1876 lines
85 KiB
Python
1876 lines
85 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 asyncpg
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from dotenv import load_dotenv
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import asyncio
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from .embeddings import Embeddings, SentenceTransformersEmbeddings
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import time
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import numpy as np
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import uuid
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import logging
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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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from .operations import EmbeddingOperationsMixin, LinkOperationsMixin, ThinkOperationsMixin
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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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# Logger for memory system
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logger = logging.getLogger(__name__)
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class TemporalSemanticMemory(
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EmbeddingOperationsMixin,
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LinkOperationsMixin,
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ThinkOperationsMixin,
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):
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"""
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Advanced memory system using temporal and semantic linking with PostgreSQL.
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Uses mixin architecture for code organization:
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- EmbeddingOperationsMixin: Embedding generation
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- LinkOperationsMixin: Entity, temporal, and semantic link creation
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- ThinkOperationsMixin: Think operations for formulating answers with opinions
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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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embeddings: Optional[Embeddings] = None,
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embedding_model: Optional[str] = None,
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pool_min_size: int = 5,
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pool_max_size: int = 100,
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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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embeddings: Embeddings implementation to use. If not provided, uses SentenceTransformersEmbeddings
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embedding_model: (Deprecated) Name of the SentenceTransformer model to use. Use embeddings parameter instead.
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pool_min_size: Minimum number of connections in the pool (default: 5)
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pool_max_size: Maximum number of connections in the pool (default: 100)
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Increase for parallel think/search operations (e.g., 200-300 for 100+ parallel thinks)
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"""
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load_dotenv()
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# Initialize PostgreSQL connection URL
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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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# Connection pool (will be created in initialize())
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self._pool = None
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self._initialized = False
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self._pool_min_size = pool_min_size
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self._pool_max_size = pool_max_size
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# Initialize entity resolver (will be created in initialize())
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self.entity_resolver = None
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# Initialize embeddings
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if embeddings is not None:
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self.embeddings = embeddings
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else:
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# Default to SentenceTransformersEmbeddings
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model_name = embedding_model or "BAAI/bge-small-en-v1.5"
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self.embeddings = SentenceTransformersEmbeddings(model_name)
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# Initialize LLM client (cached for reuse across operations)
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from openai import AsyncOpenAI
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groq_api_key = os.getenv("GROQ_API_KEY")
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if groq_api_key:
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self._llm_client = AsyncOpenAI(
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api_key=groq_api_key,
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base_url="https://api.groq.com/openai/v1"
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)
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else:
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self._llm_client = None # Will be created on-demand if needed
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# Background queue for access count updates (to avoid blocking searches)
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self._access_count_queue = asyncio.Queue()
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self._access_count_worker_task = None
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self._shutdown_event = asyncio.Event()
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# Track background opinion PUT tasks to ensure clean shutdown
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self._background_tasks = set()
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# Backpressure mechanism: limit concurrent searches to prevent overwhelming the database
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self._search_semaphore = asyncio.Semaphore(32)
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async def _access_count_worker(self):
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"""Background worker that processes access count updates in batches."""
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pool = self._pool # Pool is guaranteed to exist when worker starts
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while not self._shutdown_event.is_set():
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try:
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# Collect updates for up to 1 second or 1000 items
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updates = {}
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deadline = asyncio.get_event_loop().time() + 1.0
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while len(updates) < 1000 and asyncio.get_event_loop().time() < deadline:
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try:
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# Wait for items with short timeout
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remaining_time = max(0.1, deadline - asyncio.get_event_loop().time())
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node_ids = await asyncio.wait_for(
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self._access_count_queue.get(),
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timeout=remaining_time
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)
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# Deduplicate by adding to set
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for node_id in node_ids:
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updates[node_id] = True
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except asyncio.TimeoutError:
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break
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# Process batch if we have updates
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if updates:
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node_id_list = list(updates.keys())
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try:
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# Convert string UUIDs to UUID type for faster matching
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uuid_list = [uuid.UUID(nid) for nid in node_id_list]
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async with pool.acquire() as conn:
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await conn.execute(
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"UPDATE memory_units SET access_count = access_count + 1 WHERE id = ANY($1::uuid[])",
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uuid_list
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)
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except Exception as e:
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logger.error(f"Access count worker: Error updating access counts: {e}")
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except asyncio.CancelledError:
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break
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except Exception as e:
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logger.error(f"Access count worker: Unexpected error: {e}")
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await asyncio.sleep(1) # Backoff on error
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async def initialize(self):
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"""Initialize the connection pool and background workers."""
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if self._initialized:
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return
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# Create connection pool
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# For read-heavy workloads with many parallel think/search operations,
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# we need a larger pool. Read operations don't need strong isolation.
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self._pool = await asyncpg.create_pool(
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self.db_url,
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min_size=self._pool_min_size,
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max_size=self._pool_max_size,
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command_timeout=60,
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statement_cache_size=0 # Disable prepared statement cache
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)
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# Initialize entity resolver with pool
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self.entity_resolver = EntityResolver(self._pool)
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# Start access count worker
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self._access_count_worker_task = asyncio.create_task(self._access_count_worker())
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self._initialized = True
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logger.info("Memory system initialized (pool and workers started)")
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async def _get_pool(self) -> asyncpg.Pool:
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"""Get the connection pool (must call initialize() first)."""
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if not self._initialized:
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await self.initialize()
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return self._pool
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async def wait_for_background_tasks(self):
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"""Wait for all background tasks (e.g., opinion PUTs) to complete."""
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if self._background_tasks:
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await asyncio.gather(*self._background_tasks, return_exceptions=True)
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async def close(self):
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"""Close the connection pool and shutdown background workers."""
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logger.info("close() started")
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# Signal shutdown to worker
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self._shutdown_event.set()
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logger.info("shutdown event set")
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# Wait for background opinion PUT tasks to complete
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if self._background_tasks:
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logger.debug(f"waiting for {len(self._background_tasks)} background tasks to complete")
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await self.wait_for_background_tasks()
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logger.debug("background tasks completed")
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# Cancel and wait for worker task
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if self._access_count_worker_task is not None:
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logger.debug("cancelling worker task")
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self._access_count_worker_task.cancel()
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try:
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logger.debug("waiting for worker task to finish")
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await self._access_count_worker_task
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logger.debug("worker task finished")
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except asyncio.CancelledError:
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logger.debug("worker task cancelled successfully")
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else:
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logger.debug("no worker task to cancel")
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# Close pool
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if self._pool is not None:
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logger.debug("closing connection pool")
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self._pool.terminate()
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logger.debug("connection pool closed")
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self._pool = None
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else:
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logger.debug("no pool to close")
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logger.debug("close() completed")
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async def _find_duplicate_facts_batch(
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self,
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conn,
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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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conn: Database connection
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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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if not texts:
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return []
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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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# Fetch ALL existing facts in time window ONCE (much faster than N queries)
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import time as time_mod
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fetch_start = time_mod.time()
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existing_facts = await conn.fetch(
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"""
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SELECT id, text, embedding
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FROM memory_units
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WHERE agent_id = $1
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AND event_date BETWEEN $2 AND $3
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""",
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agent_id, time_lower, time_upper
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)
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logger.debug(f" [3.X] Fetched {len(existing_facts)} existing facts in {time_mod.time() - fetch_start:.3f}s")
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# If no existing facts, nothing is duplicate
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if not existing_facts:
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return [False] * len(texts)
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# Compute similarities in Python (vectorized with numpy)
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import numpy as np
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is_duplicate = []
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# Convert existing embeddings to numpy for faster computation
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embedding_arrays = []
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for row in existing_facts:
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raw_emb = row['embedding']
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# Handle different pgvector formats
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if isinstance(raw_emb, str):
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# Parse string format: "[1.0, 2.0, ...]"
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import json
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emb = np.array(json.loads(raw_emb), dtype=np.float32)
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elif isinstance(raw_emb, (list, tuple)):
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emb = np.array(raw_emb, dtype=np.float32)
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else:
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# Try direct conversion
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emb = np.array(raw_emb, dtype=np.float32)
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embedding_arrays.append(emb)
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if not embedding_arrays:
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existing_embeddings = np.array([])
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elif len(embedding_arrays) == 1:
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# Single embedding: reshape to (1, dim)
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existing_embeddings = embedding_arrays[0].reshape(1, -1)
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else:
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# Multiple embeddings: vstack
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existing_embeddings = np.vstack(embedding_arrays)
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comp_start = time_mod.time()
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for embedding in embeddings:
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# Compute cosine similarity with all existing facts
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emb_array = np.array(embedding)
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# Cosine similarity = 1 - cosine distance
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# For normalized vectors: cosine_sim = dot product
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similarities = np.dot(existing_embeddings, emb_array)
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|
|
|
# Check if any existing fact is too similar
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max_similarity = np.max(similarities) if len(similarities) > 0 else 0
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is_duplicate.append(max_similarity > similarity_threshold)
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|
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logger.debug(f" [3.X] Computed {len(texts)} x {len(existing_facts)} similarities in {time_mod.time() - comp_start:.3f}s")
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return is_duplicate
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|
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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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|
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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)
|
|
|
|
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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document_id: Optional[str] = None,
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|
document_metadata: Optional[Dict[str, Any]] = None,
|
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upsert: bool = False,
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fact_type_override: Optional[str] = None,
|
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confidence_score: Optional[float] = None,
|
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) -> List[str]:
|
|
"""
|
|
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
|
|
event_date: When the event occurred (defaults to now)
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document_id: Optional document ID for tracking and upsert
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|
document_metadata: Optional metadata about the document
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upsert: If True and document_id exists, delete old units and create new ones
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fact_type_override: Override fact type ('world', 'agent', 'opinion')
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confidence_score: Confidence score for opinions (0.0 to 1.0)
|
|
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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
|
|
}],
|
|
document_id=document_id,
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document_metadata=document_metadata,
|
|
upsert=upsert,
|
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fact_type_override=fact_type_override,
|
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confidence_score=confidence_score
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|
)
|
|
|
|
# Return the first (and only) list of unit IDs
|
|
return result[0] if result else []
|
|
|
|
async def put_batch_async(
|
|
self,
|
|
agent_id: str,
|
|
contents: List[Dict[str, Any]],
|
|
document_id: Optional[str] = None,
|
|
document_metadata: Optional[Dict[str, Any]] = None,
|
|
upsert: bool = False,
|
|
fact_type_override: Optional[str] = None,
|
|
confidence_score: Optional[float] = None,
|
|
) -> List[List[str]]:
|
|
"""
|
|
Store multiple content items as memory units in ONE batch operation.
|
|
|
|
This is MUCH more efficient than calling put_async multiple times:
|
|
- Extracts facts from all contents in parallel
|
|
- Generates ALL embeddings in ONE batch
|
|
- Does ALL database operations in ONE transaction
|
|
|
|
Args:
|
|
agent_id: Unique identifier for the agent
|
|
contents: List of dicts with keys:
|
|
- "content" (required): Text content to store
|
|
- "context" (optional): Context about the memory
|
|
- "event_date" (optional): When the event occurred
|
|
document_id: Optional document ID for tracking and upsert
|
|
document_metadata: Optional metadata about the document
|
|
upsert: If True and document_id exists, delete old units and create new ones
|
|
fact_type_override: Override fact type for all facts ('world', 'agent', 'opinion')
|
|
confidence_score: Confidence score for opinions (0.0 to 1.0)
|
|
|
|
Returns:
|
|
List of lists of unit IDs (one list per content item)
|
|
|
|
Example:
|
|
unit_ids = await memory.put_batch_async(
|
|
agent_id="user123",
|
|
contents=[
|
|
{"content": "Alice works at Google", "context": "conversation"},
|
|
{"content": "Bob loves Python", "context": "conversation"},
|
|
],
|
|
document_id="meeting-2024-01-15",
|
|
upsert=True
|
|
)
|
|
# Returns: [["unit-id-1"], ["unit-id-2"]]
|
|
"""
|
|
start_time = time.time()
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info(f"PUT_BATCH_ASYNC START: {agent_id}")
|
|
logger.info(f"Batch size: {len(contents)} content items")
|
|
logger.info(f"{'='*60}")
|
|
|
|
if not contents:
|
|
return []
|
|
|
|
# Step 1: Extract facts from ALL contents in parallel
|
|
step_start = time.time()
|
|
|
|
# Create tasks for parallel fact extraction
|
|
fact_extraction_tasks = []
|
|
for item in contents:
|
|
content = item["content"]
|
|
context = item.get("context", "")
|
|
event_date = item.get("event_date") or utcnow()
|
|
|
|
task = extract_facts(content, event_date, context)
|
|
fact_extraction_tasks.append((task, event_date, context))
|
|
|
|
# Wait for all fact extractions to complete
|
|
all_fact_results = await asyncio.gather(*[task for task, _, _ in fact_extraction_tasks])
|
|
logger.info(f"[1] Extract facts (parallel): {len(fact_extraction_tasks)} contents in {time.time() - step_start:.3f}s")
|
|
|
|
# Flatten and track which facts belong to which content
|
|
all_fact_texts = []
|
|
all_fact_dates = []
|
|
all_contexts = []
|
|
all_fact_entities = [] # NEW: Store LLM-extracted entities per fact
|
|
all_fact_types = [] # Store fact type (world or agent)
|
|
content_boundaries = [] # [(start_idx, end_idx), ...]
|
|
|
|
current_idx = 0
|
|
for i, ((_, event_date, context), fact_dicts) in enumerate(zip(fact_extraction_tasks, all_fact_results)):
|
|
start_idx = current_idx
|
|
|
|
for fact_dict in fact_dicts:
|
|
all_fact_texts.append(fact_dict['fact'])
|
|
try:
|
|
from dateutil import parser as date_parser
|
|
fact_date = date_parser.isoparse(fact_dict['date'])
|
|
all_fact_dates.append(fact_date)
|
|
except Exception:
|
|
all_fact_dates.append(event_date)
|
|
all_contexts.append(context)
|
|
# Extract entities from fact (default to empty list if not present)
|
|
all_fact_entities.append(fact_dict.get('entities', []))
|
|
# Extract fact type (use override if provided, else use extracted type or default to 'world')
|
|
if fact_type_override:
|
|
all_fact_types.append(fact_type_override)
|
|
else:
|
|
all_fact_types.append(fact_dict.get('fact_type', 'world'))
|
|
|
|
end_idx = current_idx + len(fact_dicts)
|
|
content_boundaries.append((start_idx, end_idx))
|
|
current_idx = end_idx
|
|
|
|
total_facts = len(all_fact_texts)
|
|
|
|
if total_facts == 0:
|
|
return [[] for _ in contents]
|
|
|
|
# Step 2: Generate ALL embeddings in ONE batch (HUGE speedup!)
|
|
step_start = time.time()
|
|
all_embeddings = await self._generate_embeddings_batch(all_fact_texts)
|
|
logger.info(f"[2] Generate embeddings (parallel): {len(all_embeddings)} embeddings in {time.time() - step_start:.3f}s")
|
|
|
|
# Step 3: Process everything in ONE database transaction
|
|
logger.debug("Getting connection pool")
|
|
pool = await self._get_pool()
|
|
logger.debug("Acquiring connection from pool")
|
|
async with pool.acquire() as conn:
|
|
logger.debug("Starting transaction")
|
|
async with conn.transaction():
|
|
logger.debug("Inside transaction")
|
|
try:
|
|
# Handle document tracking and upsert
|
|
if document_id:
|
|
logger.debug(f"Handling document tracking for {document_id}")
|
|
import hashlib
|
|
import json
|
|
|
|
# Calculate content hash from all content items
|
|
combined_content = "\n".join([c.get("content", "") for c in contents])
|
|
content_hash = hashlib.sha256(combined_content.encode()).hexdigest()
|
|
|
|
# If upsert, delete old document first (cascades to units and links)
|
|
if upsert:
|
|
deleted = await conn.fetchval(
|
|
"DELETE FROM documents WHERE id = $1 AND agent_id = $2 RETURNING id",
|
|
document_id, agent_id
|
|
)
|
|
if deleted:
|
|
logger.debug(f"[3.1] Upsert: Deleted existing document '{document_id}' and all its units")
|
|
|
|
# Insert or update document
|
|
# Always use ON CONFLICT for idempotent behavior
|
|
await conn.execute(
|
|
"""
|
|
INSERT INTO documents (id, agent_id, original_text, content_hash, metadata)
|
|
VALUES ($1, $2, $3, $4, $5)
|
|
ON CONFLICT (id, agent_id) DO UPDATE
|
|
SET original_text = EXCLUDED.original_text,
|
|
content_hash = EXCLUDED.content_hash,
|
|
metadata = EXCLUDED.metadata,
|
|
updated_at = NOW()
|
|
""",
|
|
document_id,
|
|
agent_id,
|
|
combined_content,
|
|
content_hash,
|
|
json.dumps(document_metadata or {})
|
|
)
|
|
logger.debug(f"[3.2] Document '{document_id}' stored/updated")
|
|
|
|
# Deduplication check for all facts (batched by time window)
|
|
logger.debug("Starting deduplication check")
|
|
step_start = time.time()
|
|
|
|
# Group facts by event_date (rounded to 12-hour buckets) for batching
|
|
from collections import defaultdict
|
|
time_buckets = defaultdict(list)
|
|
for idx, (sentence, embedding, fact_date) in enumerate(zip(all_fact_texts, all_embeddings, all_fact_dates)):
|
|
# Round to 12-hour bucket to group similar times
|
|
bucket_key = fact_date.replace(hour=(fact_date.hour // 12) * 12, minute=0, second=0, microsecond=0)
|
|
time_buckets[bucket_key].append((idx, sentence, embedding, fact_date))
|
|
|
|
# Process each bucket in batch
|
|
all_is_duplicate = [False] * total_facts # Initialize all as not duplicate
|
|
for bucket_date, bucket_items in time_buckets.items():
|
|
indices = [item[0] for item in bucket_items]
|
|
sentences = [item[1] for item in bucket_items]
|
|
embeddings = [item[2] for item in bucket_items]
|
|
# Use bucket_date as representative for time window
|
|
dup_flags = await self._find_duplicate_facts_batch(
|
|
conn, agent_id, sentences, embeddings, bucket_date, time_window_hours=24
|
|
)
|
|
# Map results back to original indices
|
|
for idx, is_dup in zip(indices, dup_flags):
|
|
all_is_duplicate[idx] = is_dup
|
|
|
|
duplicates_filtered = sum(all_is_duplicate)
|
|
new_facts = total_facts - duplicates_filtered
|
|
logger.debug(f"Deduplication complete: {duplicates_filtered} duplicates filtered, {new_facts} new facts ({len(time_buckets)} time buckets)")
|
|
logger.info(f"[3] Deduplication check: {duplicates_filtered} duplicates filtered, {new_facts} new facts in {time.time() - step_start:.3f}s")
|
|
|
|
# Filter out duplicates
|
|
filtered_sentences = [s for s, is_dup in zip(all_fact_texts, all_is_duplicate) if not is_dup]
|
|
filtered_embeddings = [e for e, is_dup in zip(all_embeddings, all_is_duplicate) if not is_dup]
|
|
filtered_dates = [d for d, is_dup in zip(all_fact_dates, all_is_duplicate) if not is_dup]
|
|
filtered_contexts = [c for c, is_dup in zip(all_contexts, all_is_duplicate) if not is_dup]
|
|
filtered_entities = [ents for ents, is_dup in zip(all_fact_entities, all_is_duplicate) if not is_dup]
|
|
filtered_fact_types = [ft for ft, is_dup in zip(all_fact_types, all_is_duplicate) if not is_dup]
|
|
|
|
if not filtered_sentences:
|
|
logger.debug(f"[PUT_BATCH_ASYNC] All facts were duplicates, returning empty")
|
|
return [[] for _ in contents]
|
|
|
|
# Batch insert ALL units
|
|
step_start = time.time()
|
|
# Convert embeddings to strings for asyncpg vector type
|
|
filtered_embeddings_str = [str(emb) for emb in filtered_embeddings]
|
|
# Prepare confidence scores (only for opinions)
|
|
# If fact_type is 'opinion' and no confidence_score provided, use default of 1.0
|
|
confidence_scores = [
|
|
confidence_score if confidence_score is not None else 1.0
|
|
if ft == 'opinion'
|
|
else None
|
|
for ft in filtered_fact_types
|
|
]
|
|
results = await conn.fetch(
|
|
"""
|
|
INSERT INTO memory_units (agent_id, document_id, text, context, embedding, event_date, fact_type, confidence_score, access_count)
|
|
SELECT * FROM unnest($1::text[], $2::text[], $3::text[], $4::text[], $5::vector[], $6::timestamptz[], $7::text[], $8::float[], $9::integer[])
|
|
RETURNING id
|
|
""",
|
|
[agent_id] * len(filtered_sentences),
|
|
[document_id] * len(filtered_sentences) if document_id else [None] * len(filtered_sentences),
|
|
filtered_sentences,
|
|
filtered_contexts,
|
|
filtered_embeddings_str,
|
|
filtered_dates,
|
|
filtered_fact_types,
|
|
confidence_scores,
|
|
[0] * len(filtered_sentences)
|
|
)
|
|
|
|
created_unit_ids = [str(row['id']) for row in results]
|
|
logger.debug(f"Batch insert complete: {len(created_unit_ids)} units created")
|
|
logger.info(f"[5] Batch insert units: {len(created_unit_ids)} units in {time.time() - step_start:.3f}s")
|
|
|
|
# Process entities for ALL units
|
|
logger.debug("Processing entities")
|
|
step_start = time.time()
|
|
all_entity_links = await self._extract_entities_batch_optimized(
|
|
conn, agent_id, created_unit_ids, filtered_sentences, "", filtered_dates, filtered_entities
|
|
)
|
|
logger.debug(f"Entity processing complete: {len(all_entity_links)} links")
|
|
logger.info(f"[6] Process entities (batched): {time.time() - step_start:.3f}s")
|
|
|
|
# Create temporal links
|
|
logger.debug("Creating temporal links")
|
|
step_start = time.time()
|
|
await self._create_temporal_links_batch_per_fact(conn, agent_id, created_unit_ids)
|
|
logger.debug("Temporal links complete")
|
|
logger.info(f"[7] Batch create temporal links: {time.time() - step_start:.3f}s")
|
|
|
|
# Create semantic links
|
|
logger.debug("Creating semantic links")
|
|
step_start = time.time()
|
|
await self._create_semantic_links_batch(conn, agent_id, created_unit_ids, filtered_embeddings)
|
|
logger.debug("Semantic links complete")
|
|
logger.info(f"[8] Batch create semantic links: {time.time() - step_start:.3f}s")
|
|
|
|
# Insert entity links
|
|
logger.debug("Inserting entity links")
|
|
step_start = time.time()
|
|
if all_entity_links:
|
|
await self._insert_entity_links_batch(conn, all_entity_links)
|
|
logger.debug("Entity links inserted")
|
|
logger.info(f"[9] Batch insert entity links: {time.time() - step_start:.3f}s")
|
|
|
|
# Transaction auto-commits on success
|
|
commit_start = time.time()
|
|
logger.debug(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
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info(f"PUT_BATCH_ASYNC COMPLETE: {len(created_unit_ids)} units from {len(contents)} contents in {total_time:.3f}s")
|
|
logger.info(f"{'='*60}\n")
|
|
|
|
# Trigger opinion reinforcement in background (non-blocking)
|
|
# Only trigger if there are entities in the new units
|
|
if any(filtered_entities):
|
|
asyncio.create_task(
|
|
self._reinforce_opinions_async(
|
|
agent_id=agent_id,
|
|
created_unit_ids=created_unit_ids,
|
|
unit_texts=filtered_sentences,
|
|
unit_entities=filtered_entities
|
|
)
|
|
)
|
|
logger.debug("[PUT_BATCH_ASYNC] Opinion reinforcement task queued in background")
|
|
|
|
return result_unit_ids
|
|
|
|
except Exception as e:
|
|
# Transaction auto-rolls back on exception
|
|
import traceback
|
|
traceback.print_exc()
|
|
raise Exception(f"Failed to store batch memory: {str(e)}")
|
|
|
|
def search(
|
|
self,
|
|
agent_id: str,
|
|
query: str,
|
|
thinking_budget: int = 50,
|
|
top_k: int = 10,
|
|
enable_trace: bool = False,
|
|
weight_activation: float = 0.30,
|
|
weight_semantic: float = 0.30,
|
|
weight_recency: float = 0.25,
|
|
weight_frequency: float = 0.15,
|
|
mmr_lambda: float = 0.5,
|
|
fact_type: Optional[str] = None,
|
|
) -> tuple[List[Dict[str, Any]], Optional[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
|
|
enable_trace: If True, returns detailed SearchTrace object
|
|
weight_activation: Weight for activation component (default: 0.30)
|
|
weight_semantic: Weight for semantic similarity component (default: 0.30)
|
|
weight_recency: Weight for recency component (default: 0.25)
|
|
weight_frequency: Weight for frequency component (default: 0.15)
|
|
mmr_lambda: Lambda for MMR diversification (0=max diversity, 1=no diversity, default: 0.5)
|
|
fact_type: Optional filter for fact type ('world' or 'agent')
|
|
|
|
Returns:
|
|
Tuple of (results, trace)
|
|
"""
|
|
# Run async version synchronously
|
|
return asyncio.run(self.search_async(
|
|
agent_id, query, thinking_budget, top_k, enable_trace,
|
|
weight_activation, weight_semantic, weight_recency, weight_frequency, mmr_lambda, fact_type
|
|
))
|
|
|
|
async def search_async(
|
|
self,
|
|
agent_id: str,
|
|
query: str,
|
|
thinking_budget: int = 50,
|
|
top_k: int = 10,
|
|
enable_trace: bool = False,
|
|
weight_activation: float = 0.30,
|
|
weight_semantic: float = 0.30,
|
|
weight_recency: float = 0.25,
|
|
weight_frequency: float = 0.15,
|
|
mmr_lambda: float = 0.5,
|
|
fact_type: Optional[str] = None,
|
|
max_neighbors_per_node: int = 20,
|
|
) -> tuple[List[Dict[str, Any]], Optional[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
|
|
"""
|
|
# Backpressure: limit concurrent searches to prevent overwhelming the database
|
|
async with self._search_semaphore:
|
|
# Initialize tracer if requested
|
|
from .search_tracer import SearchTracer
|
|
tracer = SearchTracer(query, thinking_budget, top_k) if enable_trace else None
|
|
if tracer:
|
|
tracer.start()
|
|
|
|
pool = await self._get_pool()
|
|
search_start = time.time()
|
|
|
|
# Buffer logs for clean output in concurrent scenarios
|
|
search_id = f"{agent_id[:8]}-{int(time.time() * 1000) % 100000}"
|
|
log_buffer = []
|
|
log_buffer.append(f"[SEARCH {search_id}] Query: '{query[:50]}...' (budget={thinking_budget}, top_k={top_k})")
|
|
|
|
try:
|
|
# Step 1: Generate query embedding (CPU-bound, no DB needed)
|
|
step_start = time.time()
|
|
query_embedding = self._generate_embedding(query)
|
|
step_duration = time.time() - step_start
|
|
log_buffer.append(f" [1] Generate query embedding: {step_duration:.3f}s")
|
|
|
|
if tracer:
|
|
tracer.record_query_embedding(query_embedding)
|
|
tracer.add_phase_metric("generate_query_embedding", step_duration)
|
|
|
|
# Step 2: Find entry points (acquire connection only for this query)
|
|
step_start = time.time()
|
|
query_embedding_str = str(query_embedding)
|
|
|
|
# Log connection acquisition
|
|
conn_acquire_start = time.time()
|
|
async with pool.acquire() as conn:
|
|
conn_acquire_time = time.time() - conn_acquire_start
|
|
if conn_acquire_time > 0.1: # Log if waiting > 100ms
|
|
log_buffer.append(f" [2.1] Waited {conn_acquire_time:.3f}s for connection (pool busy)")
|
|
|
|
# Build entry point query with optional fact_type filter
|
|
if fact_type:
|
|
entry_points = await conn.fetch(
|
|
"""
|
|
SELECT id, text, context, event_date, access_count, embedding,
|
|
1 - (embedding <=> $1::vector) AS similarity
|
|
FROM memory_units
|
|
WHERE agent_id = $2
|
|
AND embedding IS NOT NULL
|
|
AND fact_type = $3
|
|
AND (1 - (embedding <=> $1::vector)) >= 0.5
|
|
ORDER BY embedding <=> $1::vector
|
|
LIMIT 3
|
|
""",
|
|
query_embedding_str, agent_id, fact_type
|
|
)
|
|
else:
|
|
entry_points = await conn.fetch(
|
|
"""
|
|
SELECT id, text, context, event_date, access_count, embedding,
|
|
1 - (embedding <=> $1::vector) AS similarity
|
|
FROM memory_units
|
|
WHERE agent_id = $2
|
|
AND embedding IS NOT NULL
|
|
AND (1 - (embedding <=> $1::vector)) >= 0.5
|
|
ORDER BY embedding <=> $1::vector
|
|
LIMIT 3
|
|
""",
|
|
query_embedding_str, agent_id
|
|
)
|
|
|
|
step_duration = time.time() - step_start
|
|
log_buffer.append(f" [2] Find entry points: {len(entry_points)} found in {step_duration:.3f}s")
|
|
|
|
if tracer:
|
|
tracer.add_phase_metric("find_entry_points", step_duration, {"count": len(entry_points)})
|
|
for rank, ep in enumerate(entry_points, 1):
|
|
tracer.add_entry_point(
|
|
node_id=str(ep["id"]),
|
|
text=ep["text"],
|
|
similarity=ep["similarity"],
|
|
rank=rank
|
|
)
|
|
|
|
if not entry_points:
|
|
logger.debug(f"[SEARCH] Complete: 0 results in {time.time() - search_start:.3f}s")
|
|
if tracer:
|
|
trace = tracer.finalize([])
|
|
return [], trace
|
|
return [], None
|
|
|
|
# Step 3: Spreading activation with budget (in-memory processing)
|
|
step_start = time.time()
|
|
visited = set()
|
|
results = []
|
|
budget_remaining = thinking_budget
|
|
# Initialize entry points with their actual similarity scores instead of 1.0
|
|
# Format: (unit, activation, is_entry, parent_node_id, link_type, link_weight)
|
|
queue = [(dict(unit), unit["similarity"], True, None, None, None) for unit in entry_points]
|
|
|
|
# Track substep timings
|
|
calculate_weight_time = 0
|
|
query_neighbors_time = 0
|
|
process_neighbors_time = 0
|
|
|
|
# Track which nodes were visited for deferred access count update
|
|
visited_node_ids = []
|
|
|
|
# Process nodes in batches for efficient neighbor querying
|
|
# OPTIMIZATION: Increased from 50 to 100 since we now limit neighbors per node
|
|
# This reduces round trips while keeping result set manageable
|
|
BATCH_SIZE = 100
|
|
nodes_to_process = [] # (unit, activation, is_entry_point, parent_node_id, link_type, link_weight)
|
|
|
|
while queue and budget_remaining > 0:
|
|
# Collect a batch of nodes to process (in-memory, no DB)
|
|
while queue and len(nodes_to_process) < BATCH_SIZE and budget_remaining > 0:
|
|
current_unit, activation, is_entry_point, parent_node_id, link_type, link_weight = 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, parent_node_id, link_type, link_weight))
|
|
visited_node_ids.append(unit_id) # Track for deferred update
|
|
elif tracer:
|
|
# Node already visited - prune
|
|
tracer.prune_node(unit_id, "already_visited", activation)
|
|
|
|
if not nodes_to_process:
|
|
break
|
|
|
|
# Acquire connection ONLY for neighbor queries (defer access count updates)
|
|
node_ids = [str(node[0]["id"]) for node in nodes_to_process]
|
|
|
|
# Log connection acquisition for batch queries
|
|
batch_conn_start = time.time()
|
|
async with pool.acquire() as conn:
|
|
batch_conn_acquire = time.time() - batch_conn_start
|
|
if batch_conn_acquire > 0.1: # Log if waiting > 100ms
|
|
log_buffer.append(f" [3.3.1] Waited {batch_conn_acquire:.3f}s for connection (pool busy) - batch size: {len(node_ids)}")
|
|
|
|
# Query neighbors for ALL nodes in batch at once (without embeddings for speed)
|
|
# Convert string UUIDs to UUID type for faster matching
|
|
substep_start = time.time()
|
|
uuid_array = [uuid.UUID(nid) for nid in node_ids]
|
|
|
|
# Build neighbor query with optional fact_type filter
|
|
# OPTIMIZATION: Limit neighbors per node to reduce data transfer
|
|
# Dense graphs can have 100+ neighbors per node, but spreading activation
|
|
# only needs top-weighted neighbors. This reduces query from 9000→1000 rows.
|
|
# Configurable via max_neighbors_per_node parameter (default: 20)
|
|
|
|
if fact_type:
|
|
all_neighbors = await conn.fetch(
|
|
"""
|
|
SELECT * FROM (
|
|
SELECT ml.from_unit_id, ml.to_unit_id, ml.weight, ml.link_type, ml.entity_id,
|
|
mu.text, mu.context, mu.event_date, mu.access_count,
|
|
mu.id as neighbor_id,
|
|
ROW_NUMBER() OVER (PARTITION BY ml.from_unit_id ORDER BY ml.weight DESC) as rn
|
|
FROM memory_links ml
|
|
JOIN memory_units mu ON ml.to_unit_id = mu.id
|
|
WHERE ml.from_unit_id = ANY($1::uuid[])
|
|
AND ml.weight >= 0.1
|
|
AND mu.fact_type = $2
|
|
) sub
|
|
WHERE rn <= $3
|
|
ORDER BY from_unit_id, weight DESC
|
|
""",
|
|
uuid_array, fact_type, max_neighbors_per_node
|
|
)
|
|
else:
|
|
all_neighbors = await conn.fetch(
|
|
"""
|
|
SELECT * FROM (
|
|
SELECT ml.from_unit_id, ml.to_unit_id, ml.weight, ml.link_type, ml.entity_id,
|
|
mu.text, mu.context, mu.event_date, mu.access_count,
|
|
mu.id as neighbor_id,
|
|
ROW_NUMBER() OVER (PARTITION BY ml.from_unit_id ORDER BY ml.weight DESC) as rn
|
|
FROM memory_links ml
|
|
JOIN memory_units mu ON ml.to_unit_id = mu.id
|
|
WHERE ml.from_unit_id = ANY($1::uuid[])
|
|
AND ml.weight >= 0.1
|
|
) sub
|
|
WHERE rn <= $2
|
|
ORDER BY from_unit_id, weight DESC
|
|
""",
|
|
uuid_array, max_neighbors_per_node
|
|
)
|
|
neighbor_query_time = time.time() - substep_start
|
|
if neighbor_query_time > 1.0: # Log slow neighbor queries
|
|
log_buffer.append(f" [3.3.3] Slow NEIGHBOR query: {neighbor_query_time:.3f}s for {len(node_ids)} nodes → {len(all_neighbors)} neighbors")
|
|
query_neighbors_time += neighbor_query_time
|
|
|
|
# Fetch embeddings for current batch nodes (needed for weight calculation)
|
|
substep_start = time.time()
|
|
embeddings = await conn.fetch(
|
|
"SELECT id, embedding FROM memory_units WHERE id = ANY($1::uuid[])",
|
|
uuid_array
|
|
)
|
|
embedding_map = {str(row["id"]): row["embedding"] for row in embeddings}
|
|
fetch_embeddings_time = time.time() - substep_start
|
|
if fetch_embeddings_time > 0.5:
|
|
log_buffer.append(f" [3.3.4] Slow EMBEDDING fetch: {fetch_embeddings_time:.3f}s for {len(node_ids)} nodes")
|
|
query_neighbors_time += fetch_embeddings_time
|
|
|
|
# Group neighbors by from_unit_id (in-memory, no DB)
|
|
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 (CPU-bound, no DB)
|
|
for current_unit, activation, is_entry_point, parent_node_id, parent_link_type, parent_link_weight 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
|
|
# Get embedding from the map we fetched
|
|
memory_embedding = embedding_map.get(unit_id)
|
|
if memory_embedding is not None:
|
|
# Convert embedding to list of floats if it's a string or other type
|
|
if isinstance(memory_embedding, str):
|
|
import json
|
|
memory_embedding = json.loads(memory_embedding)
|
|
elif not isinstance(memory_embedding, (list, np.ndarray)):
|
|
# If it's some other type, try to convert it
|
|
memory_embedding = list(memory_embedding)
|
|
|
|
# Cosine similarity = 1 - cosine distance
|
|
query_vec = np.array(query_embedding, dtype=np.float64)
|
|
memory_vec = np.array(memory_embedding, dtype=np.float64)
|
|
# 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 using configurable parameters
|
|
final_weight = (
|
|
weight_activation * activation +
|
|
weight_semantic * semantic_similarity +
|
|
weight_recency * recency_weight +
|
|
weight_frequency * frequency_normalized
|
|
)
|
|
|
|
# Notify tracer
|
|
if tracer:
|
|
tracer.visit_node(
|
|
node_id=unit_id,
|
|
text=current_unit["text"],
|
|
context=current_unit.get("context", ""),
|
|
event_date=event_date,
|
|
access_count=current_unit.get("access_count", 0),
|
|
is_entry_point=is_entry_point,
|
|
parent_node_id=parent_node_id,
|
|
link_type=parent_link_type,
|
|
link_weight=parent_link_weight,
|
|
activation=activation,
|
|
semantic_similarity=semantic_similarity,
|
|
recency=recency_weight,
|
|
frequency=frequency_normalized,
|
|
final_weight=final_weight,
|
|
)
|
|
|
|
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,
|
|
"embedding": memory_embedding, # Store for MMR
|
|
})
|
|
|
|
# Spread to neighbors (from batch query results)
|
|
neighbors = neighbors_by_node.get(unit_id, [])
|
|
|
|
# Group neighbors by to_unit_id to handle multiple connections
|
|
neighbors_grouped = {}
|
|
for neighbor in neighbors:
|
|
neighbor_id = str(neighbor["to_unit_id"])
|
|
if neighbor_id not in neighbors_grouped:
|
|
neighbors_grouped[neighbor_id] = []
|
|
neighbors_grouped[neighbor_id].append(neighbor)
|
|
|
|
# Process each unique neighbor (aggregating multiple links)
|
|
for neighbor_id, neighbor_links in neighbors_grouped.items():
|
|
if neighbor_id in visited:
|
|
continue
|
|
|
|
# Sort links by weight descending to identify primary link
|
|
neighbor_links_sorted = sorted(neighbor_links, key=lambda x: x["weight"], reverse=True)
|
|
primary_link = neighbor_links_sorted[0]
|
|
|
|
# Aggregate link weights: max + 30% bonus for additional links
|
|
max_weight = primary_link["weight"]
|
|
bonus_weight = sum(link["weight"] for link in neighbor_links_sorted[1:]) * 0.3
|
|
combined_weight = max_weight + bonus_weight
|
|
|
|
# Calculate new activation using combined weight
|
|
new_activation = activation * combined_weight * 0.8 # 0.8 = decay factor
|
|
|
|
# Use primary link metadata for queue and trace
|
|
primary_link_type = primary_link["link_type"]
|
|
primary_entity_id = str(primary_link["entity_id"]) if primary_link["entity_id"] else None
|
|
|
|
if new_activation > 0.1:
|
|
queue.append(({
|
|
"id": primary_link["to_unit_id"],
|
|
"text": primary_link["text"],
|
|
"context": primary_link.get("context", ""),
|
|
"event_date": primary_link["event_date"],
|
|
"access_count": primary_link["access_count"],
|
|
}, new_activation, False, unit_id, primary_link_type, combined_weight)) # parent_id, link_type, combined_weight
|
|
|
|
# Record all links in trace (primary + additional)
|
|
if tracer:
|
|
# Add primary link with combined activation
|
|
tracer.add_neighbor_link(
|
|
from_node_id=unit_id,
|
|
to_node_id=neighbor_id,
|
|
link_type=primary_link_type,
|
|
link_weight=combined_weight,
|
|
entity_id=primary_entity_id,
|
|
new_activation=new_activation,
|
|
followed=True
|
|
)
|
|
|
|
# Add additional links as supplementary (if multiple connections exist)
|
|
for additional_link in neighbor_links_sorted[1:]:
|
|
additional_link_type = additional_link["link_type"]
|
|
additional_entity_id = str(additional_link["entity_id"]) if additional_link["entity_id"] else None
|
|
tracer.add_neighbor_link(
|
|
from_node_id=unit_id,
|
|
to_node_id=neighbor_id,
|
|
link_type=additional_link_type,
|
|
link_weight=additional_link["weight"],
|
|
entity_id=additional_entity_id,
|
|
new_activation=None, # Don't show activation for supplementary links
|
|
followed=True,
|
|
is_supplementary=True # Mark as supplementary link
|
|
)
|
|
elif tracer:
|
|
# Record pruned link
|
|
tracer.add_neighbor_link(
|
|
from_node_id=unit_id,
|
|
to_node_id=neighbor_id,
|
|
link_type=primary_link_type,
|
|
link_weight=combined_weight,
|
|
entity_id=primary_entity_id,
|
|
new_activation=new_activation,
|
|
followed=False,
|
|
prune_reason="activation_too_low"
|
|
)
|
|
|
|
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
|
|
log_buffer.append(f" [3] Spreading activation: {len(visited)} nodes visited in {spreading_activation_time:.3f}s")
|
|
log_buffer.append(f" [3.1] Calculate weights: {calculate_weight_time:.3f}s")
|
|
log_buffer.append(f" [3.2] Query neighbors: {query_neighbors_time:.3f}s ({num_batches} batched queries)")
|
|
log_buffer.append(f" [3.3] Process neighbors: {process_neighbors_time:.3f}s")
|
|
|
|
if tracer:
|
|
tracer.add_phase_metric("spreading_activation", spreading_activation_time, {
|
|
"nodes_visited": len(visited),
|
|
"num_batches": num_batches
|
|
})
|
|
|
|
# Step 4: Queue access count updates (background worker will process them)
|
|
if visited_node_ids:
|
|
await self._access_count_queue.put(visited_node_ids)
|
|
log_buffer.append(f" [4] Queued access count updates for {len(visited_node_ids)} nodes")
|
|
|
|
# Step 5: Sort by final weight and apply MMR for diversity
|
|
step_start = time.time()
|
|
results.sort(key=lambda x: x["weight"], reverse=True)
|
|
|
|
# Apply MMR (Maximal Marginal Relevance) for diversity if lambda < 1.0
|
|
if mmr_lambda < 1.0 and len(results) > top_k:
|
|
top_results = self._apply_mmr(results, top_k, mmr_lambda, log_buffer)
|
|
log_buffer.append(f" [5] MMR diversification (λ={mmr_lambda}): {time.time() - step_start:.3f}s")
|
|
else:
|
|
top_results = results[:top_k]
|
|
# Add original rank and remove embeddings from results
|
|
for idx, result in enumerate(top_results):
|
|
result["original_rank"] = idx + 1
|
|
result["mmr_score"] = None
|
|
result["mmr_relevance"] = None
|
|
result["mmr_max_similarity"] = None
|
|
result["mmr_diversified"] = False
|
|
result.pop("embedding", None)
|
|
log_buffer.append(f" [5] Sort and return top {top_k} (no MMR): {time.time() - step_start:.3f}s")
|
|
|
|
total_time = time.time() - search_start
|
|
log_buffer.append(f"[SEARCH {search_id}] Complete: {len(top_results)} results in {total_time:.3f}s")
|
|
|
|
# Log all buffered logs at once
|
|
logger.info("\n" + "\n".join(log_buffer))
|
|
|
|
# Finalize trace if enabled
|
|
if tracer:
|
|
trace = tracer.finalize(top_results)
|
|
return top_results, trace
|
|
return top_results, None
|
|
|
|
except Exception as e:
|
|
log_buffer.append(f"[SEARCH {search_id}] ERROR after {time.time() - search_start:.3f}s: {str(e)}")
|
|
logger.error("\n" + "\n".join(log_buffer))
|
|
raise Exception(f"Failed to search memories: {str(e)}")
|
|
|
|
def _apply_mmr(
|
|
self,
|
|
results: List[Dict[str, Any]],
|
|
top_k: int,
|
|
mmr_lambda: float,
|
|
log_buffer: List[str]
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Apply Maximal Marginal Relevance (MMR) to diversify search results.
|
|
|
|
MMR balances relevance with diversity by selecting results that are:
|
|
1. Relevant to the query (high score)
|
|
2. Different from already selected results (low similarity)
|
|
|
|
Formula: MMR = λ * relevance - (1-λ) * max_similarity_to_selected
|
|
|
|
Args:
|
|
results: Sorted list of all results with embeddings
|
|
top_k: Number of results to select
|
|
mmr_lambda: Balance parameter (0=max diversity, 1=max relevance)
|
|
log_buffer: Logging buffer
|
|
|
|
Returns:
|
|
Diversified list of top_k results
|
|
"""
|
|
if not results or top_k <= 0:
|
|
return []
|
|
|
|
# Normalize weights to [0, 1] for fair comparison with similarity
|
|
max_weight = max(r["weight"] for r in results)
|
|
min_weight = min(r["weight"] for r in results)
|
|
weight_range = max_weight - min_weight if max_weight > min_weight else 1.0
|
|
|
|
# Pre-compute normalized relevance scores for all results
|
|
for idx, result in enumerate(results):
|
|
result["original_rank"] = idx + 1
|
|
result["normalized_relevance"] = (result["weight"] - min_weight) / weight_range
|
|
|
|
# Extract embeddings as a numpy array for vectorized operations
|
|
# Shape: (num_results, embedding_dim)
|
|
embeddings_list = []
|
|
valid_indices = []
|
|
for idx, result in enumerate(results):
|
|
if result.get("embedding") is not None:
|
|
embeddings_list.append(result["embedding"])
|
|
valid_indices.append(idx)
|
|
|
|
if not embeddings_list:
|
|
# No embeddings available, just return top-k by relevance
|
|
return results[:top_k]
|
|
|
|
# Stack embeddings into a matrix (num_results, embedding_dim)
|
|
embeddings_matrix = np.array(embeddings_list, dtype=np.float32)
|
|
|
|
# Normalize embeddings for faster cosine similarity (just dot product after normalization)
|
|
norms = np.linalg.norm(embeddings_matrix, axis=1, keepdims=True)
|
|
norms[norms == 0] = 1.0 # Avoid division by zero
|
|
embeddings_matrix = embeddings_matrix / norms
|
|
|
|
selected_indices = []
|
|
remaining_indices = list(range(len(results)))
|
|
diversified_count = 0
|
|
|
|
for selection_round in range(min(top_k, len(results))):
|
|
if not remaining_indices:
|
|
break
|
|
|
|
best_mmr_score = float('-inf')
|
|
best_remaining_idx = 0
|
|
|
|
# Vectorized computation for all remaining candidates
|
|
for remaining_idx, candidate_idx in enumerate(remaining_indices):
|
|
candidate = results[candidate_idx]
|
|
normalized_relevance = candidate["normalized_relevance"]
|
|
|
|
# Calculate max similarity to selected results
|
|
max_similarity = 0.0
|
|
if selected_indices and candidate_idx in valid_indices:
|
|
# Find position in embeddings_matrix
|
|
embedding_idx = valid_indices.index(candidate_idx)
|
|
candidate_embedding = embeddings_matrix[embedding_idx]
|
|
|
|
# Vectorized similarity calculation with all selected embeddings
|
|
if selected_indices:
|
|
selected_embedding_indices = [valid_indices.index(idx) for idx in selected_indices if idx in valid_indices]
|
|
if selected_embedding_indices:
|
|
selected_embeddings = embeddings_matrix[selected_embedding_indices]
|
|
# Compute cosine similarities in one operation (already normalized, so just dot product)
|
|
similarities = np.dot(selected_embeddings, candidate_embedding)
|
|
max_similarity = float(np.max(similarities))
|
|
|
|
# MMR score: balance relevance and diversity
|
|
mmr_score = mmr_lambda * normalized_relevance - (1 - mmr_lambda) * max_similarity
|
|
|
|
if mmr_score > best_mmr_score:
|
|
best_mmr_score = mmr_score
|
|
best_remaining_idx = remaining_idx
|
|
best_max_similarity = max_similarity
|
|
|
|
# Select the best candidate
|
|
best_candidate_idx = remaining_indices.pop(best_remaining_idx)
|
|
best_candidate = results[best_candidate_idx]
|
|
|
|
# Store MMR metadata
|
|
best_candidate["mmr_score"] = best_mmr_score
|
|
best_candidate["mmr_relevance"] = best_candidate["normalized_relevance"]
|
|
best_candidate["mmr_max_similarity"] = best_max_similarity
|
|
best_candidate["mmr_diversified"] = best_remaining_idx > 0
|
|
|
|
selected_indices.append(best_candidate_idx)
|
|
|
|
if best_remaining_idx > 0:
|
|
diversified_count += 1
|
|
|
|
log_buffer.append(f" MMR: Selected {len(selected_indices)} results, {diversified_count} diversified picks")
|
|
|
|
# Return selected results in order
|
|
selected_results = [results[idx] for idx in selected_indices]
|
|
|
|
# Remove embeddings from final results (not needed in response)
|
|
for result in selected_results:
|
|
result.pop("embedding", None)
|
|
result.pop("normalized_relevance", None) # Clean up temp field
|
|
|
|
return selected_results
|
|
|
|
async def get_document(self, document_id: str, agent_id: str) -> Optional[Dict[str, Any]]:
|
|
"""
|
|
Retrieve document metadata and statistics.
|
|
|
|
Args:
|
|
document_id: Document ID to retrieve
|
|
agent_id: Agent ID that owns the document
|
|
|
|
Returns:
|
|
Dictionary with document info or None if not found
|
|
"""
|
|
pool = await self._get_pool()
|
|
async with pool.acquire() as conn:
|
|
doc = await conn.fetchrow(
|
|
"""
|
|
SELECT d.id, d.agent_id, d.original_text, d.content_hash, d.metadata,
|
|
d.created_at, d.updated_at, COUNT(mu.id) as unit_count
|
|
FROM documents d
|
|
LEFT JOIN memory_units mu ON mu.document_id = d.id
|
|
WHERE d.id = $1 AND d.agent_id = $2
|
|
GROUP BY d.id, d.agent_id, d.original_text, d.content_hash, d.metadata, d.created_at, d.updated_at
|
|
""",
|
|
document_id, agent_id
|
|
)
|
|
|
|
if not doc:
|
|
return None
|
|
|
|
import json
|
|
return {
|
|
"id": doc["id"],
|
|
"agent_id": doc["agent_id"],
|
|
"original_text": doc["original_text"],
|
|
"content_hash": doc["content_hash"],
|
|
"metadata": json.loads(doc["metadata"]) if doc["metadata"] else {},
|
|
"unit_count": doc["unit_count"],
|
|
"created_at": doc["created_at"],
|
|
"updated_at": doc["updated_at"]
|
|
}
|
|
|
|
async def delete_document(self, document_id: str, agent_id: str) -> Dict[str, int]:
|
|
"""
|
|
Delete a document and all its associated memory units and links.
|
|
|
|
Args:
|
|
document_id: Document ID to delete
|
|
agent_id: Agent ID that owns the document
|
|
|
|
Returns:
|
|
Dictionary with counts of deleted items
|
|
"""
|
|
pool = await self._get_pool()
|
|
async with pool.acquire() as conn:
|
|
async with conn.transaction():
|
|
# Count units before deletion
|
|
units_count = await conn.fetchval(
|
|
"SELECT COUNT(*) FROM memory_units WHERE document_id = $1",
|
|
document_id
|
|
)
|
|
|
|
# Delete document (cascades to memory_units and all their links)
|
|
deleted = await conn.fetchval(
|
|
"DELETE FROM documents WHERE id = $1 AND agent_id = $2 RETURNING id",
|
|
document_id, agent_id
|
|
)
|
|
|
|
return {
|
|
"document_deleted": 1 if deleted else 0,
|
|
"memory_units_deleted": units_count if deleted else 0
|
|
}
|
|
|
|
async 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
|
|
"""
|
|
pool = await self._get_pool()
|
|
async with pool.acquire() as conn:
|
|
async with conn.transaction():
|
|
try:
|
|
# Count before deletion for reporting
|
|
units_count = await conn.fetchval("SELECT COUNT(*) FROM memory_units WHERE agent_id = $1", agent_id)
|
|
entities_count = await conn.fetchval("SELECT COUNT(*) FROM entities WHERE agent_id = $1", agent_id)
|
|
|
|
# Delete memory units (cascades to unit_entities, memory_links)
|
|
await conn.execute("DELETE FROM memory_units WHERE agent_id = $1", agent_id)
|
|
|
|
# Delete entities (cascades to unit_entities, entity_cooccurrences, memory_links with entity_id)
|
|
await conn.execute("DELETE FROM entities WHERE agent_id = $1", agent_id)
|
|
|
|
return {
|
|
"memory_units_deleted": units_count,
|
|
"entities_deleted": entities_count
|
|
}
|
|
|
|
except Exception as e:
|
|
raise Exception(f"Failed to delete agent data: {str(e)}")
|
|
|
|
async def list_agents(self) -> List[str]:
|
|
"""
|
|
Get list of all agent IDs in the database.
|
|
|
|
Returns:
|
|
List of agent IDs
|
|
"""
|
|
pool = await self._get_pool()
|
|
async with pool.acquire() as conn:
|
|
# Get distinct agent IDs from memory_units
|
|
agents = await conn.fetch("""
|
|
SELECT DISTINCT agent_id
|
|
FROM memory_units
|
|
WHERE agent_id IS NOT NULL
|
|
ORDER BY agent_id
|
|
""")
|
|
|
|
return [row['agent_id'] for row in agents]
|
|
|
|
async def get_graph_data(self, agent_id: Optional[str] = None, fact_type: Optional[str] = None):
|
|
"""
|
|
Get graph data for visualization.
|
|
|
|
Args:
|
|
agent_id: Filter by agent ID
|
|
fact_type: Filter by fact type (world, agent, opinion)
|
|
|
|
Returns:
|
|
Dict with nodes, edges, and table_rows
|
|
"""
|
|
pool = await self._get_pool()
|
|
async with pool.acquire() as conn:
|
|
# Get memory units, optionally filtered by agent_id and fact_type
|
|
query_conditions = []
|
|
query_params = []
|
|
param_count = 0
|
|
|
|
if agent_id:
|
|
param_count += 1
|
|
query_conditions.append(f"agent_id = ${param_count}")
|
|
query_params.append(agent_id)
|
|
|
|
if fact_type:
|
|
param_count += 1
|
|
query_conditions.append(f"fact_type = ${param_count}")
|
|
query_params.append(fact_type)
|
|
|
|
where_clause = "WHERE " + " AND ".join(query_conditions) if query_conditions else ""
|
|
|
|
units = await conn.fetch(f"""
|
|
SELECT id, text, event_date, context
|
|
FROM memory_units
|
|
{where_clause}
|
|
ORDER BY event_date
|
|
""", *query_params)
|
|
|
|
# Get links, filtering to only include links between units of the selected agent
|
|
unit_ids = [row['id'] for row in units]
|
|
if unit_ids:
|
|
links = await conn.fetch("""
|
|
SELECT
|
|
ml.from_unit_id,
|
|
ml.to_unit_id,
|
|
ml.link_type,
|
|
ml.weight,
|
|
e.canonical_name as entity_name
|
|
FROM memory_links ml
|
|
LEFT JOIN entities e ON ml.entity_id = e.id
|
|
WHERE ml.from_unit_id = ANY($1::uuid[]) AND ml.to_unit_id = ANY($1::uuid[])
|
|
ORDER BY ml.link_type, ml.weight DESC
|
|
""", unit_ids)
|
|
else:
|
|
links = []
|
|
|
|
# Get entity information
|
|
unit_entities = await conn.fetch("""
|
|
SELECT ue.unit_id, e.canonical_name, e.entity_type
|
|
FROM unit_entities ue
|
|
JOIN entities e ON ue.entity_id = e.id
|
|
ORDER BY ue.unit_id
|
|
""")
|
|
|
|
# Build entity mapping
|
|
entity_map = {}
|
|
for row in unit_entities:
|
|
unit_id = row['unit_id']
|
|
entity_name = row['canonical_name']
|
|
entity_type = row['entity_type']
|
|
if unit_id not in entity_map:
|
|
entity_map[unit_id] = []
|
|
entity_map[unit_id].append(f"{entity_name} ({entity_type})")
|
|
|
|
# Build nodes
|
|
nodes = []
|
|
for row in units:
|
|
unit_id = row['id']
|
|
text = row['text']
|
|
event_date = row['event_date']
|
|
context = row['context']
|
|
|
|
entities = entity_map.get(unit_id, [])
|
|
entity_count = len(entities)
|
|
|
|
# Color by entity count
|
|
if entity_count == 0:
|
|
color = "#e0e0e0"
|
|
elif entity_count == 1:
|
|
color = "#90caf9"
|
|
else:
|
|
color = "#42a5f5"
|
|
|
|
nodes.append({
|
|
"data": {
|
|
"id": str(unit_id),
|
|
"label": f"{text[:30]}..." if len(text) > 30 else text,
|
|
"text": text,
|
|
"date": event_date.isoformat() if event_date else "",
|
|
"context": context if context else "",
|
|
"entities": ", ".join(entities) if entities else "None",
|
|
"color": color
|
|
}
|
|
})
|
|
|
|
# Build edges
|
|
edges = []
|
|
for row in links:
|
|
from_id = str(row['from_unit_id'])
|
|
to_id = str(row['to_unit_id'])
|
|
link_type = row['link_type']
|
|
weight = row['weight']
|
|
entity_name = row['entity_name']
|
|
|
|
# Color by link type
|
|
if link_type == 'temporal':
|
|
color = "#00bcd4"
|
|
line_style = "dashed"
|
|
elif link_type == 'semantic':
|
|
color = "#ff69b4"
|
|
line_style = "solid"
|
|
elif link_type == 'entity':
|
|
color = "#ffd700"
|
|
line_style = "solid"
|
|
else:
|
|
color = "#999999"
|
|
line_style = "solid"
|
|
|
|
edges.append({
|
|
"data": {
|
|
"id": f"{from_id}-{to_id}-{link_type}",
|
|
"source": from_id,
|
|
"target": to_id,
|
|
"linkType": link_type,
|
|
"weight": weight,
|
|
"entityName": entity_name if entity_name else "",
|
|
"color": color,
|
|
"lineStyle": line_style
|
|
}
|
|
})
|
|
|
|
# Build table rows
|
|
table_rows = []
|
|
for row in units:
|
|
unit_id = row['id']
|
|
entities = entity_map.get(unit_id, [])
|
|
|
|
table_rows.append({
|
|
"id": str(unit_id)[:8] + "...",
|
|
"text": row['text'],
|
|
"context": row['context'] if row['context'] else "N/A",
|
|
"date": row['event_date'].strftime("%Y-%m-%d %H:%M") if row['event_date'] else "N/A",
|
|
"entities": ", ".join(entities) if entities else "None"
|
|
})
|
|
|
|
return {
|
|
"nodes": nodes,
|
|
"edges": edges,
|
|
"table_rows": table_rows,
|
|
"total_units": len(units)
|
|
}
|
|
|
|
async def _evaluate_opinion_update_async(
|
|
self,
|
|
client,
|
|
opinion_text: str,
|
|
opinion_confidence: float,
|
|
new_event_text: str,
|
|
entity_name: str,
|
|
model: str = "openai/gpt-oss-120b",
|
|
) -> Optional[Dict[str, Any]]:
|
|
"""
|
|
Evaluate if an opinion should be updated based on a new event.
|
|
|
|
Args:
|
|
client: OpenAI client
|
|
opinion_text: Current opinion text (includes reasons)
|
|
opinion_confidence: Current confidence score (0.0-1.0)
|
|
new_event_text: Text of the new event
|
|
entity_name: Name of the entity this opinion is about
|
|
model: LLM model to use
|
|
|
|
Returns:
|
|
Dict with 'action' ('keep'|'update'), 'new_confidence', 'new_text' (if action=='update')
|
|
or None if no changes needed
|
|
"""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class OpinionEvaluation(BaseModel):
|
|
"""Evaluation of whether an opinion should be updated."""
|
|
action: str = Field(description="Action to take: 'keep' (no change) or 'update' (modify opinion)")
|
|
reasoning: str = Field(description="Brief explanation of why this action was chosen")
|
|
new_confidence: float = Field(description="New confidence score (0.0-1.0). Can be higher, lower, or same as before.")
|
|
new_opinion_text: Optional[str] = Field(
|
|
default=None,
|
|
description="If action is 'update', the revised opinion text that acknowledges the previous view. Otherwise None."
|
|
)
|
|
|
|
evaluation_prompt = f"""You are evaluating whether an existing opinion should be updated based on new information.
|
|
|
|
ENTITY: {entity_name}
|
|
|
|
EXISTING OPINION:
|
|
{opinion_text}
|
|
Current confidence: {opinion_confidence:.2f}
|
|
|
|
NEW EVENT:
|
|
{new_event_text}
|
|
|
|
Evaluate whether this new event:
|
|
1. REINFORCES the opinion (increase confidence, keep text)
|
|
2. WEAKENS the opinion (decrease confidence, keep text)
|
|
3. CHANGES the opinion (update both text and confidence, noting "Previously I thought X, but now Y...")
|
|
4. IRRELEVANT (keep everything as is)
|
|
|
|
Guidelines:
|
|
- Only suggest 'update' action if the new event genuinely contradicts or significantly modifies the opinion
|
|
- If updating the text, acknowledge the previous opinion and explain the change
|
|
- Confidence should reflect accumulated evidence (0.0 = no confidence, 1.0 = very confident)
|
|
- Small changes in confidence are normal; large jumps should be rare"""
|
|
|
|
try:
|
|
response = await client.beta.chat.completions.parse(
|
|
model=model,
|
|
messages=[
|
|
{"role": "system", "content": "You evaluate and update opinions based on new information."},
|
|
{"role": "user", "content": evaluation_prompt}
|
|
],
|
|
response_format=OpinionEvaluation,
|
|
temperature=0.3 # Lower temperature for more consistent evaluation
|
|
)
|
|
|
|
result = response.choices[0].message.parsed
|
|
|
|
# Only return updates if something actually changed
|
|
if result.action == 'keep' and abs(result.new_confidence - opinion_confidence) < 0.01:
|
|
return None
|
|
|
|
return {
|
|
'action': result.action,
|
|
'reasoning': result.reasoning,
|
|
'new_confidence': result.new_confidence,
|
|
'new_text': result.new_opinion_text if result.action == 'update' else None
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Failed to evaluate opinion update: {str(e)}")
|
|
return None
|
|
|
|
async def _reinforce_opinions_async(
|
|
self,
|
|
agent_id: str,
|
|
created_unit_ids: List[str],
|
|
unit_texts: List[str],
|
|
unit_entities: List[List[Dict[str, str]]],
|
|
model: str = "openai/gpt-oss-120b",
|
|
):
|
|
"""
|
|
Background task to reinforce opinions based on newly ingested events.
|
|
|
|
This runs asynchronously and does not block the put operation.
|
|
|
|
Args:
|
|
agent_id: Agent ID
|
|
created_unit_ids: List of newly created memory unit IDs
|
|
unit_texts: Texts of the newly created units
|
|
unit_entities: Entities extracted from each unit
|
|
model: LLM model to use for evaluation
|
|
"""
|
|
try:
|
|
# Extract all unique entity names from the new units
|
|
entity_names = set()
|
|
for entities_list in unit_entities:
|
|
for entity in entities_list:
|
|
entity_names.add(entity['text'])
|
|
|
|
if not entity_names:
|
|
logger.debug("[REINFORCE] No entities found in new units, skipping opinion reinforcement")
|
|
return
|
|
|
|
logger.debug(f"[REINFORCE] Starting opinion reinforcement for {len(entity_names)} entities")
|
|
|
|
pool = await self._get_pool()
|
|
async with pool.acquire() as conn:
|
|
# Find all opinions related to these entities
|
|
opinions = await conn.fetch(
|
|
"""
|
|
SELECT DISTINCT mu.id, mu.text, mu.confidence_score, e.canonical_name
|
|
FROM memory_units mu
|
|
JOIN unit_entities ue ON mu.id = ue.unit_id
|
|
JOIN entities e ON ue.entity_id = e.id
|
|
WHERE mu.agent_id = $1
|
|
AND mu.fact_type = 'opinion'
|
|
AND e.canonical_name = ANY($2::text[])
|
|
""",
|
|
agent_id,
|
|
list(entity_names)
|
|
)
|
|
|
|
if not opinions:
|
|
logger.debug("[REINFORCE] No existing opinions found for these entities")
|
|
return
|
|
|
|
logger.debug(f"[REINFORCE] Found {len(opinions)} opinions to potentially reinforce")
|
|
|
|
# Use cached LLM client
|
|
if self._llm_client is None:
|
|
logger.error("[REINFORCE] LLM client not available, skipping opinion reinforcement")
|
|
return
|
|
client = self._llm_client
|
|
|
|
# Evaluate each opinion against the new events
|
|
updates_to_apply = []
|
|
for opinion in opinions:
|
|
opinion_id = str(opinion['id'])
|
|
opinion_text = opinion['text']
|
|
opinion_confidence = opinion['confidence_score']
|
|
entity_name = opinion['canonical_name']
|
|
|
|
# Find all new events mentioning this entity
|
|
relevant_events = []
|
|
for unit_text, entities_list in zip(unit_texts, unit_entities):
|
|
if any(e['text'] == entity_name for e in entities_list):
|
|
relevant_events.append(unit_text)
|
|
|
|
if not relevant_events:
|
|
continue
|
|
|
|
# Combine all relevant events
|
|
combined_events = "\n".join(relevant_events)
|
|
|
|
# Evaluate if opinion should be updated
|
|
evaluation = await self._evaluate_opinion_update_async(
|
|
client,
|
|
opinion_text,
|
|
opinion_confidence,
|
|
combined_events,
|
|
entity_name,
|
|
model
|
|
)
|
|
|
|
if evaluation:
|
|
updates_to_apply.append({
|
|
'opinion_id': opinion_id,
|
|
'evaluation': evaluation
|
|
})
|
|
|
|
# Apply all updates in a single transaction
|
|
if updates_to_apply:
|
|
async with conn.transaction():
|
|
for update in updates_to_apply:
|
|
opinion_id = update['opinion_id']
|
|
evaluation = update['evaluation']
|
|
|
|
if evaluation['action'] == 'update' and evaluation['new_text']:
|
|
# Update both text and confidence
|
|
await conn.execute(
|
|
"""
|
|
UPDATE memory_units
|
|
SET text = $1, confidence_score = $2, updated_at = NOW()
|
|
WHERE id = $3
|
|
""",
|
|
evaluation['new_text'],
|
|
evaluation['new_confidence'],
|
|
uuid.UUID(opinion_id)
|
|
)
|
|
logger.debug(f"[REINFORCE] Updated opinion {opinion_id[:8]}... (action: {evaluation['action']}, confidence: {evaluation['new_confidence']:.2f})")
|
|
else:
|
|
# Only update confidence
|
|
await conn.execute(
|
|
"""
|
|
UPDATE memory_units
|
|
SET confidence_score = $1, updated_at = NOW()
|
|
WHERE id = $2
|
|
""",
|
|
evaluation['new_confidence'],
|
|
uuid.UUID(opinion_id)
|
|
)
|
|
logger.debug(f"[REINFORCE] Updated confidence for opinion {opinion_id[:8]}... (confidence: {evaluation['new_confidence']:.2f})")
|
|
|
|
logger.debug(f"[REINFORCE] Applied {len(updates_to_apply)} opinion updates")
|
|
else:
|
|
logger.debug("[REINFORCE] No opinion updates needed")
|
|
|
|
except Exception as e:
|
|
logger.error(f"[REINFORCE] Error during opinion reinforcement: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
|