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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LightMem Example with code data\n",
"\n",
"Tutorial author: xubuqiang"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Prepare the runtime environment"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/disk/disk_20T/xubuqiang/lightmem\n",
"env: ALL_PROXY=\n",
"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[?25hCollecting torch==2.8.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5a/63/4fdc45a0304536e75a5e1b1bbfb1b56dd0e2743c48ee83ca729f7ce44162/torch-2.8.0-cp311-cp311-manylinux_2_28_x86_64.whl (888.1 MB)\n",
"Collecting transformers==4.57.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e5/2b/4d2708ac1ff5cd708b6548f4c5812d0ae40d1c28591c4c1c762b6dbdef2d/transformers-4.57.0-py3-none-any.whl (12.0 MB)\n",
"Collecting sentence-transformers==5.1.1 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/48/21/4670d03ab8587b0ab6f7d5fa02a95c3dd6b1f39d0e40e508870201f3d76c/sentence_transformers-5.1.1-py3-none-any.whl (486 kB)\n",
"Collecting accelerate==1.10.1 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5f/a0/d9ef19f780f319c21ee90ecfef4431cbeeca95bec7f14071785c17b6029b/accelerate-1.10.1-py3-none-any.whl (374 kB)\n",
"Collecting openai==2.3.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9c/5b/4be258ff072ed8ee15f6bfd8d5a1a4618aa4704b127c0c5959212ad177d6/openai-2.3.0-py3-none-any.whl (999 kB)\n",
"Collecting tiktoken==0.12.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/6a/d0/3d9275198e067f8b65076a68894bb52fd253875f3644f0a321a720277b8a/tiktoken-0.12.0-cp311-cp311-manylinux_2_28_x86_64.whl (1.2 MB)\n",
"Collecting llmlingua==0.2.2 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/6e/3e/221fe46a3338f2babdb2082ee42df88fcaa8ea0e639e832cbb1b93c5923a/llmlingua-0.2.2-py3-none-any.whl (30 kB)\n",
"Collecting qdrant-client==1.15.1 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ef/33/d8df6a2b214ffbe4138db9a1efe3248f67dc3c671f82308bea1582ecbbb7/qdrant_client-1.15.1-py3-none-any.whl (337 kB)\n",
"Collecting pydantic==2.11.10 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/bd/1f/73c53fcbfb0b5a78f91176df41945ca466e71e9d9d836e5c522abda39ee7/pydantic-2.11.10-py3-none-any.whl (444 kB)\n",
"Collecting pydantic_core==2.33.2 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/47/bc/cd720e078576bdb8255d5032c5d63ee5c0bf4b7173dd955185a1d658c456/pydantic_core-2.33.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n",
"Collecting numpy==2.2.6 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b3/dd/2238b898e51bd6d389b7389ffb20d7f4c10066d80351187ec8e303a5a475/numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)\n",
"Collecting scipy==1.15.3 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/bd/37/89f19c8c05505d0601ed5650156e50eb881ae3918786c8fd7262b4ee66d3/scipy-1.15.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.7 MB)\n",
"Collecting scikit-learn==1.7.2 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ef/0e/97dbca66347b8cf0ea8b529e6bb9367e337ba2e8be0ef5c1a545232abfde/scikit_learn-1.7.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (9.7 MB)\n",
"Collecting nltk==3.9.2 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/60/90/81ac364ef94209c100e12579629dc92bf7a709a84af32f8c551b02c07e94/nltk-3.9.2-py3-none-any.whl (1.5 MB)\n",
"Collecting tokenizers==0.22.1 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d0/c6/dc3a0db5a6766416c32c034286d7c2d406da1f498e4de04ab1b8959edd00/tokenizers-0.22.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB)\n",
"Collecting huggingface-hub==0.35.3 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/31/a0/651f93d154cb72323358bf2bbae3e642bdb5d2f1bfc874d096f7cb159fa0/huggingface_hub-0.35.3-py3-none-any.whl (564 kB)\n",
"Collecting safetensors==0.6.2 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/fe/5d/5a514d7b88e310c8b146e2404e0dc161282e78634d9358975fd56dfd14be/safetensors-0.6.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (485 kB)\n",
"Collecting tqdm==4.67.1 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl (78 kB)\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",
"Collecting filelock==3.20.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/76/91/7216b27286936c16f5b4d0c530087e4a54eead683e6b0b73dd0c64844af6/filelock-3.20.0-py3-none-any.whl (16 kB)\n",
"Collecting regex==2025.9.18 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/fe/d0/c51d1e6a80eab11ef96a4cbad17fc0310cf68994fb01a7283276b7e5bbd6/regex-2025.9.18-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (798 kB)\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",
"Collecting h2==4.3.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/69/b2/119f6e6dcbd96f9069ce9a2665e0146588dc9f88f29549711853645e736a/h2-4.3.0-py3-none-any.whl (61 kB)\n",
"Collecting anyio==4.11.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl (109 kB)\n",
"Collecting certifi==2025.10.5 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e4/37/af0d2ef3967ac0d6113837b44a4f0bfe1328c2b9763bd5b1744520e5cfed/certifi-2025.10.5-py3-none-any.whl (163 kB)\n",
"Collecting charset-normalizer==3.4.3 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/87/df/b7737ff046c974b183ea9aa111b74185ac8c3a326c6262d413bd5a1b8c69/charset_normalizer-3.4.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (150 kB)\n",
"Collecting idna==3.10 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl (70 kB)\n",
"Collecting click==8.3.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/db/d3/9dcc0f5797f070ec8edf30fbadfb200e71d9db6b84d211e3b2085a7589a0/click-8.3.0-py3-none-any.whl (107 kB)\n",
"Collecting joblib==1.5.2 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/1e/e8/685f47e0d754320684db4425a0967f7d3fa70126bffd76110b7009a0090f/joblib-1.5.2-py3-none-any.whl (308 kB)\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",
"Collecting pillow==11.3.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/f2/2f/d7675ecae6c43e9f12aa8d58b6012683b20b6edfbdac7abcb4e6af7a3784/pillow-11.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (6.6 MB)\n",
"Collecting protobuf==6.32.1 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5c/f6/88d77011b605ef979aace37b7703e4eefad066f7e84d935e5a696515c2dd/protobuf-6.32.1-cp39-abi3-manylinux2014_x86_64.whl (322 kB)\n",
"Collecting psutil==7.1.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9d/de/04c8c61232f7244aa0a4b9a9fbd63a89d5aeaf94b2fc9d1d16e2faa5cbb0/psutil-7.1.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (291 kB)\n",
"Collecting fsspec==2025.9.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/47/71/70db47e4f6ce3e5c37a607355f80da8860a33226be640226ac52cb05ef2e/fsspec-2025.9.0-py3-none-any.whl (199 kB)\n",
"Collecting grpcio==1.75.1 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/3f/42/5f628abe360b84dfe8dd8f32be6b0606dc31dc04d3358eef27db791ea4d5/grpcio-1.75.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (6.5 MB)\n",
"Collecting portalocker==3.2.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/4b/a6/38c8e2f318bf67d338f4d629e93b0b4b9af331f455f0390ea8ce4a099b26/portalocker-3.2.0-py3-none-any.whl (22 kB)\n",
"Collecting annotated-types==0.7.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl (13 kB)\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",
"Collecting typing-inspection==0.4.2 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl (14 kB)\n",
"Collecting networkx==3.4.2 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl (1.7 MB)\n",
"Collecting sympy==1.14.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl (6.3 MB)\n",
"Collecting mpmath==1.3.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
"Collecting distro==1.9.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl (20 kB)\n",
"Collecting hf-xet==1.1.10 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/15/07/86397573efefff941e100367bbda0b21496ffcdb34db7ab51912994c32a2/hf_xet-1.1.10-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.2 MB)\n",
"Collecting hpack==4.1.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/07/c6/80c95b1b2b94682a72cbdbfb85b81ae2daffa4291fbfa1b1464502ede10d/hpack-4.1.0-py3-none-any.whl (34 kB)\n",
"Collecting hyperframe==6.1.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/48/30/47d0bf6072f7252e6521f3447ccfa40b421b6824517f82854703d0f5a98b/hyperframe-6.1.0-py3-none-any.whl (13 kB)\n",
"Collecting jiter==0.11.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9f/d8/ec74886497ea393c29dbd7651ddecc1899e86404a6b1f84a3ddab0ab59fd/jiter-0.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (348 kB)\n",
"Collecting sniffio==1.3.1 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl (10 kB)\n",
"Collecting threadpoolctl==3.6.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl (18 kB)\n",
"Collecting urllib3==2.5.0 (from lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl (129 kB)\n",
"Collecting nvidia-cuda-nvrtc-cu12==12.8.93 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/05/6b/32f747947df2da6994e999492ab306a903659555dddc0fbdeb9d71f75e52/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl (88.0 MB)\n",
"Collecting nvidia-cuda-runtime-cu12==12.8.90 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/0d/9b/a997b638fcd068ad6e4d53b8551a7d30fe8b404d6f1804abf1df69838932/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (954 kB)\n",
"Collecting nvidia-cuda-cupti-cu12==12.8.90 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/f8/02/2adcaa145158bf1a8295d83591d22e4103dbfd821bcaf6f3f53151ca4ffa/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (10.2 MB)\n",
"Collecting nvidia-cudnn-cu12==9.10.2.21 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ba/51/e123d997aa098c61d029f76663dedbfb9bc8dcf8c60cbd6adbe42f76d049/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl (706.8 MB)\n",
"Collecting nvidia-cublas-cu12==12.8.4.1 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/dc/61/e24b560ab2e2eaeb3c839129175fb330dfcfc29e5203196e5541a4c44682/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl (594.3 MB)\n",
"Collecting nvidia-cufft-cu12==11.3.3.83 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (193.1 MB)\n",
"Collecting nvidia-curand-cu12==10.3.9.90 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/fb/aa/6584b56dc84ebe9cf93226a5cde4d99080c8e90ab40f0c27bda7a0f29aa1/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl (63.6 MB)\n",
"Collecting nvidia-cusolver-cu12==11.7.3.90 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl (267.5 MB)\n",
"Collecting nvidia-cusparse-cu12==12.5.8.93 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (288.2 MB)\n",
"Collecting nvidia-cusparselt-cu12==0.7.1 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/56/79/12978b96bd44274fe38b5dde5cfb660b1d114f70a65ef962bcbbed99b549/nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl (287.2 MB)\n",
"Collecting nvidia-nccl-cu12==2.27.3 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5c/5b/4e4fff7bad39adf89f735f2bc87248c81db71205b62bcc0d5ca5b606b3c3/nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (322.4 MB)\n",
"Collecting nvidia-nvtx-cu12==12.8.90 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a2/eb/86626c1bbc2edb86323022371c39aa48df6fd8b0a1647bc274577f72e90b/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89 kB)\n",
"Collecting nvidia-nvjitlink-cu12==12.8.93 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/f6/74/86a07f1d0f42998ca31312f998bd3b9a7eff7f52378f4f270c8679c77fb9/nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl (39.3 MB)\n",
"Collecting nvidia-cufile-cu12==1.13.1.3 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/bb/fe/1bcba1dfbfb8d01be8d93f07bfc502c93fa23afa6fd5ab3fc7c1df71038a/nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (1.2 MB)\n",
"Collecting triton==3.4.0 (from torch==2.8.0->lightmem==0.1.0)\n",
" Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7d/39/43325b3b651d50187e591eefa22e236b2981afcebaefd4f2fc0ea99df191/triton-3.4.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (155.5 MB)\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",
"\u001b[33mWARNING: The candidate selected for download or install is a yanked version: 'transformers' candidate (version 4.57.0 at https://pypi.tuna.tsinghua.edu.cn/packages/e5/2b/4d2708ac1ff5cd708b6548f4c5812d0ae40d1c28591c4c1c762b6dbdef2d/transformers-4.57.0-py3-none-any.whl (from https://pypi.tuna.tsinghua.edu.cn/simple/transformers/) (requires-python:>=3.9.0))\n",
"Reason for being yanked: <none given>\u001b[0m\u001b[33m\n",
"\u001b[0mBuilding 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=b8bc5212fe5403e20a1808f7f1f0b7db4990d50b8699794f24852a5fa211cdd2\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-nmptuqgp/wheels/6e/78/3e/ad25a8ed5245b53409d8ea7f1a62638ba248b738ed4cf419a3\n",
"Successfully built lightmem\n",
"Installing collected packages: nvidia-cusparselt-cu12, mpmath, urllib3, typing-inspection, triton, tqdm, threadpoolctl, sympy, sniffio, safetensors, regex, pydantic_core, psutil, protobuf, portalocker, pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, joblib, jiter, idna, hyperframe, hpack, hf-xet, grpcio, fsspec, filelock, distro, click, charset-normalizer, certifi, annotated-types, scipy, pydantic, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, nltk, h2, anyio, tiktoken, scikit-learn, nvidia-cusolver-cu12, huggingface-hub, torch, tokenizers, openai, transformers, qdrant-client, accelerate, sentence-transformers, llmlingua, lightmem\n",
"\u001b[2K Attempting uninstall: urllib3━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 1/62\u001b[0m [mpmath]t-cu12]\n",
"\u001b[2K Found existing installation: urllib3 2.6.0━━━━━━━\u001b[0m \u001b[32m 1/62\u001b[0m [mpmath]\n",
"\u001b[2K Uninstalling urllib3-2.6.0:━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 1/62\u001b[0m [mpmath]\n",
"\u001b[2K Successfully uninstalled urllib3-2.6.0━━━━━━━━━\u001b[0m \u001b[32m 1/62\u001b[0m [mpmath]\n",
"\u001b[2K Attempting uninstall: psutil[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11/62\u001b[0m [pydantic_core]ion]\n",
"\u001b[2K Found existing installation: psutil 7.1.3━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11/62\u001b[0m [pydantic_core]\n",
"\u001b[2K Uninstalling psutil-7.1.3:━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11/62\u001b[0m [pydantic_core]\n",
"\u001b[2K Successfully uninstalled psutil-7.1.3━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11/62\u001b[0m [pydantic_core]\n",
"\u001b[2K Attempting uninstall: idnam\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27/62\u001b[0m [joblib]x]blas-cu12]u12]2]\n",
"\u001b[2K Found existing installation: idna 3.11━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27/62\u001b[0m [joblib]\n",
"\u001b[2K Uninstalling idna-3.11:90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27/62\u001b[0m [joblib]\n",
"\u001b[2K Successfully uninstalled idna-3.11━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27/62\u001b[0m [joblib]\n",
"\u001b[2K Attempting uninstall: charset-normalizer[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37/62\u001b[0m [click]ck]e]\n",
"\u001b[2K Found existing installation: charset-normalizer 3.4.4━━━━━\u001b[0m \u001b[32m37/62\u001b[0m [click]\n",
"\u001b[2K Uninstalling charset-normalizer-3.4.4:[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37/62\u001b[0m [click]\n",
"\u001b[2K Successfully uninstalled charset-normalizer-3.4.4━━━━━━━\u001b[0m \u001b[32m37/62\u001b[0m [click]\n",
"\u001b[2K Attempting uninstall: certifi━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38/62\u001b[0m [charset-normalizer]\n",
"\u001b[2K Found existing installation: certifi 2025.11.12━━━━━━━━━━━\u001b[0m \u001b[32m38/62\u001b[0m [charset-normalizer]\n",
"\u001b[2K Uninstalling certifi-2025.11.12:m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38/62\u001b[0m [charset-normalizer]\n",
"\u001b[2K Successfully uninstalled certifi-2025.11.12━━━━━━━━━━━━━\u001b[0m \u001b[32m38/62\u001b[0m [charset-normalizer]\n",
"\u001b[2K Attempting uninstall: anyio━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m47/62\u001b[0m [h2]k]a-cudnn-cu12]12]\n",
"\u001b[2K Found existing installation: anyio 4.12.0[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m47/62\u001b[0m [h2]\n",
"\u001b[2K Uninstalling anyio-4.12.0:━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m47/62\u001b[0m [h2]\n",
"\u001b[2K Successfully uninstalled anyio-4.12.0╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m47/62\u001b[0m [h2]\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62/62\u001b[0m [lightmem]lightmem]llmlingua]ransformers]\n",
"\u001b[1A\u001b[2KSuccessfully installed accelerate-1.10.1 annotated-types-0.7.0 anyio-4.11.0 certifi-2025.10.5 charset-normalizer-3.4.3 click-8.3.0 distro-1.9.0 filelock-3.20.0 fsspec-2025.9.0 grpcio-1.75.1 h2-4.3.0 hf-xet-1.1.10 hpack-4.1.0 huggingface-hub-0.35.3 hyperframe-6.1.0 idna-3.10 jiter-0.11.0 joblib-1.5.2 lightmem-0.1.0 llmlingua-0.2.2 mpmath-1.3.0 networkx-3.4.2 nltk-3.9.2 numpy-2.2.6 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.10.2.21 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.27.3 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvtx-cu12-12.8.90 openai-2.3.0 pillow-11.3.0 portalocker-3.2.0 protobuf-6.32.1 psutil-7.1.0 pydantic-2.11.10 pydantic_core-2.33.2 qdrant-client-1.15.1 regex-2025.9.18 safetensors-0.6.2 scikit-learn-1.7.2 scipy-1.15.3 sentence-transformers-5.1.1 sniffio-1.3.1 sympy-1.14.0 threadpoolctl-3.6.0 tiktoken-0.12.0 tokenizers-0.22.1 torch-2.8.0 tqdm-4.67.1 transformers-4.57.0 triton-3.4.0 typing-inspection-0.4.2 urllib3-2.5.0\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 ."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Import Dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RUN_LOG_DIR: ./logs/20251206_193204\n",
"DATA_FILE_PATH: /disk/disk_20T/xubuqiang/lightmem/dataset/longmemeval/code_single.json\n"
]
}
],
"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 = '/models/llmlingua-2-bert-base-multilingual-cased-meetingbank'\n",
"EMBEDDING_MODEL_PATH = '/models/all-MiniLM-L6-v2'\n",
"DATA_FILE_PATH = '/code_single.json'\n",
"\n",
"print(f\"RUN_LOG_DIR: {RUN_LOG_DIR}\")\n",
"print(f\"DATA_FILE_PATH: {DATA_FILE_PATH}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: ALL_PROXY=\n"
]
}
],
"source": [
"%env ALL_PROXY="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. LightMemory Initial config"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"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": [
"2025-12-06 19:11:16 - LightMemory - INFO - Initializing LightMemory with provided configuration\n",
"2025-12-06 19:11:16 - LightMemory - INFO - Token statistics tracking initialized\n",
"2025-12-06 19:11:16 - LightMemory - INFO - Initializing pre-compressor\n",
"`torch_dtype` is deprecated! Use `dtype` instead!\n",
"2025-12-06 19:12:13 - LightMemory - INFO - Initializing topic segmenter\n",
"2025-12-06 19:12:13 - LightMemory - INFO - Initializing memory manager\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DEBUG: resolved to encoding o200k_base\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:12:13 - LightMemory - INFO - Initializing text embedder\n",
"2025-12-06 19:12:13 - sentence_transformers.SentenceTransformer - INFO - Load pretrained SentenceTransformer: /disk/disk_20T/fangjizhan/models/all-MiniLM-L6-v2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ShortMemBufferManager initialized with max_tokens=512\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:12:13 - LightMemory - INFO - Initializing embedding retriever\n",
"2025-12-06 19:12:14 - 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\": \"code_demo\",\n",
" \"embedding_model_dims\": 384,\n",
" \"path\": \"./code_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": 30,
"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_faker_01] I'm reviewing the fake user data generation task we did previously. Can you remind me exactly how ma...\n",
" [q_faker_02] Going back to the fake company data task, I remember the script initially failed when trying to save...\n",
" [q_eparse_03] In our previous session using the 'Eparse' tool to convert Excel to JSON, the command failed when we...\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": 6,
"metadata": {},
"outputs": [],
"source": [
"METADATA_GENERATE_PROMPT = \"\"\"\n",
"You are a Technical Conversation Analyzer.\n",
"Your task is to extract **all technical operations, errors, and solutions** 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",
"2. **Extract ALL technical information** including:\n",
" - Commands executed (preserve EXACT command syntax)\n",
" - Error messages (preserve EXACT error text and codes)\n",
" - File paths and directories (preserve COMPLETE paths)\n",
" - Solutions and fixes applied\n",
" - Tool/package names and versions\n",
" - Configuration changes\n",
" - Problem-solution pairs (link errors with their fixes)\n",
"\n",
"3. **Preserve Specificity - DO NOT generalize**:\n",
" - ✓ \"OSError: directory `/disk/disk_20T/user/GitTaskBench/prompt/Faker_02` does not exist\"\n",
" - ✗ \"encountered a file system error\"\n",
" \n",
" - ✓ \"Fixed by running `mkdir -p /disk/disk_20T/user/GitTaskBench/prompt/Faker_02`\"\n",
" - ✗ \"created the directory\"\n",
"\n",
"4. **Link problems with solutions**:\n",
" When a problem is mentioned and later solved, create entries for both:\n",
" - The error/problem with full details\n",
" - The solution/fix with full details\n",
" - Optionally, a combined entry linking them\n",
"\n",
"5. **Time Handling**:\n",
" - Always include timestamp reference: \"on 2025-12-05\" or \"at [timestamp]\"\n",
" - For sequences: note which action happened first\n",
"\n",
"6. Output format:\n",
"Please return your response in JSON format.\n",
" {\n",
" \"data\": [\n",
" {\n",
" \"source_id\": \"<source_id>\",\n",
" \"fact\": \"<technical fact with ALL specific details>\"\n",
" }\n",
" ]\n",
" }\n",
"\n",
"Example:\n",
"--- Topic 1 ---\n",
"[2025-12-05T09:00:00.000, Fri] 0.User: python generate_users.py --count 100 --output ./data/users.csv\n",
"[2025-12-05T09:00:01.000, Fri] 0.Assistant: Error OSError: [Errno 2] No such file or directory: './data/users.csv'\n",
"[2025-12-05T09:00:02.000, Fri] 1.User: mkdir -p ./data\n",
"[2025-12-05T09:00:03.000, Fri] 2.User: python generate_users.py --count 100 --output ./data/users.csv\n",
"[2025-12-05T09:00:04.000, Fri] 2.Assistant: Successfully generated 100 user records to ./data/users.csv\n",
"\n",
"{\"data\": [\n",
" {\"source_id\": \"0\", \"fact\": \"User executed command `python generate_users.py --count 100 --output ./data/users.csv` on 2025-12-05T09:00:00.\"},\n",
" {\"source_id\": \"0\", \"fact\": \"Command failed with OSError: [Errno 2] No such file or directory: './data/users.csv' on 2025-12-05T09:00:01.\"},\n",
" {\"source_id\": \"1\", \"fact\": \"User created directory by running `mkdir -p ./data` on 2025-12-05T09:00:02.\"},\n",
" {\"source_id\": \"2\", \"fact\": \"User re-executed command `python generate_users.py --count 100 --output ./data/users.csv` on 2025-12-05T09:00:03.\"},\n",
" {\"source_id\": \"2\", \"fact\": \"Command successfully generated 100 user records to ./data/users.csv on 2025-12-05T09:00:04.\"},\n",
" {\"source_id\": \"0\", \"fact\": \"The CSV generation initially failed because directory './data' did not exist (OSError), and was fixed by creating the directory with `mkdir -p ./data` before re-running the script.\"}\n",
"]}\n",
"\n",
"Reminder: \n",
"- Preserve EXACT commands, error messages, file paths\n",
"- DO NOT paraphrase technical terms or simplify details\n",
"- Link errors with their solutions explicitly\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"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')"
]
},
{
"cell_type": "code",
"execution_count": null,
"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%| | 0/20 [00:00<?, ?it/s]2025-12-06 19:12:34 - LightMemory - INFO - ========== START add_memory_20251206_191234_747423 ==========\n",
"2025-12-06 19:12:34 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:12:34 - LightMemory - INFO - [add_memory_20251206_191234_747423] Extracted 0 visual contexts\n",
"Token indices sequence length is longer than the specified maximum sequence length for this model (1013 > 512). Running this sequence through the model will result in indexing errors\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191234_747423] Restored visual contexts after compression\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191234_747423] Target compression rate: 0.8\n",
"Adding turns: 5%|▌ | 1/20 [00:00<00:10, 1.89it/s]2025-12-06 19:12:35 - LightMemory - INFO - ========== START add_memory_20251206_191235_278208 ==========\n",
"2025-12-06 19:12:35 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_278208] Extracted 0 visual contexts\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_278208] Restored visual contexts after compression\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_278208] Target compression rate: 0.8\n",
"2025-12-06 19:12:35 - LightMemory - INFO - ========== START add_memory_20251206_191235_308053 ==========\n",
"2025-12-06 19:12:35 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_308053] Extracted 0 visual contexts\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_308053] Restored visual contexts after compression\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_308053] Target compression rate: 0.8\n",
"BertSdpaSelfAttention is used but `torch.nn.functional.scaled_dot_product_attention` does not support non-absolute `position_embedding_type` or `output_attentions=True` or `head_mask`. Falling back to the manual attention implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation=\"eager\"` when loading the model.\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_308053] Generated 1 segments\n",
"Adding turns: 15%|█▌ | 3/20 [00:00<00:03, 5.21it/s]2025-12-06 19:12:35 - LightMemory - INFO - ========== START add_memory_20251206_191235_425848 ==========\n",
"2025-12-06 19:12:35 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_425848] Extracted 0 visual contexts\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_425848] Restored visual contexts after compression\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_425848] Target compression rate: 0.8\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_425848] Generated 1 segments\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_425848] Assigned global topic IDs: total=1, mapping=[[0]]\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_425848] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_425848] Batch max_source_ids: [1]\n",
"2025-12-06 19:12:35 - LightMemory - INFO - [add_memory_20251206_191235_425848] Starting metadata generation\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"User prompt for API call 0:\n",
"--- Topic 0 ---\n",
"[2025-12-02T17:06:00.000, Tue] 0.User: Task Task Description repository content generate 100 fake data entries save CSV file two columns Username Email Repository Faker Path disk / disk _ 20T user GitTaskBench code _ base Faker Repository URL https github. com joke2k faker Understanding Guide Read README understand basic functions usage File Paths Input File Description Directory disk / disk _ 20T user GitTaskBench prompt Faker _ 01 file name output xxx start naming output 01 file format determined task requirements Supplementary Instructions understand analyze code generate execute code call tools complete user - specified task Workflow & Standards Task analyze user - provided task description task working directory work _ dir repository information repo code importance Plan Solution formulate execution steps read code library README file understand structure usage insufficient information require writing code rely language understanding tool invocation paths code generation execution paths avoid errors Repository Analysis Explore Structure understand file directory structure paths Identify Key Files Prioritize README configuration files main entry scripts Dependency Management Check requirements txt files determine installation Include installation commands code blocks pip install - r requirements txt avoid installation Configuration Python Conda pre - set no extra configuration ensure code library path PYTHONPATH generate export remote _ repo _ path } commandCode Implementation Execution detailed code implementation steps complete function class definitions parameters return values comments docstrings checkpoint model files first check download first automatic download required multiple files - O Error Handling Iteration Check code execution results errors analyze cause fix code regenerate complete script retrial task resolved completed analyze cause alternative solutions Tool Priority checkpoint model files needed first download automatic download required 7 Task Completion task completed confirmed clear summaryConstraints Mandatory Requirements Paths handling files data loading code Provided Repository Code tasks completed existing repository code prohibited rewrite code implementation Read README. md code library file understand structure usage If no README md insufficient read code\n",
"[2025-12-02T17:06:00.500, Tue] 0.Assistant: Command : cat / disk disk _ 20T user GitTaskBench code _ base Faker / README.\n",
"[2025-12-02T17:06:01.000, Tue] 1.User: Output cat : disk / disk _ 20T user / GitTaskBench / code _ base / Faker / README. md No file\n",
"[2025-12-02T17:06:01.500, Tue] 1.Assistant: Command : ls - l disk disk _ 20T user / GitTaskBench code _ base /\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:12:45 - LightMemory - INFO - [add_memory_20251206_191235_425848] API Call 0 tokens - Prompt: 1410, Completion: 662, Total: 2072\n",
"2025-12-06 19:12:45 - LightMemory - INFO - [add_memory_20251206_191235_425848] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:12:45 - LightMemory - INFO - [add_memory_20251206_191235_425848] Created 15 MemoryEntry objects\n",
"2025-12-06 19:12:45 - LightMemory - INFO - ========== START offline_update_20251206_191245_198353 ==========\n",
"2025-12-06 19:12:45 - LightMemory - INFO - [offline_update_20251206_191245_198353] Received 15 memory entries\n",
"2025-12-06 19:12:45 - LightMemory - INFO - [offline_update_20251206_191245_198353] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:12:45 - LightMemory - INFO - [offline_update_20251206_191245_198353] Starting embedding and insertion to vector database\n"
]
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"text": [
"2025-12-06 19:12:47 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [offline_update_20251206_191245_198353] Successfully inserted 15 entries to vector database\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191235_425848] Cumulative token stats - Total API calls: 1, Total tokens: 2072\n",
"Adding turns: 20%|██ | 4/20 [00:12<01:08, 4.30s/it]2025-12-06 19:12:47 - LightMemory - INFO - ========== START add_memory_20251206_191247_491551 ==========\n",
"2025-12-06 19:12:47 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191247_491551] Extracted 0 visual contexts\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191247_491551] Restored visual contexts after compression\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191247_491551] Target compression rate: 0.8\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191247_491551] Generated 1 segments\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191247_491551] Assigned global topic IDs: total=1, mapping=[[1]]\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191247_491551] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191247_491551] Batch max_source_ids: [0]\n",
"2025-12-06 19:12:47 - LightMemory - INFO - [add_memory_20251206_191247_491551] Starting metadata generation\n"
]
},
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"User prompt for API call 0:\n",
"--- Topic 1 ---\n",
"[2025-12-02T17:06:00.000, Tue] 0.User: Output total 192 - rw - rw - r - 1 user user 387 12 1 17 : 06 build - alpine. sh - rw - rw - r - user user 96220 12 1 17 : 06 CHANGELOG. md - rw - rw - r - user user 263 12 1 17 : 06 CITATION. cff - rw - rw - r - user user 2273 12 1 17 : 06 CONTRIBUTING. rst - rw - rw - r - user user 265 12 1 : 06 dev - requirements. txt drwxrwxr - 6 user user 4096 12 docs drwxrwxr - 7 user user 4096 faker - rw - rw - r - user user 9320 12 generate _ stubs. py - rw - rw - r - user 1060 12 1 LICENSE. txt - rw - rw - r - user user 410 12 1 Makefile - rw - rw - r - user user 661 12 1 MANIFEST. in - rw - rw - r - user user 295 12 1 mypy. ini - rw - rw - r - user user 14189 12 1 README. rst - rw - rw - r - user user 161 12 1 readthedocs. yml - rw - rw - r - user user 815 12 1 : RELEASE _ PROCESS. rst - rw - rw - r - user user 182 12 1 setup. cfg - rw - rw - r - user user 2543 12 1 setup. py drwxrwxr - 7 user user 4096 12 tests - rw - rw - r - user user 1416 12 1 : tox. ini - rw - rw - r - user user 0 12 1 : 06 untitledtxt - rw - r 1 user user 7 12 1 17 : 06 VERSION\n",
"[2025-12-02T17:06:00.500, Tue] 0.Assistant: Command : cat / disk disk _ 20T user GitTaskBench code _ base Faker / README.\n"
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"text": [
"2025-12-06 19:12:50 - LightMemory - INFO - [add_memory_20251206_191247_491551] API Call 0 tokens - Prompt: 1407, Completion: 228, Total: 1635\n",
"2025-12-06 19:12:50 - LightMemory - INFO - [add_memory_20251206_191247_491551] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:12:50 - LightMemory - INFO - [add_memory_20251206_191247_491551] Created 3 MemoryEntry objects\n",
"2025-12-06 19:12:50 - LightMemory - INFO - ========== START offline_update_20251206_191250_839200 ==========\n",
"2025-12-06 19:12:50 - LightMemory - INFO - [offline_update_20251206_191250_839200] Received 3 memory entries\n",
"2025-12-06 19:12:50 - LightMemory - INFO - [offline_update_20251206_191250_839200] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:12:50 - LightMemory - INFO - [offline_update_20251206_191250_839200] Starting embedding and insertion to vector database\n"
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"2025-12-06 19:12:51 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [offline_update_20251206_191250_839200] Successfully inserted 3 entries to vector database\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191247_491551] Cumulative token stats - Total API calls: 2, Total tokens: 3707\n",
"Adding turns: 25%|██▌ | 5/20 [00:16<01:01, 4.13s/it]2025-12-06 19:12:51 - LightMemory - INFO - ========== START add_memory_20251206_191251_281980 ==========\n",
"2025-12-06 19:12:51 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191251_281980] Extracted 0 visual contexts\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191251_281980] Restored visual contexts after compression\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191251_281980] Target compression rate: 0.8\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191251_281980] Generated 1 segments\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191251_281980] Assigned global topic IDs: total=1, mapping=[[2]]\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191251_281980] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191251_281980] Batch max_source_ids: [0]\n",
"2025-12-06 19:12:51 - LightMemory - INFO - [add_memory_20251206_191251_281980] Starting metadata generation\n"
]
},
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"text": [
"User prompt for API call 0:\n",
"--- Topic 2 ---\n",
"[2025-12-02T17:06:00.000, Tue] 0.User: Output : * Faker * is a Python package generates fake data for you. need to bootstrap your database, create good - looking XML documents, fill - in your persistence to stress test it, or anonymize data from production service, Faker is for you. Faker is heavily inspired by PHP Faker _, Perl Faker _, and by Ruby Faker _. - - - - : : _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ _ _ _ _ | | _ | _ | _ _ | _ | _ _ _ _ | _ | _ | _ | _ | _ | | pypi | | build | | coverage | | license | - Compatibility - Starting from version 4. 0. 0, Faker dropped support for Python 2 and from version 5. 0. 0 only supports Python 3. 8 and above. If still need Python 2 compatibility, install version 3. 0. 1 in, consider updating codebase to support Python 3 can enjoy latest features Faker has to offer. see extended docs _ for more details, especially if upgrading from version 2. 0. 4 and below might be breaking changes. This package was also previously called fake - factory already deprecated by end of 2016, and much has changed since then, ensure your project and dependencies do not depend on old package. Basic Usage - - - Install with pip :.. code : : bash pip install Faker Use faker. Faker ( ) to create and initialize a faker generator, can generate data by accessing properties named after type of data you want..code : python from faker import Faker fake = Faker ( fake. name ( # Lucy Cechtelar ' fake. address ( 426 Jordy Lodge Cartwrightshire SC 88120 - 6700 ' fake. text ( Sint velit eveniet. Rerum repellat voluptatem rerum excepturi beatae sint laudantium consequatur Magni occaecati sint sit tempore. Nesciunt # a. ( truncated\n",
"[2025-12-02T17:06:00.500, Tue] 0.Assistant: Command : pip install - r disk disk _ 20T user GitTaskBench code _ base Faker / dev - requirements.\n"
]
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"output_type": "stream",
"text": [
"2025-12-06 19:13:03 - LightMemory - INFO - [add_memory_20251206_191251_281980] API Call 0 tokens - Prompt: 1405, Completion: 409, Total: 1814\n",
"2025-12-06 19:13:03 - LightMemory - INFO - [add_memory_20251206_191251_281980] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:13:03 - LightMemory - INFO - [add_memory_20251206_191251_281980] Created 7 MemoryEntry objects\n",
"2025-12-06 19:13:03 - LightMemory - INFO - ========== START offline_update_20251206_191303_965502 ==========\n",
"2025-12-06 19:13:03 - LightMemory - INFO - [offline_update_20251206_191303_965502] Received 7 memory entries\n",
"2025-12-06 19:13:03 - LightMemory - INFO - [offline_update_20251206_191303_965502] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:13:03 - LightMemory - INFO - [offline_update_20251206_191303_965502] Starting embedding and insertion to vector database\n"
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"output_type": "stream",
"text": [
"2025-12-06 19:13:04 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:13:04 - LightMemory - INFO - [offline_update_20251206_191303_965502] Successfully inserted 7 entries to vector database\n",
"2025-12-06 19:13:04 - LightMemory - INFO - [add_memory_20251206_191251_281980] Cumulative token stats - Total API calls: 3, Total tokens: 5521\n",
"Adding turns: 30%|███ | 6/20 [00:30<01:40, 7.21s/it]2025-12-06 19:13:04 - LightMemory - INFO - ========== START add_memory_20251206_191304_997206 ==========\n",
"2025-12-06 19:13:04 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:05 - LightMemory - INFO - [add_memory_20251206_191304_997206] Extracted 0 visual contexts\n",
"2025-12-06 19:13:05 - LightMemory - INFO - [add_memory_20251206_191304_997206] Restored visual contexts after compression\n",
"2025-12-06 19:13:05 - LightMemory - INFO - [add_memory_20251206_191304_997206] Target compression rate: 0.8\n",
"2025-12-06 19:13:05 - LightMemory - INFO - [add_memory_20251206_191304_997206] Generated 1 segments\n",
"2025-12-06 19:13:05 - LightMemory - INFO - [add_memory_20251206_191304_997206] Assigned global topic IDs: total=1, mapping=[[3]]\n",
"2025-12-06 19:13:05 - LightMemory - INFO - [add_memory_20251206_191304_997206] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:13:05 - LightMemory - INFO - [add_memory_20251206_191304_997206] Batch max_source_ids: [0]\n",
"2025-12-06 19:13:05 - LightMemory - INFO - [add_memory_20251206_191304_997206] Starting metadata generation\n"
]
},
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"output_type": "stream",
"text": [
"User prompt for API call 0:\n",
"--- Topic 3 ---\n",
"[2025-12-02T17:06:00.000, Tue] 0.User: Output indexes pypi. tuna. tsinghua. edu. cn / Requirement satisfied black > = 24. 8. 0 in disk / disk _ 20T / user anaconda3 lib / python3. 13 site - packages from - r disk 20T line 1 ( 24. 10. 0 ) Requirement check - manifest in disk / disk _ 20T user anaconda3 lib python3. 13 site - packages from - r disk 20T line 2 ( 0. 51 ) Requirement coverage > = 5. 2 in disk / disk _ 20T user anaconda3 lib python3. 13 site - packages from - r disk 20T line 3 ( 7. 12. 0 ) Requirement doc8 > = 1. 1. 1 in disk disk _ 20T anaconda3 python3. 13 site - packages from - disk _ 20T line 4 ( 2. 0. 0 flake8 - comprehensions disk 20T anaconda3 python3 13 site - packages 20T line 5 3. 17. 0 Requirement flake8 = 4. 0. 0 in disk 20T anaconda3 python3. 13 site - packages - r line 6 ( 7. 1. 1 Requirement freezegun > = 1. 5. 1 in disk / disk _ 20T anaconda3 python3. 13 site - packages - r disk 20T GitTaskBench code base dev - requirements txt line 7 ) 1. 5. 5 Requirement isort > = 5. 13. 2 in disk / disk _ 20T anaconda3 lib python3. 13 site - packages - 20T code base dev - requirements txt line 8 ) 6. 0. 1 mypy - extensions > = 1. 0.disk disk _ 20T user anaconda3 lib python3. 13 site packages r disk disk 20T GitTaskBench code base dev - requirements. txt line 9\n",
"[2025-12-02T17:06:00.500, Tue] 0.Assistant: import pandas pd from faker Faker Initialize Faker fake = Faker ( ) Generate fake user data user data = Username fake. user name ( Email fake. email ( } for _ in range ( 100 ) Create DataFrame df = pd. DataFrame ( user _ data ) Save to CSV output _ path = ' disk / disk _ 20T / user / GitTaskBench / prompt / Faker _ 01 / output. csv ' df. to _ csv ( output _ path index = False\n"
]
},
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"output_type": "stream",
"text": [
"2025-12-06 19:13:23 - LightMemory - INFO - [add_memory_20251206_191304_997206] API Call 0 tokens - Prompt: 1511, Completion: 674, Total: 2185\n",
"2025-12-06 19:13:23 - LightMemory - INFO - [add_memory_20251206_191304_997206] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:13:23 - LightMemory - INFO - [add_memory_20251206_191304_997206] Created 8 MemoryEntry objects\n",
"2025-12-06 19:13:23 - LightMemory - INFO - ========== START offline_update_20251206_191323_569092 ==========\n",
"2025-12-06 19:13:23 - LightMemory - INFO - [offline_update_20251206_191323_569092] Received 8 memory entries\n",
"2025-12-06 19:13:23 - LightMemory - INFO - [offline_update_20251206_191323_569092] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:13:23 - LightMemory - INFO - [offline_update_20251206_191323_569092] Starting embedding and insertion to vector database\n"
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"text": [
"2025-12-06 19:13:24 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [offline_update_20251206_191323_569092] Successfully inserted 8 entries to vector database\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191304_997206] Cumulative token stats - Total API calls: 4, Total tokens: 7706\n",
"Adding turns: 35%|███▌ | 7/20 [00:50<02:25, 11.16s/it]2025-12-06 19:13:24 - LightMemory - INFO - ========== START add_memory_20251206_191324_784909 ==========\n",
"2025-12-06 19:13:24 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_784909] Extracted 0 visual contexts\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_784909] Restored visual contexts after compression\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_784909] Target compression rate: 0.8\n",
"2025-12-06 19:13:24 - LightMemory - INFO - ========== START add_memory_20251206_191324_829953 ==========\n",
"2025-12-06 19:13:24 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_829953] Extracted 0 visual contexts\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_829953] Restored visual contexts after compression\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_829953] Target compression rate: 0.8\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_829953] Generated 1 segments\n",
"Adding turns: 45%|████▌ | 9/20 [00:50<01:04, 5.91s/it]2025-12-06 19:13:24 - LightMemory - INFO - ========== START add_memory_20251206_191324_896476 ==========\n",
"2025-12-06 19:13:24 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_896476] Extracted 0 visual contexts\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_896476] Restored visual contexts after compression\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_896476] Target compression rate: 0.8\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_896476] Generated 1 segments\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_896476] Assigned global topic IDs: total=2, mapping=[[4, 5]]\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_896476] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_896476] Batch max_source_ids: [2]\n",
"2025-12-06 19:13:24 - LightMemory - INFO - [add_memory_20251206_191324_896476] Starting metadata generation\n"
]
},
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"output_type": "stream",
"text": [
"User prompt for API call 0:\n",
"--- Topic 4 ---\n",
"[2025-12-02T17:06:00.000, Tue] 0.User: Output Code executed no output Jupyter current working directory disk / disk _ 20T / user / GitTaskBench Jupyter Python interpreter disk / disk _ 20T / user / anaconda3 / envs / gittaskbench / bin / python\n",
"[2025-12-02T17:06:00.500, Tue] 0.Assistant: Command ls - l disk disk _ 20T user / GitTaskBench prompt / Faker _\n",
"--- Topic 5 ---\n",
"[2025-12-03T15:01:00.000, Wed] 1.User: Task Task Description repository content generate 5 company data entries Company Name Address Phone output CSV file? Repository Faker Repository Path Absolute : disk / disk _ 20T user GitTaskBench code _ base / Faker Repository URL https github. com joke2k / faker Understanding Guide Read README understand basic functions usage File Paths : Input : Directory : disk / disk _ 20T user GitTaskBench prompt Faker file name output start naming output output file format determined task requirements Supplementary Instructions Goal analyze code repository execute code call tools complete user - task Workflow & Standards analyze user - provided task description task working directory work dir repository information repo code importance hints Plan Solution formulate execution steps read code library README file understand structure usage require writing code rely language tool invocation paths code generation execution paths avoid errors Repository Analysis Explore Structure understand file directory structure paths Identify Key Files Prioritize README configuration files main entry scripts Dependency Management Check requirements determine installation needed Include installation commands code blocks pip install - r requirements. txt package avoid installation not conda Configuration Python Conda environment pre - set no extra configuration ensure code library path PYTHONPATH generate export remote _ repo _ path } \" commandCode Implementation Execution detailed code implementation steps complete function class definitions parameters return values comments docstrings checkpoint model files first check download first automatic download required multiple files - O Error Handling Iteration Check code execution results errors analyze cause fix code regenerate complete script retrial task resolved completed analyze cause alternative solutions Priority checkpoint model files needed first download first automatic download required 7 Task Completion task completed confirmed impossible clear summaryConstraints Mandatory Requirements Paths handling files data loading code Provided Repository Code tasks completed existing repository code prohibited rewrite code implementation Read README. md code library file understand structure usage If no README md insufficient read code\n",
"[2025-12-03T15:01:00.500, Wed] 1.Assistant: Command : cat / disk disk _ 20T user GitTaskBench code _ base Faker / README.\n",
"[2025-12-03T15:01:01.000, Wed] 2.User: Output cat : disk / disk _ 20T user / GitTaskBench / code _ base / Faker / README. md No file\n",
"[2025-12-03T15:01:01.500, Wed] 2.Assistant: Command : ls - l disk disk _ 20T user / GitTaskBench code _ base /\n"
]
},
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"text": [
"2025-12-06 19:13:37 - LightMemory - INFO - [add_memory_20251206_191324_896476] API Call 0 tokens - Prompt: 1535, Completion: 758, Total: 2293\n",
"2025-12-06 19:13:37 - LightMemory - INFO - [add_memory_20251206_191324_896476] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:13:37 - LightMemory - INFO - [add_memory_20251206_191324_896476] Created 12 MemoryEntry objects\n",
"2025-12-06 19:13:37 - LightMemory - INFO - ========== START offline_update_20251206_191337_039987 ==========\n",
"2025-12-06 19:13:37 - LightMemory - INFO - [offline_update_20251206_191337_039987] Received 12 memory entries\n",
"2025-12-06 19:13:37 - LightMemory - INFO - [offline_update_20251206_191337_039987] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:13:37 - LightMemory - INFO - [offline_update_20251206_191337_039987] Starting embedding and insertion to vector database\n"
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"text": [
"2025-12-06 19:13:38 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [offline_update_20251206_191337_039987] Successfully inserted 12 entries to vector database\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191324_896476] Cumulative token stats - Total API calls: 5, Total tokens: 9999\n",
"Adding turns: 50%|█████ | 10/20 [01:04<01:19, 7.95s/it]2025-12-06 19:13:38 - LightMemory - INFO - ========== START add_memory_20251206_191338_884833 ==========\n",
"2025-12-06 19:13:38 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191338_884833] Extracted 0 visual contexts\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191338_884833] Restored visual contexts after compression\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191338_884833] Target compression rate: 0.8\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191338_884833] Generated 1 segments\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191338_884833] Assigned global topic IDs: total=1, mapping=[[6]]\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191338_884833] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191338_884833] Batch max_source_ids: [0]\n",
"2025-12-06 19:13:38 - LightMemory - INFO - [add_memory_20251206_191338_884833] Starting metadata generation\n"
]
},
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"output_type": "stream",
"text": [
"User prompt for API call 0:\n",
"--- Topic 6 ---\n",
"[2025-12-03T15:01:00.000, Wed] 0.User: Output total 192 - rw - rw - r - 1 user user 387 12 1 17 : 06 build - alpine. sh - rw - rw - r - user user 96220 12 1 17 : 06 CHANGELOG. md - rw - rw - r - user user 263 12 1 17 : 06 CITATION. cff - rw - rw - r - user user 2273 12 1 17 : 06 CONTRIBUTING. rst - rw - rw - r - user user 265 12 1 : 06 dev - requirements. txt drwxrwxr - 6 user user 4096 12 docs drwxrwxr - 7 user user 4096 faker - rw - rw - r - user user 9320 12 generate _ stubs. py - rw - rw - r - user 1060 12 1 LICENSE. txt - rw - rw - r - user user 410 12 1 Makefile - rw - rw - r - user user 661 12 1 MANIFEST. in - rw - rw - r - user user 295 12 1 mypy. ini - rw - rw - r - user user 14189 12 1 README. rst - rw - rw - r - user user 161 12 1 readthedocs. yml - rw - rw - r - user user 815 12 1 : RELEASE _ PROCESS. rst - rw - rw - r - user user 182 12 1 setup. cfg - rw - rw - r - user user 2543 12 1 setup. py drwxrwxr - 7 user user 4096 12 tests - rw - rw - r - user user 1416 12 1 : tox. ini - rw - rw - r - user user 0 12 1 : 06 untitledtxt - rw - r 1 user user 7 12 1 17 : 06 VERSION\n",
"[2025-12-03T15:01:00.500, Wed] 0.Assistant: Command : cat / disk disk _ 20T user GitTaskBench code _ base Faker / README.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191338_884833] API Call 0 tokens - Prompt: 1407, Completion: 224, Total: 1631\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191338_884833] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191338_884833] Created 3 MemoryEntry objects\n",
"2025-12-06 19:13:43 - LightMemory - INFO - ========== START offline_update_20251206_191343_310554 ==========\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [offline_update_20251206_191343_310554] Received 3 memory entries\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [offline_update_20251206_191343_310554] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [offline_update_20251206_191343_310554] Starting embedding and insertion to vector database\n"
]
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"2025-12-06 19:13:43 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [offline_update_20251206_191343_310554] Successfully inserted 3 entries to vector database\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191338_884833] Cumulative token stats - Total API calls: 6, Total tokens: 11630\n",
"Adding turns: 55%|█████▌ | 11/20 [01:09<01:04, 7.14s/it]2025-12-06 19:13:43 - LightMemory - INFO - ========== START add_memory_20251206_191343_792780 ==========\n",
"2025-12-06 19:13:43 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191343_792780] Extracted 0 visual contexts\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191343_792780] Restored visual contexts after compression\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191343_792780] Target compression rate: 0.8\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191343_792780] Generated 1 segments\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191343_792780] Assigned global topic IDs: total=1, mapping=[[7]]\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191343_792780] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191343_792780] Batch max_source_ids: [0]\n",
"2025-12-06 19:13:43 - LightMemory - INFO - [add_memory_20251206_191343_792780] Starting metadata generation\n"
]
},
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"text": [
"User prompt for API call 0:\n",
"--- Topic 7 ---\n",
"[2025-12-03T15:01:00.000, Wed] 0.User: Output : * Faker * is a Python package generates fake data for you. need to bootstrap your database, create good - looking XML documents, fill - in your persistence to stress test it, or anonymize data from production service, Faker is for you. Faker is heavily inspired by PHP Faker _, Perl Faker _, and by Ruby Faker _. - - - - : : _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ | _ _ _ _ _ | | _ | _ | _ _ | _ | _ _ _ _ | _ | _ | _ | _ | _ | | pypi | | build | | coverage | | license | - Compatibility - Starting from version 4. 0. 0, Faker dropped support for Python 2 and from version 5. 0. 0 only supports Python 3. 8 and above. If still need Python 2 compatibility, install version 3. 0. 1 in, consider updating codebase to support Python 3 can enjoy latest features Faker has to offer. see extended docs _ for more details, especially if upgrading from version 2. 0. 4 and below might be breaking changes. This package was also previously called fake - factory already deprecated by end of 2016, and much has changed since then, ensure your project and dependencies do not depend on old package. Basic Usage - - - Install with pip :.. code : : bash pip install Faker Use faker. Faker ( ) to create and initialize a faker generator, can generate data by accessing properties named after type of data you want..code : python from faker import Faker fake = Faker ( fake. name ( # Lucy Cechtelar ' fake. address ( 426 Jordy Lodge Cartwrightshire SC 88120 - 6700 ' fake. text ( Sint velit eveniet. Rerum repellat voluptatem rerum excepturi beatae sint laudantium consequatur Magni occaecati sint sit tempore. Nesciunt # a. ( truncated\n",
"[2025-12-03T15:01:00.500, Wed] 0.Assistant: Code faker import Faker pandas pd Initialize Faker fake = Faker ( ) Generate fake company data company _ data = [ for _ in range ( 5 ) : company _ data. append ( Company Name ' fake. company Address ' fake. address replace ( ' \\ n ' Phone ' : fake. phone _ number ( Create DataFrame df = pd. DataFrame ( company _ data ) Define output file path output file _ path = ' / disk / disk _ 20T / user / GitTaskBench / prompt / Faker _ 02 / output. csv ' Save to CSV df. to _ csv ( output _ file _ path, index = False\n"
]
},
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"2025-12-06 19:13:48 - LightMemory - INFO - [add_memory_20251206_191343_792780] API Call 0 tokens - Prompt: 1511, Completion: 339, Total: 1850\n",
"2025-12-06 19:13:48 - LightMemory - INFO - [add_memory_20251206_191343_792780] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:13:48 - LightMemory - INFO - [add_memory_20251206_191343_792780] Created 7 MemoryEntry objects\n",
"2025-12-06 19:13:48 - LightMemory - INFO - ========== START offline_update_20251206_191348_795591 ==========\n",
"2025-12-06 19:13:48 - LightMemory - INFO - [offline_update_20251206_191348_795591] Received 7 memory entries\n",
"2025-12-06 19:13:48 - LightMemory - INFO - [offline_update_20251206_191348_795591] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:13:48 - LightMemory - INFO - [offline_update_20251206_191348_795591] Starting embedding and insertion to vector database\n"
]
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"2025-12-06 19:13:49 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:13:49 - LightMemory - INFO - [offline_update_20251206_191348_795591] Successfully inserted 7 entries to vector database\n",
"2025-12-06 19:13:49 - LightMemory - INFO - [add_memory_20251206_191343_792780] Cumulative token stats - Total API calls: 7, Total tokens: 13480\n",
"Adding turns: 60%|██████ | 12/20 [01:15<00:54, 6.87s/it]2025-12-06 19:13:49 - LightMemory - INFO - ========== START add_memory_20251206_191349_950855 ==========\n",
"2025-12-06 19:13:49 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:49 - LightMemory - INFO - [add_memory_20251206_191349_950855] Extracted 0 visual contexts\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191349_950855] Restored visual contexts after compression\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191349_950855] Target compression rate: 0.8\n",
"Adding turns: 65%|██████▌ | 13/20 [01:15<00:34, 4.98s/it]2025-12-06 19:13:50 - LightMemory - INFO - ========== START add_memory_20251206_191350_114036 ==========\n",
"2025-12-06 19:13:50 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191350_114036] Extracted 0 visual contexts\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191350_114036] Restored visual contexts after compression\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191350_114036] Target compression rate: 0.8\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191350_114036] Generated 1 segments\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191350_114036] Assigned global topic IDs: total=1, mapping=[[8]]\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191350_114036] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191350_114036] Batch max_source_ids: [0]\n",
"2025-12-06 19:13:50 - LightMemory - INFO - [add_memory_20251206_191350_114036] Starting metadata generation\n"
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"User prompt for API call 0:\n",
"--- Topic 8 ---\n",
"[2025-12-03T15:01:00.000, Wed] 0.User: Output OSError Traceback recent call last Cell In [ 1 ] line 23 20 output file path = ' disk / disk _ 20T user GitTaskBench prompt / Faker _ 02 output. csv ' 22 Save to CSV > 23 df. to csv ( output file path index = False File disk / disk _ 20T user / anaconda3 envs gittaskbench lib / python3. 12 site - packages pandas / util / decorators. py : 333 deprecate nonkeyword arguments locals > decorate locals wrapper ( * args, kwargs ) 327 if len ( args ) > num allow args : 328 warnings. warn ( 329 msg. format ( arguments = format argument list allow args ) ) 330 FutureWarning 331 stacklevel = find _ stack _ level ( ) 332 ) - > 333 return func ( * args, kwargs ) File disk / disk _ 20T user anaconda3 envs gittaskbench lib / python3. 12 site - packages / pandas / core / generic. py : 3967 NDFrameto _ csv ( self path buf sep na rep float format columns header index index label mode encoding compression quoting quotechar lineterminator chunksize date _ format doublequote escapechar decimal errors storage options 3956 df = self isinstance self ABCDataFrame self frame 3958 formatter = DataFrameFormatter 3959 frame = df 3960 header header 3964 decimal = decimal 3965 - > 3967 return DataFrameRenderer formatter to _ csv 3968 path _ buf 3969 lineterminator = 3970 sep = sep 3971 encoding 3972 errors = errors 3973 compression = compression 3974 quoting = quoting 3975 columns = columns 3976 index _ label = index 3977 mode = mode 3978 chunksize = 3979 quotechar = quotechar 3980 date _ format = date _ format 3981 doublequote 3982 escapechar = escapechar 3983 storage options = storage options 3984 File / disk / disk _ 20T / user... (\n",
"[2025-12-03T15:01:00.500, Wed] 0.Assistant: Command mkdir - p disk disk _ 20T user / GitTaskBench prompt / Faker _\n"
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"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191350_114036] API Call 0 tokens - Prompt: 1418, Completion: 308, Total: 1726\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191350_114036] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191350_114036] Created 4 MemoryEntry objects\n",
"2025-12-06 19:13:54 - LightMemory - INFO - ========== START offline_update_20251206_191354_128284 ==========\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [offline_update_20251206_191354_128284] Received 4 memory entries\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [offline_update_20251206_191354_128284] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [offline_update_20251206_191354_128284] Starting embedding and insertion to vector database\n"
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"2025-12-06 19:13:54 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [offline_update_20251206_191354_128284] Successfully inserted 4 entries to vector database\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191350_114036] Cumulative token stats - Total API calls: 8, Total tokens: 15206\n",
"Adding turns: 70%|███████ | 14/20 [01:20<00:29, 4.90s/it]2025-12-06 19:13:54 - LightMemory - INFO - ========== START add_memory_20251206_191354_809748 ==========\n",
"2025-12-06 19:13:54 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_809748] Extracted 0 visual contexts\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_809748] Restored visual contexts after compression\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_809748] Target compression rate: 0.8\n",
"2025-12-06 19:13:54 - LightMemory - INFO - ========== START add_memory_20251206_191354_857339 ==========\n",
"2025-12-06 19:13:54 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_857339] Extracted 0 visual contexts\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_857339] Restored visual contexts after compression\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_857339] Target compression rate: 0.8\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_857339] Generated 1 segments\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_857339] Assigned global topic IDs: total=1, mapping=[[9]]\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_857339] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_857339] Batch max_source_ids: [1]\n",
"2025-12-06 19:13:54 - LightMemory - INFO - [add_memory_20251206_191354_857339] Starting metadata generation\n"
]
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"text": [
"User prompt for API call 0:\n",
"--- Topic 9 ---\n",
"[2025-12-03T15:01:00.000, Wed] 0.User: Output\n",
"[2025-12-03T15:01:00.500, Wed] 0.Assistant: Code Save CSV after creating directory output _ file path = disk / disk _ 20T user / GitTaskBench prompt / Faker _ 02 output. csv ' df. to _ csv ( output _ file _ path index = False\n",
"[2025-12-03T15:32:00.000, Wed] 1.User: Task Task Description content json format? Name Eparse Path disk disk _ 20T GitTaskBench code _ base URL github. com ChrisPappalardo Eparse Understanding Guide Read README understand basic functions usage File Paths Input Path disk disk _ 20T GitTaskBench queries Eparse _ 03 input File Description Excel parse Directory disk 20T GitTaskBench prompt Eparse 03 file name output xxx start naming output 01 suffix output file format determined task requirements Instructions analyze code repository generate execute code call tools complete user - specified task Workflow Standards 1 analyze user - provided task description task working directory work dir repository information code importance Plan Solution formulate execution steps read code library README file understand structure usage insufficient information steps require writing code rely language tool invocation paths code generation execution paths avoid errors 3Repository Analysis Explore Structure understand file directory structure paths Identify Key Files Prioritize README configuration files main entry scripts Dependency Management Check requirements txt files determine dependencies installation needed Include installation commands code blocks pip install - r requirementstxt package packages avoid installation not use conda install pip Environment Configuration Python / Conda environment pre - set no extra configuration ensure code library path PYTHONPATH generate export = $ remote _ repo _ path } commandCode Implementation Execution detailed code implementation steps complete function class definitions parameters return values comments docstrings checkpoint model files first check download automatic use wget download multiple files wget - O Error Handling Iteration Check code results errors analyze cause fix code regenerate complete script retrial task resolved analyze cause consider alternative solutions Priority checkpoint model files needed first check exist download automatic download 7 Task Completion task completed impossible summary Key Constraints Mandatory Requirements Absolute Paths paths handling files data loading code Repository Code tasks completed existing repository code prohibited rewrite code implementation Read READMEmd code library file understand structure usage no README insufficient read code\n",
"[2025-12-03T15:32:00.500, Wed] 1.Assistant: Command cat disk disk _ 20T user / GitTaskBench code _ base / Eparse / README.\n"
]
},
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"name": "stderr",
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"text": [
"2025-12-06 19:14:02 - LightMemory - INFO - [add_memory_20251206_191354_857339] API Call 0 tokens - Prompt: 1432, Completion: 453, Total: 1885\n",
"2025-12-06 19:14:02 - LightMemory - INFO - [add_memory_20251206_191354_857339] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:14:02 - LightMemory - INFO - [add_memory_20251206_191354_857339] Created 7 MemoryEntry objects\n",
"2025-12-06 19:14:02 - LightMemory - INFO - ========== START offline_update_20251206_191402_196651 ==========\n",
"2025-12-06 19:14:02 - LightMemory - INFO - [offline_update_20251206_191402_196651] Received 7 memory entries\n",
"2025-12-06 19:14:02 - LightMemory - INFO - [offline_update_20251206_191402_196651] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:14:02 - LightMemory - INFO - [offline_update_20251206_191402_196651] Starting embedding and insertion to vector database\n"
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"2025-12-06 19:14:03 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [offline_update_20251206_191402_196651] Successfully inserted 7 entries to vector database\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191354_857339] Cumulative token stats - Total API calls: 9, Total tokens: 17091\n",
"Adding turns: 80%|████████ | 16/20 [01:28<00:18, 4.57s/it]2025-12-06 19:14:03 - LightMemory - INFO - ========== START add_memory_20251206_191403_133937 ==========\n",
"2025-12-06 19:14:03 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191403_133937] Extracted 0 visual contexts\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191403_133937] Restored visual contexts after compression\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191403_133937] Target compression rate: 0.8\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191403_133937] Generated 1 segments\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191403_133937] Assigned global topic IDs: total=1, mapping=[[10]]\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191403_133937] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191403_133937] Batch max_source_ids: [1]\n",
"2025-12-06 19:14:03 - LightMemory - INFO - [add_memory_20251206_191403_133937] Starting metadata generation\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"User prompt for API call 0:\n",
"--- Topic 10 ---\n",
"[2025-12-03T15:32:00.000, Wed] 0.User: Output cat : disk / disk _ 20T user / GitTaskBench / code _ base / Eparse / README. md No file\n",
"[2025-12-03T15:32:00.500, Wed] 0.Assistant: Command ls - l disk disk _ 20T user GitTaskBench code _ base / Eparse\n",
"[2025-12-03T15:32:01.000, Wed] 1.User: Output total 72 - rw - rw - r - 1 user user 133 12 1 17 : 06 AUTHORS. rst - rw - rw - r 1088 12 1 17 : 06 conftest. py drwxrwxr 3 user user 4096 12 17 : 06 contrib - rw - rw - r 3353 12 : 06 CONTRIBUTING. rst drwxrwxr 2 4096 12 1 17 : 06 docs drwxrwxr 2 4096 eparse - rw - rw - r 1405 12 1 17 : 06 HISTORY. rst - rw - rw - r - user 1074 12 1 17 : 06 LICENSE - rw - rw - r - user 2367 12 1 : 06 Makefile - rw - rw - r 262 12 : 06 MANIFEST. in - rw - rw - r - user 1269 12 1 17 : 06 pyproject. toml - rw - rw - r - user 13647 12 1 17 : 06 README. rst - rw - rw - r - user 286 12 1 17 : 06 setup. cfg drwxrwxr - 2 user user 4096 12 1 17 : 06 tests - rw - rw - r - user 370 12 1 17 : 06 tox.\n",
"[2025-12-03T15:32:01.500, Wed] 1.Assistant: Command cat disk disk _ 20T user / GitTaskBench code _ base / Eparse / README.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:14:08 - LightMemory - INFO - [add_memory_20251206_191403_133937] API Call 0 tokens - Prompt: 1389, Completion: 340, Total: 1729\n",
"2025-12-06 19:14:08 - LightMemory - INFO - [add_memory_20251206_191403_133937] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:14:08 - LightMemory - INFO - [add_memory_20251206_191403_133937] Created 5 MemoryEntry objects\n",
"2025-12-06 19:14:08 - LightMemory - INFO - ========== START offline_update_20251206_191408_180604 ==========\n",
"2025-12-06 19:14:08 - LightMemory - INFO - [offline_update_20251206_191408_180604] Received 5 memory entries\n",
"2025-12-06 19:14:08 - LightMemory - INFO - [offline_update_20251206_191408_180604] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:14:08 - LightMemory - INFO - [offline_update_20251206_191408_180604] Starting embedding and insertion to vector database\n"
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"2025-12-06 19:14:08 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:14:08 - LightMemory - INFO - [offline_update_20251206_191408_180604] Successfully inserted 5 entries to vector database\n",
"2025-12-06 19:14:08 - LightMemory - INFO - [add_memory_20251206_191403_133937] Cumulative token stats - Total API calls: 10, Total tokens: 18820\n",
"Adding turns: 85%|████████▌ | 17/20 [01:34<00:14, 4.88s/it]2025-12-06 19:14:09 - LightMemory - INFO - ========== START add_memory_20251206_191409_000631 ==========\n",
"2025-12-06 19:14:09 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_000631] Extracted 0 visual contexts\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_000631] Restored visual contexts after compression\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_000631] Target compression rate: 0.8\n",
"2025-12-06 19:14:09 - LightMemory - INFO - ========== START add_memory_20251206_191409_044607 ==========\n",
"2025-12-06 19:14:09 - LightMemory - INFO - force_segment=False, force_extract=False\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_044607] Extracted 0 visual contexts\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_044607] Restored visual contexts after compression\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_044607] Target compression rate: 0.8\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_044607] Generated 1 segments\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_044607] Assigned global topic IDs: total=1, mapping=[[11]]\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_044607] Extraction triggered 1 times, extract_list length: 1\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_044607] Batch max_source_ids: [0]\n",
"2025-12-06 19:14:09 - LightMemory - INFO - [add_memory_20251206_191409_044607] Starting metadata generation\n"
]
},
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"name": "stdout",
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"text": [
"User prompt for API call 0:\n",
"--- Topic 11 ---\n",
"[2025-12-03T15:32:00.000, Wed] 0.User: Output : = eparse. image : https img. shields. io / pypi / v / eparse. svg : target https pypi. python. org pypi eparse image img shields. io badge / License - MIT - blue. svg : target : https opensource. org / licenses / MIT : alt : License : MIT Description = Excel spreadsheet crawler table parser for data extraction querying Features * Command - line interface * Recursive Excel file discovery * Sub - tabular data extraction ( logical tables ) * SQLite PostgreSQL database interfaces * CLI query tool * Summary data metrics install eparse use pip latest version on PyPI :.. code - block : : $ pip install eparse clone repo install from source latest version not PyPI code - block : $ git clone https : github. com / ChrisPappalardo / eparse. git $ cd eparse $ pip install. eparse project? add PyPI version latest source to requirements. txt file : : : eparse # latest pypi version eparse = = 0. 8. 0 # sepcific pypi version eparse @ git + eparse git # latest source postgres interface install postgres package psycopg2. Instructions found. psycopg. org / docs / install. html # quick - install > _ package optional use other interfaces SQLite3 interface without install psycopg2 install psycopg2 package environment may install pre - compiled binary driver follows :.code - block : : $ pip install psycopg2 - binary error use postgres endpoint user : pass @ host : port / my _ db mentions postgres driver missing know haven ' t properly installed compiled ) psycopg2. U... ( truncated\n",
"[2025-12-03T15:32:00.500, Wed] 0.Assistant: Command pip install\n"
]
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"output_type": "stream",
"text": [
"2025-12-06 19:14:14 - LightMemory - INFO - [add_memory_20251206_191409_044607] API Call 0 tokens - Prompt: 1310, Completion: 397, Total: 1707\n",
"2025-12-06 19:14:14 - LightMemory - INFO - [add_memory_20251206_191409_044607] Metadata generation completed with 1 API calls\n",
"2025-12-06 19:14:14 - LightMemory - INFO - [add_memory_20251206_191409_044607] Created 9 MemoryEntry objects\n",
"2025-12-06 19:14:14 - LightMemory - INFO - ========== START offline_update_20251206_191414_458377 ==========\n",
"2025-12-06 19:14:14 - LightMemory - INFO - [offline_update_20251206_191414_458377] Received 9 memory entries\n",
"2025-12-06 19:14:14 - LightMemory - INFO - [offline_update_20251206_191414_458377] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:14:14 - LightMemory - INFO - [offline_update_20251206_191414_458377] Starting embedding and insertion to vector database\n"
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"text": [
"2025-12-06 19:14:15 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [offline_update_20251206_191414_458377] Successfully inserted 9 entries to vector database\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191409_044607] Cumulative token stats - Total API calls: 11, Total tokens: 20527\n",
"Adding turns: 95%|█████████▌| 19/20 [01:41<00:04, 4.29s/it]2025-12-06 19:14:15 - LightMemory - INFO - ========== START add_memory_20251206_191415_893489 ==========\n",
"2025-12-06 19:14:15 - LightMemory - INFO - force_segment=True, force_extract=True\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191415_893489] Extracted 0 visual contexts\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191415_893489] Restored visual contexts after compression\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191415_893489] Target compression rate: 0.8\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191415_893489] Generated 1 segments\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191415_893489] Assigned global topic IDs: total=2, mapping=[[12], [13]]\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191415_893489] Extraction triggered 2 times, extract_list length: 2\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191415_893489] Batch max_source_ids: [1, 1]\n",
"2025-12-06 19:14:15 - LightMemory - INFO - [add_memory_20251206_191415_893489] Starting metadata generation\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"User prompt for API call 0:\n",
"--- Topic 12 ---\n",
"[2025-12-03T15:32:00.000, Wed] 0.User: Output indexes pypi. tuna. tsinghua. edu. cn Collecting eparse Downloading pypi packages 26 / e8 / acf68d42e11c192225db3c32be6b6690841d26caff9ee3482b6ac0cd37b4 / eparse - 0. 7. 3 - py2. py3 - none - any. whl ( 19 kB ) click > 8. 0. 0 disk 20T user anaconda3 lib python3. 13 site - packages eparse 8. 1. 8 openpyxl 3. 0. 0 20T 13 packages. 5 lxml 4. 9. 3 pandas. 3 peewee 3. 16. 0. 18. 3 unstructured 0. 8. 5 Downloading pypi. tuna. tsinghua. educn / packages / c2 / 98 / e8ddcfadd762f8f69d84e14498c28adefdd8e2008f443077495984405c45 / unstructured - 0. 18. 21 - py3 - none - any. whl ( 1. 8 MB ) MB 5. 9 MB s 0 : 00 : 00 Requirement : et - xmlfile disk disk _ 20T user anaconda3 lib / python3. 13 site - packages openpyxl > 3. 0 eparse 2. 0. 0 numpy 1. 26. 0 disk 20T anaconda3. 13 site - packages pandas 2. 0 eparse 2. 1. 3 python - dateutil 2. 8. 2 disk disk 20T anaconda3 python3. 13 site - packages pandas 2. 0. 0 eparse 2. 9. 0post0 pytz 2020. 1 disk disk 20T user anaconda3 lib python3. 13 site packages pandas 2. 0. 0 eparse 2024. 1 tzdata 2022. 7 disk disk 20T user anaconda3 lib python3. 13 site packages pandas 2 0 0 eparse 2025. 2\n",
"[2025-12-03T15:32:00.500, Wed] 0.Assistant: Command mkdir - p disk disk _ 20T user / GitTaskBench prompt / Eparse _\n",
"[2025-12-03T15:32:01.000, Wed] 1.User: Output\n",
"[2025-12-03T15:32:01.500, Wed] 1.Assistant: Command eparse - v - f disk disk _ 20T user / GitTaskBench queries / Eparse _ 03 input Eparse 03 _ input. xlsx - o json disk / disk _ 20T user / GitTaskBench prompt / Eparse _ 03 / output. json\n",
"User prompt for API call 1:\n",
"--- Topic 13 ---\n",
"[2025-12-03T15:32:02.000, Wed] 2.User: Output found 1 files output error - json disk / disk _ 20T user / GitTaskBench / prompt / Eparse _ 03 / output. json not recognized\n",
"[2025-12-03T15:32:02.500, Wed] 2.Assistant: Command eparse - v - f disk / disk _ 20T user GitTaskBench queries / Eparse _ 03 input Eparse _ 03 _ input. xlsx - o stdout parse - z > disk / disk _ 20T user GitTaskBench prompt / Eparse _ 03 / output.\n",
"[2025-12-03T15:32:03.000, Wed] 3.User: Output\n",
"[2025-12-03T15:32:03.500, Wed] 3.Assistant: Command cat disk disk _ 20T user / GitTaskBench prompt / Eparse _ 03 / output.\n"
]
},
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"name": "stderr",
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"text": [
"2025-12-06 19:14:23 - LightMemory - INFO - [add_memory_20251206_191415_893489] API Call 0 tokens - Prompt: 1596, Completion: 344, Total: 1940\n",
"2025-12-06 19:14:23 - LightMemory - INFO - [add_memory_20251206_191415_893489] API Call 1 tokens - Prompt: 1127, Completion: 241, Total: 1368\n",
"2025-12-06 19:14:23 - LightMemory - INFO - [add_memory_20251206_191415_893489] Metadata generation completed with 2 API calls\n",
"2025-12-06 19:14:23 - LightMemory - WARNING - LLM returned invalid source_id=2 (valid range: [0, 1]) in batch 1. Auto-corrected to source_id=1. Fact: User encountered an output error stating '1 files output error - json disk / disk _ 20T user / GitTa...\n",
"2025-12-06 19:14:23 - LightMemory - WARNING - LLM returned invalid source_id=2 (valid range: [0, 1]) in batch 1. Auto-corrected to source_id=1. Fact: Assistant provided command `eparse - v - f disk / disk _ 20T user GitTaskBench queries / Eparse _ 03...\n",
"2025-12-06 19:14:23 - LightMemory - WARNING - LLM returned invalid source_id=3 (valid range: [0, 1]) in batch 1. Auto-corrected to source_id=1. Fact: User executed command `cat disk disk _ 20T user / GitTaskBench prompt / Eparse _ 03 / output.` on 20...\n",
"2025-12-06 19:14:23 - LightMemory - INFO - [add_memory_20251206_191415_893489] Created 9 MemoryEntry objects\n",
"2025-12-06 19:14:23 - LightMemory - INFO - ========== START offline_update_20251206_191423_160631 ==========\n",
"2025-12-06 19:14:23 - LightMemory - INFO - [offline_update_20251206_191423_160631] Received 9 memory entries\n",
"2025-12-06 19:14:23 - LightMemory - INFO - [offline_update_20251206_191423_160631] construct_update_queue_trigger=False, offline_update_trigger=False\n",
"2025-12-06 19:14:23 - LightMemory - INFO - [offline_update_20251206_191423_160631] Starting embedding and insertion to vector database\n"
]
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"text": [
"2025-12-06 19:14:23 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n"
]
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"text": [
"2025-12-06 19:14:24 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n"
]
},
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"text": [
"2025-12-06 19:14:24 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n"
]
},
{
"data": {
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:14:24 - lightmem.factory.retriever.embeddingretriever.qdrant - INFO - Inserting 1 vectors into collection code_demo\n",
"2025-12-06 19:14:24 - LightMemory - INFO - [offline_update_20251206_191423_160631] Successfully inserted 9 entries to vector database\n",
"2025-12-06 19:14:24 - LightMemory - INFO - [add_memory_20251206_191415_893489] Cumulative token stats - Total API calls: 13, Total tokens: 23835\n",
"Adding turns: 100%|██████████| 20/20 [01:49<00:00, 5.49s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"All historical sessions have been added!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"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",
" \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": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:14:35 - LightMemory - INFO - ========== START construct_queue_20251206_191435_771858 ==========\n",
"2025-12-06 19:14:35 - LightMemory - INFO - [construct_queue_20251206_191435_771858] Parameters: top_k=20, keep_top_n=10, max_workers=8\n",
"2025-12-06 19:14:35 - LightMemory - INFO - [construct_queue_20251206_191435_771858] Retrieved 89 entries from vector database\n",
"2025-12-06 19:14:35 - LightMemory - INFO - [construct_queue_20251206_191435_771858] Starting parallel queue construction with 8 workers\n",
"2025-12-06 19:14:48 - LightMemory - INFO - [construct_queue_20251206_191435_771858] Queue construction completed: 89 updated, 0 skipped, nonempty_queues=89, empty_queues=0\n",
"2025-12-06 19:14:48 - LightMemory - INFO - ========== END construct_queue_20251206_191435_771858 ==========\n",
"2025-12-06 19:14:48 - LightMemory - INFO - ========== START offline_update_all_20251206_191448_699374 ==========\n",
"2025-12-06 19:14:48 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] Parameters: score_threshold=0.8, max_workers=5\n",
"2025-12-06 19:14:48 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] Retrieved 89 entries from vector database\n",
"2025-12-06 19:14:48 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] Starting parallel offline update with 5 workers\n",
"2025-12-06 19:15:07 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] Offline update completed:\n",
"2025-12-06 19:15:07 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] - Processed: 41 entries\n",
"2025-12-06 19:15:07 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] - Updated: 17 entries\n",
"2025-12-06 19:15:07 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] - Deleted: 19 entries\n",
"2025-12-06 19:15:07 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] - Skipped (no candidates): 48 entries\n",
"2025-12-06 19:15:07 - LightMemory - INFO - [offline_update_all_20251206_191448_699374] - Update API calls: 41, Total tokens: 32401\n",
"2025-12-06 19:15:07 - LightMemory - INFO - ========== END offline_update_all_20251206_191448_699374 ==========\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": 27,
"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": 31,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:32:13 - LightMemory - INFO - ========== START retrieve_20251206_193213_714931 ==========\n",
"2025-12-06 19:32:13 - LightMemory - INFO - [retrieve_20251206_193213_714931] Query: I'm reviewing the fake user data generation task we did previously. Can you remind me exactly how many user records were generated and what were the column headers in the output CSV?\n",
"2025-12-06 19:32:13 - LightMemory - INFO - [retrieve_20251206_193213_714931] 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_faker_01]\n",
"================================================================================\n",
"Question: I'm reviewing the fake user data generation task we did previously. Can you remind me exactly how many user records were generated and what were the column headers in the output CSV?\n",
"Question Date: 2025/12/05 (Fri) 09:00\n",
"Question Type: single-session-assistant\n",
"Expected Answer: We generated 100 fake user records. The column headers in the output CSV were 'Username' and 'Email'.\n"
]
},
{
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"model_id": "fd5d166fbb134d06afe33a8cb6f9bc8f",
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"version_minor": 0
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"Batches: 0%| | 0/1 [00:00<?, ?it/s]"
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{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:32:13 - LightMemory - INFO - [retrieve_20251206_193213_714931] Searching vector database\n",
"2025-12-06 19:32:13 - LightMemory - INFO - [retrieve_20251206_193213_714931] Found 20 results\n",
"2025-12-06 19:32:13 - LightMemory - INFO - [retrieve_20251206_193213_714931] Formatted 20 results into output string\n",
"2025-12-06 19:32:13 - LightMemory - INFO - ========== END retrieve_20251206_193213_714931 ==========\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Retrieved 20 relevant memories\n",
"--------------------------------------------------------------------------------\n",
"Memory 1: 2025-12-02T17:06:00.000 Tue User provided a code snippet to generate fake user data using pandas and Faker on 2025-12-02T17:06:00.500.\n",
"Memory 2: 2025-12-03T15:01:00.000 Wed User requested output on 2025-12-03T15:01:00.\n",
"Memory 3: 2025-12-02T17:06:00.000 Tue The code initializes Faker with 'fake = Faker()' and generates fake user data with 'user_data = [{'Username': fake.user_name(), 'Email': fake.email()} for _ in range(100)]' on 2025-12-02T17:06:00.500.\n",
"Memory 4: 2025-12-03T15:01:00.000 Wed User outputted file details including permissions and sizes for multiple files on 2025-12-03T15:01:00.\n",
"Memory 5: 2025-12-03T15:01:00.000 Wed User shared a code snippet for generating fake data using Faker, including initializing the Faker generator and generating fake company data.\n",
"Memory 6: 2025-12-02T17:06:00.000 Tue User outputted file details including permissions and sizes for various files in the directory on 2025-12-03T15:01:00.\n",
"Memory 7: 2025-12-03T15:01:00.000 Wed Assistant provided code snippet for saving a CSV file after creating a directory with the file path '/disk/disk_20T/user/GitTaskBench/prompt/Faker_02/output.csv' using 'df.to_csv(output_file_path, index=False)' on 2025-12-03T15:01:00.500.\n",
"Memory 8: 2025-12-03T15:01:00.000 Wed The specific line causing the error is indicated as 'df.to_csv(output_file_path, index=False)' on line 23 of the user's code.\n",
"Memory 9: 2025-12-03T15:32:00.000 Wed User mentioned the output file name as 'xxx' with a suffix '01' and indicated that the output file format is determined by task requirements on 2025-12-03T15:32:00.\n",
"Memory 10: 2025-12-03T15:01:00.000 Wed User included a command to save the DataFrame to CSV: `df.to_csv(output_file_path, index=False)`.\n",
"Memory 11: 2025-12-03T15:01:00.000 Wed User described a task to generate 5 company data entries with details including repository path 'disk/disk_20T/user/GitTaskBench/code_base/Faker', repository URL 'https://github.com/joke2k/faker', and input directory 'disk/disk_20T/user/GitTaskBench/prompt/Faker' on 2025-12-03T15:01:00.\n",
"Memory 12: 2025-12-03T15:01:00.000 Wed User encountered an OSError while trying to save a DataFrame to CSV with the output file path 'disk/disk_20T/user/GitTaskBench/prompt/Faker_02/output.csv'. The error traceback indicates issues in the pandas library related to the DataFrame's to_csv method.\n",
"Memory 13: 2025-12-02T17:06:00.000 Tue User demonstrated basic usage of Faker with the code snippet: `from faker import Faker; fake = Faker(); fake.name(); fake.address(); fake.text();` on 2025-12-02T17:06:00.\n",
"Memory 14: 2025-12-03T15:32:00.000 Wed User outlined a workflow for analyzing a code repository, generating and executing code, and completing user-specified tasks on 2025-12-03T15:32:00.\n",
"Memory 15: 2025-12-03T15:01:00.000 Wed User provided information about the Faker package, stating it is a Python package that generates fake data, and mentioned compatibility changes starting from version 4.0.0 which dropped support for Python 2 and only supports Python 3.8 and above. User noted that to maintain Python 2 compatibility, one should install version 3.0.1 of Faker and suggested updating the codebase to support Python 3 for the latest features.\n",
"Memory 16: 2025-12-03T15:01:00.000 Wed The task to generate company data entries could not proceed due to the missing README.md file, which was confirmed by the output error from the `cat` command.\n",
"Memory 17: 2025-12-02T17:06:00.000 Tue User provided supplementary instructions to analyze the code repository and generate execution paths to avoid errors, emphasizing the importance of reading the README file and understanding file paths.\n",
"Memory 18: 2025-12-03T15:01:00.000 Wed User provided supplementary instructions for analyzing the code repository and executing code, emphasizing the importance of reading the README file and understanding file paths on 2025-12-03T15:01:00.\n",
"Memory 19: 2025-12-02T17:06:00.000 Tue User requested to formulate execution steps based on the provided task description.\n",
"Memory 20: 2025-12-03T15:32:01.000 Wed User executed the command `ls - l disk disk _ 20T user GitTaskBench code _ base / Eparse` and received output indicating the contents of the directory on 2025-12-03T15:32:01.\n",
"\n",
"Generating answer...\n",
"\n",
"Generated Answer: A total of 100 user records were generated. The column headers in the output CSV were 'Username' and 'Email'.\n",
"\n",
"Evaluating answer...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:32:17 - LightMemory - INFO - ========== START retrieve_20251206_193217_929164 ==========\n",
"2025-12-06 19:32:17 - LightMemory - INFO - [retrieve_20251206_193217_929164] Query: Going back to the fake company data task, I remember the script initially failed when trying to save the CSV file. What was the specific reason for that failure and how did we fix it?\n",
"2025-12-06 19:32:17 - LightMemory - INFO - [retrieve_20251206_193217_929164] Parameters: limit=20, filters=None\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Evaluation Result: True (✓ Correct)\n",
"\n",
"================================================================================\n",
"Question 2/3 [ID: q_faker_02]\n",
"================================================================================\n",
"Question: Going back to the fake company data task, I remember the script initially failed when trying to save the CSV file. What was the specific reason for that failure and how did we fix it?\n",
"Question Date: 2025/12/05 (Fri) 09:30\n",
"Question Type: single-session-assistant\n",
"Expected Answer: The failure was an OSError because the output directory did not exist. We fixed it by creating the directory before executing the Python script again.\n"
]
},
{
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"model_id": "6dbd554a926e4b7798a23364c5721cd1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Batches: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:32:17 - LightMemory - INFO - [retrieve_20251206_193217_929164] Searching vector database\n",
"2025-12-06 19:32:17 - LightMemory - INFO - [retrieve_20251206_193217_929164] Found 20 results\n",
"2025-12-06 19:32:17 - LightMemory - INFO - [retrieve_20251206_193217_929164] Formatted 20 results into output string\n",
"2025-12-06 19:32:17 - LightMemory - INFO - ========== END retrieve_20251206_193217_929164 ==========\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Retrieved 20 relevant memories\n",
"--------------------------------------------------------------------------------\n",
"Memory 1: 2025-12-03T15:01:00.000 Wed The task to generate company data entries could not proceed due to the missing README.md file, which was confirmed by the output error from the `cat` command.\n",
"Memory 2: 2025-12-03T15:01:00.000 Wed The specific line causing the error is indicated as 'df.to_csv(output_file_path, index=False)' on line 23 of the user's code.\n",
"Memory 3: 2025-12-03T15:01:00.000 Wed User encountered an OSError while trying to save a DataFrame to CSV with the output file path 'disk/disk_20T/user/GitTaskBench/prompt/Faker_02/output.csv'. The error traceback indicates issues in the pandas library related to the DataFrame's to_csv method.\n",
"Memory 4: 2025-12-03T15:01:00.000 Wed Assistant provided code snippet for saving a CSV file after creating a directory with the file path '/disk/disk_20T/user/GitTaskBench/prompt/Faker_02/output.csv' using 'df.to_csv(output_file_path, index=False)' on 2025-12-03T15:01:00.500.\n",
"Memory 5: 2025-12-03T15:01:00.000 Wed User shared a code snippet for generating fake data using Faker, including initializing the Faker generator and generating fake company data.\n",
"Memory 6: 2025-12-02T17:06:00.000 Tue User provided a code snippet to generate fake user data using pandas and Faker on 2025-12-02T17:06:00.500.\n",
"Memory 7: 2025-12-03T15:01:00.000 Wed The error traceback includes the following details: 'File disk/disk_20T/user/anaconda3/envs/gittaskbench/lib/python3.12/site-packages/pandas/core/generic.py:3967 NDFrame.to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)'\n",
"Memory 8: 2025-12-03T15:01:00.000 Wed User included a command to save the DataFrame to CSV: `df.to_csv(output_file_path, index=False)`.\n",
"Memory 9: 2025-12-03T15:32:00.000 Wed User mentioned the output file name as 'xxx' with a suffix '01' and indicated that the output file format is determined by task requirements on 2025-12-03T15:32:00.\n",
"Memory 10: 2025-12-03T15:01:00.000 Wed User provided information about the Faker package, stating it is a Python package that generates fake data, and mentioned compatibility changes starting from version 4.0.0 which dropped support for Python 2 and only supports Python 3.8 and above. User noted that to maintain Python 2 compatibility, one should install version 3.0.1 of Faker and suggested updating the codebase to support Python 3 for the latest features.\n",
"Memory 11: 2025-12-03T15:01:00.000 Wed User described a task to generate 5 company data entries with details including repository path 'disk/disk_20T/user/GitTaskBench/code_base/Faker', repository URL 'https://github.com/joke2k/faker', and input directory 'disk/disk_20T/user/GitTaskBench/prompt/Faker' on 2025-12-03T15:01:00.\n",
"Memory 12: 2025-12-02T17:06:00.000 Tue User noted that starting from version 4.0.0, Faker dropped support for Python 2 and from version 5.0.0 only supports Python 3.8 and above. To maintain Python 2 compatibility, one should install version 3.0.1 of Faker and it is suggested to update the codebase to support Python 3 for the latest features.\n",
"Memory 13: 2025-12-02T17:06:00.000 Tue User mentioned the need for automatic downloading of required files and handling errors during code execution.\n",
"Memory 14: 2025-12-02T17:06:00.000 Tue User provided information about the Faker Python package, including its purpose as a package that generates fake data, compatibility changes starting from version 4.0.0 which dropped support for Python 2 and only supports Python 3.8 and above, installation instructions with the command `pip install Faker`, and noted that to maintain Python 2 compatibility, one should install version 3.0.1 of Faker and suggested updating the codebase to support Python 3 for the latest features.\n",
"Memory 15: 2025-12-02T17:06:00.000 Tue User indicated that the package was previously called fake-factory, which has been deprecated since the end of 2016 on 2025-12-02T17:06:00.\n",
"Memory 16: 2025-12-03T15:01:00.000 Wed User noted that to maintain Python 2 compatibility, one should install version 3.0.1 of Faker and suggested updating the codebase to support Python 3 for the latest features. They also provided information that compatibility changes for the Faker package started from version 4.0.0, which dropped support for Python 2 and only supports Python 3.8 and above.\n",
"Memory 17: 2025-12-03T15:01:00.000 Wed User outputted file details including permissions and sizes for multiple files on 2025-12-03T15:01:00.\n",
"Memory 18: 2025-12-03T15:01:00.000 Wed User mentioned that the package was previously called fake-factory, which has been deprecated since the end of 2016.\n",
"Memory 19: 2025-12-02T17:06:00.000 Tue User specified the input file directory as `/disk/disk_20T/user/GitTaskBench/prompt/Faker_01`.\n",
"Memory 20: 2025-12-02T17:06:00.000 Tue User provided installation command `pip install Faker` for the Faker package on 2025-12-02T17:06:00, along with basic usage instructions and information about the package's purpose and compatibility.\n",
"\n",
"Generating answer...\n",
"\n",
"Generated Answer: The script initially failed to save the CSV file due to an OSError encountered while trying to execute the line `df.to_csv(output_file_path, index=False)`. The error traceback indicated issues within the pandas library related to the DataFrame's `to_csv` method. \n",
"\n",
"To fix this issue, the assistant provided a code snippet that included creating the necessary directory for the output file path before attempting to save the DataFrame to CSV. This ensured that the directory structure existed, allowing the `to_csv` method to execute successfully.\n",
"\n",
"Evaluating answer...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:32:22 - LightMemory - INFO - ========== START retrieve_20251206_193222_165818 ==========\n",
"2025-12-06 19:32:22 - LightMemory - INFO - [retrieve_20251206_193222_165818] Query: In our previous session using the 'Eparse' tool to convert Excel to JSON, the command failed when we tried to use the 'json://' endpoint. What command line argument did we use instead to successfully save the output?\n",
"2025-12-06 19:32:22 - LightMemory - INFO - [retrieve_20251206_193222_165818] Parameters: limit=20, filters=None\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Evaluation Result: True (✓ Correct)\n",
"\n",
"================================================================================\n",
"Question 3/3 [ID: q_eparse_03]\n",
"================================================================================\n",
"Question: In our previous session using the 'Eparse' tool to convert Excel to JSON, the command failed when we tried to use the 'json://' endpoint. What command line argument did we use instead to successfully save the output?\n",
"Question Date: 2025/12/05 (Fri) 10:00\n",
"Question Type: single-session-assistant\n",
"Expected Answer: We used `-o stdout` to successfully save the output.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a64d6c2056ef491493fd33f188d4f6ab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Batches: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-06 19:32:22 - LightMemory - INFO - [retrieve_20251206_193222_165818] Searching vector database\n",
"2025-12-06 19:32:22 - LightMemory - INFO - [retrieve_20251206_193222_165818] Found 20 results\n",
"2025-12-06 19:32:22 - LightMemory - INFO - [retrieve_20251206_193222_165818] Formatted 20 results into output string\n",
"2025-12-06 19:32:22 - LightMemory - INFO - ========== END retrieve_20251206_193222_165818 ==========\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Retrieved 20 relevant memories\n",
"--------------------------------------------------------------------------------\n",
"Memory 1: 2025-12-03T15:32:02.000 Wed User encountered an output error stating '1 files output error - json disk / disk _ 20T user / GitTaskBench / prompt / Eparse _ 03 / output. json not recognized' on 2025-12-03T15:32:02.\n",
"Memory 2: 2025-12-03T15:32:00.000 Wed User described a task involving JSON format, specifying paths such as 'disk/disk_20T/GitTaskBench/code_base' and 'disk/disk_20T/GitTaskBench/queries/Eparse_03/input' on 2025-12-03T15:32:00.\n",
"Memory 3: 2025-12-03T15:01:00.000 Wed User encountered an OSError while trying to save a DataFrame to CSV with the output file path 'disk/disk_20T/user/GitTaskBench/prompt/Faker_02/output.csv'. The error traceback indicates issues in the pandas library related to the DataFrame's to_csv method.\n",
"Memory 4: 2025-12-03T15:32:02.000 Wed Assistant provided command `eparse - v - f disk / disk _ 20T user GitTaskBench queries / Eparse _ 03 input Eparse _ 03 _ input. xlsx - o stdout parse - z > disk / disk _ 20T user GitTaskBench prompt / Eparse _ 03 / output.` on 2025-12-03T15:32:02.500.\n",
"Memory 5: 2025-12-03T15:01:00.000 Wed Assistant provided code snippet for saving a CSV file after creating a directory with the file path '/disk/disk_20T/user/GitTaskBench/prompt/Faker_02/output.csv' using 'df.to_csv(output_file_path, index=False)' on 2025-12-03T15:01:00.500.\n",
"Memory 6: 2025-12-03T15:01:00.000 Wed User included a command to save the DataFrame to CSV: `df.to_csv(output_file_path, index=False)`.\n",
"Memory 7: 2025-12-03T15:32:00.000 Wed User mentioned the output file name as 'xxx' with a suffix '01' and indicated that the output file format is determined by task requirements on 2025-12-03T15:32:00.\n",
"Memory 8: 2025-12-03T15:01:00.000 Wed The specific line causing the error is indicated as 'df.to_csv(output_file_path, index=False)' on line 23 of the user's code.\n",
"Memory 9: 2025-12-03T15:32:00.000 Wed User mentioned the command to install eparse using pip: `$ pip install eparse`, and also provided a command to install eparse from source: `$ pip install.`.\n",
"Memory 10: 2025-12-03T15:01:00.000 Wed The error traceback includes the following details: 'File disk/disk_20T/user/anaconda3/envs/gittaskbench/lib/python3.12/site-packages/pandas/core/generic.py:3967 NDFrame.to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)'\n",
"Memory 11: 2025-12-03T15:32:00.000 Wed User provided a command to change directory into the eparse folder: `$ cd eparse`.\n",
"Memory 12: 2025-12-03T15:01:00.000 Wed User indicated that the Python Conda environment is pre-set with no extra configuration needed and that the PYTHONPATH should be generated and exported on 2025-12-03T15:01:00.\n",
"Memory 13: 2025-12-03T15:01:00.000 Wed The task to generate company data entries could not proceed due to the missing README.md file, which was confirmed by the output error from the `cat` command.\n",
"Memory 14: 2025-12-02T17:06:00.000 Tue User executed code in Jupyter with the current working directory set to 'disk/disk_20T/user/GitTaskBench' and Python interpreter located at 'disk/disk_20T/user/anaconda3/envs/gittaskbench/bin/python' on 2025-12-02T17:06:00.\n",
"Memory 15: 2025-12-03T15:32:00.000 Wed User mentioned adding the latest PyPI version of eparse to the requirements.txt file: `eparse==0.8.0`.\n",
"Memory 16: 2025-12-03T15:32:00.000 Wed User attempted to output indexes from pypi.tuna.tsinghua.edu.cn and downloaded packages including eparse-0.7.3 and unstructured-0.18.21.\n",
"Memory 17: 2025-12-03T15:01:00.000 Wed User requested output on 2025-12-03T15:01:00.\n",
"Memory 18: 2025-12-03T15:32:00.000 Wed User emphasized the importance of reading the README file to understand basic functions and usage, and mentioned paths like 'disk/disk_20T/GitTaskBench/queries/Eparse_03' on 2025-12-03T15:32:00.\n",
"Memory 19: 2025-12-03T15:32:00.000 Wed User provided a command to clone the eparse repository: `$ git clone https://github.com/ChrisPappalardo/eparse.git`.\n",
"Memory 20: 2025-12-03T15:32:00.000 Wed User provided information about the eparse package, including installation instructions and features on 2025-12-03T15:32:00.\n",
"\n",
"Generating answer...\n",
"\n",
"Generated Answer: In our previous session, instead of using the 'json://' endpoint, we successfully saved the output using the command line argument `-o stdout`.\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",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "LightMem",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.14"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|