{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightMem Example with travel data\n",
    "\n",
    "Tutorial author: xubuqiang"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Prepare the runtime environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/disk/disk_20T/xubuqiang/lightmem\n",
      "env: ALL_PROXY=\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Obtaining file:///disk/disk_20T/xubuqiang/lightmem\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Checking if build backend supports build_editable ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build editable ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: torch==2.8.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2.8.0)\n",
      "Requirement already satisfied: transformers==4.57.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (4.57.0)\n",
      "Requirement already satisfied: sentence-transformers==5.1.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (5.1.1)\n",
      "Requirement already satisfied: accelerate==1.10.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.10.1)\n",
      "Requirement already satisfied: openai==2.3.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2.3.0)\n",
      "Requirement already satisfied: tiktoken==0.12.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.12.0)\n",
      "Requirement already satisfied: llmlingua==0.2.2 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.2.2)\n",
      "Requirement already satisfied: qdrant-client==1.15.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.15.1)\n",
      "Requirement already satisfied: pydantic==2.11.10 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2.11.10)\n",
      "Requirement already satisfied: pydantic_core==2.33.2 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2.33.2)\n",
      "Requirement already satisfied: numpy==2.2.6 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2.2.6)\n",
      "Requirement already satisfied: scipy==1.15.3 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.15.3)\n",
      "Requirement already satisfied: scikit-learn==1.7.2 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.7.2)\n",
      "Requirement already satisfied: nltk==3.9.2 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.9.2)\n",
      "Requirement already satisfied: tokenizers==0.22.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.22.1)\n",
      "Requirement already satisfied: huggingface-hub==0.35.3 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.35.3)\n",
      "Requirement already satisfied: safetensors==0.6.2 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.6.2)\n",
      "Requirement already satisfied: tqdm==4.67.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (4.67.1)\n",
      "Requirement already satisfied: PyYAML==6.0.3 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (6.0.3)\n",
      "Requirement already satisfied: requests==2.32.5 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2.32.5)\n",
      "Requirement already satisfied: filelock==3.20.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.20.0)\n",
      "Requirement already satisfied: regex==2025.9.18 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2025.9.18)\n",
      "Requirement already satisfied: packaging==25.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (25.0)\n",
      "Requirement already satisfied: httpx==0.28.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.28.1)\n",
      "Requirement already satisfied: httpcore==1.0.9 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.0.9)\n",
      "Requirement already satisfied: h11==0.16.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.16.0)\n",
      "Requirement already satisfied: h2==4.3.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (4.3.0)\n",
      "Requirement already satisfied: anyio==4.11.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (4.11.0)\n",
      "Requirement already satisfied: certifi==2025.10.5 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2025.10.5)\n",
      "Requirement already satisfied: charset-normalizer==3.4.3 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.4.3)\n",
      "Requirement already satisfied: idna==3.10 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.10)\n",
      "Requirement already satisfied: click==8.3.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (8.3.0)\n",
      "Requirement already satisfied: joblib==1.5.2 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.5.2)\n",
      "Requirement already satisfied: Jinja2==3.1.6 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.1.6)\n",
      "Requirement already satisfied: MarkupSafe==3.0.3 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.0.3)\n",
      "Requirement already satisfied: pillow==11.3.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (11.3.0)\n",
      "Requirement already satisfied: protobuf==6.32.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (6.32.1)\n",
      "Requirement already satisfied: psutil==7.1.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (7.1.0)\n",
      "Requirement already satisfied: fsspec==2025.9.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2025.9.0)\n",
      "Requirement already satisfied: grpcio==1.75.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.75.1)\n",
      "Requirement already satisfied: portalocker==3.2.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.2.0)\n",
      "Requirement already satisfied: annotated-types==0.7.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.7.0)\n",
      "Requirement already satisfied: typing_extensions==4.15.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (4.15.0)\n",
      "Requirement already satisfied: typing-inspection==0.4.2 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.4.2)\n",
      "Requirement already satisfied: networkx==3.4.2 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.4.2)\n",
      "Requirement already satisfied: sympy==1.14.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.14.0)\n",
      "Requirement already satisfied: mpmath==1.3.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.3.0)\n",
      "Requirement already satisfied: distro==1.9.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.9.0)\n",
      "Requirement already satisfied: hf-xet==1.1.10 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.1.10)\n",
      "Requirement already satisfied: hpack==4.1.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (4.1.0)\n",
      "Requirement already satisfied: hyperframe==6.1.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (6.1.0)\n",
      "Requirement already satisfied: jiter==0.11.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (0.11.0)\n",
      "Requirement already satisfied: sniffio==1.3.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (1.3.1)\n",
      "Requirement already satisfied: threadpoolctl==3.6.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (3.6.0)\n",
      "Requirement already satisfied: urllib3==2.5.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from lightmem==0.1.0) (2.5.0)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (12.8.93)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (12.8.90)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (12.8.90)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (9.10.2.21)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (12.8.4.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (11.3.3.83)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (10.3.9.90)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (11.7.3.90)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (12.5.8.93)\n",
      "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (0.7.1)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.27.3 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (2.27.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (12.8.90)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (12.8.93)\n",
      "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (1.13.1.3)\n",
      "Requirement already satisfied: triton==3.4.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from torch==2.8.0->lightmem==0.1.0) (3.4.0)\n",
      "Requirement already satisfied: setuptools>=40.8.0 in /disk/disk_20T/xubuqiang/anaconda3/envs/LightMem/lib/python3.11/site-packages (from triton==3.4.0->torch==2.8.0->lightmem==0.1.0) (80.9.0)\n",
      "Building wheels for collected packages: lightmem\n",
      "  Building editable for lightmem (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for lightmem: filename=lightmem-0.1.0-0.editable-py3-none-any.whl size=11716 sha256=7af01086068eb7a894b4bc406b2f9a2f0257090ea4f3ebd62fc2753ca3dbe9bc\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-85oq2t07/wheels/6e/78/3e/ad25a8ed5245b53409d8ea7f1a62638ba248b738ed4cf419a3\n",
      "Successfully built lightmem\n",
      "Installing collected packages: lightmem\n",
      "  Attempting uninstall: lightmem\n",
      "    Found existing installation: lightmem 0.1.0\n",
      "    Uninstalling lightmem-0.1.0:\n",
      "      Successfully uninstalled lightmem-0.1.0\n",
      "Successfully installed lightmem-0.1.0\n",
      "/disk/disk_20T/xubuqiang/lightmem/tutorial-notebooks\n"
     ]
    }
   ],
   "source": [
    "# Set your LightMemory project path\n",
    "LIGHTMEM_PROJECT_PATH = '/disk/disk_20T/xubuqiang/lightmem'\n",
    "\n",
    "# Install in editable mode\n",
    "%cd {LIGHTMEM_PROJECT_PATH}\n",
    "%env ALL_PROXY=\n",
    "!pip install -e .\n",
    "%cd tutorial-notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import datetime\n",
    "from lightmem.memory.lightmem import LightMemory\n",
    "from typing import List, Dict, Any\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# logging setup\n",
    "LOGS_ROOT = \"./logs\"\n",
    "RUN_TIMESTAMP = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "RUN_LOG_DIR = os.path.join(LOGS_ROOT, RUN_TIMESTAMP)\n",
    "os.makedirs(RUN_LOG_DIR, exist_ok=True)\n",
    "\n",
    "# API\n",
    "API_KEY = ''\n",
    "API_BASE_URL = ''\n",
    "LLM_MODEL = 'gpt-4o-mini'\n",
    "\n",
    "LLMLINGUA_MODEL_PATH = '/disk/disk_20T/fangjizhan/models/llmlingua-2-bert-base-multilingual-cased-meetingbank'\n",
    "EMBEDDING_MODEL_PATH = '/disk/disk_20T/fangjizhan/models/all-MiniLM-L6-v2'\n",
    "DATA_FILE_PATH = '/travel_single.json'\n",
    "\n",
    "print(f\"RUN_LOG_DIR: {RUN_LOG_DIR}\")\n",
    "print(f\"DATA_FILE_PATH: {DATA_FILE_PATH}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. LightMemory Initial config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:40:23 - LightMemory - INFO - Initializing LightMemory with provided configuration\n",
      "2025-12-07 23:40:23 - LightMemory - INFO - Token statistics tracking initialized\n",
      "2025-12-07 23:40:23 - LightMemory - INFO - Initializing pre-compressor\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial LightMem...\n",
      "pre_compressor:llmlingua-2\n",
      "pre_compressor:llmlingua_config={'model_name': '/disk/disk_20T/fangjizhan/models/llmlingua-2-bert-base-multilingual-cased-meetingbank', 'device_map': 'cuda', 'use_llmlingua2': True} llmlingua2_config={'max_batch_size': 50, 'max_force_token': 100} compress_config={'instruction': '', 'rate': 0.8, 'target_token': -1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:40:23 - LightMemory - INFO - Initializing topic segmenter\n",
      "2025-12-07 23:40:23 - LightMemory - INFO - Initializing memory manager\n",
      "2025-12-07 23:40:23 - LightMemory - INFO - Initializing text embedder\n",
      "2025-12-07 23:40:23 - sentence_transformers.SentenceTransformer - INFO - Load pretrained SentenceTransformer: /disk/disk_20T/fangjizhan/models/all-MiniLM-L6-v2\n",
      "2025-12-07 23:40:24 - LightMemory - INFO - Initializing embedding retriever\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG: resolved to encoding o200k_base\n",
      "ShortMemBufferManager initialized with max_tokens=512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:40:24 - LightMemory - INFO - LightMemory initialization completed successfully\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightMem initialized!\n"
     ]
    }
   ],
   "source": [
    "config_dict = {\n",
    "    \"pre_compress\": True,\n",
    "    \"pre_compressor\": {\n",
    "        \"model_name\": \"llmlingua-2\",\n",
    "        \"configs\": {\n",
    "            \"llmlingua_config\": {\n",
    "                \"model_name\": LLMLINGUA_MODEL_PATH,\n",
    "                \"device_map\": \"cuda\",\n",
    "                \"use_llmlingua2\": True,\n",
    "            },\n",
    "        }\n",
    "    },\n",
    "    \"topic_segment\": True,\n",
    "    \"precomp_topic_shared\": True,\n",
    "    \"topic_segmenter\": {\n",
    "        \"model_name\": \"llmlingua-2\",\n",
    "    },\n",
    "    \"messages_use\": \"hybrid\",\n",
    "    \"metadata_generate\": True,\n",
    "    \"text_summary\": True,\n",
    "    \"memory_manager\": {\n",
    "        \"model_name\": 'openai',\n",
    "        \"configs\": {\n",
    "            \"model\": LLM_MODEL,\n",
    "            \"api_key\": API_KEY,\n",
    "            \"max_tokens\": 16000,\n",
    "            \"openai_base_url\": API_BASE_URL\n",
    "        }\n",
    "    },\n",
    "    \"extract_threshold\": 0.1,\n",
    "    \"index_strategy\": \"embedding\",\n",
    "    \"text_embedder\": {\n",
    "        \"model_name\": \"huggingface\",\n",
    "        \"configs\": {\n",
    "            \"model\": EMBEDDING_MODEL_PATH,\n",
    "            \"embedding_dims\": 384,\n",
    "            \"model_kwargs\": {\"device\": \"cuda\"},\n",
    "        },\n",
    "    },\n",
    "    \"retrieve_strategy\": \"embedding\",\n",
    "    \"embedding_retriever\": {\n",
    "        \"model_name\": \"qdrant\",\n",
    "        \"configs\": {\n",
    "            \"collection_name\": \"travel_demo\",\n",
    "            \"embedding_model_dims\": 384,\n",
    "            \"path\": \"./travel_demo_db\", \n",
    "        }\n",
    "    },\n",
    "    \"update\": \"offline\",\n",
    "    \"logging\": {\n",
    "        \"level\": \"DEBUG\",\n",
    "        \"file_enabled\": True,\n",
    "        \"log_dir\": RUN_LOG_DIR,\n",
    "    }\n",
    "}\n",
    "\n",
    "print(\"Initial LightMem...\")\n",
    "lightmem = LightMemory.from_config(config_dict)\n",
    "print(\"LightMem initialized!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Load dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset statistics:\n",
      "- Number of questions: 3\n",
      "- Number of historical sessions: 3\n",
      "- Session ID list: ['session_0', 'session_1', 'session_2']\n",
      "\n",
      "Question preview:\n",
      "  [q_travel_01] I'm reviewing my expenses from the solo trip to Tampa I took back in March 2022. Can you remind me w...\n",
      "  [q_travel_02] I'm going back to our previous conversation about the 2-day Tampa trip from Washington. Can you remi...\n",
      "  [q_travel_03] For the family trip we took over the July 4th weekend, I recall we had to be very careful about food...\n"
     ]
    }
   ],
   "source": [
    "with open(DATA_FILE_PATH, 'r', encoding='utf-8') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "if isinstance(data, list):\n",
    "    data_item = data[0]\n",
    "else:\n",
    "    data_item = data\n",
    "\n",
    "question_ids = data_item.get('question_id', [])\n",
    "question_types = data_item.get('question_type', [])\n",
    "questions = data_item.get('question', [])\n",
    "question_dates = data_item.get('question_date', [])\n",
    "answers = data_item.get('answer', [])\n",
    "answer_session_ids = data_item.get('answer_session_ids', [])\n",
    "haystack_session_ids = data_item.get('haystack_session_ids', [])\n",
    "haystack_dates = data_item.get('haystack_dates', [])\n",
    "haystack_sessions = data_item.get('haystack_sessions', [])\n",
    "\n",
    "print(f\"Dataset statistics:\")\n",
    "print(f\"- Number of questions: {len(questions)}\")\n",
    "print(f\"- Number of historical sessions: {len(haystack_sessions)}\")\n",
    "print(f\"- Session ID list: {haystack_session_ids}\")\n",
    "print(f\"\\nQuestion preview:\")\n",
    "for i, (qid, q) in enumerate(zip(question_ids[:3], questions[:3])):\n",
    "    print(f\"  [{qid}] {q[:100]}...\" if len(q) > 100 else f\"  [{qid}] {q}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. ADD memory into LightMem\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "METADATA_GENERATE_PROMPT = \"\"\"\n",
    "You are a Travel Planning Conversation Analyzer.\n",
    "Your task is to extract **all travel-related facts and preferences** from a conversation.\n",
    "\n",
    "Input format:\n",
    "--- Topic X ---\n",
    "[timestamp, weekday] source_id.SpeakerName: message\n",
    "...\n",
    "\n",
    "Critical Instructions:\n",
    "1. **Process messages strictly in ascending source_id order** (one by one)\n",
    "\n",
    "2. **Extract ALL travel information** including:\n",
    "   - Travel plans: dates, destinations, budget, duration\n",
    "   - Personal preferences: dietary restrictions, activity preferences, accommodation needs\n",
    "   - Bookings: flights (with flight numbers), hotels (with addresses), restaurants, attractions\n",
    "   - Specific details: prices, addresses, ratings, operating hours\n",
    "   - Transportation: modes, routes, costs\n",
    "   - Itinerary details: daily plans, timing, locations\n",
    "\n",
    "3. **Preserve ALL Specific Details - DO NOT generalize**:\n",
    "   Flight information:\n",
    "   - ✓ \"User booked Flight F3694191 departing Washington at 09:30, arriving Tampa at 12:01 on March 25, 2022 for $173.\"\n",
    "   - ✗ \"User booked a flight\"\n",
    "   \n",
    "   Accommodation:\n",
    "   - ✓ \"User booked 'Spacious 1+bedroom apt NYC' at 100 N Ashley Dr, Tampa for 2 nights (March 25-27) at $547/night, total $1,094.\"\n",
    "   - ✗ \"User booked an apartment\"\n",
    "   \n",
    "   Preferences:\n",
    "   - ✓ \"User is vegetarian with a per-meal budget of $30-$60.\"\n",
    "   - ✗ \"User has dietary restrictions\"\n",
    "   \n",
    "   Restaurants:\n",
    "   - ✓ \"Assistant recommended The Zaffran at 35 W Gasparilla Plaza, vegetarian-friendly with veggie wraps, $35, 3.3 rating, 5-minute walk from apartment.\"\n",
    "   - ✗ \"Assistant recommended a restaurant\"\n",
    "\n",
    "4. **Time Handling**:\n",
    "   - Always include specific dates: \"March 25, 2022\" not \"the trip\"\n",
    "   - Include times when mentioned: \"09:30\" not \"morning\"\n",
    "   - Reference the message timestamp for context\n",
    "\n",
    "5. **For multi-day itineraries**:\n",
    "   - Extract each day's plan separately\n",
    "   - Include all activities, timings, locations, costs\n",
    "   - Preserve the sequence of events\n",
    "\n",
    "6. Output format:\n",
    "Please return your response in JSON format.\n",
    "   {\n",
    "     \"data\": [\n",
    "       {\n",
    "         \"source_id\": \"<source_id>\",\n",
    "         \"fact\": \"<complete fact with ALL specific details>\"\n",
    "       }\n",
    "     ]\n",
    "   }\n",
    "\n",
    "Example:\n",
    "--- Topic 1 ---\n",
    "[2022-03-20T13:21:00.000, Sun] 0.User: I need help planning a 3-day trip to Paris from NYC, March 10-12, budget $2000. I'm vegetarian.\n",
    "[2022-03-20T13:21:00.500, Sun] 0.Assistant: For your Paris trip, I recommend Flight AF123 (departs 18:00, arrives 08:00 next day, $450). Hotel Le Marais at 15 Rue des Archives ($180/night).\n",
    "\n",
    "{\"data\": [\n",
    "  {\"source_id\": \"0\", \"fact\": \"User is planning a 3-day trip from NYC to Paris from March 10-12, 2022 with a budget of $2,000.\"},\n",
    "  {\"source_id\": \"0\", \"fact\": \"User is vegetarian.\"},\n",
    "  {\"source_id\": \"0\", \"fact\": \"Assistant recommended Flight AF123 departing NYC at 18:00, arriving Paris at 08:00 the next day for $450.\"},\n",
    "  {\"source_id\": \"0\", \"fact\": \"Assistant recommended Hotel Le Marais located at 15 Rue des Archives, Paris for $180 per night.\"}\n",
    "]}\n",
    "\n",
    "Reminder:\n",
    "- Preserve ALL specific details: flight numbers, addresses, prices, ratings, times\n",
    "- DO NOT summarize or generalize\n",
    "- Extract information for EVERY day in multi-day itineraries\n",
    "- Include both user preferences and assistant recommendations\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_timestamp(timestamp: str) -> str:\n",
    "    \"\"\"\n",
    "    Convert timestamp from '2025/12/02 (Tue) 17:06' to '2025-12-02 17:06:00'\n",
    "    \n",
    "    Args:\n",
    "        timestamp: Original timestamp string\n",
    "        \n",
    "    Returns:\n",
    "        Converted timestamp string in format '%Y-%m-%d %H:%M:%S'\n",
    "    \"\"\"\n",
    "    from datetime import datetime\n",
    "    \n",
    "    # Remove day of week (e.g., \"(Tue)\")\n",
    "    timestamp_clean = timestamp.split('(')[0].strip() + ' ' + timestamp.split(')')[1].strip()\n",
    "    # Now it's like: '2025/12/02 17:06'\n",
    "    \n",
    "    # Parse the timestamp\n",
    "    dt = datetime.strptime(timestamp_clean, '%Y/%m/%d %H:%M')\n",
    "    \n",
    "    # Convert to target format\n",
    "    return dt.strftime('%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "def add_sessions_to_memory(lightmem: LightMemory, \n",
    "                          sessions: List[List[Dict]], \n",
    "                          session_ids: List[str],\n",
    "                          dates: List[str]) -> None:\n",
    "    \"\"\"\n",
    "    Add historical sessions to the LightMemory system.\n",
    "    Sessions are added turn by turn (each turn contains a user message and an assistant message).\n",
    "    \n",
    "    Args:\n",
    "        lightmem: LightMemory instance\n",
    "        sessions: List of sessions, each session contains multiple conversation turns\n",
    "        session_ids: List of session IDs\n",
    "        dates: List of session timestamps (will be converted to standard format)\n",
    "    \"\"\"\n",
    "    print(\"Starting to add historical sessions to memory repository...\")\n",
    "    \n",
    "    # Convert all timestamps to standard format\n",
    "    print(\"Converting timestamps to standard format...\")\n",
    "    converted_dates = [convert_timestamp(date) for date in dates]\n",
    "    \n",
    "    # Calculate total number of turns for progress bar\n",
    "    total_turns = 0\n",
    "    for session in sessions:\n",
    "        # Ensure first message is from user\n",
    "        session_copy = session.copy()\n",
    "        while session_copy and session_copy[0][\"role\"] != \"user\":\n",
    "            session_copy.pop(0)\n",
    "        num_turns = len(session_copy) // 2\n",
    "        total_turns += num_turns\n",
    "    \n",
    "    progress_bar = tqdm(total=total_turns, desc=\"Adding turns\")\n",
    "    \n",
    "    for session_idx, (session, session_id, date) in enumerate(zip(sessions, session_ids, converted_dates)):\n",
    "        # Ensure the first message is from user\n",
    "        while session and session[0][\"role\"] != \"user\":\n",
    "            session.pop(0)\n",
    "        \n",
    "        num_turns = len(session) // 2\n",
    "        \n",
    "        for turn_idx in range(num_turns):\n",
    "            # Extract one turn (user + assistant messages)\n",
    "            turn_messages = session[turn_idx*2 : turn_idx*2 + 2]\n",
    "            \n",
    "            # Validate turn structure\n",
    "            if len(turn_messages) < 2 or turn_messages[0][\"role\"] != \"user\" or turn_messages[1][\"role\"] != \"assistant\":\n",
    "                continue\n",
    "            \n",
    "            # Add timestamp and speaker information to each message\n",
    "            for msg in turn_messages:\n",
    "                msg[\"time_stamp\"] = date\n",
    "                # Add default speaker information if not present\n",
    "                if \"speaker_name\" not in msg:\n",
    "                    msg[\"speaker_name\"] = \"User\" if msg[\"role\"] == \"user\" else \"Assistant\"\n",
    "                if \"speaker_id\" not in msg:\n",
    "                    msg[\"speaker_id\"] = \"speaker_a\" if msg[\"role\"] == \"user\" else \"speaker_b\"\n",
    "            \n",
    "            # Only force_segment and force_extract on the last turn of the last session\n",
    "            is_last_turn = (session_idx == len(sessions) - 1 and turn_idx == num_turns - 1)\n",
    "            \n",
    "            # Add turn to memory system\n",
    "            try:\n",
    "                lightmem.add_memory(\n",
    "                    messages=turn_messages,\n",
    "                    METADATA_GENERATE_PROMPT=METADATA_GENERATE_PROMPT,\n",
    "                    force_segment=is_last_turn,\n",
    "                    force_extract=is_last_turn,\n",
    "                )\n",
    "                progress_bar.update(1)\n",
    "            except Exception as e:\n",
    "                print(f\"\\nWarning: Failed to add turn {turn_idx} from session {session_id}: {str(e)}\")\n",
    "                continue\n",
    "    \n",
    "    progress_bar.close()\n",
    "    print(\"\\nAll historical sessions have been added!\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to add historical sessions to memory repository...\n",
      "Converting timestamps to standard format...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding turns:   0%|          | 0/27 [00:00<?, ?it/s]2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_096631 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_096631] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_096631] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_096631] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_172518 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_172518] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_172518] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_172518] Target compression rate: 0.8\n",
      "Adding turns:   7%|▋         | 2/27 [00:00<00:01, 17.23it/s]2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_213576 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_213576] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_213576] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_213576] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_252656 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_252656] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_252656] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_252656] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_293463 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_293463] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_293463] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_293463] Target compression rate: 0.8\n",
      "Adding turns:  19%|█▊        | 5/27 [00:00<00:00, 22.05it/s]2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_331807 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_331807] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_331807] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_331807] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_372559 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_372559] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_372559] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_372559] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_422130 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_422130] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_422130] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_422130] Target compression rate: 0.8\n",
      "Adding turns:  30%|██▉       | 8/27 [00:00<00:00, 22.70it/s]2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_459989 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_459989] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_459989] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_459989] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_495775 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_495775] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_495775] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_495775] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_537473 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_537473] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_537473] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_537473] Target compression rate: 0.8\n",
      "Adding turns:  41%|████      | 11/27 [00:00<00:00, 23.79it/s]2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_577453 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_577453] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_577453] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_577453] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_620505 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_620505] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_620505] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_620505] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_659294 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_659294] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_659294] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_659294] Target compression rate: 0.8\n",
      "Adding turns:  52%|█████▏    | 14/27 [00:00<00:00, 24.02it/s]2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_700362 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_700362] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_700362] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_700362] Target compression rate: 0.8\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - ========== START add_memory_20251207_234206_743423 ==========\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_743423] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_743423] Restored visual contexts after compression\n",
      "2025-12-07 23:42:06 - LightMemory - INFO - [add_memory_20251207_234206_743423] Target compression rate: 0.8\n"
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      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234206_743423] Generated 1 segments\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - ========== START add_memory_20251207_234207_068402 ==========\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_068402] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_068402] Restored visual contexts after compression\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_068402] Target compression rate: 0.8\n",
      "Adding turns:  63%|██████▎   | 17/27 [00:01<00:00, 13.49it/s]2025-12-07 23:42:07 - LightMemory - INFO - ========== START add_memory_20251207_234207_106778 ==========\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_106778] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_106778] Restored visual contexts after compression\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_106778] Target compression rate: 0.8\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - ========== START add_memory_20251207_234207_147462 ==========\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_147462] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_147462] Restored visual contexts after compression\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_147462] Target compression rate: 0.8\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - ========== START add_memory_20251207_234207_184176 ==========\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_184176] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_184176] Restored visual contexts after compression\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_184176] Target compression rate: 0.8\n",
      "Adding turns:  74%|███████▍  | 20/27 [00:01<00:00, 16.01it/s]2025-12-07 23:42:07 - LightMemory - INFO - ========== START add_memory_20251207_234207_223138 ==========\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_223138] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_223138] Restored visual contexts after compression\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_223138] Target compression rate: 0.8\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - ========== START add_memory_20251207_234207_263560 ==========\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_263560] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_263560] Restored visual contexts after compression\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_263560] Target compression rate: 0.8\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - ========== START add_memory_20251207_234207_302482 ==========\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_302482] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_302482] Restored visual contexts after compression\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_302482] Target compression rate: 0.8\n",
      "Adding turns:  85%|████████▌ | 23/27 [00:01<00:00, 18.06it/s]2025-12-07 23:42:07 - LightMemory - INFO - ========== START add_memory_20251207_234207_344308 ==========\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_344308] Extracted 0 visual contexts\n",
      "Token indices sequence length is longer than the specified maximum sequence length for this model (516 > 512). Running this sequence through the model will result in indexing errors\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_344308] Restored visual contexts after compression\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_344308] Target compression rate: 0.8\n"
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     "text": [
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_344308] Generated 1 segments\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_344308] Assigned global topic IDs: total=1, mapping=[[21]]\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_344308] Extraction triggered 1 times, extract_list length: 1\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_344308] Batch max_source_ids: [6]\n",
      "2025-12-07 23:42:07 - LightMemory - INFO - [add_memory_20251207_234207_344308] Starting metadata generation\n"
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     "text": [
      "User prompt for API call 0:\n",
      "--- Topic 21 ---\n",
      "[2022-03-20T13:21:00.000, Sun] 0.User: Hi! need help developing 3 - day travel itinerary for one person trip starts in Washington ends in Tampa, March 25th to March 27th, 2022 budget is $ 1, 800 total first suggest transportation options fit this\n",
      "[2022-03-20T13:21:00.500, Sun] 0.Assistant: Hello 3 - day solo trip March 25 - 27 Washington→Tampa $ 1, 800 budget flights most time - efficient option 13 + hours by car / taxi practical short trip. budget - friendly round - trip Washington → Tampa ( March 25 Flight F3694191 ( departs 09 : 30 arrives 12 : 01, $ 173 ) - Tampa → Washington ( March 27 ) Flight F3873003 ( departs 19 : 07 arrives 21 : 15, $ 187 ) Total flights : $ 360. Alternatives self - driving ( $ 72 total 13h15m ) or taxi ( $ 1, 452 over budget ). lock in flights or explore other options\n",
      "[2022-03-20T13:21:01.000, Sun] 1.User: Let s lock in these flights! Next, recommend accommodations in Tampa convenient for attractions fit my single occupancy budget\n",
      "[2022-03-20T13:21:01.500, Sun] 1.Assistant: Great choice! single occupancy convenience Tampa s downtown area ideal. recommend : Spacious 1 + bedroom apt located in NYC ( Entire home / apt, $ 547 / night, 1 - night minimum 5. 0 review rate ) Key details Near Riverwalk walkable many attractions ) no smoking / parties fits 2 people space ) total 2 nights : $ 1, 094 keeps remaining budget at $ 1, 800 - $ 360 ( flights ) - $ 1, 094 ( accommodations ) = $ 346 for meals attractions local transport. accommodation work for you\n",
      "[2022-03-20T13:21:02.000, Sun] 2.User: apartment sounds good! I d like attractions focused on mix of nature history no extreme theme park rides like roller coasters. apartment near Riverwalk? want to walk to some spots if possible\n",
      "[2022-03-20T13:21:02.500, Sun] 2.Assistant: Perfect! answer your question : yes apartment at 100 N Ashley Dr steps from Riverwalk walk to Tampa Bay History Center, MacDill Park downtown attractions ( aligns with walkable preference ). I ll integrate nature + history attractions ( no roller coasters ) into your itinerary. Next share per - meal budget dietary restrictions\n",
      "[2022-03-20T13:21:03.000, Sun] 3.User: per - meal budget $ 30 - $ 60 m vegetarian. outline Day 1 s itinerary ( arrival + downtown exploration ) first\n",
      "[2022-03-20T13:21:03.500, Sun] 3.Assistant: vegetarian meals + $ 30 - $ 60 / meal budget Day 1 refined itinerary March 25 Day 1 Arrival & Downtown Exploration 09 : 30 Depart Washington on Flight F3694191 ( $ 173 ) 12 : 01 Arrive in Tampa, Uber / Lyft to apartment ~ $ 20 from airport 13 Check - in to Riverwalk near apartment ( $ 547 / night 14 : Lunch at The Zaffran ( 35 W Gasparilla Plaza ) vegetarian - friendly ( veggie wraps salads tea $ 35 within budget 3. 3 rating 5 - minute walk from apartment 15 : Visit Tampa Bay History Center ( 801 Water St ) maritime / cultural history $ 25 admission 4. 7 rating 5 - minute walk from lunch 17 : Stroll Riverwalk to MacDill Park ( 100 N Ashley Dr ) scenic waterfront nature - focused ) free entry 19 : Dinner at Uptown Fresh Beer Cafe ( 1221 E Fowler Ave ) vegetarian pizza / Mexican bowls $ 61 over budget highly rated 15 - minute Uber ( ~ $ 15 ). Day 1 plan work closer dinner option\n",
      "[2022-03-20T13:21:04.000, Sun] 4.User: Let s stick with Uptown Fresh Beer Cafe menu looks worth it! share Day 2 s itinerary focus on nature prefer ZooTampa over Busch Gardens )\n",
      "[2022-03-20T13:21:04.500, Sun] 4.Assistant: Day 2 ( March 26 focused on nature at ZooTampa with 08 : 30 Breakfast at apartment grab snacks from nearby Publix 5 - minute walk ~ $ 10 saves money 09 : 30 Uber to ZooTampa at Lowry Park ( 1101 W Sligh Ave $ 18 ride ) 10 : Enter zoo 500 + animals botanical gardens interactive exhibits $ 30 admission student discount 4. 5 rating 12 : 30 Lunch at zoo vegetarian café $ 15 within budget 15 : Free time to explore zoo nature trails 17 : 30 Uber back to apartment ( $ 12 ) 19 : Dinner at Butterburrs ( 123 E Kennedy Blvd ) vegetarian pizza $ 28 within budget ) 3. 6 rating 10 - minute walk apartment. Budget update : $ 360 ( flights ) + $ 1, 094 ( accommodations + $ 96 ( meals + $ 55 ( local transport + $ 25 ( attraction ) = $ 1, 630 Remaining $ 170. Day 2\n",
      "[2022-03-20T13:21:05.000, Sun] 5.User: Day 2 perfect! For Day 3 add botanical garden or aquarium ( nature - focused ) and some history - related spots? Also have tips for saving money on local transport\n",
      "[2022-03-20T13:21:05.500, Sun] 5.Assistant: Day 3 ( March 27 integrates request botanical garden / aquarium + history plus money - saving transport tips 09 : Breakfast at apartment ( Publix snacks ~ $ 10 ) 10 Walk to Florida Aquarium ( 701 Channelside Dr 15 - minute walk marine life + wetlands trail $ 30 admission 4. 6 rating 12 Lunch at Lalit Kathi Rolls Momos ( 101 S Franklin St ) vegetarian momos $ 23 budget 5 - minute walk from aquarium 14 Uber to Ybor City Museum State Park ( 1818 E 9th Ave ~ $ 10 ) historic cigar factories history - focused ) free entry 16 : Uber back to apartment check out ( $ 10 ) 17 Uber to Tampa International Airport ( $ 20 ) 19 : Depart on Flight F3873003 ( $ 187 ) Money - saving tip Use TBARTA bus ( $ 2. 25 / single ride ) instead Uber Ybor City trip bus # 1 from Riverwalk to Ybor City 20 minutes saves $ 7. 75 adjust Day 3 transport use bus\n",
      "[2022-03-20T13:21:06.000, Sun] 6.User: ll stick with Uber for convenience thanks for tip! share final budget breakdown booking links ( flights, apartment, attractions )\n",
      "[2022-03-20T13:21:06.500, Sun] 6.Assistant: No problem! Final Budget Breakdown ( total : $ 1, 783 under $ 1, 800! ) : | Category | Cost | Details | | - - - - - - - - | | | Round - Trip Flights | $ 360 | F3694191 ( $ 173 ) + F3873003 ( $ 187 ) | | Accommodations ( 2 nights ) | $ 1, 094 | Spacious 1 + bedroom apt | | Meals | $ 172 | Breakfast ( $ 20 ) + Lunch ( $ 53 ) + Dinner ( $ 99 ) | | Local Transport | $ 100 | Uber ( includes airport transfers ) | | Attractions | $ 85 | ZooTampa ( $ 30 ) + Aquarium ( $ 30 ) + History Center ( $ 25 ) | | Miscellaneous ( snacks ) | $ 72 | Publix snacks + water | Booking Links : 1. Flights : [ F3694191 ] ( https : / / example. com / flights / F3694191 ) | [ F3873003 ] ( example. flights / F3873003 ) 2. Accommodation : [ Spacious 1 + bedroom apt ] ( / / www. vrbo. com / 123456 ) ( mention single occupancy ) 3. Attractions : ZooTampa [ 10 % Off Ticket ] ( https / zootampa. org / tickets / ) | Florida Aquarium [ Advance Booking ] ( https / www. flaquarium. org / tickets /\n"
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      "Adding turns:  85%|████████▌ | 23/27 [00:18<00:00, 18.06it/s]2025-12-07 23:42:24 - LightMemory - INFO - [add_memory_20251207_234207_344308] API Call 0 tokens - Prompt: 2729, Completion: 1032, Total: 3761\n",
      "2025-12-07 23:42:24 - LightMemory - INFO - [add_memory_20251207_234207_344308] Metadata generation completed with 1 API calls\n",
      "2025-12-07 23:42:24 - LightMemory - INFO - [add_memory_20251207_234207_344308] Created 10 MemoryEntry objects\n",
      "2025-12-07 23:42:24 - LightMemory - INFO - ========== START offline_update_20251207_234224_330071 ==========\n",
      "2025-12-07 23:42:24 - LightMemory - INFO - [offline_update_20251207_234224_330071] Received 10 memory entries\n",
      "2025-12-07 23:42:24 - LightMemory - INFO - [offline_update_20251207_234224_330071] construct_update_queue_trigger=False, offline_update_trigger=False\n",
      "2025-12-07 23:42:24 - LightMemory - INFO - [offline_update_20251207_234224_330071] Starting embedding and insertion to vector database\n"
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     "text": [
      "2025-12-07 23:42:25 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [offline_update_20251207_234224_330071] Successfully inserted 10 entries to vector database\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234207_344308] Cumulative token stats - Total API calls: 1, Total tokens: 3761\n",
      "Adding turns:  89%|████████▉ | 24/27 [00:19<00:07,  2.49s/it]2025-12-07 23:42:25 - LightMemory - INFO - ========== START add_memory_20251207_234225_768408 ==========\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_768408] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_768408] Restored visual contexts after compression\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_768408] Target compression rate: 0.8\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - ========== START add_memory_20251207_234225_816144 ==========\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - force_segment=False, force_extract=False\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_816144] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_816144] Restored visual contexts after compression\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_816144] Target compression rate: 0.8\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - ========== START add_memory_20251207_234225_851023 ==========\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - force_segment=True, force_extract=True\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_851023] Extracted 0 visual contexts\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_851023] Restored visual contexts after compression\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_851023] Target compression rate: 0.8\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_851023] Generated 1 segments\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_851023] Assigned global topic IDs: total=2, mapping=[[22], [23]]\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_851023] Extraction triggered 2 times, extract_list length: 2\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_851023] Batch max_source_ids: [15, 3]\n",
      "2025-12-07 23:42:25 - LightMemory - INFO - [add_memory_20251207_234225_851023] Starting metadata generation\n"
     ]
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     "text": [
      "User prompt for API call 0:\n",
      "--- Topic 22 ---\n",
      "[2022-03-20T13:21:00.000, Sun] 0.User: One last question : there place to rent bike near Riverwalk for Day 1 s stroll\n",
      "[2022-03-20T13:21:00.500, Sun] 0.Assistant: Yes! Tampa Bay Bike Share station at 100 N Ashley Dr 5 - minute walk from apartment ( Riverwalk - near ) Details : - $ 15 / day pass unlimited 30 - minute rides - Booking link : [ Tampa Bay Bike Share ] https tampabaybikeshare. org / ) Last tips Florida Aquarium busy weekends arrive by 10 AM ) apartment has kitchenette stock up on water at Publix to save ) Enjoy trip need last - minute tweaks\n",
      "[2022-03-20T13:21:01.000, Sun] 1.User: Thanks much this exactly what I needed\n",
      "[2022-03-20T13:21:01.500, Sun] 1.Assistant: Happy travels! saved preferences : 3 - day solo trip $ 1, 800 budget nature + history attractions ( no roller coasters ) vegetarian meals Riverwalk - near accommodation Uber transport Hope you amazing time in Tampa!\n",
      "[2022-04-03T10:57:00.000, Sun] 2.User: Hi! partner and I planning 2 - day trip to Tampa from Washington, April 10th - 11th, 2022 want to visit theme parks with thrilling rides like roller coasters stay within $ 1, 200 budget for two people first suggest flight options fit schedule and budget\n",
      "[2022-04-03T10:57:00.500, Sun] 2.Assistant: Hi 2 - day theme park trip April 10 - 11, 2 people $ 1, 200 budget thrilling rides focus flights most time - efficient option 13h15m self - driving ideal short trips. affordable round - trips : Washington → Tampa ( April 10 ) Flight F3883584 ( departs 22 : 05 arrives 00 : 29 next day $ 232 / person → $ 464 total ) late arrival saves vacation time Tampa → Washington ( April 11 Flight F3873003 departs 19 : 07 arrives 21 : 15 $ 187 / person → $ 374 total ) fits theme park closing times. Alternative : Earlier inbound flight ( F3694191, $ 173 / person adds $ 98 total okay late arrival April 10th\n",
      "[2022-04-03T10:57:01.000, Sun] 3.User: Late arrival fine don t mind staying up late! Next recommend accommodations near Busch Gardens or Adventure Island for convenience within budget\n",
      "[2022-04-03T10:57:01.500, Sun] 3.Assistant: choice! proximity to parks recommend : Bright duplex apartment Entire home / apt, $ 164 / night, 2 - night minimum → $ 328 total ) Key details Fits 3 people space for 2 ) no children under 10 rule basic clean 15 - minute Uber to Busch Gardens Current budget used : $ 464 ( flights ) + $ 328 ( accommodations ) = $ 792 remaining $ 408 for meals transport park tickets accommodation work for you\n",
      "[2022-04-03T10:57:02.000, Sun] 4.User: apartment sounds perfect! split time between parks : Busch Gardens April 10th after ) and Adventure Island April 11th. outline Day 1 s itinerary ( arrival + Busch Gardens ) first\n",
      "[2022-04-03T10:57:02.500, Sun] 4.Assistant: split parks Busch Gardens 4 / 10 Adventure Island 4 / 11 ) Day 1 tailored itinerary : - April 10 ( Day 1 Busch Gardens Tampa Bay 00 : 29 Arrive Tampa on Flight F3883584 ( $ 464 total 01 : Uber to Bright duplex apartment $ 30 ride from airport 01 : 30 Check - in 24 - hour key drop host 09 : Breakfast at apartment grab snacks from nearby Walmart $ 20 total for 2 10 : Uber to Busch Gardens ( 10165 McKinley Dr, $ 15 ride ) 10 : 30 Enter park top thrilling rides Kumba ( roller coaster Falcon ' s Fury drop tower ) Congo River Rapids ( water ride 12 : 30 Lunch at park Congo River Cookhouse ( casual BBQ, $ 15 / person → $ 30 total ). Day 1 structure work adjustments to ride order timing\n",
      "[2022-04-03T10:57:03.000, Sun] 5.User: Day 1 looks great! also want budget - friendly casual restaurants near parks for dinner nothing too fancy add that to Day 1 share rest of evening plan\n",
      "[2022-04-03T10:57:03.500, Sun] 5.Assistant: budget - friendly casual dinner complete Day 1 itinerary 17 : 00 Leave Busch Gardens, Uber to The Tin Cow ( 123 E Kennedy Blvd, $ 20 ride casual BBQ / seafood $ 67 total for 2, 3. 7 rating near apartment 19 : 00 Return to apartment via Uber ( $ 20 ride ) tip Tin Cow offers meal 2 BBQ plates + 2 drinks ) for $ 55 save $ 12! Budget update $ 792 + $ 30 ( lunch ) + $ 67 ( dinner ) + $ 65 ( transport ) + $ 20 ( snacks ) = $ 974 Remaining $ 226 for Day 2 look\n",
      "[2022-04-03T10:57:04.000, Sun] 6.User: Love the couple s meal tip thanks! Now share Day 2 s itinerary for Adventure Island including breakfast lunch travel to airport\n",
      "[2022-04-03T10:57:04.500, Sun] 6.Assistant: welcome! s Day 2 April 11 ) Adventure Island itinerary with flight schedule 08 : 30 Breakfast at apartment Walmart snacks : $ 15 total for 2 09 : 30 Uber to Adventure Island ( 10001 McKinley Dr, $ 15 ride ) water slides wave pools lazy river thrilling water rides 10 : 00 Enter park Day pass : $ 60 / person → $ 120 total adventureisland. com 12 : 30 Lunch at park s Mango Joe s casual sandwiches $ 12 / person → $ 24 total 15 : 30 Leave park Uber back to apartment check out ( $ 15 ride 16 : 30 Pack bags Uber to Tampa International Airport $ 30 ride ) 19 : 07 Depart Tampa on Flight F3873003 ( $ 374 total ) 21 : 15 Arrive in Washington Adventure Island tip : Arrive before 12 PM avoid lines for top slides! Day 2 plan work\n",
      "[2022-04-03T10:57:05.000, Sun] 7.User: This is perfect! share final budget breakdown booking links ( flights, apartment, park tickets )\n",
      "[2022-04-03T10:57:05.500, Sun] 7.Assistant: Wonderful! Final Budget Breakdown ( total : $ 1, 199 under $ 1, 200! ) : | Category | Cost | Details | | - - - - | | Round - Trip Flights | $ 838 | F3883584 ( $ 464 ) + F3873003 ( $ 374 ) | | Accommodations ( 2 nights ) | $ 328 | Bright duplex apartment | | Meals | $ 149 | Breakfast ( $ 35 ) + Lunch ( $ 54 ) + Dinner ( $ 60 ) | | Local Transport | $ 120 | Uber ( airport + park transfers ) | | Attractions | $ 280 | Busch Gardens ( $ 160 ) + Adventure Island ( $ 120 ) | | Miscellaneous ( snacks ) | $ 20 | Walmart snacks + water | Booking Links : 1. Flights : [ F3883584 ] ( https : / / example. com / flights / F3883584 ) | [ F3873003 ] ( / example. com flights F3873003 ) 2. Accommodation : [ Bright duplex apartment ] ( / www. airbnb. com / 789012 ) ( mention theme park trip for free late check - out ) 3. Park Tickets : - Busch Gardens : [ Day Pass ( 10 % Off ) ] ( https : / / buschgardens. com / tampa / tickets ) - Adventure Island : [ Day Pass ] ( https : / / adventureisland. com / tickets\n",
      "[2022-04-03T10:57:06.000, Sun] 8.User: quick confirmation : Do park tickets include all rides? don t want extra fees once inside\n",
      "[2022-04-03T10:57:06.500, Sun] 8.Assistant: Great question happy to confirm! parks day passes include * * all rides attractions no extra fees * : Busch Gardens : Kumba Falcon ' s Fury Congo River Rapids all other rides covered. Adventure Island : All water slides wave pools lazy river included. apartment host confirmed late check - out ( 16 : 30 ) guaranteed for theme park guests won t be rushed No hidden costs total stays under $ 1, 200\n",
      "[2022-04-03T10:57:07.000, Sun] 9.User: Thanks much we can t wait\n",
      "[2022-04-03T10:57:07.500, Sun] 9.Assistant: Enjoy thrilling Tampa trip! saved preferences : 2 - day couple s trip $ 1, 200 budget theme parks with roller coasters / water rides split park days budget - friendly casual meals late arrival Hope make tons of fun memories on rides!\n",
      "[2022-06-20T19:51:00.000, Mon] 10.User: Hi! I planning 4 - day family trip to Tampa with two kids ( ages 6 and 8 ) from July 1st to 4th, 2022 need rental car for flexibility flights fit schedule Budget $ 2, 500 total for 3 people first suggest flight and rental car options\n",
      "[2022-06-20T19:51:00.500, Mon] 10.Assistant: 4 - day family trip July 1 - 4, 3 people : adult + 2 kids 6 / 8yo, $ 2, 500 budget start with transportation Flights round - trip Evening flights work best for kids sleep plane Washington → Tampa ( July 1 ) F4012813 ( departs 19 : 24 arrives 21 : 42, $ 212 / adult + $ 150 / kid → $ 512 total - Tampa → Washington ( July 4 ) F4037222 ( departs 20 : 38 arrives 22 : 43, $ 282 / adult + $ 180 / kid → $ 642 total ) Rental Car Enterprise Economy car fits 5 people TIA : $ 50 / day × 4 days = $ 200 includes insurance, child seats Total transport : $ 512 + $ 642 + $ 200 = $ 1, 354 evening flights or prefer earlier options ( adds ~ $ 300 )\n",
      "[2022-06-20T19:51:01.000, Mon] 11.User: Evening flights great thanks! Next recommend family - friendly accommodations near attractions ( like children s museums zoos ) fit budget\n",
      "[2022-06-20T19:51:01.500, Mon] 11.Assistant: Perfect! proximity to family - friendly attractions recommend : Two Bed Railroad Style Apartment Entire home / apt $ 1093 for 4 nights Key details 2 bedrooms fits 4 people kid - friendly near Glazer Children ' s Museum no pets / parties rule 3. 0 review rate Current budget used : $ 1, 354 + $ 1, 093 = $ 2, 447 remaining $ 53 for gas snacks small adjustments accommodation work for you\n",
      "[2022-06-20T19:51:02.000, Mon] 12.User: apartment sounds ideal! need family - friendly attractions no scary rides ( children s museums zoos aquariums ) nut - free restaurants ( my youngest has nut allergy ) d love beach day on July 3rd fireworks July 4th if available\n",
      "[2022-06-20T19:51:02.500, Mon] 12.Assistant: preferences noted no scary rides nut - free restaurants beach day ( 7 / 3 ) July 4th fireworks! Day 1 July 1 core itinerary start : 19 : 24 : Depart Washington on Flight F4012813 ( $ 512 total ) - 21 : 42 : Arrive in Tampa pick up rental car at TIA ( $ 200 ) - 22 : 30 : Drive to apartment 20 - minute drive ~ $ 10 gas ) - 23 : 00 : Check - in host provides nut - free snacks for kids confirmed Day 1 light let kids adjust add quick activity after check - in or keep relaxed\n",
      "[2022-06-20T19:51:03.000, Mon] 13.User: Keep Day 1 relaxed thanks outline Day 2 itinerary ZooTampa + Glazer Children ' s Museum nut - free lunch options\n",
      "[2022-06-20T19:51:03.500, Mon] 13.Assistant: choice ZooTampa Glazer Museum perfect for 6 - 8yo kids s Day 2 ( July 2 ) detailed plan 08 : 30 Breakfast at apartment host - provided nut - free cereal + fruit : $ 0 ) 09 : 30 Drive to ZooTampa at Lowry Park ( 15 - minute drive, $ 5 gas ) 10 : 00 Enter zoo kids highlights : petting zoo giraffe feeding aquatic center. Tickets : $ 25 / adult + $ 18 / kid → $ 61 total zootampa 12 : 30 Lunch at zoo Nut - Free Café pre - arranged $ 12 / person → $ 36 total ) 15 : 30 Drive to Publix ( 5 - minute drive ) for nut - free snacks / water : $ 30 total morning - to - afternoon structure work adjust timing for kids\n",
      "[2022-06-20T19:51:04.000, Mon] 14.User: Timing perfect! Please add Glazer Children ' s Museum nut - free dinner to Day 2, plus update budget\n",
      "[2022-06-20T19:51:04.500, Mon] 14.Assistant: Added complete Day 2 itinerary 16 : 30 Drive to Glazer Children ' s Museum 20 - minute drive $ 5 gas 17 : 00 Enter museum hands - on exhibits water play construction zone science lab. Tickets $ 18 / adult + $ 15 / kid → $ 48 total 19 : 00 Drive to Butterburrs 10 - minute drive $ 3 gas nut - free pizza / seafood $ 28 total for 3, 3. 6 rating confirmed nut - free policy 20 : 30 Return to apartment 5 - minute drive $ 2 gas ) Budget update $ 2, 447 + $ 61 ( zoo ) + $ 48 ( museum ) + $ 36 ( lunch ) + $ 28 ( dinner ) + $ 25 ( gas ) + $ 30 ( snacks ) = $ 2, 675 trim costs Day 3 Day 2 plan work\n",
      "[2022-06-20T19:51:05.000, Mon] 15.User: Day 2 great no worries about budget now! share Day 3 s itinerary ( beach day + Florida Aquarium ) nut - free meals\n",
      "[2022-06-20T19:51:05.500, Mon] 15.Assistant: Thanks understanding! trim Day 3 costs plan July 3 ) 08 : 30 Breakfast at apartment Publix snacks : $ 15 total 09 : 30 Drive to Ballast Point Park Beach 30 - minute drive $ 8 gas calm waters kid - friendly FREE entry saves cost 12 : 30 Lunch at beach concession stand nut - free hot dogs / fries $ 10 / person → $ 30 total 14 : 30 Drive to Florida Aquarium 25 - minute drive $ 7 gas 15 : 00 Enter aquarium touch tanks shark exhibit wetlands trail Tickets $ 28 / adult + $ 22 / kid → $ 72 total 17 : 30 Drive to Lalit Kathi Rolls Momos 10 - minute drive $ 3 gas nut - free momos / tea $ 23 total 3, 3. 1 rating 19 : 00 Return to apartment 10 - minute drive $ 3 gas ) Budget $ 2, 675 + $ 72 ( aquarium ) + $ 30 lunch ) + $ 23 ( dinner ) + $ 21 ( gas ) + $ 15 ( breakfast ) = $ 2, 836 cut Day 4 costs Day 3 workUser prompt for API call 1:\n",
      "--- Topic 23 ---\n",
      "[2022-06-20T19:51:06.000, Mon] 16.User: Day 3 looks perfect love free beach! share Day 4 s itinerary Ybor City + fireworks ) 返 程 安 排 get total budget back under $ 2, 500\n",
      "[2022-06-20T19:51:06.500, Mon] 16.Assistant: trimming Day 4 costs keeping fireworks! Day 4 July 4 ) plan : 08 : 30 Breakfast at apartment Publix snacks : $ 15 total 09 : 30 Drive to Ybor City Museum State Park 15 - minute drive $ 4 gas ) FREE guided kids tour historic exhibits saves attraction fees 12 : 30 Lunch at Uptown Fresh Beer Cafe 20 - minute drive $ 5 gas ) nut - free pizza / Mexican bowls $ 61 total for 3, 41 rating - 14 : 30 : Drive back to apartment to pack ( 20 - minute drive, $ 5 gas ) - 15 : 30 : Check out - 16 : 00 : Drive to Tampa Riverwalk ( 10 - minute drive, $ 3 gas ) FREE July 4th fireworks at 20 : 00 ( MacDill Park viewing spot ) - 19 : 00 : Return rental car to TIA ( 20 - minute drive, $ 7 gas ) - 20 : 38 : Depart on Flight F4037222 ( $ 642 total ) Final Budget Breakdown ( total : $ 2, 498 under $ 2, 500! ) : | Category | Cost | Details | | | Round - Trip Flights | $ 1, 154 | F4012813 ( $ 512 ) + F4037222 ( $ 642 ) | | Rental Car + Gas | $ 270 | Car ( $ 200 ) + Gas ( $ 70 ) | | Accommodations ( 4 nights ) | $ 1, 093 | Perfect Two Bed Apartment | | Meals | $ 336 | Breakfast ( $ 30 ) + Lunch ( $ 96 ) + Dinner ( $ 210 ) | | Attractions | $ 181 | Zoo ( $ 61 ) + Museum ( $ 48 ) + Aquarium ( $ 72 ) | | Miscellaneous ( snacks ) | $ 30 | Publix nut - free snacks | Cost cuts : Free beach, free Ybor tour, free fireworks! Does Day 4 work\n",
      "[2022-06-20T19:51:07.000, Mon] 17.User: This amazing thank you! share all booking links ( flights rental car apartment attractions ) nut - free restaurant contact info for rechecking\n",
      "[2022-06-20T19:51:07.500, Mon] 17.Assistant: re welcome! Here s details need : - Booking Links : 1. Flights : [ F4012813 ] ( https example. com / flights / F4012813 ) | [ F4037222 ] flights F4037222 ) 2 Rental Car : [ Enterprise Economy Car ] ( enterprise. com cars economy html location = TPA ) ( request child seats ) 3 Accommodation : [ Perfect Two Bed Apartment ] (. vrbo. com / 345678 ) mention nut allergy for free snacks ) 4 Attractions : - ZooTampa : [ Family Pack ( 10 % Off ) ] ( zootampa. org family - tickets - Glazer Museum : [ Group Tickets ] glazermuseum org group - tickets - Florida Aquarium : [ Family Admission ] flaquarium. org family / - Nut - Free Restaurant Contacts : 1. Butterburrs : ( 813 ) 123 - 4567 | info @ butterburrs. com nut - free kitchen ) 2 Lalit Kathi Rolls Momos : ( 813 ) 234 - 5678 | contact @ lalitkathi com nut - free menu ) 3. Uptown Fresh Beer Cafe : ( 813 ) 345 - 6789 | info @ uptownfresh. com ( separate nut - free prep area\n",
      "[2022-06-20T19:51:08.000, Mon] 18.User: last thing extra tips for traveling kids in July ( heat, hydration )\n",
      "[2022-06-20T19:51:08.500, Mon] 18.Assistant: Great question! July Tampa tips for kids : 1 Hydration : Tampa Riverwalk attractions free water refill stations bring reusable bottles 2 Heat : Pack sunscreen ( SPF 50 + ) hats water shoes beach / aquarium wetlands ) 3 Timing : Visit outdoor attractions ( zoo beach ) morning afternoons hottest aquarium / museum air - conditioned! 4 Rental Car : Confirm child seats booking free with Enterprise ) saves time at pickup. saved preferences 4 - day family trip nut - free no scary rides beach / fireworks $ 2, 500 budget Enjoy safe fun trip happy July 4th!\n",
      "[2022-06-20T19:51:09.000, Mon] 19.User: This is everything need thanks again\n",
      "[2022-06-20T19:51:09.500, Mon] 19.Assistant: You re welcome! Wishing family wonderful time exploring Tampa s kid - friendly spots hope fireworks beach day unforgettable\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:42:55 - LightMemory - INFO - [add_memory_20251207_234225_851023] API Call 0 tokens - Prompt: 4465, Completion: 2262, Total: 6727\n",
      "2025-12-07 23:42:55 - LightMemory - INFO - [add_memory_20251207_234225_851023] API Call 1 tokens - Prompt: 1949, Completion: 890, Total: 2839\n",
      "2025-12-07 23:42:55 - LightMemory - INFO - [add_memory_20251207_234225_851023] Metadata generation completed with 2 API calls\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: User is planning Day 4 itinerary for July 4, 2022, including Ybor City and fireworks, with a total b...\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Day 4 plan includes: 08:30 Breakfast at apartment with Publix snacks costing $15 total....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: 09:30 Drive to Ybor City Museum State Park, 15-minute drive costing $4 in gas, with a free guided ki...\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: 12:30 Lunch at Uptown Fresh Beer Cafe, 20-minute drive costing $5 in gas, with nut-free pizza and Me...\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: 14:30 Drive back to apartment to pack, 20-minute drive costing $5 in gas....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: 15:30 Check out of the apartment....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: 16:00 Drive to Tampa Riverwalk, 10-minute drive costing $3 in gas....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: 20:00 Free July 4th fireworks at MacDill Park viewing spot....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: 19:00 Return rental car to TIA, 20-minute drive costing $7 in gas....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: 20:38 Depart on Flight F4037222 for $642....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Final budget breakdown for the trip totals $2,498, under $2,500....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Round-trip flights cost $1,154: F4012813 for $512 and F4037222 for $642....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Rental car and gas cost $270: car for $200 and gas for $70....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Accommodations for 4 nights cost $1,093 at Perfect Two Bed Apartment....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Meals cost $336: Breakfast for $30, Lunch for $96, and Dinner for $210....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Attractions cost $181: Zoo for $61, Museum for $48, and Aquarium for $72....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=16 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Miscellaneous costs $30 for Publix nut-free snacks....\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=17 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Assistant provided booking links: Flights F4012813 and F4037222, Rental Car from Enterprise, Accommo...\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=17 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Nut-free restaurant contacts provided: Butterburrs (813-123-4567), Lalit Kathi Rolls Momos (813-234-...\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=18 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: Assistant provided tips for traveling with kids in July in Tampa: Hydration with free water refill s...\n",
      "2025-12-07 23:42:55 - LightMemory - WARNING - LLM returned invalid source_id=18 (valid range: [0, 3]) in batch 1. Auto-corrected to source_id=3. Fact: User's saved preferences include a 4-day family trip, nut-free meals, no scary rides, beach, firewor...\n",
      "2025-12-07 23:42:55 - LightMemory - INFO - [add_memory_20251207_234225_851023] Created 60 MemoryEntry objects\n",
      "2025-12-07 23:42:55 - LightMemory - INFO - ========== START offline_update_20251207_234255_631299 ==========\n",
      "2025-12-07 23:42:55 - LightMemory - INFO - [offline_update_20251207_234255_631299] Received 60 memory entries\n",
      "2025-12-07 23:42:55 - LightMemory - INFO - [offline_update_20251207_234255_631299] construct_update_queue_trigger=False, offline_update_trigger=False\n",
      "2025-12-07 23:42:55 - LightMemory - INFO - [offline_update_20251207_234255_631299] Starting embedding and insertion to vector database\n"
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      "2025-12-07 23:43:03 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
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      "2025-12-07 23:43:03 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
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      "2025-12-07 23:43:03 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
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      "2025-12-07 23:43:03 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
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      "2025-12-07 23:43:03 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
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      "2025-12-07 23:43:03 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
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      "2025-12-07 23:43:04 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
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      "2025-12-07 23:43:04 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
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      "2025-12-07 23:43:04 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n"
     ]
    },
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:43:04 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection travel_demo\n",
      "2025-12-07 23:43:04 - LightMemory - INFO - [offline_update_20251207_234255_631299] Successfully inserted 60 entries to vector database\n",
      "2025-12-07 23:43:04 - LightMemory - INFO - [add_memory_20251207_234225_851023] Cumulative token stats - Total API calls: 3, Total tokens: 13327\n",
      "Adding turns: 100%|██████████| 27/27 [00:58<00:00,  2.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "All historical sessions have been added!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "add_sessions_to_memory(lightmem, haystack_sessions, haystack_session_ids, haystack_dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Offline update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:43:15 - LightMemory - INFO - ========== START construct_queue_20251207_234315_201806 ==========\n",
      "2025-12-07 23:43:15 - LightMemory - INFO - [construct_queue_20251207_234315_201806] Parameters: top_k=20, keep_top_n=10, max_workers=8\n",
      "2025-12-07 23:43:15 - LightMemory - INFO - [construct_queue_20251207_234315_201806] Retrieved 70 entries from vector database\n",
      "2025-12-07 23:43:15 - LightMemory - INFO - [construct_queue_20251207_234315_201806] Starting parallel queue construction with 8 workers\n",
      "2025-12-07 23:43:25 - LightMemory - INFO - [construct_queue_20251207_234315_201806] Queue construction completed: 70 updated, 0 skipped, nonempty_queues=70, empty_queues=0\n",
      "2025-12-07 23:43:25 - LightMemory - INFO - ========== END construct_queue_20251207_234315_201806 ==========\n",
      "2025-12-07 23:43:25 - LightMemory - INFO - ========== START offline_update_all_20251207_234325_161431 ==========\n",
      "2025-12-07 23:43:25 - LightMemory - INFO - [offline_update_all_20251207_234325_161431] Parameters: score_threshold=0.8, max_workers=5\n",
      "2025-12-07 23:43:25 - LightMemory - INFO - [offline_update_all_20251207_234325_161431] Retrieved 70 entries from vector database\n",
      "2025-12-07 23:43:25 - LightMemory - INFO - [offline_update_all_20251207_234325_161431] Starting parallel offline update with 5 workers\n",
      "2025-12-07 23:43:28 - LightMemory - INFO - [offline_update_all_20251207_234325_161431] Offline update completed:\n",
      "2025-12-07 23:43:28 - LightMemory - INFO - [offline_update_all_20251207_234325_161431]   - Processed: 7 entries\n",
      "2025-12-07 23:43:28 - LightMemory - INFO - [offline_update_all_20251207_234325_161431]   - Updated: 1 entries\n",
      "2025-12-07 23:43:28 - LightMemory - INFO - [offline_update_all_20251207_234325_161431]   - Deleted: 3 entries\n",
      "2025-12-07 23:43:28 - LightMemory - INFO - [offline_update_all_20251207_234325_161431]   - Skipped (no candidates): 63 entries\n",
      "2025-12-07 23:43:28 - LightMemory - INFO - [offline_update_all_20251207_234325_161431]   - Update API calls: 7, Total tokens: 5448\n",
      "2025-12-07 23:43:28 - LightMemory - INFO - ========== END offline_update_all_20251207_234325_161431 ==========\n"
     ]
    }
   ],
   "source": [
    "lightmem.construct_update_queue_all_entries()\n",
    "lightmem.offline_update_all_entries(score_threshold=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Retrieval and answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_retrieval_and_answer(lightmem: LightMemory, \n",
    "                              questions: List[str], \n",
    "                              question_ids: List[str],\n",
    "                              question_types: List[str],\n",
    "                              question_dates: List[str],\n",
    "                              answers: List[str],\n",
    "                              top_k: int = 20) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Perform memory retrieval, generate answers using LLM, and evaluate correctness.\n",
    "    \n",
    "    Args:\n",
    "        lightmem: LightMemory instance\n",
    "        questions: List of questions\n",
    "        question_ids: List of question IDs\n",
    "        question_types: List of question types\n",
    "        question_dates: List of question dates\n",
    "        answers: List of expected answers\n",
    "        top_k: Number of memory entries to retrieve\n",
    "    \n",
    "    Returns:\n",
    "        DataFrame containing retrieval and evaluation results\n",
    "    \"\"\"\n",
    "    results = []\n",
    "    \n",
    "    print(f\"Starting memory retrieval and answer generation for {len(questions)} questions...\\n\")\n",
    "    \n",
    "    # Initialize LLM for answer generation (using the same config as LightMemory)\n",
    "    from openai import OpenAI\n",
    "    \n",
    "    llm_client = OpenAI(\n",
    "        api_key=API_KEY,\n",
    "        base_url=API_BASE_URL\n",
    "    )\n",
    "    \n",
    "    # LLM for judging (can be the same)\n",
    "    llm_judge = llm_client\n",
    "    \n",
    "    for idx, (qid, question, qtype, qdate, expected_answer) in enumerate(\n",
    "        zip(question_ids, questions, question_types, question_dates, answers), 1\n",
    "    ):\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"Question {idx}/{len(questions)} [ID: {qid}]\")\n",
    "        print(f\"{'='*80}\")\n",
    "        print(f\"Question: {question}\")\n",
    "        print(f\"Question Date: {qdate}\")\n",
    "        print(f\"Question Type: {qtype}\")\n",
    "        print(f\"Expected Answer: {expected_answer}\")\n",
    "        \n",
    "        try:\n",
    "            # Step 1: Retrieve relevant memories\n",
    "            result_string = lightmem.retrieve(question, limit=top_k)\n",
    "            related_memories = [m.strip() for m in result_string.split('\\n') if m.strip()]\n",
    "            \n",
    "            print(f\"\\nRetrieved {len(related_memories)} relevant memories\")\n",
    "            print(\"-\" * 80)\n",
    "            \n",
    "            # Display first few memories\n",
    "            for mem_idx, memory in enumerate(related_memories, 1):\n",
    "                print(f\"Memory {mem_idx}: {memory}\")\n",
    "            \n",
    "            # Step 2: Generate answer using LLM\n",
    "            print(\"\\nGenerating answer...\")\n",
    "            messages = [\n",
    "                {\"role\": \"system\", \"content\": \"You can ONLY answer based on the provided memories.\"},\n",
    "                {\n",
    "                    \"role\": \"user\",\n",
    "                    \"content\": f\"Question: {question}\\n\\nPlease answer the question based on the following memories:\\n{result_string}\"\n",
    "                }\n",
    "            ]\n",
    "            \n",
    "            response = llm_client.chat.completions.create(\n",
    "                model=LLM_MODEL,\n",
    "                messages=messages,\n",
    "                max_tokens=1024,\n",
    "                temperature=0.0\n",
    "            )\n",
    "            \n",
    "            generated_answer = response.choices[0].message.content\n",
    "            print(f\"\\nGenerated Answer: {generated_answer}\")\n",
    "            \n",
    "            # Step 3: Evaluate answer correctness\n",
    "            print(\"\\nEvaluating answer...\")\n",
    "            \n",
    "            # Build evaluation prompt\n",
    "\n",
    "            eval_prompt = f\"\"\"You are an expert evaluator. Compare the generated answer with the expected answer.\n",
    "            Question: {question}\n",
    "            Expected Answer: {expected_answer}\n",
    "            Generated Answer: {generated_answer}\n",
    "\n",
    "            Determine if the generated answer is correct compared to the expected answer.\n",
    "            Answer only \"True\" or \"False\".\"\"\"\n",
    "            \n",
    "            eval_messages = [{\"role\": \"user\", \"content\": eval_prompt}]\n",
    "            \n",
    "            eval_response = llm_judge.chat.completions.create(\n",
    "                model=LLM_MODEL,\n",
    "                messages=eval_messages,\n",
    "                max_tokens=10,\n",
    "                temperature=0.0\n",
    "            )\n",
    "            \n",
    "            eval_result = eval_response.choices[0].message.content.strip()\n",
    "            correct = 1 if \"true\" in eval_result.lower() else 0\n",
    "            \n",
    "            print(f\"Evaluation Result: {eval_result} ({'✓ Correct' if correct else '✗ Incorrect'})\")\n",
    "            \n",
    "            # Record results\n",
    "            results.append({\n",
    "                'question_id': qid,\n",
    "                'question_type': qtype,\n",
    "                'question': question,\n",
    "                'question_date': qdate,\n",
    "                'expected_answer': expected_answer,\n",
    "                'retrieved_count': len(related_memories),\n",
    "                'retrieved_memories': related_memories,\n",
    "                'generated_answer': generated_answer,\n",
    "                'eval_result': eval_result,\n",
    "                'correct': correct\n",
    "            })\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"\\nError: Processing failed - {str(e)}\")\n",
    "            import traceback\n",
    "            traceback.print_exc()\n",
    "            \n",
    "            results.append({\n",
    "                'question_id': qid,\n",
    "                'question_type': qtype,\n",
    "                'question': question,\n",
    "                'question_date': qdate,\n",
    "                'expected_answer': expected_answer,\n",
    "                'retrieved_count': 0,\n",
    "                'retrieved_memories': [],\n",
    "                'generated_answer': '',\n",
    "                'eval_result': '',\n",
    "                'correct': 0,\n",
    "                'error': str(e)\n",
    "            })\n",
    "    \n",
    "    print(f\"\\n{'='*80}\")\n",
    "    print(\"Retrieval and answer generation completed!\")\n",
    "    print(f\"{'='*80}\\n\")\n",
    "    \n",
    "    df = pd.DataFrame(results)\n",
    "    \n",
    "    # Print summary statistics\n",
    "    if len(df) > 0 and 'correct' in df.columns:\n",
    "        accuracy = df['correct'].mean() * 100\n",
    "        print(f\"Overall Accuracy: {accuracy:.2f}% ({df['correct'].sum()}/{len(df)})\")\n",
    "    \n",
    "    return df\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:44:21 - LightMemory - INFO - ========== START retrieve_20251207_234421_637955 ==========\n",
      "2025-12-07 23:44:21 - LightMemory - INFO - [retrieve_20251207_234421_637955] Query: I'm reviewing my expenses from the solo trip to Tampa I took back in March 2022. Can you remind me which specific apartment I booked near the Riverwalk and what was the nightly rate?\n",
      "2025-12-07 23:44:21 - LightMemory - INFO - [retrieve_20251207_234421_637955] Parameters: limit=20, filters=None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting memory retrieval and answer generation for 3 questions...\n",
      "\n",
      "\n",
      "================================================================================\n",
      "Question 1/3 [ID: q_travel_01]\n",
      "================================================================================\n",
      "Question: I'm reviewing my expenses from the solo trip to Tampa I took back in March 2022. Can you remind me which specific apartment I booked near the Riverwalk and what was the nightly rate?\n",
      "Question Date: 2022/06/01 (Thu) 14:30\n",
      "Question Type: single-session-assistant\n",
      "Expected Answer: You booked the 'Spacious 1+ bedroom apt' at 100 N Ashley Dr. The rate was $547 per night.\n"
     ]
    },
    {
     "data": {
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       "model_id": "dcd97e7ef44046a3a3b5c1c3e068a2b3",
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:44:21 - LightMemory - INFO - [retrieve_20251207_234421_637955] Searching vector database\n",
      "2025-12-07 23:44:21 - LightMemory - INFO - [retrieve_20251207_234421_637955] Found 20 results\n",
      "2025-12-07 23:44:21 - LightMemory - INFO - [retrieve_20251207_234421_637955] Formatted 20 results into output string\n",
      "2025-12-07 23:44:21 - LightMemory - INFO - ========== END retrieve_20251207_234421_637955 ==========\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Retrieved 20 relevant memories\n",
      "--------------------------------------------------------------------------------\n",
      "Memory 1: 2022-03-20T13:21:01.000 Sun User booked accommodations at 'Spacious 1+ bedroom apt' located at 100 N Ashley Dr, Tampa for 2 nights (March 25-27) at $547/night, total $1,094.\n",
      "Memory 2: 2022-06-20T19:51:06.000 Mon 16:00 Drive to Tampa Riverwalk, 10-minute drive costing $3 in gas.\n",
      "Memory 3: 2022-03-20T13:21:00.000 Sun User is planning a 3-day solo trip from Washington to Tampa from March 25-27, 2022 with a budget of $1,800.\n",
      "Memory 4: 2022-03-20T13:21:03.000 Sun Day 1 itinerary (March 25): 09:30 Depart Washington on Flight F3694191 ($173); 12:01 Arrive in Tampa; Uber/Lyft to apartment ~ $20; 13:00 Check-in to apartment; 14:00 Lunch at The Zaffran (35 W Gasparilla Plaza) vegetarian-friendly (veggie wraps, salads, tea) $35, 3.3 rating, 5-minute walk from apartment; 15:00 Visit Tampa Bay History Center (801 Water St) maritime/cultural history $25 admission, 4.7 rating, 5-minute walk from lunch; 17:00 Stroll Riverwalk to MacDill Park (100 N Ashley Dr) scenic waterfront, free entry; 19:00 Dinner at Uptown Fresh Beer Cafe (1221 E Fowler Ave) vegetarian pizza/Mexican bowls $61, 15-minute Uber ~ $15.\n",
      "Memory 5: 2022-06-20T19:51:02.000 Mon Assistant outlined Day 1 itinerary for July 1: Depart Washington on Flight F4012813 at 19:24, arrive in Tampa at 21:42, pick up rental car at TIA ($200), drive to apartment (20-minute drive, ~$10 gas), check-in at 23:00 with nut-free snacks for kids.\n",
      "Memory 6: 2022-06-20T19:51:00.000 Mon User is planning a 4-day family trip to Tampa with two kids (ages 6 and 8) from July 1st to 4th, 2022, needing a rental car for flexibility with a total budget of $2,500 for 3 people.\n",
      "Memory 7: 2022-06-20T19:51:06.000 Mon 14:30 Drive back to apartment to pack, 20-minute drive costing $5 in gas.\n",
      "Memory 8: 2022-03-20T13:21:05.000 Sun Day 3 itinerary (March 27): 09:00 Breakfast at apartment (Publix snacks ~ $10); 10:00 Walk to Florida Aquarium (701 Channelside Dr) 15-minute walk, marine life + wetlands trail $30 admission, 4.6 rating; 12:00 Lunch at Lalit Kathi Rolls Momos (101 S Franklin St) vegetarian momos $23, 5-minute walk from aquarium; 14:00 Uber to Ybor City Museum State Park (1818 E 9th Ave) ~ $10, historic cigar factories, free entry; 16:00 Uber back to apartment, check out ~ $10; 17:00 Uber to Tampa International Airport ~ $20; 19:00 Depart on Flight F3873003 ($187).\n",
      "Memory 9: 2022-06-20T19:51:06.000 Mon Accommodations for 4 nights cost $1,093 at Perfect Two Bed Apartment.\n",
      "Memory 10: 2022-04-03T10:57:02.000 Sun Day 1 itinerary includes: April 10, 2022 - Flight F3883584 arrives in Tampa at 00:29, Uber to Bright duplex apartment ($30), check-in at 01:30, breakfast at apartment ($20 total for 2), Uber to Busch Gardens (10165 McKinley Dr, $15 ride), enter park at 10:30, lunch at Congo River Cookhouse ($15/person, $30 total).\n",
      "Memory 11: 2022-04-03T10:57:01.000 Sun Assistant recommended Bright duplex apartment for $164/night with a 2-night minimum, totaling $328, which fits 3 people and is a 15-minute Uber ride to Busch Gardens.\n",
      "Memory 12: 2022-04-03T10:57:04.000 Sun Day 2 itinerary includes: April 11, 2022 - Breakfast at apartment ($15 total for 2), Uber to Adventure Island (10001 McKinley Dr, $15 ride), enter park at 10:00 with a day pass ($60/person, $120 total), lunch at Mango Joe's ($12/person, $24 total), leave park at 15:30, Uber back to apartment ($15 ride), pack bags, Uber to Tampa International Airport ($30 ride), depart on Flight F3873003 at 19:07.\n",
      "Memory 13: 2022-04-03T10:57:00.000 Sun User and partner are planning a 2-day trip to Tampa from Washington on April 10th-11th, 2022, with a budget of $1,200 for two people, wanting to visit theme parks with thrilling rides like roller coasters.\n",
      "Memory 14: 2022-03-20T13:21:00.000 Sun Assistant recommended Tampa Bay Bike Share station at 100 N Ashley Dr, 5-minute walk from the apartment, with a $15/day pass for unlimited 30-minute rides.\n",
      "Memory 15: 2022-06-20T19:51:01.000 Mon Assistant recommended a Two Bed Railroad Style Apartment for $1,093 for 4 nights, which fits 4 people and is near Glazer Children's Museum.\n",
      "Memory 16: 2022-04-03T10:57:00.000 Sun Assistant provided flight options: Flight F3883584 departing Washington on April 10 at 22:05, arriving Tampa at 00:29 the next day for $232/person ($464 total), and Flight F3873003 departing Tampa on April 11 at 19:07, arriving Washington at 21:15 for $187/person ($374 total).\n",
      "Memory 17: 2022-06-20T19:51:06.000 Mon 15:30 Check out of the apartment.\n",
      "Memory 18: 2022-06-20T19:51:06.000 Mon Final budget breakdown for the trip totals $2,498, under $2,500.\n",
      "Memory 19: 2022-04-03T10:57:05.000 Sun User requested a final budget breakdown and booking links for flights, apartment, and park tickets.\n",
      "Memory 20: 2022-06-20T19:51:00.000 Mon Assistant provided flight options: Flight F4012813 departing Washington on July 1 at 19:24, arriving Tampa at 21:42 for $512 total, and Flight F4037222 departing Tampa on July 4 at 20:38, arriving Washington at 22:43 for $642 total. Additionally, the Day 1 itinerary for July 1 includes picking up a rental car at TIA for $200, driving to the apartment (20-minute drive, ~$10 gas), and checking in at 23:00 with nut-free snacks for kids.\n",
      "\n",
      "Generating answer...\n",
      "\n",
      "Generated Answer: You booked the \"Spacious 1+ bedroom apt\" located at 100 N Ashley Dr, Tampa for your solo trip. The nightly rate was $547, totaling $1,094 for the 2 nights from March 25-27, 2022.\n",
      "\n",
      "Evaluating answer...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:44:23 - LightMemory - INFO - ========== START retrieve_20251207_234423_978258 ==========\n",
      "2025-12-07 23:44:23 - LightMemory - INFO - [retrieve_20251207_234423_978258] Query: I'm going back to our previous conversation about the 2-day Tampa trip from Washington. Can you remind me what the departure time and arrival time of the return flight F3873003 are?\n",
      "2025-12-07 23:44:23 - LightMemory - INFO - [retrieve_20251207_234423_978258] Parameters: limit=20, filters=None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Result: True (✓ Correct)\n",
      "\n",
      "================================================================================\n",
      "Question 2/3 [ID: q_travel_02]\n",
      "================================================================================\n",
      "Question: I'm going back to our previous conversation about the 2-day Tampa trip from Washington. Can you remind me what the departure time and arrival time of the return flight F3873003 are?\n",
      "Question Date: 2022/06/01 (Thu) 14:30\n",
      "Question Type: single-session-assistant\n",
      "Expected Answer: Departure time: 19:07 on April 11, 2022; Arrival time: 21:15 on April 11, 2022\n"
     ]
    },
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       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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     "output_type": "display_data"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:44:24 - LightMemory - INFO - [retrieve_20251207_234423_978258] Searching vector database\n",
      "2025-12-07 23:44:24 - LightMemory - INFO - [retrieve_20251207_234423_978258] Found 20 results\n",
      "2025-12-07 23:44:24 - LightMemory - INFO - [retrieve_20251207_234423_978258] Formatted 20 results into output string\n",
      "2025-12-07 23:44:24 - LightMemory - INFO - ========== END retrieve_20251207_234423_978258 ==========\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Retrieved 20 relevant memories\n",
      "--------------------------------------------------------------------------------\n",
      "Memory 1: 2022-04-03T10:57:00.000 Sun Assistant provided flight options: Flight F3883584 departing Washington on April 10 at 22:05, arriving Tampa at 00:29 the next day for $232/person ($464 total), and Flight F3873003 departing Tampa on April 11 at 19:07, arriving Washington at 21:15 for $187/person ($374 total).\n",
      "Memory 2: 2022-06-20T19:51:00.000 Mon Assistant provided flight options: Flight F4012813 departing Washington on July 1 at 19:24, arriving Tampa at 21:42 for $512 total, and Flight F4037222 departing Tampa on July 4 at 20:38, arriving Washington at 22:43 for $642 total. Additionally, the Day 1 itinerary for July 1 includes picking up a rental car at TIA for $200, driving to the apartment (20-minute drive, ~$10 gas), and checking in at 23:00 with nut-free snacks for kids.\n",
      "Memory 3: 2022-06-20T19:51:02.000 Mon Assistant outlined Day 1 itinerary for July 1: Depart Washington on Flight F4012813 at 19:24, arrive in Tampa at 21:42, pick up rental car at TIA ($200), drive to apartment (20-minute drive, ~$10 gas), check-in at 23:00 with nut-free snacks for kids.\n",
      "Memory 4: 2022-03-20T13:21:00.000 Sun User selected round-trip flights: Flight F3694191 departing Washington at 09:30, arriving Tampa at 12:01 on March 25 for $173; Flight F3873003 departing Tampa at 19:07, arriving Washington at 21:15 on March 27 for $187. Total flights cost: $360.\n",
      "Memory 5: 2022-03-20T13:21:05.000 Sun Day 3 itinerary (March 27): 09:00 Breakfast at apartment (Publix snacks ~ $10); 10:00 Walk to Florida Aquarium (701 Channelside Dr) 15-minute walk, marine life + wetlands trail $30 admission, 4.6 rating; 12:00 Lunch at Lalit Kathi Rolls Momos (101 S Franklin St) vegetarian momos $23, 5-minute walk from aquarium; 14:00 Uber to Ybor City Museum State Park (1818 E 9th Ave) ~ $10, historic cigar factories, free entry; 16:00 Uber back to apartment, check out ~ $10; 17:00 Uber to Tampa International Airport ~ $20; 19:00 Depart on Flight F3873003 ($187).\n",
      "Memory 6: 2022-06-20T19:51:06.000 Mon 20:38 Depart on Flight F4037222 for $642.\n",
      "Memory 7: 2022-03-20T13:21:03.000 Sun Day 1 itinerary (March 25): 09:30 Depart Washington on Flight F3694191 ($173); 12:01 Arrive in Tampa; Uber/Lyft to apartment ~ $20; 13:00 Check-in to apartment; 14:00 Lunch at The Zaffran (35 W Gasparilla Plaza) vegetarian-friendly (veggie wraps, salads, tea) $35, 3.3 rating, 5-minute walk from apartment; 15:00 Visit Tampa Bay History Center (801 Water St) maritime/cultural history $25 admission, 4.7 rating, 5-minute walk from lunch; 17:00 Stroll Riverwalk to MacDill Park (100 N Ashley Dr) scenic waterfront, free entry; 19:00 Dinner at Uptown Fresh Beer Cafe (1221 E Fowler Ave) vegetarian pizza/Mexican bowls $61, 15-minute Uber ~ $15.\n",
      "Memory 8: 2022-04-03T10:57:02.000 Sun Day 1 itinerary includes: April 10, 2022 - Flight F3883584 arrives in Tampa at 00:29, Uber to Bright duplex apartment ($30), check-in at 01:30, breakfast at apartment ($20 total for 2), Uber to Busch Gardens (10165 McKinley Dr, $15 ride), enter park at 10:30, lunch at Congo River Cookhouse ($15/person, $30 total).\n",
      "Memory 9: 2022-03-20T13:21:00.000 Sun User is planning a 3-day solo trip from Washington to Tampa from March 25-27, 2022 with a budget of $1,800.\n",
      "Memory 10: 2022-04-03T10:57:04.000 Sun Day 2 itinerary includes: April 11, 2022 - Breakfast at apartment ($15 total for 2), Uber to Adventure Island (10001 McKinley Dr, $15 ride), enter park at 10:00 with a day pass ($60/person, $120 total), lunch at Mango Joe's ($12/person, $24 total), leave park at 15:30, Uber back to apartment ($15 ride), pack bags, Uber to Tampa International Airport ($30 ride), depart on Flight F3873003 at 19:07.\n",
      "Memory 11: 2022-06-20T19:51:06.000 Mon 16:00 Drive to Tampa Riverwalk, 10-minute drive costing $3 in gas.\n",
      "Memory 12: 2022-04-03T10:57:00.000 Sun User and partner are planning a 2-day trip to Tampa from Washington on April 10th-11th, 2022, with a budget of $1,200 for two people, wanting to visit theme parks with thrilling rides like roller coasters.\n",
      "Memory 13: 2022-06-20T19:51:06.000 Mon Round-trip flights cost $1,154: F4012813 for $512 and F4037222 for $642.\n",
      "Memory 14: 2022-04-03T10:57:00.000 Sun Assistant mentioned an alternative earlier inbound flight F3694191 for $173/person, adding $98 total to the trip cost.\n",
      "Memory 15: 2022-06-20T19:51:06.000 Mon 15:30 Check out of the apartment.\n",
      "Memory 16: 2022-06-20T19:51:07.000 Mon Assistant provided booking links: Flights F4012813 and F4037222, Rental Car from Enterprise, Accommodation at Perfect Two Bed Apartment, and attractions ZooTampa, Glazer Museum, and Florida Aquarium.\n",
      "Memory 17: 2022-03-20T13:21:01.000 Sun User booked accommodations at 'Spacious 1+ bedroom apt' located at 100 N Ashley Dr, Tampa for 2 nights (March 25-27) at $547/night, total $1,094.\n",
      "Memory 18: 2022-06-20T19:51:00.000 Mon User is planning a 4-day family trip to Tampa with two kids (ages 6 and 8) from July 1st to 4th, 2022, needing a rental car for flexibility with a total budget of $2,500 for 3 people.\n",
      "Memory 19: 2022-06-20T19:51:06.000 Mon Final budget breakdown for the trip totals $2,498, under $2,500.\n",
      "Memory 20: 2022-04-03T10:57:01.000 Sun User confirmed that a late arrival is fine and requested accommodations near Busch Gardens or Adventure Island within budget.\n",
      "\n",
      "Generating answer...\n",
      "\n",
      "Generated Answer: The return flight F3873003 departs Tampa on April 11 at 19:07 and arrives in Washington at 21:15.\n",
      "\n",
      "Evaluating answer...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-07 23:44:26 - LightMemory - INFO - ========== START retrieve_20251207_234426_209208 ==========\n",
      "2025-12-07 23:44:26 - LightMemory - INFO - [retrieve_20251207_234426_209208] Query: For the family trip we took over the July 4th weekend, I recall we had to be very careful about food. What was the specific dietary restriction for my youngest child, and where did we end up watching the fireworks on the last night?\n",
      "2025-12-07 23:44:26 - LightMemory - INFO - [retrieve_20251207_234426_209208] Parameters: limit=20, filters=None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Result: True (✓ Correct)\n",
      "\n",
      "================================================================================\n",
      "Question 3/3 [ID: q_travel_03]\n",
      "================================================================================\n",
      "Question: For the family trip we took over the July 4th weekend, I recall we had to be very careful about food. What was the specific dietary restriction for my youngest child, and where did we end up watching the fireworks on the last night?\n",
      "Question Date: 2022/06/01 (Thu) 14:30\n",
      "Question Type: single-session-assistant\n",
      "Expected Answer: The specific dietary restriction was a nut allergy (nut-free). You watched the July 4th fireworks at MacDill Park along the Tampa Riverwalk.\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2025-12-07 23:44:26 - LightMemory - INFO - [retrieve_20251207_234426_209208] Searching vector database\n",
      "2025-12-07 23:44:26 - LightMemory - INFO - [retrieve_20251207_234426_209208] Found 20 results\n",
      "2025-12-07 23:44:26 - LightMemory - INFO - [retrieve_20251207_234426_209208] Formatted 20 results into output string\n",
      "2025-12-07 23:44:26 - LightMemory - INFO - ========== END retrieve_20251207_234426_209208 ==========\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Retrieved 20 relevant memories\n",
      "--------------------------------------------------------------------------------\n",
      "Memory 1: 2022-06-20T19:51:02.000 Mon User requested family-friendly attractions with no scary rides, nut-free restaurants due to a nut allergy, a beach day on July 3rd, and fireworks on July 4th if available.\n",
      "Memory 2: 2022-06-20T19:51:05.000 Mon User requested Day 3's itinerary for a beach day and Florida Aquarium with nut-free meals.\n",
      "Memory 3: 2022-06-20T19:51:08.000 Mon User's saved preferences include a 4-day family trip, nut-free meals, no scary rides, beach, fireworks, and a $2,500 budget.\n",
      "Memory 4: 2022-06-20T19:51:03.000 Mon User requested a detailed itinerary for Day 2 including ZooTampa and Glazer Children's Museum with nut-free lunch options.\n",
      "Memory 5: 2022-06-20T19:51:06.000 Mon Meals cost $336: Breakfast for $30, Lunch for $96, and Dinner for $210.\n",
      "Memory 6: 2022-06-20T19:51:08.000 Mon Assistant provided tips for traveling with kids in July in Tampa: Hydration with free water refill stations, packing sunscreen and hats, timing visits to outdoor attractions in the morning, and confirming child seats with rental car.\n",
      "Memory 7: 2022-06-20T19:51:06.000 Mon 20:00 Free July 4th fireworks at MacDill Park viewing spot.\n",
      "Memory 8: 2022-03-20T13:21:03.000 Sun User has a per-meal budget of $30-$60 and is vegetarian.\n",
      "Memory 9: 2022-06-20T19:51:04.000 Mon User requested to add Glazer Children's Museum and nut-free dinner to Day 2, plus update the budget.\n",
      "Memory 10: 2022-06-20T19:51:03.000 Mon Day 2 itinerary includes: July 2 - Breakfast at apartment (nut-free cereal + fruit, $0), drive to ZooTampa (15-minute drive, $5 gas), enter zoo at 10:00 with tickets ($61 total), lunch at Nut-Free Café ($36 total), drive to Publix for nut-free snacks/water ($30 total).\n",
      "Memory 11: 2022-06-20T19:51:04.000 Mon Budget update: $2,447 + $61 (zoo) + $48 (museum) + $36 (lunch) + $28 (dinner) + $25 (gas) + $30 (snacks) = $2,675.\n",
      "Memory 12: 2022-04-03T10:57:03.000 Sun Budget update: $792 (previous total) + $30 (lunch) + $67 (dinner) + $65 (transport) + $20 (snacks) = $974, remaining $226 for Day 2.\n",
      "Memory 13: 2022-06-20T19:51:06.000 Mon User is planning Day 4 itinerary for July 4, 2022, including Ybor City and fireworks, with a total budget under $2,500.\n",
      "Memory 14: 2022-06-20T19:51:06.000 Mon Day 4 plan includes: 08:30 Breakfast at apartment with Publix snacks costing $15 total.\n",
      "Memory 15: 2022-06-20T19:51:05.000 Mon Day 3 itinerary includes: July 3 - Breakfast at apartment (Publix snacks, $15 total), drive to Ballast Point Park Beach (30-minute drive, $8 gas), free entry, lunch at beach concession stand (nut-free hot dogs/fries, $30 total), drive to Florida Aquarium (25-minute drive, $7 gas), enter aquarium at 15:00 with tickets ($72 total), dinner at Lalit Kathi Rolls Momos (nut-free, $23 total), return to apartment (10-minute drive, $3 gas).\n",
      "Memory 16: 2022-06-20T19:51:06.000 Mon Miscellaneous costs $30 for Publix nut-free snacks.\n",
      "Memory 17: 2022-04-03T10:57:03.000 Sun User requested budget-friendly casual restaurants near parks for dinner and to add that to Day 1's plan.\n",
      "Memory 18: 2022-04-03T10:57:05.000 Sun Final budget breakdown: Total $1,199 under $1,200 - Round-trip flights $838, accommodations (2 nights) $328, meals $149, local transport $120, attractions $280, miscellaneous (snacks) $20.\n",
      "Memory 19: 2022-06-20T19:51:06.000 Mon 12:30 Lunch at Uptown Fresh Beer Cafe, 20-minute drive costing $5 in gas, with nut-free pizza and Mexican bowls costing $61 total, rated 3.4.\n",
      "Memory 20: 2022-03-20T13:21:03.000 Sun Day 1 itinerary (March 25): 09:30 Depart Washington on Flight F3694191 ($173); 12:01 Arrive in Tampa; Uber/Lyft to apartment ~ $20; 13:00 Check-in to apartment; 14:00 Lunch at The Zaffran (35 W Gasparilla Plaza) vegetarian-friendly (veggie wraps, salads, tea) $35, 3.3 rating, 5-minute walk from apartment; 15:00 Visit Tampa Bay History Center (801 Water St) maritime/cultural history $25 admission, 4.7 rating, 5-minute walk from lunch; 17:00 Stroll Riverwalk to MacDill Park (100 N Ashley Dr) scenic waterfront, free entry; 19:00 Dinner at Uptown Fresh Beer Cafe (1221 E Fowler Ave) vegetarian pizza/Mexican bowls $61, 15-minute Uber ~ $15.\n",
      "\n",
      "Generating answer...\n",
      "\n",
      "Generated Answer: The specific dietary restriction for your youngest child was a nut allergy, which required you to be careful about food choices during the trip. You ended up watching the fireworks on the last night at MacDill Park.\n",
      "\n",
      "Evaluating answer...\n",
      "Evaluation Result: True (✓ Correct)\n",
      "\n",
      "================================================================================\n",
      "Retrieval and answer generation completed!\n",
      "================================================================================\n",
      "\n",
      "Overall Accuracy: 100.00% (3/3)\n"
     ]
    }
   ],
   "source": [
    "# Execute retrieval, answer generation, and evaluation\n",
    "retrieval_results = test_retrieval_and_answer(\n",
    "    lightmem, \n",
    "    questions, \n",
    "    question_ids,\n",
    "    question_types,\n",
    "    question_dates,\n",
    "    answers, \n",
    "    top_k=20\n",
    ")"
   ]
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   "source": []
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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