|
|
(query_embedding, agent_id, query_embedding, query_embedding)
|
|
)
|
|
|
|
entry_points = cursor.fetchall()
|
|
print(f" [2] Find entry points: {len(entry_points)} found in {time.time() - step_start:.3f}s")
|
|
|
|
if not entry_points:
|
|
print(f"[SEARCH] Complete: 0 results in {time.time() - search_start:.3f}s")
|
|
return []
|
|
|
|
# Step 3: Spreading activation with budget
|
|
step_start = time.time()
|
|
visited = set()
|
|
results = []
|
|
budget_remaining = thinking_budget
|
|
# Initialize entry points with their actual similarity scores instead of 1.0
|
|
queue = [(dict(unit), unit["similarity"], True) for unit in entry_points] # (unit, activation, is_entry)
|
|
|
|
# Track substep timings
|
|
update_access_time = 0
|
|
calculate_weight_time = 0
|
|
query_neighbors_time = 0
|
|
process_neighbors_time = 0
|
|
|
|
# Process nodes in batches for efficient neighbor querying
|
|
BATCH_SIZE = 50
|
|
nodes_to_process = [] # (unit, activation, is_entry_point)
|
|
|
|
while queue and budget_remaining > 0:
|
|
# Collect a batch of nodes to process
|
|
while queue and len(nodes_to_process) < BATCH_SIZE and budget_remaining > 0:
|
|
current_unit, activation, is_entry_point = queue.pop(0)
|
|
unit_id = str(current_unit["id"])
|
|
|
|
if unit_id not in visited:
|
|
visited.add(unit_id)
|
|
budget_remaining -= 1
|
|
nodes_to_process.append((current_unit, activation, is_entry_point))
|
|
|
|
if not nodes_to_process:
|
|
break
|
|
|
|
# Update access counts for batch
|
|
substep_start = time.time()
|
|
node_ids = [str(node[0]["id"]) for node in nodes_to_process]
|
|
cursor.execute(
|
|
"UPDATE memory_units SET access_count = access_count + 1 WHERE id::text = ANY(%s)",
|
|
(node_ids,)
|
|
)
|
|
update_access_time += time.time() - substep_start
|
|
|
|
# Query neighbors for ALL nodes in batch at once
|
|
substep_start = time.time()
|
|
cursor.execute(
|
|
"""
|
|
SELECT ml.from_unit_id, ml.to_unit_id, ml.weight,
|
|
mu.text, mu.context, mu.event_date, mu.access_count, mu.embedding
|
|
FROM memory_links ml
|
|
JOIN memory_units mu ON ml.to_unit_id = mu.id
|
|
WHERE ml.from_unit_id::text = ANY(%s)
|
|
AND ml.weight >= 0.1
|
|
ORDER BY ml.from_unit_id, ml.weight DESC
|
|
""",
|
|
(node_ids,)
|
|
)
|
|
all_neighbors = cursor.fetchall()
|
|
query_neighbors_time += time.time() - substep_start
|
|
|
|
# Group neighbors by from_unit_id
|
|
substep_start = time.time()
|
|
neighbors_by_node = {}
|
|
for neighbor in all_neighbors:
|
|
from_id = str(neighbor["from_unit_id"])
|
|
if from_id not in neighbors_by_node:
|
|
neighbors_by_node[from_id] = []
|
|
neighbors_by_node[from_id].append(neighbor)
|
|
|
|
# Process each node in the batch
|
|
for current_unit, activation, is_entry_point in nodes_to_process:
|
|
unit_id = str(current_unit["id"])
|
|
|
|
# Calculate combined weight
|
|
event_date = current_unit["event_date"]
|
|
days_since = (utcnow() - event_date).total_seconds() / 86400
|
|
|
|
recency_weight = calculate_recency_weight(days_since)
|
|
frequency_weight = calculate_frequency_weight(current_unit.get("access_count", 0))
|
|
|
|
# Normalize frequency to [0, 1] range
|
|
frequency_normalized = (frequency_weight - 1.0) / 1.0
|
|
|
|
# Calculate semantic similarity between query and this memory
|
|
memory_embedding = current_unit.get("embedding")
|
|
if memory_embedding is not None:
|
|
# Cosine similarity = 1 - cosine distance
|
|
query_vec = np.array(query_embedding)
|
|
memory_vec = np.array(memory_embedding)
|
|
# Cosine similarity
|
|
dot_product = np.dot(query_vec, memory_vec)
|
|
norm_query = np.linalg.norm(query_vec)
|
|
norm_memory = np.linalg.norm(memory_vec)
|
|
semantic_similarity = dot_product / (norm_query * norm_memory) if norm_query > 0 and norm_memory > 0 else 0.0
|
|
else:
|
|
semantic_similarity = 0.0
|
|
|
|
# Combined weight: 30% activation, 30% semantic similarity, 25% recency, 15% frequency
|
|
final_weight = 0.3 * activation + 0.3 * semantic_similarity + 0.25 * recency_weight + 0.15 * frequency_normalized
|
|
|
|
# Notify tracer
|
|
if live_tracer:
|
|
live_tracer.visit_node(
|
|
node_id=unit_id,
|
|
text=current_unit["text"],
|
|
activation=activation,
|
|
recency=recency_weight,
|
|
frequency=frequency_weight,
|
|
weight=final_weight,
|
|
is_entry_point=is_entry_point,
|
|
)
|
|
|
|
results.append({
|
|
"id": unit_id,
|
|
"text": current_unit["text"],
|
|
"context": current_unit.get("context", ""),
|
|
"event_date": event_date.isoformat(),
|
|
"weight": final_weight,
|
|
"activation": activation,
|
|
"semantic_similarity": semantic_similarity,
|
|
"recency": recency_weight,
|
|
"frequency": frequency_weight,
|
|
})
|
|
|
|
# Spread to neighbors (from batch query results)
|
|
neighbors = neighbors_by_node.get(unit_id, [])
|
|
for neighbor in neighbors:
|
|
neighbor_id = str(neighbor["to_unit_id"])
|
|
if neighbor_id not in visited:
|
|
link_weight = neighbor["weight"]
|
|
new_activation = activation * link_weight * 0.8 # 0.8 = decay factor
|
|
|
|
if new_activation > 0.1:
|
|
queue.append(({
|
|
"id": neighbor["to_unit_id"],
|
|
"text": neighbor["text"],
|
|
"context": neighbor.get("context", ""),
|
|
"event_date": neighbor["event_date"],
|
|
"access_count": neighbor["access_count"],
|
|
"embedding": neighbor.get("embedding"),
|
|
}, new_activation, False)) # Not an entry point
|
|
|
|
calculate_weight_time += time.time() - substep_start
|
|
process_neighbors_time += time.time() - substep_start
|
|
|
|
# Clear batch for next iteration
|
|
nodes_to_process = []
|
|
|
|
spreading_activation_time = time.time() - step_start
|
|
num_batches = (len(visited) + BATCH_SIZE - 1) // BATCH_SIZE # Ceiling division
|
|
print(f" [3] Spreading activation: {len(visited)} nodes visited in {spreading_activation_time:.3f}s")
|
|
print(f" [3.1] Update access counts: {update_access_time:.3f}s")
|
|
print(f" [3.2] Calculate weights: {calculate_weight_time:.3f}s")
|
|
print(f" [3.3] Query neighbors: {query_neighbors_time:.3f}s ({num_batches} batched queries)")
|
|
print(f" [3.4] Process neighbors: {process_neighbors_time:.3f}s")
|
|
|
|
step_start = time.time()
|
|
self.conn.commit()
|
|
print(f" [4] Commit: {time.time() - step_start:.3f}s")
|
|
|
|
# Step 4: Sort by final weight and return top results
|
|
step_start = time.time()
|
|
results.sort(key=lambda x: x["weight"], reverse=True)
|
|
top_results = results[:top_k]
|
|
print(f" [5] Sort and return top {top_k}: {time.time() - step_start:.3f}s")
|
|
|
|
print(f"[SEARCH] Complete: {len(top_results)} results in {time.time() - search_start:.3f}s\n")
|
|
return top_results
|
|
|
|
except Exception as e:
|
|
print(f"[SEARCH] ERROR after {time.time() - search_start:.3f}s: {str(e)}")
|
|
self.conn.rollback()
|
|
raise Exception(f"Failed to search memories: {str(e)}")
|
|
finally:
|
|
cursor.close()
|
|
|
|
def delete_agent(self, agent_id: str) -> Dict[str, int]:
|
|
"""
|
|
Delete all data for a specific agent (multi-tenant cleanup).
|
|
|
|
This is much more efficient than dropping all tables and allows
|
|
multiple agents to coexist in the same database.
|
|
|
|
Deletes (with CASCADE):
|
|
- All memory units for this agent
|
|
- All entities for this agent
|
|
- All associated links, unit-entity associations, and co-occurrences
|
|
|
|
Args:
|
|
agent_id: Agent ID to delete
|
|
|
|
Returns:
|
|
Dictionary with counts of deleted items
|
|
"""
|
|
cursor = self.conn.cursor()
|
|
|
|
try:
|
|
# Count before deletion for reporting
|
|
cursor.execute("SELECT COUNT(*) FROM memory_units WHERE agent_id = %s", (agent_id,))
|
|
units_count = cursor.fetchone()[0]
|
|
|
|
cursor.execute("SELECT COUNT(*) FROM entities WHERE agent_id = %s", (agent_id,))
|
|
entities_count = cursor.fetchone()[0]
|
|
|
|
# Delete memory units (cascades to unit_entities, memory_links)
|
|
cursor.execute("DELETE FROM memory_units WHERE agent_id = %s", (agent_id,))
|
|
|
|
# Delete entities (cascades to unit_entities, entity_cooccurrences, memory_links with entity_id)
|
|
cursor.execute("DELETE FROM entities WHERE agent_id = %s", (agent_id,))
|
|
|
|
self.conn.commit()
|
|
|
|
return {
|
|
"memory_units_deleted": units_count,
|
|
"entities_deleted": entities_count
|
|
}
|
|
|
|
except Exception as e:
|
|
self.conn.rollback()
|
|
raise Exception(f"Failed to delete agent data: {str(e)}")
|
|
finally:
|
|
cursor.close()
|
|
|
|
def get_memory_graph_data(self, agent_id: str = None) -> Tuple[List[Dict], List[Dict]]:
|
|
"""
|
|
Get memory graph data for visualization.
|
|
|
|
Args:
|
|
agent_id: Optional agent ID (if None, returns all data)
|
|
|
|
Returns:
|
|
Tuple of (units, links) for visualization
|
|
"""
|
|
cursor = self.conn.cursor(cursor_factory=RealDictCursor)
|
|
|
|
try:
|
|
# Get all units (optionally filtered by agent)
|
|
if agent_id:
|
|
cursor.execute(
|
|
"SELECT id, text, context, event_date, access_count FROM memory_units WHERE agent_id = %s",
|
|
(agent_id,)
|
|
)
|
|
else:
|
|
cursor.execute(
|
|
"SELECT id, text, context, event_date, access_count FROM memory_units"
|
|
)
|
|
units = [dict(row) for row in cursor.fetchall()]
|
|
|
|
# Get all links (optionally filtered by agent)
|
|
if agent_id:
|
|
cursor.execute(
|
|
"""
|
|
SELECT ml.from_unit_id, ml.to_unit_id, ml.link_type, ml.weight
|
|
FROM memory_links ml
|
|
JOIN memory_units mu1 ON ml.from_unit_id = mu1.id
|
|
JOIN memory_units mu2 ON ml.to_unit_id = mu2.id
|
|
WHERE mu1.agent_id = %s
|
|
""",
|
|
(agent_id,)
|
|
)
|
|
else:
|
|
cursor.execute(
|
|
"SELECT from_unit_id, to_unit_id, link_type, weight FROM memory_links"
|
|
)
|
|
links = [dict(row) for row in cursor.fetchall()]
|
|
|
|
return units, links
|
|
|
|
except Exception as e:
|
|
raise Exception(f"Failed to get memory graph data: {str(e)}")
|
|
finally:
|
|
cursor.close()
|
|
|
|
def _extract_entities_batch_optimized(
|
|
self,
|
|
cursor,
|
|
agent_id: str,
|
|
unit_ids: List[str],
|
|
sentences: List[str],
|
|
context: str,
|
|
fact_dates: List,
|
|
) -> List[tuple]:
|
|
"""
|
|
Extract entities from ALL sentences in one batch (MUCH faster than sequential).
|
|
|
|
Uses spaCy's batch processing to extract entities from all texts at once,
|
|
then resolves and links them in bulk.
|
|
|
|
Returns list of tuples for batch insertion: (from_unit_id, to_unit_id, link_type, weight, entity_id)
|
|
"""
|
|
from .entity_resolver import extract_entities_batch
|
|
|
|
try:
|
|
# Step 1: Extract entities from ALL sentences in one batch (fast!)
|
|
substep_start = time.time()
|
|
all_entities = extract_entities_batch(sentences)
|
|
total_entities = sum(len(ents) for ents in all_entities)
|
|
print(f" [6.1] spaCy NER (batch): {total_entities} entities from {len(sentences)} sentences in {time.time() - substep_start:.3f}s")
|
|
|
|
# Step 2: Resolve entities in BATCH (much faster!)
|
|
substep_start = time.time()
|
|
step_6_2_start = time.time()
|
|
|
|
# [6.2.1] Prepare all entities for batch resolution
|
|
substep_6_2_1_start = time.time()
|
|
all_entities_flat = []
|
|
entity_to_unit = [] # Maps flat index to (unit_id, local_index)
|
|
|
|
for unit_id, entities, fact_date in zip(unit_ids, all_entities, fact_dates):
|
|
if not entities:
|
|
continue
|
|
|
|
for local_idx, entity in enumerate(entities):
|
|
all_entities_flat.append({
|
|
'text': entity['text'],
|
|
'type': entity['type'],
|
|
'nearby_entities': entities,
|
|
})
|
|
entity_to_unit.append((unit_id, local_idx, fact_date))
|
|
print(f" [6.2.1] Prepare entities: {len(all_entities_flat)} entities in {time.time() - substep_6_2_1_start:.3f}s")
|
|
|
|
# Resolve ALL entities in one batch call
|
|
if all_entities_flat:
|
|
# [6.2.2] Batch resolve entities
|
|
substep_6_2_2_start = time.time()
|
|
# Group by date for batch resolution (most will have same date)
|
|
entities_by_date = {}
|
|
for idx, (unit_id, local_idx, fact_date) in enumerate(entity_to_unit):
|
|
date_key = fact_date
|
|
if date_key not in entities_by_date:
|
|
entities_by_date[date_key] = []
|
|
entities_by_date[date_key].append((idx, all_entities_flat[idx]))
|
|
|
|
# Resolve each date group in batch
|
|
resolved_entity_ids = [None] * len(all_entities_flat)
|
|
for fact_date, entities_group in entities_by_date.items():
|
|
indices = [idx for idx, _ in entities_group]
|
|
entities_data = [entity_data for _, entity_data in entities_group]
|
|
|
|
batch_resolved = self.entity_resolver.resolve_entities_batch(
|
|
agent_id=agent_id,
|
|
entities_data=entities_data,
|
|
context=context,
|
|
unit_event_date=fact_date
|
|
)
|
|
|
|
for idx, entity_id in zip(indices, batch_resolved):
|
|
resolved_entity_ids[idx] = entity_id
|
|
print(f" [6.2.2] Resolve entities: {len(all_entities_flat)} entities in {time.time() - substep_6_2_2_start:.3f}s")
|
|
|
|
# [6.2.3] Create unit-entity links in BATCH
|
|
substep_6_2_3_start = time.time()
|
|
# Map resolved entities back to units and collect all (unit, entity) pairs
|
|
unit_to_entity_ids = {}
|
|
unit_entity_pairs = []
|
|
for idx, (unit_id, local_idx, fact_date) in enumerate(entity_to_unit):
|
|
if unit_id not in unit_to_entity_ids:
|
|
unit_to_entity_ids[unit_id] = []
|
|
|
|
entity_id = resolved_entity_ids[idx]
|
|
unit_to_entity_ids[unit_id].append(entity_id)
|
|
unit_entity_pairs.append((unit_id, entity_id))
|
|
|
|
# Batch insert all unit-entity links (MUCH faster!)
|
|
self.entity_resolver.link_units_to_entities_batch(unit_entity_pairs)
|
|
print(f" [6.2.3] Create unit-entity links (batched): {len(unit_entity_pairs)} links in {time.time() - substep_6_2_3_start:.3f}s")
|
|
|
|
print(f" [6.2] Entity resolution (batched): {len(all_entities_flat)} entities resolved in {time.time() - step_6_2_start:.3f}s")
|
|
else:
|
|
unit_to_entity_ids = {}
|
|
print(f" [6.2] Entity resolution (batched): 0 entities in {time.time() - step_6_2_start:.3f}s")
|
|
|
|
# Step 3: Create entity links between units that share entities
|
|
substep_start = time.time()
|
|
# Collect all unique entity IDs
|
|
all_entity_ids = set()
|
|
for entity_ids in unit_to_entity_ids.values():
|
|
all_entity_ids.update(entity_ids)
|
|
|
|
# For each entity, find all units that reference it (one query per entity)
|
|
entity_to_units = {}
|
|
for entity_id in all_entity_ids:
|
|
cursor.execute(
|
|
"""
|
|
SELECT unit_id
|
|
FROM unit_entities
|
|
WHERE entity_id = %s
|
|
""",
|
|
(entity_id,)
|
|
)
|
|
entity_to_units[entity_id] = [row[0] for row in cursor.fetchall()]
|
|
|
|
# Create bidirectional links between units that share entities
|
|
links = []
|
|
for entity_id, units_with_entity in entity_to_units.items():
|
|
# For each pair of units with this entity, create bidirectional links
|
|
for i, unit_id_1 in enumerate(units_with_entity):
|
|
for unit_id_2 in units_with_entity[i+1:]:
|
|
# Bidirectional links
|
|
links.append((unit_id_1, unit_id_2, 'entity', 1.0, entity_id))
|
|
links.append((unit_id_2, unit_id_1, 'entity', 1.0, entity_id))
|
|
|
|
print(f" [6.3] Entity link creation: {len(links)} links for {len(all_entity_ids)} unique entities in {time.time() - substep_start:.3f}s")
|
|
|
|
return links
|
|
|
|
except Exception as e:
|
|
print(f"ERROR: Failed to extract entities in batch: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
# Re-raise to trigger rollback at put_async level
|
|
raise
|
|
|
|
def _create_temporal_links_batch(
|
|
self,
|
|
cursor,
|
|
agent_id: str,
|
|
unit_ids: List[str],
|
|
event_date: datetime,
|
|
time_window_hours: int = 24,
|
|
):
|
|
"""
|
|
Create temporal links for multiple units in one batch query.
|
|
|
|
Uses a single query to find all relevant temporal connections.
|
|
"""
|
|
if not unit_ids:
|
|
return
|
|
|
|
try:
|
|
from psycopg2.extras import execute_values
|
|
|
|
# Get ALL recent units within time window (single query)
|
|
# Cast string IDs to UUIDs for comparison
|
|
cursor.execute(
|
|
"""
|
|
SELECT id, event_date
|
|
FROM memory_units
|
|
WHERE agent_id = %s
|
|
AND id::text != ALL(%s)
|
|
AND event_date >= %s
|
|
ORDER BY event_date DESC
|
|
""",
|
|
(agent_id, unit_ids, event_date - timedelta(hours=time_window_hours))
|
|
)
|
|
|
|
recent_units = cursor.fetchall()
|
|
|
|
# Create links from each new unit to all recent units
|
|
links = []
|
|
for unit_id in unit_ids:
|
|
for recent_id, recent_event_date in recent_units:
|
|
# Calculate temporal proximity weight
|
|
time_diff_hours = abs((event_date - recent_event_date).total_seconds() / 3600)
|
|
weight = max(0.3, 1.0 - (time_diff_hours / time_window_hours))
|
|
links.append((unit_id, recent_id, 'temporal', weight, None))
|
|
|
|
if links:
|
|
execute_values(
|
|
cursor,
|
|
"""
|
|
INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
|
|
VALUES %s
|
|
ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
|
|
""",
|
|
links
|
|
)
|
|
|
|
except Exception as e:
|
|
print(f"ERROR: Failed to create temporal links: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
# Re-raise to trigger rollback at put_async level
|
|
raise
|
|
|
|
def _create_temporal_links_batch_per_fact(
|
|
self,
|
|
cursor,
|
|
agent_id: str,
|
|
unit_ids: List[str],
|
|
time_window_hours: int = 24,
|
|
):
|
|
"""
|
|
Create temporal links for multiple units, each with their own event_date.
|
|
|
|
Queries the event_date for each unit from the database and creates temporal
|
|
links based on individual dates (supports per-fact dating).
|
|
"""
|
|
if not unit_ids:
|
|
return
|
|
|
|
try:
|
|
from psycopg2.extras import execute_values
|
|
|
|
# Get the event_date for each new unit
|
|
cursor.execute(
|
|
"""
|
|
SELECT id, event_date
|
|
FROM memory_units
|
|
WHERE id::text = ANY(%s)
|
|
""",
|
|
(unit_ids,)
|
|
)
|
|
new_units = {str(row[0]): row[1] for row in cursor.fetchall()}
|
|
|
|
# Create links based on each unit's individual event_date
|
|
links = []
|
|
for unit_id, unit_event_date in new_units.items():
|
|
# Find units within the time window of THIS specific unit
|
|
cursor.execute(
|
|
"""
|
|
SELECT id, event_date
|
|
FROM memory_units
|
|
WHERE agent_id = %s
|
|
AND id != %s
|
|
AND event_date BETWEEN %s AND %s
|
|
ORDER BY event_date DESC
|
|
LIMIT 10
|
|
""",
|
|
(
|
|
agent_id,
|
|
unit_id,
|
|
unit_event_date - timedelta(hours=time_window_hours),
|
|
unit_event_date + timedelta(hours=time_window_hours)
|
|
)
|
|
)
|
|
|
|
recent_units = cursor.fetchall()
|
|
|
|
for recent_id, recent_event_date in recent_units:
|
|
# Calculate temporal proximity weight
|
|
time_diff_hours = abs((unit_event_date - recent_event_date).total_seconds() / 3600)
|
|
weight = max(0.3, 1.0 - (time_diff_hours / time_window_hours))
|
|
links.append((unit_id, recent_id, 'temporal', weight, None))
|
|
|
|
if links:
|
|
execute_values(
|
|
cursor,
|
|
"""
|
|
INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
|
|
VALUES %s
|
|
ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
|
|
""",
|
|
links
|
|
)
|
|
|
|
except Exception as e:
|
|
print(f"ERROR: Failed to create temporal links: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
# Re-raise to trigger rollback at put_async level
|
|
raise
|
|
|
|
def _create_semantic_links_batch(
|
|
self,
|
|
cursor,
|
|
agent_id: str,
|
|
unit_ids: List[str],
|
|
embeddings: List[List[float]],
|
|
top_k: int = 5,
|
|
threshold: float = 0.7,
|
|
):
|
|
"""
|
|
Create semantic links for multiple units efficiently.
|
|
|
|
For each unit, finds similar units and creates links.
|
|
"""
|
|
if not unit_ids or not embeddings:
|
|
return
|
|
|
|
try:
|
|
from psycopg2.extras import execute_values
|
|
|
|
all_links = []
|
|
|
|
for unit_id, embedding in zip(unit_ids, embeddings):
|
|
# Find similar units using vector similarity
|
|
cursor.execute(
|
|
"""
|
|
SELECT id, 1 - (embedding <=> %s::vector) AS similarity
|
|
FROM memory_units
|
|
WHERE agent_id = %s
|
|
AND id != %s
|
|
AND embedding IS NOT NULL
|
|
AND (1 - (embedding <=> %s::vector)) >= %s
|
|
ORDER BY embedding <=> %s::vector
|
|
LIMIT %s
|
|
""",
|
|
(embedding, agent_id, unit_id, embedding, threshold, embedding, top_k)
|
|
)
|
|
|
|
similar_units = cursor.fetchall()
|
|
|
|
for similar_id, similarity in similar_units:
|
|
all_links.append((unit_id, similar_id, 'semantic', float(similarity), None))
|
|
|
|
if all_links:
|
|
execute_values(
|
|
cursor,
|
|
"""
|
|
INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
|
|
VALUES %s
|
|
ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
|
|
""",
|
|
all_links
|
|
)
|
|
|
|
except Exception as e:
|
|
print(f"ERROR: Failed to create semantic links: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
# Re-raise to trigger rollback at put_async level
|
|
raise
|
|
|
|
def _insert_entity_links_batch(self, cursor, links: List[tuple]):
|
|
"""Insert all entity links in a single batch."""
|
|
if not links:
|
|
return
|
|
|
|
try:
|
|
from psycopg2.extras import execute_values
|
|
execute_values(
|
|
cursor,
|
|
"""
|
|
INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
|
|
VALUES %s
|
|
ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
|
|
""",
|
|
links
|
|
)
|
|
except Exception as e:
|
|
print(f"Warning: Failed to insert entity links: {str(e)}")
|