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Google Colab\n", + "import sys\n", + "import torch\n", + "\n", + "if 'google.colab' in sys.modules:\n", + "\n", + " def format_pytorch_version(version):\n", + " return version.split('+')[0]\n", + "\n", + " TORCH_version = torch.__version__\n", + " TORCH = format_pytorch_version(TORCH_version)\n", + "\n", + " def format_cuda_version(version):\n", + " return 'cu' + version.replace('.', '')\n", + "\n", + " CUDA_version = torch.version.cuda\n", + " CUDA = format_cuda_version(CUDA_version)\n", + "\n", + " !pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-geometric" + ], + "metadata": { + "id": "vWNgkL71ALf7" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Let's create a toy dataset" + ], + "metadata": { + "id": "aH3Q56MGDARI" + } + }, + { + "cell_type": "code", + "source": [ + "from torch_geometric.data import Data\n", + "\n", + "# Assume a toy dataset with 3 papers (nodes), edges, and labels\n", + "data = Data(\n", + " x=torch.rand(3, 10), # let's use random features for simplicity\n", + " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long), # Edges\n", + " y=torch.tensor([0, 1, 2], dtype=torch.long), # True labels\n", + " text=[\"Paper A abstract\", \"Paper B abstract\", \"Paper C abstract\"], # Text data\n", + ")" + ], + "metadata": { + "id": "lDLHAhIlDEvm" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388, + "referenced_widgets": [ + "a0f9087c1517444d9d02d3ee239871c0", + "49409686002944ba913043f9e566dec6", + 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"bab624c7fda8476d974253c9fba8a827" + ] + }, + "id": "YxK0emCj_0ad", + "outputId": "f823051a-2dab-49df-b472-5373cde384a1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/48.0 [00:00 ollama.log &\n", + " !sleep 5\n", + " !time curl -i localhost:11434" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ha3JtCsVj5ij", + "outputId": "bf15bcb3-cd93-45f4-912f-9dfa5a35d012" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "nohup: redirecting stderr to stdout\n", + "HTTP/1.1 200 OK\n", + "\u001b[1mContent-Type\u001b[0m: text/plain; charset=utf-8\n", + "\u001b[1mDate\u001b[0m: Sun, 02 Feb 2025 21:36:50 GMT\n", + "\u001b[1mContent-Length\u001b[0m: 17\n", + "\n", + "Ollama is running\n", + "real\t0m0.009s\n", + "user\t0m0.005s\n", + "sys\t0m0.004s\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Dowload the language model 'minicpm-llama3-v-2_5' and the text embedder\n", + "# WARNING: models are > 5GB\n", + "if LLM_BACKEND == \"ollama\":\n", + " !ollama pull minicpm-v\n", + " !ollama pull nomic-embed-text\n", + " !ollama list" + ], + "metadata": { + "id": "JPGUVFm_1eoB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": 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manifest \n", + "pulling 262843d4806a... 12% ▕▏ 539 MB/4.4 GB 235 MB/s 16s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 13% ▕▏ 558 MB/4.4 GB 235 MB/s 16s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 13% ▕▏ 570 MB/4.4 GB 235 MB/s 16s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 13% ▕▏ 594 MB/4.4 GB 235 MB/s 16s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 14% ▕▏ 622 MB/4.4 GB 235 MB/s 16s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 14% ▕▏ 628 MB/4.4 GB 235 MB/s 16s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 15% ▕▏ 654 MB/4.4 GB 235 MB/s 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5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 73% ▕▏ 3.2 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 74% ▕▏ 3.3 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 74% ▕▏ 3.3 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 75% ▕▏ 3.3 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 76% ▕▏ 3.4 GB/4.4 GB 218 MB/s 4s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 76% ▕▏ 3.4 GB/4.4 GB 218 MB/s 4s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 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3s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 84% ▕▏ 3.7 GB/4.4 GB 231 MB/s 3s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 84% ▕▏ 3.7 GB/4.4 GB 231 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 85% ▕▏ 3.8 GB/4.4 GB 231 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 86% ▕▏ 3.8 GB/4.4 GB 231 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 86% ▕▏ 3.8 GB/4.4 GB 231 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 87% ▕▏ 3.8 GB/4.4 GB 231 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 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"pulling f8a805e9e620... 19% ▕▏ 203 MB/1.0 GB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 23% ▕▏ 238 MB/1.0 GB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 25% ▕▏ 261 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 27% ▕▏ 282 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 29% ▕▏ 304 MB/1.0 GB 261 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 43% ▕▏ 449 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 47% ▕▏ 494 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 49% ▕▏ 515 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 53% ▕▏ 554 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 57% ▕▏ 595 MB/1.0 GB 277 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 66% ▕▏ 692 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 66% ▕▏ 692 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 66% ▕▏ 692 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 66% ▕▏ 693 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 695 MB/1.0 GB 231 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 696 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 696 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 696 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 697 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 697 MB/1.0 GB 231 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 701 MB/1.0 GB 175 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 68% ▕▏ 715 MB/1.0 GB 175 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 73% ▕▏ 757 MB/1.0 GB 175 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 78% ▕▏ 812 MB/1.0 GB 175 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 80% ▕▏ 834 MB/1.0 GB 175 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 94% ▕▏ 982 MB/1.0 GB 181 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 97% ▕▏ 1.0 GB/1.0 GB 181 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 98% ▕▏ 1.0 GB/1.0 GB 181 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 99% ▕▏ 1.0 GB/1.0 GB 181 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB/1.0 GB 181 MB/s 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"pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", + "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", + "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", + "pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 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"pulling 60ed67c565f8... 100% ▕▏ 506 B \n", + "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", + "pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", + "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", + "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", + "pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest 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"pulling 60ed67c565f8... 100% ▕▏ 506 B \n", + "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", + "pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", + "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", + "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", + "pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest 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"pulling 60ed67c565f8... 100% ▕▏ 506 B \n", + "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", + "pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", + "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", + "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", + "pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest 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"pulling ce4a164fc046... 100% ▕▏ 17 B \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \n", + "pulling ce4a164fc046... 100% ▕▏ 17 B \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \n", + "pulling ce4a164fc046... 100% ▕▏ 17 B \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \n", + "pulling ce4a164fc046... 100% ▕▏ 17 B \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + 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"verifying sha256 digest \n", + "writing manifest \n", + "success \u001b[?25h\n", + "NAME ID SIZE MODIFIED \n", + "nomic-embed-text:latest 0a109f422b47 274 MB Less than a second ago \n", + "minicpm-v:latest c92bfad01205 5.5 GB 8 seconds ago \n", + "phi4:latest ac896e5b8b34 9.1 GB 17 minutes ago \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Neo4j" + ], + "metadata": { + "id": "bK17PDF7KxCv" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import sys\n", + "\n", + "if 'google.colab' in sys.modules:\n", + " !pip install -q \"neo4j-graphrag[ollama]\"\n", + " !pip install -q langchain_community langchain_experimental langchain_neo4j langchain_openai\n", + "\n", + " # Install the JDK\n", + " !apt-get install openjdk-17-jdk-headless -qq > /dev/null\n", + " os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-17-openjdk-amd64\"\n", + " !update-alternatives --set java /usr/lib/jvm/java-17-openjdk-amd64/jre/bin/java\n", + " !java -version\n", + "\n", + " # Download Neo4j\n", + " !curl -O https://dist.neo4j.org/neo4j-community-5.26.1-unix.tar.gz\n", + " # Decompress and rename the directory\n", + " !tar -xf neo4j-community-5.26.1-unix.tar.gz\n", + " !mv neo4j-community-5.26.1 nj\n", + "\n", + " # (Optional) Disable authentication (if desired)\n", + " !sed -i '/#dbms.security.auth_enabled/s/^#//g' nj/conf/neo4j.conf\n", + "\n", + " # Copy the APOC jar from the labs directory to plugins\n", + " !cp nj/labs/apoc-*.jar nj/plugins/\n", + "\n", + " # Update configuration to allow APOC procedures\n", + " !echo \"dbms.security.procedures.unrestricted=apoc.*\" >> nj/conf/neo4j.conf\n", + " !echo \"dbms.security.procedures.allowlist=apoc.*\" >> nj/conf/neo4j.conf\n", + "\n", + " # Start Neo4j\n", + " !nj/bin/neo4j start" + ], + "metadata": { + "id": "r53ouOX_M3of", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6ab70449-f250-4216-b0f5-66e6147cd1ae" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "update-alternatives: error: alternative /usr/lib/jvm/java-17-openjdk-amd64/jre/bin/java for java not registered; not setting\n", + "openjdk version \"17.0.13\" 2024-10-15\n", + "OpenJDK Runtime Environment (build 17.0.13+11-Ubuntu-2ubuntu122.04)\n", + "OpenJDK 64-Bit Server VM (build 17.0.13+11-Ubuntu-2ubuntu122.04, mixed mode, sharing)\n", + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 151M 100 151M 0 0 217M 0 --:--:-- --:--:-- --:--:-- 217M\n", + "Directories in use:\n", + "home: /content/nj\n", + "config: /content/nj/conf\n", + "logs: /content/nj/logs\n", + "plugins: /content/nj/plugins\n", + "import: /content/nj/import\n", + "data: /content/nj/data\n", + "certificates: /content/nj/certificates\n", + "licenses: /content/nj/licenses\n", + "run: /content/nj/run\n", + "Starting Neo4j.\n", + "Started neo4j (pid:8890). It is available at http://localhost:7474\n", + "There may be a short delay until the server is ready.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# colab may kill the ollama process, start again\n", + "if 'google.colab' in sys.modules:\n", + " !nohup ollama serve > ollama.log &\n", + " !sleep 5\n", + " !time curl -i localhost:11434" + ], + "metadata": { + "id": "uII2aMpvmT74" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from neo4j import GraphDatabase\n", + "from langchain_neo4j import Neo4jGraph\n", + "\n", + "# ---- Step 1: Setup Neo4j Connection ----\n", + "NEO4J_URI = \"bolt://localhost:7687\"\n", + "NEO4J_USER = \"neo4j\"\n", + "NEO4J_PASSWORD = \"your_password\"\n", + "\n", + "driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))\n", + "graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USER, password=NEO4J_PASSWORD)\n", + "\n", + "# ---- Step 2: Create knowledge graph from text ----\n", + "import os\n", + "from langchain_experimental.graph_transformers.llm import LLMGraphTransformer\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "llm = ChatOpenAI(temperature=0,\n", + " model_name=llm_model,\n", + " base_url=base_url,\n", + " api_key=api_key)\n", + "\n", + "llm_transformer = LLMGraphTransformer(llm=llm)\n", + "\n", + "from langchain_core.documents import Document\n", + "\n", + "text = \"\"\"\n", + "Marie Curie, born in 1867, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.\n", + "She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.\n", + "Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.\n", + "She was, in 1906, the first woman to become a professor at the University of Paris.\n", + "\"\"\"\n", + "documents = [Document(page_content=text)]\n", + "graph_documents = llm_transformer.convert_to_graph_documents(documents)\n", + "print(f\"Nodes:{graph_documents[0].nodes}\")\n", + "print(f\"Relationships:{graph_documents[0].relationships}\")\n", + "\n", + "# Add graph to neo4j\n", + "graph.add_graph_documents(graph_documents)\n", + "\n", + "# ---- Step 3: Perform GraphRAG ----\n", + "\n", + "def escape(s):\n", + " return s.replace(\"{\",\"\").replace(\"}\",\"\")\n", + "\n", + "CYPHER_GENERATION_TEMPLATE = f\"\"\"You are a Neo4j expert. Generate a Cypher query to answer the given question.\n", + "\n", + "Database Schema: {escape(graph.schema)}\n", + "\n", + "Rules:\n", + "1. Always use explicit `MATCH` for relationships.\n", + "2. Never use `WHERE` for relationship matching.\n", + "3. Use `RETURN DISTINCT` when appropriate.\n", + "\n", + "Example Queries:\n", + "1. Question: \"Who won the Nobel Prize?\"\n", + " Cypher: MATCH (p:Person)-[:WON_NOBEL_PRIZE]->(:Awarded) RETURN p.id AS winner\n", + "\n", + "Question: {{query}}\n", + "Return only the Cypher query without any explanation or additional text.\n", + "Cypher:\"\"\"\n", + "\n", + "from langchain_neo4j import GraphCypherQAChain\n", + "from langchain_core.prompts import PromptTemplate\n", + "\n", + "chain = GraphCypherQAChain.from_llm(\n", + " llm=llm,\n", + " graph=graph,\n", + " verbose=True,\n", + " cypher_prompt=PromptTemplate(\n", + " input_variables=[\"query\"],\n", + " template=CYPHER_GENERATION_TEMPLATE\n", + " ),\n", + " allow_dangerous_requests=True\n", + ")\n", + "\n", + "# ---- Step 5: Test Queries ----\n", + "print(\"\\nTesting queries...\")\n", + "\n", + "question = \"Who married a Nobel Prize?\"\n", + "\n", + "print(f\"\\nQuestion: {question}\")\n", + "response = chain.invoke(question)\n", + "print(\"Response:\", response['result'])\n", + "\n", + "# Close the driver\n", + "driver.close()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OUWxS0p0-d-e", + "outputId": "527d26fc-0dfc-479b-f1c8-330ec8b7d52b" + }, + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='1867', type='Year', properties={}), Node(id='Polish', type='Nationality', properties={}), Node(id='French', type='Nationality', properties={}), Node(id='Physicist', type='Profession', properties={}), Node(id='Chemist', type='Profession', properties={}), Node(id='Radioactivity', type='Research field', properties={}), Node(id='Nobel Prize', type='Award', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Institution', properties={})]\n", + "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='1867', type='Year', properties={}), type='BORN_IN_YEAR', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Polish', type='Nationality', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='French', type='Nationality', properties={}), type='NATURALISED_NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Physicist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Chemist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Radioactivity', type='Research field', properties={}), type='RESEARCHED_IN_FIELD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARDED', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Marie Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='CO-WINNER_OF_AWARD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Institution', properties={}), type='PROFESSOR_AT_INSTITUTION', properties={})]\n", + "\n", + "Testing queries...\n", + "\n", + "Question: Who married a Nobel Prize?\n", + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mcypher\n", + "MATCH (person1:Person)-[:SPOUSE]->(person2:Person), \n", + " (person2)-[:AWARDED]->(:Award)\n", + "RETURN DISTINCT person1.id AS marriedToNobelPrizeWinner\n", + "\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'marriedToNobelPrizeWinner': 'Pierre Curie'}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "Response: Marie Curie married Pierre Curie, who was a Nobel Prize winner.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "A4gzGQzeeqFC" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 574b2cc1c4d04ddfb5e4d72bd108716dc7b7c656 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 5 Apr 2025 15:07:37 +0200 Subject: [PATCH 02/15] [Chapter13] Graph and GenAI --- Chapter13/LMM_and_Graphs.ipynb | 10291 +++++++++++++++---------------- Chapter13/poetry.lock | 4203 +++++++++++++ Chapter13/pyproject.toml | 35 + Chapter13/setup_ollama.sh | 9 + docker/Dockerfile | 7 + 5 files changed, 9377 insertions(+), 5168 deletions(-) create mode 100644 Chapter13/poetry.lock create mode 100644 Chapter13/pyproject.toml create mode 100755 Chapter13/setup_ollama.sh diff --git a/Chapter13/LMM_and_Graphs.ipynb b/Chapter13/LMM_and_Graphs.ipynb index ce991bc..63a468a 100644 --- a/Chapter13/LMM_and_Graphs.ipynb +++ b/Chapter13/LMM_and_Graphs.ipynb @@ -1,5201 +1,5156 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "vWNgkL71ALf7" + }, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "# install PyG if running on Google Colab\n", + "import sys\n", + "import torch\n", + "\n", + "if 'google.colab' in sys.modules:\n", + "\n", + " def format_pytorch_version(version):\n", + " return version.split('+')[0]\n", + "\n", + " TORCH_version = torch.__version__\n", + " TORCH = format_pytorch_version(TORCH_version)\n", + "\n", + " def format_cuda_version(version):\n", + " return 'cu' + version.replace('.', '')\n", + "\n", + " CUDA_version = torch.version.cuda\n", + " CUDA = format_cuda_version(CUDA_version)\n", + "\n", + " !pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-geometric" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aH3Q56MGDARI" + }, + "source": [ + "Let's create a toy dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "lDLHAhIlDEvm" + }, + "outputs": [], + "source": [ + "import sys\n", + "import torch\n", + "\n", + "from torch_geometric.data import Data\n", + "\n", + "# Assume a toy dataset with 3 papers (nodes), edges, and labels\n", + "data = Data(\n", + " x=torch.rand(3, 10), # let's use random features for simplicity\n", + " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long), # Edges\n", + " y=torch.tensor([0, 1, 2], dtype=torch.long), # True labels\n", + " text=[\"Paper A abstract\", \"Paper B abstract\", \"Paper C abstract\"], # Text data\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { "colab": { - "provenance": [], - "gpuType": "T4" + "base_uri": "https://localhost:8080/", + "height": 388, + "referenced_widgets": [ + "a0f9087c1517444d9d02d3ee239871c0", + "49409686002944ba913043f9e566dec6", + "9ca2c22d54a14543a0ccb418f84dfc7a", + "6ef1fa9fc050493a88aa2ba634c36cb4", + "95dde357b3704448bfdb0b3a8818413e", + "918e3e0abaaa43119b27a725d9996622", + "c45bf94c8ae1497bb5093dd459e35868", + "78a75c6a1b0444baa32a8dc0444c6e42", + "efae36a6383c472c8d8fe7f39223a13f", + "722fd967e7a640468b2c1c828c6ad983", + "9b6d7d228f25418aaf2489fedc465492", + "0b92a27e384b4ccfa1717e2898c44499", + "694e7d436acc4f5288c6dddcb1e8eb43", + "f8dd5aa75d5342b9be4b9ef0914b3330", + 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Define the Graph Neural Network (GNN)\n", + "class GNN(torch.nn.Module):\n", + " def __init__(self, input_dim, hidden_dim, output_dim):\n", + " super(GNN, self).__init__()\n", + " self.conv1 = GCNConv(input_dim, hidden_dim)\n", + " self.conv2 = GCNConv(hidden_dim, output_dim)\n", + "\n", + " def forward(self, x, edge_index):\n", + " x = self.conv1(x, edge_index).relu()\n", + " x = self.conv2(x, edge_index)\n", + " return x\n", + "\n", + "# 2. Define the LLM (e.g., BERT for text encoding)\n", + "class TextEncoder(torch.nn.Module):\n", + " def __init__(self, model_name=\"bert-base-uncased\", output_dim=128):\n", + " super(TextEncoder, self).__init__()\n", + " self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " self.model = AutoModel.from_pretrained(model_name)\n", + " self.fc = torch.nn.Linear(self.model.config.hidden_size, output_dim)\n", + "\n", + " def forward(self, texts):\n", + " # Tokenize and encode text data\n", + " inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True)\n", + " outputs = self.model(**inputs)\n", + " cls_embedding = outputs.last_hidden_state[:, 0, :] # [CLS] token embedding\n", + " return self.fc(cls_embedding)\n", + "\n", + "# 3. Training Loop with Pseudo-label Exchange\n", + "def train_prediction_alignment(data, gnn, text_encoder, num_iterations=5):\n", + " optimizer_gnn = torch.optim.Adam(gnn.parameters(), lr=0.01)\n", + " optimizer_text = torch.optim.Adam(text_encoder.parameters(), lr=0.0001)\n", + "\n", + " for iteration in range(num_iterations):\n", + " # 3.1 Train GNN\n", + " gnn.train()\n", + " optimizer_gnn.zero_grad()\n", + " gnn_labels = gnn(data.x, data.edge_index) # GNN generates pseudo-labels\n", + " gnn_loss = torch.nn.CrossEntropyLoss()(gnn_labels, data.y) # Assume `data.y` is the target\n", + " gnn_loss.backward()\n", + " optimizer_gnn.step()\n", + "\n", + " # 3.2 Train LLM with GNN-generated pseudo-labels\n", + " text_encoder.train()\n", + " optimizer_text.zero_grad()\n", + " text_features = text_encoder(data.text) # Text encoding\n", + " llm_loss = torch.nn.CrossEntropyLoss()(text_features, torch.argmax(gnn_labels, dim=1))\n", + " llm_loss.backward()\n", + " optimizer_text.step()\n", + "\n", + " print(f\"Iteration {iteration+1}: GNN Loss = {gnn_loss.item()}, LLM Loss = {llm_loss.item()}\")\n", + "\n", + "# Initialize models and train\n", + "gnn = GNN(input_dim=10, hidden_dim=16, output_dim=3)\n", + "text_encoder = TextEncoder()\n", + "train_prediction_alignment(data, gnn, text_encoder)" + ] }, - "cells": [ - { - "cell_type": "code", - "source": [ - "%%capture\n", - "\n", - "# install PyG if running on Google Colab\n", - "import sys\n", - "import torch\n", - "\n", - "if 'google.colab' in sys.modules:\n", - "\n", - " def format_pytorch_version(version):\n", - " return version.split('+')[0]\n", - "\n", - " TORCH_version = torch.__version__\n", - " TORCH = format_pytorch_version(TORCH_version)\n", - "\n", - " def format_cuda_version(version):\n", - " return 'cu' + version.replace('.', '')\n", - "\n", - " CUDA_version = torch.version.cuda\n", - " CUDA = format_cuda_version(CUDA_version)\n", - "\n", - " !pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", - " !pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", - " !pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", - " !pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", - " !pip install torch-geometric" - ], - "metadata": { - "id": "vWNgkL71ALf7" - }, - "execution_count": 1, - "outputs": [] + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "AwmGzCmH_669", + "outputId": "2577a628-8f1c-4efb-c732-1dfbbade06bf" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "Let's create a toy dataset" - ], - "metadata": { - "id": "aH3Q56MGDARI" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1: Loss = 1.059374213218689\n", + "Epoch 2: Loss = 1.0176868438720703\n", + "Epoch 3: Loss = 1.0710476636886597\n", + "Epoch 4: Loss = 1.0982847213745117\n", + "Epoch 5: Loss = 1.098282814025879\n", + "Epoch 6: Loss = 1.0987071990966797\n", + "Epoch 7: Loss = 1.0966845750808716\n", + "Epoch 8: Loss = 1.0122456550598145\n", + "Epoch 9: Loss = 1.0971144437789917\n", + "Epoch 10: Loss = 1.0901302099227905\n" + ] + } + ], + "source": [ + "# Import libraries\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from transformers import AutoTokenizer, AutoModel\n", + "from torch_geometric.nn import GraphConv\n", + "from torch_geometric.data import Data\n", + "\n", + "# 1. Define the GNN\n", + "class GNN(torch.nn.Module):\n", + " def __init__(self, input_dim, hidden_dim):\n", + " super(GNN, self).__init__()\n", + " self.conv = GraphConv(input_dim, hidden_dim)\n", + "\n", + " def forward(self, x, edge_index):\n", + " return self.conv(x, edge_index)\n", + "\n", + "# 2. Define the Text Encoder (LLM)\n", + "class TextEncoder(torch.nn.Module):\n", + " def __init__(self, model_name=\"bert-base-uncased\", output_dim=128):\n", + " super(TextEncoder, self).__init__()\n", + " self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " self.model = AutoModel.from_pretrained(model_name)\n", + " self.fc = torch.nn.Linear(self.model.config.hidden_size, output_dim)\n", + "\n", + " def forward(self, texts):\n", + " inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True)\n", + " outputs = self.model(**inputs)\n", + " cls_embedding = outputs.last_hidden_state[:, 0, :] # [CLS] token embedding\n", + " return self.fc(cls_embedding)\n", + "\n", + "# 3. Contrastive Learning Objective\n", + "def contrastive_loss(graph_emb, text_emb, tau=0.1):\n", + " sim = F.cosine_similarity(graph_emb.unsqueeze(1), text_emb.unsqueeze(0), dim=2)\n", + " labels = torch.arange(sim.size(0)).to(sim.device)\n", + " loss = F.cross_entropy(sim / tau, labels)\n", + " return loss\n", + "\n", + "# 4. Training Loop for Latent Space Alignment\n", + "def train_latent_alignment(data, gnn, text_encoder, epochs=10):\n", + " optimizer = torch.optim.Adam(list(gnn.parameters()) + list(text_encoder.parameters()), lr=0.001)\n", + " for epoch in range(epochs):\n", + " optimizer.zero_grad()\n", + "\n", + " # Encode graph and text\n", + " graph_emb = gnn(data.x, data.edge_index) # Graph embeddings\n", + " text_emb = text_encoder(data.text) # Text embeddings\n", + "\n", + " # Compute contrastive loss\n", + " loss = contrastive_loss(graph_emb, text_emb)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " print(f\"Epoch {epoch+1}: Loss = {loss.item()}\")\n", + "\n", + "# 5. Example Data\n", + "# Toy data with 3 products and their relationships\n", + "data = Data(\n", + " x=torch.rand(3, 10), # Node features\n", + " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long), # Edges\n", + " text=[\"Product A description\", \"Product B description\", \"Product C description\"], # Text data\n", + ")\n", + "\n", + "# Initialize models and train\n", + "gnn = GNN(input_dim=10, hidden_dim=128)\n", + "text_encoder = TextEncoder()\n", + "train_latent_alignment(data, gnn, text_encoder)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mqcZZn6qCCmj" + }, + "source": [ + "# GraphRAG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xM73g-bDiPPG" + }, + "source": [ + "If using Colab you can simply run the following cells.\n", + "\n", + "Otherwise, if you want to use the local backend, please:\n", + "- download neo4j desktop on [docker](https://neo4j.com/docs/graph-data-science/current/installation/installation-docker/)*\n", + "- download [lm-studio](https://lmstudio.ai/) and download the minicpm-llama3-v-2_5 and nomic-embed-text model\n", + "\n", + "*run docker as:\n", + "\n", + "\n", + "```\n", + "docker run --rm --env NEO4J_AUTH=neo4j/defaultpass -p 7474:7474 -p 7687:7687 -v $PWD/data:/data -v $PWD/plugins:/plugins --name neo4j-apoc -e NEO4J_apoc_export_file_enabled=true -e NEO4J_apoc_import_file_enabled=true -e NEO4J_apoc_import_file_use__neo4j__config=true -e NEO4J_PLUGINS=\\[\\\"apoc-extended\\\"\\] neo4j\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "JKEORJfWwI7w" + }, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "LLM_BACKEND = \"ollama\" # choose [\"ollama\" | \"lm-studio\"]\n", + "# LLM_BACKEND = \"lm-studio\"\n", + "\n", + "assert LLM_BACKEND in [\"ollama\", \"lm-studio\"]\n", + "\n", + "if LLM_BACKEND == \"ollama\":\n", + " base_url = \"http://localhost:11434/v1\"\n", + " api_key = \"ollama\"\n", + " # llm_model = \"phi4\"\n", + " llm_model = \"minicpm-v\"\n", + "else:\n", + " base_url = \"http://localhost:1234/v1\"\n", + " api_key = \"lm-studio\"\n", + " llm_model = \"minicpm-llama3-v-2_5\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FIEW2lBMqp2V" + }, + "source": [ + "If Colab you need to download ollama and start the server" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ha3JtCsVj5ij", + "outputId": "bf15bcb3-cd93-45f4-912f-9dfa5a35d012" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "from torch_geometric.data import Data\n", - "\n", - "# Assume a toy dataset with 3 papers (nodes), edges, and labels\n", - "data = Data(\n", - " x=torch.rand(3, 10), # let's use random features for simplicity\n", - " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long), # Edges\n", - " y=torch.tensor([0, 1, 2], dtype=torch.long), # True labels\n", - " text=[\"Paper A abstract\", \"Paper B abstract\", \"Paper C abstract\"], # Text data\n", - ")" - ], - "metadata": { - "id": "lDLHAhIlDEvm" - }, - "execution_count": 2, - "outputs": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "nohup: redirecting stderr to stdout\n", + "HTTP/1.1 200 OK\n", + "\u001b[1mContent-Type\u001b[0m: text/plain; charset=utf-8\n", + "\u001b[1mDate\u001b[0m: Sun, 02 Feb 2025 21:36:50 GMT\n", + "\u001b[1mContent-Length\u001b[0m: 17\n", + "\n", + "Ollama is running\n", + "real\t0m0.009s\n", + "user\t0m0.005s\n", + "sys\t0m0.004s\n" + ] + } + ], + "source": [ + "if 'google.colab' in sys.modules:\n", + " !curl https://ollama.ai/install.sh | sh\n", + " !nohup ollama serve > ollama.log &\n", + " !sleep 5\n", + " !time curl -i localhost:11434" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "JPGUVFm_1eoB", + "outputId": "391fd502-f552-45df-d655-6b1af61a7830" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 388, - "referenced_widgets": [ - "a0f9087c1517444d9d02d3ee239871c0", - "49409686002944ba913043f9e566dec6", - "9ca2c22d54a14543a0ccb418f84dfc7a", - "6ef1fa9fc050493a88aa2ba634c36cb4", - "95dde357b3704448bfdb0b3a8818413e", - "918e3e0abaaa43119b27a725d9996622", - "c45bf94c8ae1497bb5093dd459e35868", - "78a75c6a1b0444baa32a8dc0444c6e42", - "efae36a6383c472c8d8fe7f39223a13f", - "722fd967e7a640468b2c1c828c6ad983", - "9b6d7d228f25418aaf2489fedc465492", - "0b92a27e384b4ccfa1717e2898c44499", - "694e7d436acc4f5288c6dddcb1e8eb43", - "f8dd5aa75d5342b9be4b9ef0914b3330", - "577d03563d0e429da232b47df6dc2cfb", - "da4c66ea361b4ea4be05010c02866f51", - "f500df21edf1499c83e6d0036e5b771c", - "26329100e08f452bba6a2cb0199c84b7", - "bcbc986b382a4ba49bfc4d5fd428488c", - "bced6297dc0b46ea826ee40344c21b13", - "04e572cf4bfe4c4b91b08f47cdb88c77", - "1c795237b0964f4182a2f2948b747f94", - "63bc10c1908945928df1d9b492912a04", - "a87bb9d072ee43a08a97c9effb82db9c", - "000c7c5876a04721ac8ccc89d9e6163e", - "ccbc8df9abab48948c5317bcc31612e1", - "87df42e6746e4847864eda694620750f", - "a88f61637e6541f589187d5d9abcb503", - "bb0d43a5c4f34b1d9a39230b9c129fd5", - "5bf85ad0b72a4e68914487ffabef5ea6", - "f8d2202f01874c30a3ae563e6fdd6428", - "853f6e10e86f4d8aba5822790d43a34e", - "581e482879434621bb48245bc886baa2", - "b369bdad965b4b3794af271f5416d42a", - "07c929ea8b0e4f6a85bfa54b60beec27", - "e2e8b3e0ceb7405fa6c0940730f60804", - "0c381c9ceeb04677903611735c1b7fcf", - "90fc7f82fa7c4fe79386e766fe687370", - "02035c4cf9f142c6a7167d4d81befce0", - "0ef9969aa23348e39e56981b0ce9ba35", - "ba9d04c9c86e45f9a80f3a61ee220c5e", - "317a758830264800bbd6f17ee10d5e22", - "a54841dde9cd42db85b326faaf4ab0b5", - "72c8076ed09748e1a37b21f25d12dafe", - "b78234a537214afebcfdee5f32d4d9a0", - "4090d3b6cdbf45e9b54f65e55a994713", - "41bb03671ae14450aaeb13b9ee9a9ffa", - "0825bfd7305b497abaac3d5edbcfb72d", - "03c8fbe4a1ab403dae44d77ff0240d9a", - "204a396320264c7fa63b2e539f1b25d1", - "6a2f31e069e54cfb96dfbb5b80e5d15b", - "45dc666b5ac94bbf9164058d0c7cd3e2", - "07718c8acd3049c4a38778e6f8d4eca3", - "e053ba80dc79470d8b1d1c9dc67db792", - "bab624c7fda8476d974253c9fba8a827" - ] - }, - "id": "YxK0emCj_0ad", - "outputId": "f823051a-2dab-49df-b472-5373cde384a1" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/48.0 [00:00 5GB\n", + "if LLM_BACKEND == \"ollama\":\n", + " !ollama pull minicpm-v\n", + " !ollama pull nomic-embed-text\n", + " !ollama list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bK17PDF7KxCv" + }, + "source": [ + "# Neo4j" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "r53ouOX_M3of", + "outputId": "6ab70449-f250-4216-b0f5-66e6147cd1ae" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "# Import libraries\n", - "import torch\n", - "import torch.nn.functional as F\n", - "from transformers import AutoTokenizer, AutoModel\n", - "from torch_geometric.nn import GraphConv\n", - "from torch_geometric.data import Data\n", - "\n", - "# 1. Define the GNN\n", - "class GNN(torch.nn.Module):\n", - " def __init__(self, input_dim, hidden_dim):\n", - " super(GNN, self).__init__()\n", - " self.conv = GraphConv(input_dim, hidden_dim)\n", - "\n", - " def forward(self, x, edge_index):\n", - " return self.conv(x, edge_index)\n", - "\n", - "# 2. Define the Text Encoder (LLM)\n", - "class TextEncoder(torch.nn.Module):\n", - " def __init__(self, model_name=\"bert-base-uncased\", output_dim=128):\n", - " super(TextEncoder, self).__init__()\n", - " self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n", - " self.model = AutoModel.from_pretrained(model_name)\n", - " self.fc = torch.nn.Linear(self.model.config.hidden_size, output_dim)\n", - "\n", - " def forward(self, texts):\n", - " inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True)\n", - " outputs = self.model(**inputs)\n", - " cls_embedding = outputs.last_hidden_state[:, 0, :] # [CLS] token embedding\n", - " return self.fc(cls_embedding)\n", - "\n", - "# 3. Contrastive Learning Objective\n", - "def contrastive_loss(graph_emb, text_emb, tau=0.1):\n", - " sim = F.cosine_similarity(graph_emb.unsqueeze(1), text_emb.unsqueeze(0), dim=2)\n", - " labels = torch.arange(sim.size(0)).to(sim.device)\n", - " loss = F.cross_entropy(sim / tau, labels)\n", - " return loss\n", - "\n", - "# 4. Training Loop for Latent Space Alignment\n", - "def train_latent_alignment(data, gnn, text_encoder, epochs=10):\n", - " optimizer = torch.optim.Adam(list(gnn.parameters()) + list(text_encoder.parameters()), lr=0.001)\n", - " for epoch in range(epochs):\n", - " optimizer.zero_grad()\n", - "\n", - " # Encode graph and text\n", - " graph_emb = gnn(data.x, data.edge_index) # Graph embeddings\n", - " text_emb = text_encoder(data.text) # Text embeddings\n", - "\n", - " # Compute contrastive loss\n", - " loss = contrastive_loss(graph_emb, text_emb)\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " print(f\"Epoch {epoch+1}: Loss = {loss.item()}\")\n", - "\n", - "# 5. Example Data\n", - "# Toy data with 3 products and their relationships\n", - "data = Data(\n", - " x=torch.rand(3, 10), # Node features\n", - " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long), # Edges\n", - " text=[\"Product A description\", \"Product B description\", \"Product C description\"], # Text data\n", - ")\n", - "\n", - "# Initialize models and train\n", - "gnn = GNN(input_dim=10, hidden_dim=128)\n", - "text_encoder = TextEncoder()\n", - "train_latent_alignment(data, gnn, text_encoder)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AwmGzCmH_669", - "outputId": "2577a628-8f1c-4efb-c732-1dfbbade06bf" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1: Loss = 1.1030889749526978\n", - "Epoch 2: Loss = 0.8914819359779358\n", - "Epoch 3: Loss = 0.654697060585022\n", - "Epoch 4: Loss = 1.1298424005508423\n", - "Epoch 5: Loss = 1.1417795419692993\n", - "Epoch 6: Loss = 1.0708292722702026\n", - "Epoch 7: Loss = 0.987954318523407\n", - "Epoch 8: Loss = 1.0997623205184937\n", - "Epoch 9: Loss = 1.0972650051116943\n", - "Epoch 10: Loss = 1.0914229154586792\n" - ] - } - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "update-alternatives: error: alternative /usr/lib/jvm/java-17-openjdk-amd64/jre/bin/java for java not registered; not setting\n", + "openjdk version \"17.0.13\" 2024-10-15\n", + "OpenJDK Runtime Environment (build 17.0.13+11-Ubuntu-2ubuntu122.04)\n", + "OpenJDK 64-Bit Server VM (build 17.0.13+11-Ubuntu-2ubuntu122.04, mixed mode, sharing)\n", + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 151M 100 151M 0 0 217M 0 --:--:-- --:--:-- --:--:-- 217M\n", + "Directories in use:\n", + "home: /content/nj\n", + "config: /content/nj/conf\n", + "logs: /content/nj/logs\n", + "plugins: /content/nj/plugins\n", + "import: /content/nj/import\n", + "data: /content/nj/data\n", + "certificates: /content/nj/certificates\n", + "licenses: /content/nj/licenses\n", + "run: /content/nj/run\n", + "Starting Neo4j.\n", + "Started neo4j (pid:8890). It is available at http://localhost:7474\n", + "There may be a short delay until the server is ready.\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "\n", + "if 'google.colab' in sys.modules:\n", + " !pip install -q \"neo4j-graphrag[ollama]\"\n", + " !pip install -q langchain_community langchain_experimental langchain_neo4j langchain_openai\n", + "\n", + " # Install the JDK\n", + " !apt-get install openjdk-17-jdk-headless -qq > /dev/null\n", + " os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-17-openjdk-amd64\"\n", + " !update-alternatives --set java /usr/lib/jvm/java-17-openjdk-amd64/jre/bin/java\n", + " !java -version\n", + "\n", + " # Download Neo4j\n", + " !curl -O https://dist.neo4j.org/neo4j-community-5.26.1-unix.tar.gz\n", + " # Decompress and rename the directory\n", + " !tar -xf neo4j-community-5.26.1-unix.tar.gz\n", + " !mv neo4j-community-5.26.1 nj\n", + "\n", + " # (Optional) Disable authentication (if desired)\n", + " !sed -i '/#dbms.security.auth_enabled/s/^#//g' nj/conf/neo4j.conf\n", + "\n", + " # Copy the APOC jar from the labs directory to plugins\n", + " !cp nj/labs/apoc-*.jar nj/plugins/\n", + "\n", + " # Update configuration to allow APOC procedures\n", + " !echo \"dbms.security.procedures.unrestricted=apoc.*\" >> nj/conf/neo4j.conf\n", + " !echo \"dbms.security.procedures.allowlist=apoc.*\" >> nj/conf/neo4j.conf\n", + "\n", + " # Start Neo4j\n", + " !nj/bin/neo4j start" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uII2aMpvmT74" + }, + "outputs": [], + "source": [ + "# colab may kill the ollama process, start again\n", + "if 'google.colab' in sys.modules:\n", + " !nohup ollama serve > ollama.log &\n", + " !sleep 5\n", + " !time curl -i localhost:11434" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "OUWxS0p0-d-e", + "outputId": "527d26fc-0dfc-479b-f1c8-330ec8b7d52b" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "# GraphRAG" - ], - "metadata": { - "id": "mqcZZn6qCCmj" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={})]\n", + "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='CO-WINNER_OF_NOBEL_PRIZE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel_Prize', type='Award', properties={}), type='RECIPIENT_OF', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel_Prize', type='Award', properties={}), type='RECIPIENT_OF', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University_Of_Paris', type='Institution', properties={}), type='PROFESSOR_AT', properties={})]\n", + "\n", + "Testing queries...\n", + "\n", + "Question: Who married a Nobel Prize?\n", + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:CO-WINNER_OF_NOBEL_PRIZE]->(n:Award)\n", + "RETURN n.id AS nobel_prize_winner, p.id AS person_id\u001b[0m\n" + ] }, { - "cell_type": "markdown", - "source": [ - "If using Colab you can simply run the following cells.\n", - "\n", - "Otherwise, if you want to use the local backend, please:\n", - "- download neo4j desktop on [docker](https://neo4j.com/docs/graph-data-science/current/installation/installation-docker/)*\n", - "- download [lm-studio](https://lmstudio.ai/) and download the minicpm-llama3-v-2_5 and nomic-embed-text model\n", - "\n", - "*run docker as:\n", - "\n", - "\n", - "```\n", - "docker run --rm --env NEO4J_AUTH=neo4j/defaultpass -p 7474:7474 -p 7687:7687 -v $PWD/data:/data -v $PWD/plugins:/plugins --name neo4j-apoc -e NEO4J_apoc_export_file_enabled=true -e NEO4J_apoc_import_file_enabled=true -e NEO4J_apoc_import_file_use__neo4j__config=true -e NEO4J_PLUGINS=\\[\\\"apoc-extended\\\"\\] neo4j\n", - "```\n", - "\n" - ], - "metadata": { - "id": "xM73g-bDiPPG" - } + "ename": "CypherSyntaxError", + "evalue": "{code: Neo.ClientError.Statement.SyntaxError} {message: Invalid input '-': expected a parameter, '&', '*', ':', 'WHERE', ']', '{' or '|' (line 1, column 22 (offset: 21))\n\"MATCH (p:Person)-[:CO-WINNER_OF_NOBEL_PRIZE]->(n:Award)\"\n ^}", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCypherSyntaxError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 82\u001b[0m\n\u001b[1;32m 79\u001b[0m question \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWho married a Nobel Prize?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mQuestion: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquestion\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 82\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResponse:\u001b[39m\u001b[38;5;124m\"\u001b[39m, response[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# Close the driver\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/langchain/chains/base.py:170\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 168\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 169\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 170\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 171\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m include_run_info:\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/langchain/chains/base.py:160\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_inputs(inputs)\n\u001b[1;32m 159\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 160\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[1;32m 163\u001b[0m )\n\u001b[1;32m 165\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[1;32m 166\u001b[0m inputs, outputs, return_only_outputs\n\u001b[1;32m 167\u001b[0m )\n\u001b[1;32m 168\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/langchain_neo4j/chains/graph_qa/cypher.py:356\u001b[0m, in \u001b[0;36mGraphCypherQAChain._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[38;5;66;03m# Retrieve and limit the number of results\u001b[39;00m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;66;03m# Generated Cypher be null if query corrector identifies invalid schema\u001b[39;00m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m generated_cypher:\n\u001b[0;32m--> 356\u001b[0m context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerated_cypher\u001b[49m\u001b[43m)\u001b[49m[: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtop_k]\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 358\u001b[0m context \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/langchain_neo4j/graphs/neo4j_graph.py:230\u001b[0m, in \u001b[0;36mNeo4jGraph.query\u001b[0;34m(self, query, params, session_params)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m session_params:\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 230\u001b[0m data, _, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_driver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 231\u001b[0m \u001b[43m \u001b[49m\u001b[43mQuery\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[43m \u001b[49m\u001b[43mdatabase_\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_database\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 233\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 234\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 235\u001b[0m json_data \u001b[38;5;241m=\u001b[39m [r\u001b[38;5;241m.\u001b[39mdata() \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m data]\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msanitize:\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/driver.py:970\u001b[0m, in \u001b[0;36mDriver.execute_query\u001b[0;34m(self, query_, parameters_, routing_, database_, impersonated_user_, bookmark_manager_, auth_, result_transformer_, **kwargs)\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 967\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid routing control value: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrouting_\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 968\u001b[0m )\n\u001b[1;32m 969\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m session\u001b[38;5;241m.\u001b[39m_pipelined_begin:\n\u001b[0;32m--> 970\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_transaction\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 971\u001b[0m \u001b[43m \u001b[49m\u001b[43maccess_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 972\u001b[0m \u001b[43m \u001b[49m\u001b[43mTelemetryAPI\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDRIVER\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 973\u001b[0m \u001b[43m \u001b[49m\u001b[43mwork\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 974\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_str\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult_transformer_\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 975\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 976\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/session.py:583\u001b[0m, in \u001b[0;36mSession._run_transaction\u001b[0;34m(self, access_mode, api, transaction_function, args, kwargs)\u001b[0m\n\u001b[1;32m 581\u001b[0m tx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transaction\n\u001b[1;32m 582\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 583\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mtransaction_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mCancelledError:\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# if cancellation callback has not been called yet:\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transaction \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_work/query.py:144\u001b[0m, in \u001b[0;36munit_of_work..wrapper..wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/driver.py:1306\u001b[0m, in \u001b[0;36m_work\u001b[0;34m(tx, query, parameters, transformer)\u001b[0m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_work\u001b[39m(\n\u001b[1;32m 1301\u001b[0m tx: ManagedTransaction,\n\u001b[1;32m 1302\u001b[0m query: te\u001b[38;5;241m.\u001b[39mLiteralString,\n\u001b[1;32m 1303\u001b[0m parameters: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, t\u001b[38;5;241m.\u001b[39mAny],\n\u001b[1;32m 1304\u001b[0m transformer: t\u001b[38;5;241m.\u001b[39mCallable[[Result], t\u001b[38;5;241m.\u001b[39mUnion[_T]],\n\u001b[1;32m 1305\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m _T:\n\u001b[0;32m-> 1306\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mtx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1307\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m transformer(res)\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/transaction.py:206\u001b[0m, in \u001b[0;36mTransactionBase.run\u001b[0;34m(self, query, parameters, **kwparameters)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results\u001b[38;5;241m.\u001b[39mappend(result)\n\u001b[1;32m 205\u001b[0m parameters \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(parameters \u001b[38;5;129;01mor\u001b[39;00m {}, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwparameters)\n\u001b[0;32m--> 206\u001b[0m \u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tx_ready_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/result.py:177\u001b[0m, in \u001b[0;36mResult._tx_ready_run\u001b[0;34m(self, query, parameters)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_tx_ready_run\u001b[39m(\u001b[38;5;28mself\u001b[39m, query, parameters):\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# BEGIN+RUN does not carry any extra on the RUN message.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;66;03m# BEGIN {extra}\u001b[39;00m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;66;03m# RUN \"query\" {parameters} {extra}\u001b[39;00m\n\u001b[0;32m--> 177\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/result.py:236\u001b[0m, in \u001b[0;36mResult._run\u001b[0;34m(self, query, parameters, db, imp_user, access_mode, bookmarks, notifications_min_severity, notifications_disabled_classifications)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pull()\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39msend_all()\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_attach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/result.py:430\u001b[0m, in \u001b[0;36mResult._attach\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exhausted \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attached \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/io/_common.py:184\u001b[0m, in \u001b[0;36mConnectionErrorHandler.__getattr__..outer..inner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21minner\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 184\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (Neo4jError, ServiceUnavailable, SessionExpired) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39miscoroutinefunction(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__on_error)\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/io/_bolt.py:864\u001b[0m, in \u001b[0;36mBolt.fetch_message\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 860\u001b[0m \u001b[38;5;66;03m# Receive exactly one message\u001b[39;00m\n\u001b[1;32m 861\u001b[0m tag, fields \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minbox\u001b[38;5;241m.\u001b[39mpop(\n\u001b[1;32m 862\u001b[0m hydration_hooks\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresponses[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mhydration_hooks\n\u001b[1;32m 863\u001b[0m )\n\u001b[0;32m--> 864\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtag\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midle_since \u001b[38;5;241m=\u001b[39m monotonic()\n\u001b[1;32m 866\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/io/_bolt5.py:1208\u001b[0m, in \u001b[0;36mBolt5x7._process_message\u001b[0;34m(self, tag, fields)\u001b[0m\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enrich_error_diagnostic_record(summary_metadata)\n\u001b[1;32m 1207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1208\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_failure\u001b[49m\u001b[43m(\u001b[49m\u001b[43msummary_metadata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ServiceUnavailable, DatabaseUnavailable):\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpool:\n", + "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/io/_common.py:254\u001b[0m, in \u001b[0;36mResponse.on_failure\u001b[0;34m(self, metadata)\u001b[0m\n\u001b[1;32m 252\u001b[0m handler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandlers\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_summary\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 253\u001b[0m Util\u001b[38;5;241m.\u001b[39mcallback(handler)\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hydrate_error(metadata)\n", + "\u001b[0;31mCypherSyntaxError\u001b[0m: {code: Neo.ClientError.Statement.SyntaxError} {message: Invalid input '-': expected a parameter, '&', '*', ':', 'WHERE', ']', '{' or '|' (line 1, column 22 (offset: 21))\n\"MATCH (p:Person)-[:CO-WINNER_OF_NOBEL_PRIZE]->(n:Award)\"\n ^}" + ] + } + ], + "source": [ + "from neo4j import GraphDatabase\n", + "from langchain_neo4j import Neo4jGraph\n", + "\n", + "# ---- Step 1: Setup Neo4j Connection ----\n", + "NEO4J_URI = \"bolt://localhost:7687\"\n", + "NEO4J_USER = \"neo4j\"\n", + "NEO4J_PASSWORD = \"neo5j\"\n", + "\n", + "driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))\n", + "graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USER, password=NEO4J_PASSWORD)\n", + "\n", + "# ---- Step 2: Create knowledge graph from text ----\n", + "import os\n", + "from langchain_experimental.graph_transformers.llm import LLMGraphTransformer\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "llm = ChatOpenAI(temperature=0,\n", + " model_name=llm_model,\n", + " base_url=base_url,\n", + " api_key=api_key)\n", + "\n", + "llm_transformer = LLMGraphTransformer(llm=llm)\n", + "\n", + "from langchain_core.documents import Document\n", + "\n", + "text = \"\"\"\n", + "Marie Curie, born in 1867, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.\n", + "She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.\n", + "Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.\n", + "She was, in 1906, the first woman to become a professor at the University of Paris.\n", + "\"\"\"\n", + "documents = [Document(page_content=text)]\n", + "graph_documents = llm_transformer.convert_to_graph_documents(documents)\n", + "print(f\"Nodes:{graph_documents[0].nodes}\")\n", + "print(f\"Relationships:{graph_documents[0].relationships}\")\n", + "\n", + "# Add graph to neo4j\n", + "graph.add_graph_documents(graph_documents)\n", + "\n", + "# ---- Step 3: Perform GraphRAG ----\n", + "\n", + "def escape(s):\n", + " return s.replace(\"{\",\"\").replace(\"}\",\"\")\n", + "\n", + "CYPHER_GENERATION_TEMPLATE = f\"\"\"You are a Neo4j expert. Generate a Cypher query to answer the given question.\n", + "\n", + "Database Schema: {escape(graph.schema)}\n", + "\n", + "Rules:\n", + "1. Always use explicit `MATCH` for relationships.\n", + "2. Never use `WHERE` for relationship matching.\n", + "3. Use `RETURN DISTINCT` when appropriate.\n", + "\n", + "Example Queries:\n", + "1. Question: \"Who won the Nobel Prize?\"\n", + " Cypher: MATCH (p:Person)-[:WON_NOBEL_PRIZE]->(:Awarded) RETURN p.id AS winner\n", + "\n", + "Question: {{query}}\n", + "Return only the Cypher query without any explanation or additional text.\n", + "Cypher:\"\"\"\n", + "\n", + "from langchain_neo4j import GraphCypherQAChain\n", + "from langchain_core.prompts import PromptTemplate\n", + "\n", + "chain = GraphCypherQAChain.from_llm(\n", + " llm=llm,\n", + " graph=graph,\n", + " verbose=True,\n", + " cypher_prompt=PromptTemplate(\n", + " input_variables=[\"query\"],\n", + " template=CYPHER_GENERATION_TEMPLATE\n", + " ),\n", + " allow_dangerous_requests=True\n", + ")\n", + "\n", + "# ---- Step 5: Test Queries ----\n", + "print(\"\\nTesting queries...\")\n", + "\n", + "question = \"Who married a Nobel Prize?\"\n", + "\n", + "print(f\"\\nQuestion: {question}\")\n", + "response = chain.invoke(question)\n", + "print(\"Response:\", response['result'])\n", + "\n", + "# Close the driver\n", + 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llm_model = \"minicpm-llama3-v-2_5\"" - ], - "metadata": { - "id": "JKEORJfWwI7w" - }, - "execution_count": 21, - "outputs": [] + "02035c4cf9f142c6a7167d4d81befce0": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "markdown", - "source": [ - "If Colab you need to download ollama and start the server" - ], - "metadata": { - "id": "FIEW2lBMqp2V" - } + "03c8fbe4a1ab403dae44d77ff0240d9a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + 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"metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ha3JtCsVj5ij", - "outputId": "bf15bcb3-cd93-45f4-912f-9dfa5a35d012" - }, - "execution_count": 31, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "nohup: redirecting stderr to stdout\n", - "HTTP/1.1 200 OK\n", - "\u001b[1mContent-Type\u001b[0m: text/plain; charset=utf-8\n", - "\u001b[1mDate\u001b[0m: Sun, 02 Feb 2025 21:36:50 GMT\n", - "\u001b[1mContent-Length\u001b[0m: 17\n", - "\n", - "Ollama is running\n", - "real\t0m0.009s\n", - "user\t0m0.005s\n", - "sys\t0m0.004s\n" - ] - } - ] + "04e572cf4bfe4c4b91b08f47cdb88c77": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": 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18s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 39% ▕▏ 1.7 GB/4.4 GB 144 MB/s 18s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 40% ▕▏ 1.8 GB/4.4 GB 144 MB/s 18s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 41% ▕▏ 1.8 GB/4.4 GB 149 MB/s 17s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 42% ▕▏ 1.8 GB/4.4 GB 149 MB/s 17s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 42% ▕▏ 1.9 GB/4.4 GB 149 MB/s 17s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 43% ▕▏ 1.9 GB/4.4 GB 149 MB/s 16s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 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15s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 48% ▕▏ 2.1 GB/4.4 GB 160 MB/s 14s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 49% ▕▏ 2.2 GB/4.4 GB 160 MB/s 14s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 50% ▕▏ 2.2 GB/4.4 GB 160 MB/s 13s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 50% ▕▏ 2.2 GB/4.4 GB 160 MB/s 13s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 51% ▕▏ 2.3 GB/4.4 GB 160 MB/s 13s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 51% ▕▏ 2.3 GB/4.4 GB 160 MB/s 13s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 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2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 34% ▕▏ 350 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 37% ▕▏ 389 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 39% ▕▏ 406 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 43% ▕▏ 449 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 47% ▕▏ 494 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 49% ▕▏ 515 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 53% ▕▏ 554 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 57% ▕▏ 595 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 59% ▕▏ 616 MB/1.0 GB 277 MB/s 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"pulling 60ed67c565f8... 100% ▕▏ 506 B \n", - "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", - "pulling f02dd72bb242... 100% ▕▏ 59 B \n", - "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", - "verifying sha256 digest ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", - "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", - "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", - "pulling f02dd72bb242... 100% ▕▏ 59 B \n", - "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", - "verifying sha256 digest ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest 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"pulling 60ed67c565f8... 100% ▕▏ 506 B \n", - "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", - "pulling f02dd72bb242... 100% ▕▏ 59 B \n", - "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", - "verifying sha256 digest ⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", - "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", - "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", - "pulling f02dd72bb242... 100% ▕▏ 59 B \n", - "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", - "verifying sha256 digest ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest 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"pulling 60ed67c565f8... 100% ▕▏ 506 B \n", - "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", - "pulling f02dd72bb242... 100% ▕▏ 59 B \n", - "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", - "verifying sha256 digest ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", - "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", - "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", - "pulling f02dd72bb242... 100% ▕▏ 59 B \n", - "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", - "verifying sha256 digest ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest 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"metadata": { - "id": "uII2aMpvmT74" - }, - "execution_count": null, - "outputs": [] + "layout": "IPY_MODEL_90fc7f82fa7c4fe79386e766fe687370" + } }, - { - "cell_type": "code", - "source": [ - "from neo4j import GraphDatabase\n", - "from langchain_neo4j import Neo4jGraph\n", - "\n", - "# ---- Step 1: Setup Neo4j Connection ----\n", - "NEO4J_URI = \"bolt://localhost:7687\"\n", - "NEO4J_USER = \"neo4j\"\n", - "NEO4J_PASSWORD = \"your_password\"\n", - "\n", - "driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))\n", - "graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USER, password=NEO4J_PASSWORD)\n", - "\n", - "# ---- Step 2: Create knowledge graph from text ----\n", - "import os\n", - "from langchain_experimental.graph_transformers.llm import LLMGraphTransformer\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "llm = ChatOpenAI(temperature=0,\n", - " model_name=llm_model,\n", - " base_url=base_url,\n", - " api_key=api_key)\n", - "\n", - "llm_transformer = LLMGraphTransformer(llm=llm)\n", - "\n", - "from langchain_core.documents import Document\n", - "\n", - "text = \"\"\"\n", - "Marie Curie, born in 1867, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.\n", - "She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.\n", - "Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.\n", - "She was, in 1906, the first woman to become a professor at the University of Paris.\n", - "\"\"\"\n", - "documents = [Document(page_content=text)]\n", - "graph_documents = llm_transformer.convert_to_graph_documents(documents)\n", - "print(f\"Nodes:{graph_documents[0].nodes}\")\n", - "print(f\"Relationships:{graph_documents[0].relationships}\")\n", - "\n", - "# Add graph to neo4j\n", - "graph.add_graph_documents(graph_documents)\n", - "\n", - "# ---- Step 3: Perform GraphRAG ----\n", - "\n", - "def escape(s):\n", - " return s.replace(\"{\",\"\").replace(\"}\",\"\")\n", - "\n", - "CYPHER_GENERATION_TEMPLATE = f\"\"\"You are a Neo4j expert. Generate a Cypher query to answer the given question.\n", - "\n", - "Database Schema: {escape(graph.schema)}\n", - "\n", - "Rules:\n", - "1. Always use explicit `MATCH` for relationships.\n", - "2. Never use `WHERE` for relationship matching.\n", - "3. Use `RETURN DISTINCT` when appropriate.\n", - "\n", - "Example Queries:\n", - "1. Question: \"Who won the Nobel Prize?\"\n", - " Cypher: MATCH (p:Person)-[:WON_NOBEL_PRIZE]->(:Awarded) RETURN p.id AS winner\n", - "\n", - "Question: {{query}}\n", - "Return only the Cypher query without any explanation or additional text.\n", - "Cypher:\"\"\"\n", - "\n", - "from langchain_neo4j import GraphCypherQAChain\n", - "from langchain_core.prompts import PromptTemplate\n", - "\n", - "chain = GraphCypherQAChain.from_llm(\n", - " llm=llm,\n", - " graph=graph,\n", - " verbose=True,\n", - " cypher_prompt=PromptTemplate(\n", - " input_variables=[\"query\"],\n", - " template=CYPHER_GENERATION_TEMPLATE\n", - " ),\n", - " allow_dangerous_requests=True\n", - ")\n", - "\n", - "# ---- Step 5: Test Queries ----\n", - "print(\"\\nTesting queries...\")\n", - "\n", - "question = \"Who married a Nobel Prize?\"\n", - "\n", - "print(f\"\\nQuestion: {question}\")\n", - "response = chain.invoke(question)\n", - "print(\"Response:\", response['result'])\n", - "\n", - "# Close the driver\n", - 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Node(id='Polish', type='Nationality', properties={}), Node(id='French', type='Nationality', properties={}), Node(id='Physicist', type='Profession', properties={}), Node(id='Chemist', type='Profession', properties={}), Node(id='Radioactivity', type='Research field', properties={}), Node(id='Nobel Prize', type='Award', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Institution', properties={})]\n", - "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='1867', type='Year', properties={}), type='BORN_IN_YEAR', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Polish', type='Nationality', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='French', type='Nationality', properties={}), type='NATURALISED_NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Physicist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Chemist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Radioactivity', type='Research field', properties={}), type='RESEARCHED_IN_FIELD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARDED', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Marie Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), 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"==1.21.6" +# networkx = "==2.5" +torch = "^2.1.0" +torch_geometric = "^2.5.2" +torchvision = "^0.16.0" +torchmetrics="^1.3.0" +torch-sparse = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp310-cp310-linux_x86_64.whl"} +torch-scatter = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp310-cp310-linux_x86_64.whl"} +neo4j-graphrag = {extras = ["ollama"], version="^1.6.1"} +langchain-community = "^0.3.21" +langchain-experimental = "^0.3.4" +langchain-neo4j = "^0.4.0" +langchain-openai = "^0.3.12" +transformers = {extras = ["torch"], version = "^4.50.3"} + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + diff --git a/Chapter13/setup_ollama.sh b/Chapter13/setup_ollama.sh new file mode 100755 index 0000000..beabc85 --- /dev/null +++ b/Chapter13/setup_ollama.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +curl -fsSL https://ollama.com/install.sh | sh + +ollama -v + +ollama pull minicpm-v +ollama pull nomic-embed-text + diff --git a/docker/Dockerfile b/docker/Dockerfile index 3530d5e..efb3daa 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -85,3 +85,10 @@ RUN ls -d -1 */ | grep -v -e Chapter10 | xargs rm -rf RUN conda create -n chap10 python=3.10 RUN conda run -n chap10 pip install -r Chapter10/requirements.txt RUN conda run -n chap10 python -m ipykernel install --name chap10 --user + +FROM base as chap13 +RUN ls -d -1 */ | grep -v -e Chapter13 | xargs rm -rf +RUN conda create -n chap13 python=3.10 +RUN conda run -n chap13 pip install -r Chapter13/requirements.txt +RUN conda run -n chap13 python -m ipykernel install --name chap13 --user +RUN /bin/bash ./Chapter13/setup_ollama.sh From 37c2dd41c4a592f8bf2d8c8e10682955b6d7d839 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 5 Apr 2025 15:11:38 +0200 Subject: [PATCH 03/15] fix deps and generate requirements.txt --- Chapter13/poetry.lock | 19 +++++- Chapter13/requirements.txt | 136 +++++++++++++++++++++++++++++++++++++ 2 files changed, 154 insertions(+), 1 deletion(-) create mode 100644 Chapter13/requirements.txt diff --git a/Chapter13/poetry.lock b/Chapter13/poetry.lock index 27fc8d3..004e741 100644 --- a/Chapter13/poetry.lock +++ b/Chapter13/poetry.lock @@ -1859,6 +1859,7 @@ files = [ fsspec = ">=2024.9.0,<2025.0.0" json-repair = ">=0.39.1,<0.40.0" neo4j = ">=5.17.0,<6.0.0" +ollama = {version = ">=0.4.4,<0.5.0", optional = true, markers = "extra == \"ollama\""} pydantic = ">=2.6.3,<3.0.0" pypdf = ">=5.1.0,<6.0.0" pyyaml = ">=6.0.2,<7.0.0" @@ -2141,6 +2142,22 @@ files = [ {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"}, ] +[[package]] +name = "ollama" +version = "0.4.7" +description = "The official Python client for Ollama." +optional = false +python-versions = "<4.0,>=3.8" +groups = ["main"] +files = [ + {file = "ollama-0.4.7-py3-none-any.whl", hash = "sha256:85505663cca67a83707be5fb3aeff0ea72e67846cea5985529d8eca4366564a1"}, + {file = "ollama-0.4.7.tar.gz", hash = "sha256:891dcbe54f55397d82d289c459de0ea897e103b86a3f1fad0fdb1895922a75ff"}, +] + +[package.dependencies] +httpx = ">=0.27,<0.29" +pydantic = ">=2.9.0,<3.0.0" + [[package]] name = "openai" version = "1.70.0" @@ -4200,4 +4217,4 @@ cffi = ["cffi (>=1.11)"] [metadata] lock-version = "2.1" python-versions = "~3.10" -content-hash = "1cee11514cf70f92f362e0f608fc3800be3b4c024a7d06a0b0a47c7791939667" +content-hash = "9898bd5a26a13ee6d345251c4e789fd57b2f0f011f6c5e2c648d5c724144f47a" diff --git a/Chapter13/requirements.txt b/Chapter13/requirements.txt new file mode 100644 index 0000000..1e80cf4 --- /dev/null +++ b/Chapter13/requirements.txt @@ -0,0 +1,136 @@ +accelerate==1.6.0 ; python_version == "3.10" +aiohappyeyeballs==2.6.1 ; python_version == "3.10" +aiohttp==3.11.16 ; python_version == "3.10" +aiosignal==1.3.2 ; python_version == "3.10" +annotated-types==0.7.0 ; python_version == "3.10" +anyio==4.9.0 ; python_version == "3.10" +appnope==0.1.4 ; platform_system == "Darwin" and python_version == "3.10" +asttokens==3.0.0 ; python_version == "3.10" +async-timeout==4.0.3 ; python_version == "3.10" +attrs==25.3.0 ; python_version == "3.10" +certifi==2025.1.31 ; python_version == "3.10" +cffi==1.17.1 ; (implementation_name == "pypy" or platform_python_implementation == "PyPy") and python_version == "3.10" +charset-normalizer==3.4.1 ; python_version == "3.10" +colorama==0.4.6 ; python_version == "3.10" and (sys_platform == "win32" or platform_system == "Windows") +comm==0.2.2 ; python_version == "3.10" +cycler==0.12.1 ; python_version == "3.10" +dataclasses-json==0.6.7 ; python_version == "3.10" +debugpy==1.8.13 ; python_version == "3.10" +decorator==5.2.1 ; python_version == "3.10" +distro==1.9.0 ; python_version == "3.10" +exceptiongroup==1.2.2 ; python_version == "3.10" +executing==2.2.0 ; python_version == "3.10" +filelock==3.18.0 ; python_version == "3.10" +frozenlist==1.5.0 ; python_version == "3.10" +fsspec==2024.12.0 ; python_version == "3.10" +greenlet==3.1.1 ; python_version == "3.10" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32") +h11==0.14.0 ; python_version == "3.10" +httpcore==1.0.7 ; python_version == "3.10" +httpx-sse==0.4.0 ; python_version == "3.10" +httpx==0.28.1 ; python_version == "3.10" +huggingface-hub==0.30.1 ; python_version == "3.10" +idna==3.10 ; python_version == "3.10" +ipykernel==6.29.5 ; python_version == "3.10" +ipython==8.34.0 ; python_version == "3.10" +jedi==0.19.2 ; python_version == "3.10" +jinja2==3.1.6 ; python_version == "3.10" +jiter==0.9.0 ; python_version == "3.10" +json-repair==0.39.1 ; python_version == "3.10" +jsonpatch==1.33 ; python_version == "3.10" +jsonpointer==3.0.0 ; python_version == "3.10" +jupyter-client==8.6.3 ; python_version == "3.10" +jupyter-core==5.7.2 ; python_version == "3.10" +kiwisolver==1.4.8 ; python_version == "3.10" +langchain-community==0.3.21 ; python_version == "3.10" +langchain-core==0.3.51 ; python_version == "3.10" +langchain-experimental==0.3.4 ; python_version == "3.10" +langchain-neo4j==0.4.0 ; python_version == "3.10" +langchain-openai==0.3.12 ; python_version == "3.10" +langchain-text-splitters==0.3.8 ; python_version == "3.10" +langchain==0.3.23 ; python_version == "3.10" +langsmith==0.3.24 ; python_version == "3.10" +lightning-utilities==0.14.3 ; python_version == "3.10" +markupsafe==3.0.2 ; python_version == "3.10" +marshmallow==3.26.1 ; python_version == "3.10" +matplotlib-inline==0.1.7 ; python_version == "3.10" +matplotlib==3.2.2 ; python_version == "3.10" +mpmath==1.3.0 ; python_version == "3.10" +multidict==6.3.2 ; python_version == "3.10" +mypy-extensions==1.0.0 ; python_version == "3.10" +neo4j-graphrag==1.6.1 ; python_version == "3.10" +neo4j-graphrag[ollama]==1.6.1 ; python_version == "3.10" +neo4j==5.28.1 ; python_version == "3.10" +nest-asyncio==1.6.0 ; python_version == "3.10" +networkx==3.4.2 ; python_version == "3.10" +numpy==2.2.4 ; python_version == "3.10" +nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-nvjitlink-cu12==12.8.93 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +ollama==0.4.7 ; python_version == "3.10" +openai==1.70.0 ; python_version == "3.10" +orjson==3.10.16 ; platform_python_implementation != "PyPy" and python_version == "3.10" +packaging==24.2 ; python_version == "3.10" +parso==0.8.4 ; python_version == "3.10" +pexpect==4.9.0 ; sys_platform != "win32" and sys_platform != "emscripten" and python_version == "3.10" +pillow==11.1.0 ; python_version == "3.10" +platformdirs==4.3.7 ; python_version == "3.10" +prompt-toolkit==3.0.50 ; python_version == "3.10" +propcache==0.3.1 ; python_version == "3.10" +psutil==7.0.0 ; python_version == "3.10" +ptyprocess==0.7.0 ; sys_platform != "win32" and sys_platform != "emscripten" and python_version == "3.10" +pure-eval==0.2.3 ; python_version == "3.10" +pycparser==2.22 ; (implementation_name == "pypy" or platform_python_implementation == "PyPy") and python_version == "3.10" +pydantic-core==2.33.1 ; python_version == "3.10" +pydantic-settings==2.8.1 ; python_version == "3.10" +pydantic==2.11.2 ; python_version == "3.10" +pygments==2.19.1 ; python_version == "3.10" +pyparsing==3.2.3 ; python_version == "3.10" +pypdf==5.4.0 ; python_version == "3.10" +python-dateutil==2.9.0.post0 ; python_version == "3.10" +python-dotenv==1.1.0 ; python_version == "3.10" +pytz==2025.2 ; python_version == "3.10" +pywin32==310 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version == "3.10" +pyyaml==6.0.2 ; python_version == "3.10" +pyzmq==26.4.0 ; python_version == "3.10" +regex==2024.11.6 ; python_version == "3.10" +requests-toolbelt==1.0.0 ; python_version == "3.10" +requests==2.32.3 ; python_version == "3.10" +safetensors==0.5.3 ; python_version == "3.10" +scipy==1.15.2 ; python_version == "3.10" +setuptools==78.1.0 ; python_version == "3.10" +six==1.17.0 ; python_version == "3.10" +sniffio==1.3.1 ; python_version == "3.10" +sqlalchemy==2.0.40 ; python_version == "3.10" +stack-data==0.6.3 ; python_version == "3.10" +sympy==1.13.3 ; python_version == "3.10" +tenacity==9.1.2 ; python_version == "3.10" +tiktoken==0.9.0 ; python_version == "3.10" +tokenizers==0.21.1 ; python_version == "3.10" +torch-geometric==2.6.1 ; python_version == "3.10" +torch-scatter @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp310-cp310-linux_x86_64.whl ; python_version == "3.10" +torch-sparse @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp310-cp310-linux_x86_64.whl ; python_version == "3.10" +torch==2.1.2 ; python_version == "3.10" +torchmetrics==1.7.0 ; python_version == "3.10" +torchvision==0.16.2 ; python_version == "3.10" +tornado==6.4.2 ; python_version == "3.10" +tqdm==4.67.1 ; python_version == "3.10" +traitlets==5.14.3 ; python_version == "3.10" +transformers[torch]==4.50.3 ; python_version == "3.10" +triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +types-pyyaml==6.0.12.20250402 ; python_version == "3.10" +typing-extensions==4.13.1 ; python_version == "3.10" +typing-inspect==0.9.0 ; python_version == "3.10" +typing-inspection==0.4.0 ; python_version == "3.10" +urllib3==2.3.0 ; python_version == "3.10" +wcwidth==0.2.13 ; python_version == "3.10" +yarl==1.18.3 ; python_version == "3.10" +zstandard==0.23.0 ; python_version == "3.10" From b2c9e7e2ec826afaccc81a1c1d99de889e040a80 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 5 Apr 2025 15:23:55 +0200 Subject: [PATCH 04/15] sudo privileges to install ollama --- Chapter13/setup_ollama.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Chapter13/setup_ollama.sh b/Chapter13/setup_ollama.sh index beabc85..1571c39 100755 --- a/Chapter13/setup_ollama.sh +++ b/Chapter13/setup_ollama.sh @@ -1,6 +1,6 @@ #!/bin/bash -curl -fsSL https://ollama.com/install.sh | sh +curl -fsSL https://ollama.com/install.sh | sudo sh ollama -v From 67d2fa48670e4a450cb5fbb1381f08490cabcce5 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 5 Apr 2025 23:45:05 +0200 Subject: [PATCH 05/15] fix --- .github/workflows/ci.yaml | 62 +- Chapter13/LMM_and_Graphs.ipynb | 2989 +------------------------------- docker/Dockerfile | 2 +- 3 files changed, 110 insertions(+), 2943 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index f98bb50..0d3b5ac 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -15,26 +15,28 @@ jobs: max-parallel: 5 matrix: chapter: - - name: chap1 - folder: Chapter01 - - name: chap2 - folder: Chapter02 - - name: chap3 - folder: Chapter03 - - name: chap4 - folder: Chapter04 - - name: chap5 - folder: Chapter05 - - name: chap6 - folder: Chapter06 - - name: chap7 - folder: Chapter07 - - name: chap8 - folder: Chapter08 - - name: chap9 - folder: Chapter09 - - name: chap10 - folder: Chapter10 +# - name: chap1 +# folder: Chapter01 +# - name: chap2 +# folder: Chapter02 +# - name: chap3 +# folder: Chapter03 +# - name: chap4 +# folder: Chapter04 +# - name: chap5 +# folder: Chapter05 +# - name: chap6 +# folder: Chapter06 +# - name: chap7 +# folder: Chapter07 +# - name: chap8 +# folder: Chapter08 +# - name: chap9 +# folder: Chapter09 +# - name: chap10 +# folder: Chapter10 + - name: chap13 + folder: Chapter13 runs-on: ubuntu-latest name: Image ${{ matrix.chapter.name }} steps: @@ -80,6 +82,25 @@ jobs: docker network connect my-network neo4j fi + # Start Neo4j if we are testing chapter 13 + if [ "${{ matrix.chapter.name }}" == "chap13" ]; + then + docker run --rm --detach --name neo4j \ + --publish=7474:7474 --publish=7687:7687 \ + --user="$(id -u):$(id -g)" \ + --env NEO4J_AUTH=none \ + --env NEO4J_PLUGINS='["graph-data-science"]' \ + neo4j:5.26.0 + docker network connect my-network neo4j + + docker run --rm --detach --name ollama \ + --publish=11434:11434 \ + --volume olama:/root/.ollama \ + ollama/ollama:0.6.4 + docker network connect my-network ollama + fi + + mkdir -p data chmod -R 777 data docker run \ @@ -89,6 +110,7 @@ jobs: --env KAGGLE_KEY=${KAGGLE_TOKEN} \ --env NEO4J_HOST=neo4j \ --env JANUSGRAPH_HOST=janusgraph \ + --env OLLAMA_HOST=ollama \ graph-machine-learning:latest docker network connect my-network graph-machine-learning-box diff --git a/Chapter13/LMM_and_Graphs.ipynb b/Chapter13/LMM_and_Graphs.ipynb index 63a468a..5656140 100644 --- a/Chapter13/LMM_and_Graphs.ipynb +++ b/Chapter13/LMM_and_Graphs.ipynb @@ -1,38 +1,10 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "vWNgkL71ALf7" - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "%%capture\n", - "\n", - "# install PyG if running on Google Colab\n", - "import sys\n", - "import torch\n", - "\n", - "if 'google.colab' in sys.modules:\n", - "\n", - " def format_pytorch_version(version):\n", - " return version.split('+')[0]\n", - "\n", - " TORCH_version = torch.__version__\n", - " TORCH = format_pytorch_version(TORCH_version)\n", - "\n", - " def format_cuda_version(version):\n", - " return 'cu' + version.replace('.', '')\n", - "\n", - " CUDA_version = torch.version.cuda\n", - " CUDA = format_cuda_version(CUDA_version)\n", - "\n", - " !pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", - " !pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", - " !pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", - " !pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", - " !pip install torch-geometric" + "# LMM and Graphs" ] }, { @@ -344,12 +316,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 38, "metadata": { "id": "JKEORJfWwI7w" }, "outputs": [], "source": [ + "import os\n", "import sys\n", "\n", "LLM_BACKEND = \"ollama\" # choose [\"ollama\" | \"lm-studio\"]\n", @@ -358,10 +331,10 @@ "assert LLM_BACKEND in [\"ollama\", \"lm-studio\"]\n", "\n", "if LLM_BACKEND == \"ollama\":\n", - " base_url = \"http://localhost:11434/v1\"\n", + " base_url = f\"http://{os.environ.get('OLLAMA_HOST', 'localhost')}:11434/v1\"\n", " api_key = \"ollama\"\n", - " # llm_model = \"phi4\"\n", - " llm_model = \"minicpm-v\"\n", + " # llm_model = \"minicpm-v\"\n", + " llm_model = \"phi4\"\n", "else:\n", " base_url = \"http://localhost:1234/v1\"\n", " api_key = \"lm-studio\"\n", @@ -379,2786 +352,46 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ha3JtCsVj5ij", - "outputId": "bf15bcb3-cd93-45f4-912f-9dfa5a35d012" - }, + "execution_count": 39, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "nohup: redirecting stderr to stdout\n", - "HTTP/1.1 200 OK\n", - "\u001b[1mContent-Type\u001b[0m: text/plain; charset=utf-8\n", - "\u001b[1mDate\u001b[0m: Sun, 02 Feb 2025 21:36:50 GMT\n", - "\u001b[1mContent-Length\u001b[0m: 17\n", - "\n", - "Ollama is running\n", - "real\t0m0.009s\n", - "user\t0m0.005s\n", - "sys\t0m0.004s\n" - ] + "data": { + "text/plain": [ + "ProgressResponse(status='success', completed=None, total=None, digest=None)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "if 'google.colab' in sys.modules:\n", - " !curl https://ollama.ai/install.sh | sh\n", - " !nohup ollama serve > ollama.log &\n", - " !sleep 5\n", - " !time curl -i localhost:11434" + "import ollama\n", + "\n", + "# ollama.pull(llm_model)\n", + "# ollama.pull(\"nomic-embed-text\")\n", + "ollama.pull(\"phi4\")" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JPGUVFm_1eoB", - "outputId": "391fd502-f552-45df-d655-6b1af61a7830" - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[?25lpulling manifest ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 67% ▕▏ 696 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 67% ▕▏ 697 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 67% ▕▏ 697 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 67% ▕▏ 697 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 67% ▕▏ 700 MB/1.0 GB 175 MB/s 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\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", - "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", - "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", - "pulling f02dd72bb242... 100% ▕▏ 59 B \n", - "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", - "verifying sha256 digest ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", - "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", - "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", - "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", - "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", - "pulling 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"verifying sha256 digest \n", - "writing manifest \n", - "success \u001b[?25h\n", - "NAME ID SIZE MODIFIED \n", - "nomic-embed-text:latest 0a109f422b47 274 MB Less than a second ago \n", - "minicpm-v:latest c92bfad01205 5.5 GB 8 seconds ago \n", - "phi4:latest ac896e5b8b34 9.1 GB 17 minutes ago \n" - ] + "data": { + "text/plain": [ + "ListResponse(models=[Model(model='phi4:latest', modified_at=datetime.datetime(2025, 4, 5, 21, 8, 26, 809068, tzinfo=TzInfo(UTC)), digest='ac896e5b8b34a1f4efa7b14d7520725140d5512484457fab45d2a4ea14c69dba', size=9053116391, details=ModelDetails(parent_model='', format='gguf', family='phi3', families=['phi3'], parameter_size='14.7B', quantization_level='Q4_K_M')), Model(model='minicpm-v:latest', modified_at=datetime.datetime(2025, 4, 5, 21, 4, 17, 111987, tzinfo=TzInfo(UTC)), digest='c92bfad0120556eda311984f1ac2f0d0a589b8d68c4053c13486b526276aa205', size=5473838466, details=ModelDetails(parent_model='', format='gguf', family='qwen2', families=['qwen2', 'clip'], parameter_size='7.6B', quantization_level='Q4_0'))])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Dowload the language model 'minicpm-llama3-v-2_5' and the text embedder\n", - "# WARNING: models are > 5GB\n", - "if LLM_BACKEND == \"ollama\":\n", - " !ollama pull minicpm-v\n", - " !ollama pull nomic-embed-text\n", - " !ollama list" + "ollama.list()" ] }, { @@ -3172,94 +405,7 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "r53ouOX_M3of", - "outputId": "6ab70449-f250-4216-b0f5-66e6147cd1ae" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update-alternatives: error: alternative /usr/lib/jvm/java-17-openjdk-amd64/jre/bin/java for java not registered; not setting\n", - "openjdk version \"17.0.13\" 2024-10-15\n", - "OpenJDK Runtime Environment (build 17.0.13+11-Ubuntu-2ubuntu122.04)\n", - "OpenJDK 64-Bit Server VM (build 17.0.13+11-Ubuntu-2ubuntu122.04, mixed mode, sharing)\n", - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 151M 100 151M 0 0 217M 0 --:--:-- --:--:-- --:--:-- 217M\n", - "Directories in use:\n", - "home: /content/nj\n", - "config: /content/nj/conf\n", - "logs: /content/nj/logs\n", - "plugins: /content/nj/plugins\n", - "import: /content/nj/import\n", - "data: /content/nj/data\n", - "certificates: /content/nj/certificates\n", - "licenses: /content/nj/licenses\n", - "run: /content/nj/run\n", - "Starting Neo4j.\n", - "Started neo4j (pid:8890). It is available at http://localhost:7474\n", - "There may be a short delay until the server is ready.\n" - ] - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "if 'google.colab' in sys.modules:\n", - " !pip install -q \"neo4j-graphrag[ollama]\"\n", - " !pip install -q langchain_community langchain_experimental langchain_neo4j langchain_openai\n", - "\n", - " # Install the JDK\n", - " !apt-get install openjdk-17-jdk-headless -qq > /dev/null\n", - " os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-17-openjdk-amd64\"\n", - " !update-alternatives --set java /usr/lib/jvm/java-17-openjdk-amd64/jre/bin/java\n", - " !java -version\n", - "\n", - " # Download Neo4j\n", - " !curl -O https://dist.neo4j.org/neo4j-community-5.26.1-unix.tar.gz\n", - " # Decompress and rename the directory\n", - " !tar -xf neo4j-community-5.26.1-unix.tar.gz\n", - " !mv neo4j-community-5.26.1 nj\n", - "\n", - " # (Optional) Disable authentication (if desired)\n", - " !sed -i '/#dbms.security.auth_enabled/s/^#//g' nj/conf/neo4j.conf\n", - "\n", - " # Copy the APOC jar from the labs directory to plugins\n", - " !cp nj/labs/apoc-*.jar nj/plugins/\n", - "\n", - " # Update configuration to allow APOC procedures\n", - " !echo \"dbms.security.procedures.unrestricted=apoc.*\" >> nj/conf/neo4j.conf\n", - " !echo \"dbms.security.procedures.allowlist=apoc.*\" >> nj/conf/neo4j.conf\n", - "\n", - " # Start Neo4j\n", - " !nj/bin/neo4j start" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uII2aMpvmT74" - }, - "outputs": [], - "source": [ - "# colab may kill the ollama process, start again\n", - "if 'google.colab' in sys.modules:\n", - " !nohup ollama serve > ollama.log &\n", - " !sleep 5\n", - " !time curl -i localhost:11434" - ] - }, - { - "cell_type": "code", - "execution_count": 14, + "execution_count": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3272,54 +418,53 @@ "name": "stdout", "output_type": "stream", "text": [ - "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={})]\n", - "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='CO-WINNER_OF_NOBEL_PRIZE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel_Prize', type='Award', properties={}), type='RECIPIENT_OF', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel_Prize', type='Award', properties={}), type='RECIPIENT_OF', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University_Of_Paris', type='Institution', properties={}), type='PROFESSOR_AT', properties={})]\n", + "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='1867', type='Date', properties={}), Node(id='Polish And Naturalised-French', type='Nationality', properties={}), Node(id='Physicist', type='Profession', properties={}), Node(id='Chemist', type='Profession', properties={}), Node(id='Radioactivity', type='Concept', properties={}), Node(id='Nobel Prize', type='Award', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]\n", + "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='1867', type='Date', properties={}), type='BORN_IN_YEAR', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Polish And Naturalised-French', type='Nationality', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Physicist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Chemist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Radioactivity', type='Concept', properties={}), type='RESEARCHED_ON', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARDED', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Marie Curie', type='Person', properties={}), type='SPOUSE_OF', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='CO-WINNER_OF', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='PROFESSOR_AT', properties={})]\n", "\n", "Testing queries...\n", "\n", "Question: Who married a Nobel Prize?\n", "\n", "\n", - "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", - "Generated Cypher:\n", - "\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:CO-WINNER_OF_NOBEL_PRIZE]->(n:Award)\n", - "RETURN n.id AS nobel_prize_winner, p.id AS person_id\u001b[0m\n" + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n" ] }, { - "ename": "CypherSyntaxError", - "evalue": "{code: Neo.ClientError.Statement.SyntaxError} {message: Invalid input '-': expected a parameter, '&', '*', ':', 'WHERE', ']', '{' or '|' (line 1, column 22 (offset: 21))\n\"MATCH (p:Person)-[:CO-WINNER_OF_NOBEL_PRIZE]->(n:Award)\"\n ^}", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mCypherSyntaxError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 82\u001b[0m\n\u001b[1;32m 79\u001b[0m question \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWho married a Nobel Prize?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mQuestion: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquestion\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 82\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResponse:\u001b[39m\u001b[38;5;124m\"\u001b[39m, response[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# Close the driver\u001b[39;00m\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/langchain/chains/base.py:170\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 168\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 169\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 170\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 171\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m include_run_info:\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/langchain/chains/base.py:160\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_inputs(inputs)\n\u001b[1;32m 159\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 160\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[1;32m 163\u001b[0m )\n\u001b[1;32m 165\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[1;32m 166\u001b[0m inputs, outputs, return_only_outputs\n\u001b[1;32m 167\u001b[0m )\n\u001b[1;32m 168\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/langchain_neo4j/chains/graph_qa/cypher.py:356\u001b[0m, in \u001b[0;36mGraphCypherQAChain._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[38;5;66;03m# Retrieve and limit the number of results\u001b[39;00m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;66;03m# Generated Cypher be null if query corrector identifies invalid schema\u001b[39;00m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m generated_cypher:\n\u001b[0;32m--> 356\u001b[0m context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerated_cypher\u001b[49m\u001b[43m)\u001b[49m[: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtop_k]\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 358\u001b[0m context \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/langchain_neo4j/graphs/neo4j_graph.py:230\u001b[0m, in \u001b[0;36mNeo4jGraph.query\u001b[0;34m(self, query, params, session_params)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m session_params:\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 230\u001b[0m data, _, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_driver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 231\u001b[0m \u001b[43m \u001b[49m\u001b[43mQuery\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[43m \u001b[49m\u001b[43mdatabase_\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_database\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 233\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 234\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 235\u001b[0m json_data \u001b[38;5;241m=\u001b[39m [r\u001b[38;5;241m.\u001b[39mdata() \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m data]\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msanitize:\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/driver.py:970\u001b[0m, in \u001b[0;36mDriver.execute_query\u001b[0;34m(self, query_, parameters_, routing_, database_, impersonated_user_, bookmark_manager_, auth_, result_transformer_, **kwargs)\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 967\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid routing control value: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrouting_\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 968\u001b[0m )\n\u001b[1;32m 969\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m session\u001b[38;5;241m.\u001b[39m_pipelined_begin:\n\u001b[0;32m--> 970\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_transaction\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 971\u001b[0m \u001b[43m \u001b[49m\u001b[43maccess_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 972\u001b[0m \u001b[43m \u001b[49m\u001b[43mTelemetryAPI\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDRIVER\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 973\u001b[0m \u001b[43m \u001b[49m\u001b[43mwork\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 974\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_str\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult_transformer_\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 975\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 976\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/session.py:583\u001b[0m, in \u001b[0;36mSession._run_transaction\u001b[0;34m(self, access_mode, api, transaction_function, args, kwargs)\u001b[0m\n\u001b[1;32m 581\u001b[0m tx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transaction\n\u001b[1;32m 582\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 583\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mtransaction_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mCancelledError:\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# if cancellation callback has not been called yet:\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transaction \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_work/query.py:144\u001b[0m, in \u001b[0;36munit_of_work..wrapper..wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/driver.py:1306\u001b[0m, in \u001b[0;36m_work\u001b[0;34m(tx, query, parameters, transformer)\u001b[0m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_work\u001b[39m(\n\u001b[1;32m 1301\u001b[0m tx: ManagedTransaction,\n\u001b[1;32m 1302\u001b[0m query: te\u001b[38;5;241m.\u001b[39mLiteralString,\n\u001b[1;32m 1303\u001b[0m parameters: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, t\u001b[38;5;241m.\u001b[39mAny],\n\u001b[1;32m 1304\u001b[0m transformer: t\u001b[38;5;241m.\u001b[39mCallable[[Result], t\u001b[38;5;241m.\u001b[39mUnion[_T]],\n\u001b[1;32m 1305\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m _T:\n\u001b[0;32m-> 1306\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mtx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1307\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m transformer(res)\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/transaction.py:206\u001b[0m, in \u001b[0;36mTransactionBase.run\u001b[0;34m(self, query, parameters, **kwparameters)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_results\u001b[38;5;241m.\u001b[39mappend(result)\n\u001b[1;32m 205\u001b[0m parameters \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(parameters \u001b[38;5;129;01mor\u001b[39;00m {}, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwparameters)\n\u001b[0;32m--> 206\u001b[0m \u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tx_ready_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/result.py:177\u001b[0m, in \u001b[0;36mResult._tx_ready_run\u001b[0;34m(self, query, parameters)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_tx_ready_run\u001b[39m(\u001b[38;5;28mself\u001b[39m, query, parameters):\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# BEGIN+RUN does not carry any extra on the RUN message.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;66;03m# BEGIN {extra}\u001b[39;00m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;66;03m# RUN \"query\" {parameters} {extra}\u001b[39;00m\n\u001b[0;32m--> 177\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/result.py:236\u001b[0m, in \u001b[0;36mResult._run\u001b[0;34m(self, query, parameters, db, imp_user, access_mode, bookmarks, notifications_min_severity, notifications_disabled_classifications)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pull()\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39msend_all()\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_attach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/work/result.py:430\u001b[0m, in \u001b[0;36mResult._attach\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exhausted \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attached \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/io/_common.py:184\u001b[0m, in \u001b[0;36mConnectionErrorHandler.__getattr__..outer..inner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21minner\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 184\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (Neo4jError, ServiceUnavailable, SessionExpired) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39miscoroutinefunction(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__on_error)\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/io/_bolt.py:864\u001b[0m, in \u001b[0;36mBolt.fetch_message\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 860\u001b[0m \u001b[38;5;66;03m# Receive exactly one message\u001b[39;00m\n\u001b[1;32m 861\u001b[0m tag, fields \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minbox\u001b[38;5;241m.\u001b[39mpop(\n\u001b[1;32m 862\u001b[0m hydration_hooks\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresponses[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mhydration_hooks\n\u001b[1;32m 863\u001b[0m )\n\u001b[0;32m--> 864\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtag\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midle_since \u001b[38;5;241m=\u001b[39m monotonic()\n\u001b[1;32m 866\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/io/_bolt5.py:1208\u001b[0m, in \u001b[0;36mBolt5x7._process_message\u001b[0;34m(self, tag, fields)\u001b[0m\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enrich_error_diagnostic_record(summary_metadata)\n\u001b[1;32m 1207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1208\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_failure\u001b[49m\u001b[43m(\u001b[49m\u001b[43msummary_metadata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ServiceUnavailable, DatabaseUnavailable):\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpool:\n", - "File \u001b[0;32m~/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/neo4j/_sync/io/_common.py:254\u001b[0m, in \u001b[0;36mResponse.on_failure\u001b[0;34m(self, metadata)\u001b[0m\n\u001b[1;32m 252\u001b[0m handler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandlers\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_summary\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 253\u001b[0m Util\u001b[38;5;241m.\u001b[39mcallback(handler)\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hydrate_error(metadata)\n", - "\u001b[0;31mCypherSyntaxError\u001b[0m: {code: Neo.ClientError.Statement.SyntaxError} {message: Invalid input '-': expected a parameter, '&', '*', ':', 'WHERE', ']', '{' or '|' (line 1, column 22 (offset: 21))\n\"MATCH (p:Person)-[:CO-WINNER_OF_NOBEL_PRIZE]->(n:Award)\"\n ^}" + "name": "stderr", + "output_type": "stream", + "text": [ + "Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.UnknownPropertyKeyWarning} {category: UNRECOGNIZED} {title: The provided property key is not in the database} {description: One of the property names in your query is not available in the database, make sure you didn't misspell it or that the label is available when you run this statement in your application (the missing property name is: name)} {position: line: 2, column: 54, offset: 60} for query: 'cypher\\nMATCH (person:Person)-[:RECIPIENT_OF]->(award:Award {name: \"Nobel Prize\"})\\nMATCH (spouse:Person)-[:MARRIED_TO]->(person)\\nRETURN DISTINCT spouse.id AS marriedToNobelPrizeWinner\\n'\n", + "Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.UnknownRelationshipTypeWarning} {category: UNRECOGNIZED} {title: The provided relationship type is not in the database.} {description: One of the relationship types in your query is not available in the database, make sure you didn't misspell it or that the label is available when you run this statement in your application (the missing relationship type is: MARRIED_TO)} {position: line: 3, column: 25, offset: 106} for query: 'cypher\\nMATCH (person:Person)-[:RECIPIENT_OF]->(award:Award {name: \"Nobel Prize\"})\\nMATCH (spouse:Person)-[:MARRIED_TO]->(person)\\nRETURN DISTINCT spouse.id AS marriedToNobelPrizeWinner\\n'\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mcypher\n", + "MATCH (person:Person)-[:RECIPIENT_OF]->(award:Award {name: \"Nobel Prize\"})\n", + "MATCH (spouse:Person)-[:MARRIED_TO]->(person)\n", + "RETURN DISTINCT spouse.id AS marriedToNobelPrizeWinner\n", + "\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "Response: I don't know the answer.\n" ] } ], "source": [ + "import os\n", + "\n", "from neo4j import GraphDatabase\n", "from langchain_neo4j import Neo4jGraph\n", "\n", + "host = os.environ.get(\"NEO4J_HOST\", \"localhost\")\n", + "\n", "# ---- Step 1: Setup Neo4j Connection ----\n", - "NEO4J_URI = \"bolt://localhost:7687\"\n", + "NEO4J_URI = f\"bolt://{host}:7687\"\n", "NEO4J_USER = \"neo4j\"\n", "NEO4J_PASSWORD = \"neo5j\"\n", "\n", @@ -3420,9 +565,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "graph-machine-learning-310", + "display_name": "chap13", "language": "python", - "name": "graph-machine-learning-310" + "name": "chap13" }, "language_info": { "codemirror_mode": { diff --git a/docker/Dockerfile b/docker/Dockerfile index efb3daa..705501f 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -91,4 +91,4 @@ RUN ls -d -1 */ | grep -v -e Chapter13 | xargs rm -rf RUN conda create -n chap13 python=3.10 RUN conda run -n chap13 pip install -r Chapter13/requirements.txt RUN conda run -n chap13 python -m ipykernel install --name chap13 --user -RUN /bin/bash ./Chapter13/setup_ollama.sh +# RUN /bin/bash ./Chapter13/setup_ollama.sh From 5f3891617a9a7648ca7f977fe5427d8e75c90553 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 5 Apr 2025 23:46:48 +0200 Subject: [PATCH 06/15] fix ci --- .github/workflows/ci.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 0d3b5ac..55d1d9e 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -110,7 +110,7 @@ jobs: --env KAGGLE_KEY=${KAGGLE_TOKEN} \ --env NEO4J_HOST=neo4j \ --env JANUSGRAPH_HOST=janusgraph \ - --env OLLAMA_HOST=ollama \ + --env OLLAMA_HOST=ollama \ graph-machine-learning:latest docker network connect my-network graph-machine-learning-box From 31c3dfef9b214e8aef9198275d4e2df4f0cedd02 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 5 Apr 2025 23:59:17 +0200 Subject: [PATCH 07/15] freeing space --- .github/workflows/ci.yaml | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 55d1d9e..772129f 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -48,6 +48,17 @@ jobs: run: echo "branch=${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}" >> $GITHUB_OUTPUT id: extract_branch + - name: (GitHub hosted) Free up disk space + shell: bash + run: | + printf '\nDisk usage before cleanup\n' + df --human-readable + # Based on https://github.com/actions/runner-images/issues/2840#issuecomment-790492173 + rm -r /usr/share/dotnet + rm -r /opt/hostedtoolcache/ + printf '\nDisk usage after cleanup\n' + df --human-readable + - name: Build Image id: build run: | From ef9c4195d1776b448f69248554af4e524dd73390 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sun, 6 Apr 2025 00:00:34 +0200 Subject: [PATCH 08/15] use jammy --- .github/workflows/ci.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 772129f..512788f 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -37,7 +37,7 @@ jobs: # folder: Chapter10 - name: chap13 folder: Chapter13 - runs-on: ubuntu-latest + runs-on: ubuntu-22.04 name: Image ${{ matrix.chapter.name }} steps: - name: Checkout repository From a36451c62ed2543aef2e7ee94160d69448ed955c Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sun, 6 Apr 2025 00:16:21 +0200 Subject: [PATCH 09/15] enable apoc plugins --- .github/workflows/ci.yaml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 512788f..2df7d00 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -100,8 +100,11 @@ jobs: --publish=7474:7474 --publish=7687:7687 \ --user="$(id -u):$(id -g)" \ --env NEO4J_AUTH=none \ - --env NEO4J_PLUGINS='["graph-data-science"]' \ - neo4j:5.26.0 + --env NEO4J_PLUGINS='["apoc-extended"]' \ + --env NEO4J_apoc_export_file_enabled=true \ + --env NEO4J_apoc_import_file_enabled=true \ + --env NEO4J_apoc_import_file_use__neo4j_config=true \ + neo4j:5.26.0 docker network connect my-network neo4j docker run --rm --detach --name ollama \ From a8e772b058ffee64a633ee5e50f31a973ff3368b Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sun, 6 Apr 2025 00:19:18 +0200 Subject: [PATCH 10/15] fix format --- .github/workflows/ci.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 2df7d00..206b4e7 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -104,7 +104,7 @@ jobs: --env NEO4J_apoc_export_file_enabled=true \ --env NEO4J_apoc_import_file_enabled=true \ --env NEO4J_apoc_import_file_use__neo4j_config=true \ - neo4j:5.26.0 + neo4j:5.26.0 docker network connect my-network neo4j docker run --rm --detach --name ollama \ From c5528cae41f57bb9c48b1927c266f3c2003ba3b1 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sun, 6 Apr 2025 00:28:14 +0200 Subject: [PATCH 11/15] add apoc --- .github/workflows/ci.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 206b4e7..554b529 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -100,7 +100,7 @@ jobs: --publish=7474:7474 --publish=7687:7687 \ --user="$(id -u):$(id -g)" \ --env NEO4J_AUTH=none \ - --env NEO4J_PLUGINS='["apoc-extended"]' \ + --env NEO4J_PLUGINS='["apoc","apoc-extended"]' \ --env NEO4J_apoc_export_file_enabled=true \ --env NEO4J_apoc_import_file_enabled=true \ --env NEO4J_apoc_import_file_use__neo4j_config=true \ From 1167758db0becc151f4e6e85ccbe317a1150ca5e Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 31 May 2025 15:20:12 +0200 Subject: [PATCH 12/15] renaming chapter13 into chapter12 --- .github/workflows/ci.yaml | 46 +++++++++---------- .../LLM_and_Graphs.ipynb | 4 +- {Chapter13 => Chapter12}/poetry.lock | 0 {Chapter13 => Chapter12}/pyproject.toml | 0 {Chapter13 => Chapter12}/requirements.txt | 0 {Chapter13 => Chapter12}/setup_ollama.sh | 0 docker/Dockerfile | 12 ++--- 7 files changed, 31 insertions(+), 31 deletions(-) rename Chapter13/LMM_and_Graphs.ipynb => Chapter12/LLM_and_Graphs.ipynb (99%) rename {Chapter13 => Chapter12}/poetry.lock (100%) rename {Chapter13 => Chapter12}/pyproject.toml (100%) rename {Chapter13 => Chapter12}/requirements.txt (100%) rename {Chapter13 => Chapter12}/setup_ollama.sh (100%) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 554b529..4c087f4 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -15,28 +15,28 @@ jobs: max-parallel: 5 matrix: chapter: -# - name: chap1 -# folder: Chapter01 -# - name: chap2 -# folder: Chapter02 -# - name: chap3 -# folder: Chapter03 -# - name: chap4 -# folder: Chapter04 -# - name: chap5 -# folder: Chapter05 -# - name: chap6 -# folder: Chapter06 -# - name: chap7 -# folder: Chapter07 -# - name: chap8 -# folder: Chapter08 -# - name: chap9 -# folder: Chapter09 -# - name: chap10 -# folder: Chapter10 - - name: chap13 - folder: Chapter13 + - name: chap1 + folder: Chapter01 + - name: chap2 + folder: Chapter02 + - name: chap3 + folder: Chapter03 + - name: chap4 + folder: Chapter04 + - name: chap5 + folder: Chapter05 + - name: chap6 + folder: Chapter06 + - name: chap7 + folder: Chapter07 + - name: chap8 + folder: Chapter08 + - name: chap9 + folder: Chapter09 + - name: chap10 + folder: Chapter10 + - name: chap12 + folder: Chapter12 runs-on: ubuntu-22.04 name: Image ${{ matrix.chapter.name }} steps: @@ -94,7 +94,7 @@ jobs: fi # Start Neo4j if we are testing chapter 13 - if [ "${{ matrix.chapter.name }}" == "chap13" ]; + if [ "${{ matrix.chapter.name }}" == "chap12" ]; then docker run --rm --detach --name neo4j \ --publish=7474:7474 --publish=7687:7687 \ diff --git a/Chapter13/LMM_and_Graphs.ipynb b/Chapter12/LLM_and_Graphs.ipynb similarity index 99% rename from Chapter13/LMM_and_Graphs.ipynb rename to Chapter12/LLM_and_Graphs.ipynb index 5656140..31c79ee 100644 --- a/Chapter13/LMM_and_Graphs.ipynb +++ b/Chapter12/LLM_and_Graphs.ipynb @@ -565,9 +565,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "chap13", + "display_name": "chap12", "language": "python", - "name": "chap13" + "name": "chap12" }, "language_info": { "codemirror_mode": { diff --git a/Chapter13/poetry.lock b/Chapter12/poetry.lock similarity index 100% rename from Chapter13/poetry.lock rename to Chapter12/poetry.lock diff --git a/Chapter13/pyproject.toml b/Chapter12/pyproject.toml similarity index 100% rename from Chapter13/pyproject.toml rename to Chapter12/pyproject.toml diff --git a/Chapter13/requirements.txt b/Chapter12/requirements.txt similarity index 100% rename from Chapter13/requirements.txt rename to Chapter12/requirements.txt diff --git a/Chapter13/setup_ollama.sh b/Chapter12/setup_ollama.sh similarity index 100% rename from Chapter13/setup_ollama.sh rename to Chapter12/setup_ollama.sh diff --git a/docker/Dockerfile b/docker/Dockerfile index 705501f..14655ee 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -86,9 +86,9 @@ RUN conda create -n chap10 python=3.10 RUN conda run -n chap10 pip install -r Chapter10/requirements.txt RUN conda run -n chap10 python -m ipykernel install --name chap10 --user -FROM base as chap13 -RUN ls -d -1 */ | grep -v -e Chapter13 | xargs rm -rf -RUN conda create -n chap13 python=3.10 -RUN conda run -n chap13 pip install -r Chapter13/requirements.txt -RUN conda run -n chap13 python -m ipykernel install --name chap13 --user -# RUN /bin/bash ./Chapter13/setup_ollama.sh +FROM base as chap12 +RUN ls -d -1 */ | grep -v -e Chapter12 | xargs rm -rf +RUN conda create -n chap12 python=3.10 +RUN conda run -n chap12 pip install -r Chapter12/requirements.txt +RUN conda run -n chap12 python -m ipykernel install --name chap12 --user +# RUN /bin/bash ./Chapter12/setup_ollama.sh From 58c3d11c49575e07627251ba4bb5f7dbedc2ef01 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 21 Jun 2025 15:17:43 +0200 Subject: [PATCH 13/15] update requirements to use numpy<2.0 --- Chapter12/poetry.lock | 2855 ++++++++++++++++++------------------ Chapter12/pyproject.toml | 2 +- Chapter12/requirements.txt | 101 +- 3 files changed, 1508 insertions(+), 1450 deletions(-) diff --git a/Chapter12/poetry.lock b/Chapter12/poetry.lock index 004e741..2485f78 100644 --- a/Chapter12/poetry.lock +++ b/Chapter12/poetry.lock @@ -2,18 +2,18 @@ [[package]] name = "accelerate" -version = "1.6.0" +version = "1.8.1" description = "Accelerate" optional = false python-versions = ">=3.9.0" groups = ["main"] files = [ - {file = "accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa"}, - {file = "accelerate-1.6.0.tar.gz", hash = "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32"}, + {file = "accelerate-1.8.1-py3-none-any.whl", hash = "sha256:c47b8994498875a2b1286e945bd4d20e476956056c7941d512334f4eb44ff991"}, + {file = "accelerate-1.8.1.tar.gz", hash = "sha256:f60df931671bc4e75077b852990469d4991ce8bd3a58e72375c3c95132034db9"}, ] [package.dependencies] -huggingface-hub = ">=0.21.0" +huggingface_hub = ">=0.21.0" numpy = ">=1.17,<3.0.0" packaging = ">=20.0" psutil = "*" @@ -28,8 +28,9 @@ quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.11.2,< rich = ["rich"] sagemaker = ["sagemaker"] test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +test-fp8 = ["torchao"] test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"] -test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "tensorboard", "wandb"] +test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "swanlab", "tensorboard", "wandb"] testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] 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python_version == "3.10" -jiter==0.9.0 ; python_version == "3.10" +jiter==0.10.0 ; python_version == "3.10" json-repair==0.39.1 ; python_version == "3.10" jsonpatch==1.33 ; python_version == "3.10" jsonpointer==3.0.0 ; python_version == "3.10" jupyter-client==8.6.3 ; python_version == "3.10" -jupyter-core==5.7.2 ; python_version == "3.10" +jupyter-core==5.8.1 ; python_version == "3.10" kiwisolver==1.4.8 ; python_version == "3.10" -langchain-community==0.3.21 ; python_version == "3.10" -langchain-core==0.3.51 ; python_version == "3.10" +langchain-community==0.3.26 ; python_version == "3.10" +langchain-core==0.3.66 ; python_version == "3.10" langchain-experimental==0.3.4 ; python_version == "3.10" langchain-neo4j==0.4.0 ; python_version == "3.10" -langchain-openai==0.3.12 ; python_version == "3.10" +langchain-openai==0.3.24 ; python_version == "3.10" langchain-text-splitters==0.3.8 ; python_version == "3.10" -langchain==0.3.23 ; python_version == "3.10" -langsmith==0.3.24 ; python_version == "3.10" +langchain==0.3.26 ; python_version == "3.10" +langsmith==0.4.1 ; python_version == "3.10" lightning-utilities==0.14.3 ; python_version == "3.10" markupsafe==3.0.2 ; python_version == "3.10" marshmallow==3.26.1 ; python_version == "3.10" matplotlib-inline==0.1.7 ; python_version == "3.10" matplotlib==3.2.2 ; python_version == "3.10" mpmath==1.3.0 ; python_version == "3.10" -multidict==6.3.2 ; python_version == "3.10" -mypy-extensions==1.0.0 ; python_version == "3.10" -neo4j-graphrag==1.6.1 ; python_version == "3.10" -neo4j-graphrag[ollama]==1.6.1 ; python_version == "3.10" +multidict==6.5.0 ; python_version == "3.10" +mypy-extensions==1.1.0 ; python_version == "3.10" +neo4j-graphrag==1.7.0 ; python_version == "3.10" +neo4j-graphrag[ollama]==1.7.0 ; python_version == "3.10" neo4j==5.28.1 ; python_version == "3.10" nest-asyncio==1.6.0 ; python_version == "3.10" networkx==3.4.2 ; python_version == "3.10" -numpy==2.2.4 ; python_version == "3.10" +numpy==1.26.4 ; python_version == "3.10" nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" @@ -73,45 +74,45 @@ nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" -nvidia-nvjitlink-cu12==12.8.93 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +nvidia-nvjitlink-cu12==12.9.86 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" -ollama==0.4.7 ; python_version == "3.10" -openai==1.70.0 ; python_version == "3.10" -orjson==3.10.16 ; platform_python_implementation != "PyPy" and python_version == "3.10" +ollama==0.4.9 ; python_version == "3.10" +openai==1.90.0 ; python_version == "3.10" +orjson==3.10.18 ; platform_python_implementation != "PyPy" and python_version == "3.10" packaging==24.2 ; python_version == "3.10" parso==0.8.4 ; python_version == "3.10" pexpect==4.9.0 ; sys_platform != "win32" and sys_platform != "emscripten" and python_version == "3.10" -pillow==11.1.0 ; python_version == "3.10" -platformdirs==4.3.7 ; python_version == "3.10" -prompt-toolkit==3.0.50 ; python_version == "3.10" -propcache==0.3.1 ; python_version == "3.10" +pillow==11.2.1 ; python_version == "3.10" +platformdirs==4.3.8 ; python_version == "3.10" +prompt-toolkit==3.0.51 ; python_version == "3.10" +propcache==0.3.2 ; python_version == "3.10" psutil==7.0.0 ; python_version == "3.10" ptyprocess==0.7.0 ; sys_platform != "win32" and sys_platform != "emscripten" and python_version == "3.10" pure-eval==0.2.3 ; python_version == "3.10" pycparser==2.22 ; (implementation_name == "pypy" or platform_python_implementation == "PyPy") and python_version == "3.10" -pydantic-core==2.33.1 ; python_version == "3.10" -pydantic-settings==2.8.1 ; python_version == "3.10" -pydantic==2.11.2 ; python_version == "3.10" +pydantic-core==2.33.2 ; python_version == "3.10" +pydantic-settings==2.9.1 ; python_version == "3.10" +pydantic==2.11.7 ; python_version == "3.10" pygments==2.19.1 ; python_version == "3.10" pyparsing==3.2.3 ; python_version == "3.10" -pypdf==5.4.0 ; python_version == "3.10" +pypdf==5.6.0 ; python_version == "3.10" python-dateutil==2.9.0.post0 ; python_version == "3.10" python-dotenv==1.1.0 ; python_version == "3.10" pytz==2025.2 ; python_version == "3.10" pywin32==310 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version == "3.10" pyyaml==6.0.2 ; python_version == "3.10" -pyzmq==26.4.0 ; python_version == "3.10" +pyzmq==27.0.0 ; python_version == "3.10" regex==2024.11.6 ; python_version == "3.10" requests-toolbelt==1.0.0 ; python_version == "3.10" -requests==2.32.3 ; python_version == "3.10" +requests==2.32.4 ; python_version == "3.10" safetensors==0.5.3 ; python_version == "3.10" -scipy==1.15.2 ; python_version == "3.10" -setuptools==78.1.0 ; python_version == "3.10" +scipy==1.15.3 ; python_version == "3.10" +setuptools==80.9.0 ; python_version == "3.10" six==1.17.0 ; python_version == "3.10" sniffio==1.3.1 ; python_version == "3.10" -sqlalchemy==2.0.40 ; python_version == "3.10" +sqlalchemy==2.0.41 ; python_version == "3.10" stack-data==0.6.3 ; python_version == "3.10" -sympy==1.13.3 ; python_version == "3.10" +sympy==1.14.0 ; python_version == "3.10" tenacity==9.1.2 ; python_version == "3.10" tiktoken==0.9.0 ; python_version == "3.10" tokenizers==0.21.1 ; python_version == "3.10" @@ -119,18 +120,18 @@ torch-geometric==2.6.1 ; python_version == "3.10" torch-scatter @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp310-cp310-linux_x86_64.whl ; python_version == "3.10" torch-sparse @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp310-cp310-linux_x86_64.whl ; python_version == "3.10" torch==2.1.2 ; python_version == "3.10" -torchmetrics==1.7.0 ; python_version == "3.10" +torchmetrics==1.7.3 ; python_version == "3.10" torchvision==0.16.2 ; python_version == "3.10" -tornado==6.4.2 ; python_version == "3.10" +tornado==6.5.1 ; python_version == "3.10" tqdm==4.67.1 ; python_version == "3.10" traitlets==5.14.3 ; python_version == "3.10" -transformers[torch]==4.50.3 ; python_version == "3.10" +transformers[torch]==4.52.4 ; python_version == "3.10" triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" -types-pyyaml==6.0.12.20250402 ; python_version == "3.10" -typing-extensions==4.13.1 ; python_version == "3.10" +types-pyyaml==6.0.12.20250516 ; python_version == "3.10" +typing-extensions==4.14.0 ; python_version == "3.10" typing-inspect==0.9.0 ; python_version == "3.10" -typing-inspection==0.4.0 ; python_version == "3.10" -urllib3==2.3.0 ; python_version == "3.10" +typing-inspection==0.4.1 ; python_version == "3.10" +urllib3==2.5.0 ; python_version == "3.10" wcwidth==0.2.13 ; python_version == "3.10" -yarl==1.18.3 ; python_version == "3.10" +yarl==1.20.1 ; python_version == "3.10" zstandard==0.23.0 ; python_version == "3.10" From 04559cc0de77975e1a82b081af3478775762f318 Mon Sep 17 00:00:00 2001 From: Enrico Deusebio Date: Sat, 21 Jun 2025 22:49:23 +0200 Subject: [PATCH 14/15] import logic from pr 40 --- Chapter12/LLM_and_Graphs.ipynb | 1966 +++----------------------------- 1 file changed, 143 insertions(+), 1823 deletions(-) diff --git a/Chapter12/LLM_and_Graphs.ipynb b/Chapter12/LLM_and_Graphs.ipynb index 31c79ee..ce5fbf5 100644 --- a/Chapter12/LLM_and_Graphs.ipynb +++ b/Chapter12/LLM_and_Graphs.ipynb @@ -18,11 +18,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "id": "lDLHAhIlDEvm" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/euler/.conda/envs/chap12/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "import sys\n", "import torch\n", @@ -31,16 +40,47 @@ "\n", "# Assume a toy dataset with 3 papers (nodes), edges, and labels\n", "data = Data(\n", - " x=torch.rand(3, 10), # let's use random features for simplicity\n", - " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long), # Edges\n", - " y=torch.tensor([0, 1, 2], dtype=torch.long), # True labels\n", - " text=[\"Paper A abstract\", \"Paper B abstract\", \"Paper C abstract\"], # Text data\n", + " x=torch.rand(3, 10), # Random node features\n", + " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long).t().contiguous(), # Edges (transposed for PyG)\n", + " y=torch.tensor([0, 1, 2], dtype=torch.long), # True labels (3 classes)\n", + " text=[\"Paper A abstract about machine learning\", \n", + " \"Paper B abstract about deep learning\", \n", + " \"Paper C abstract about neural networks\"], # Text data\n", ")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset info:\n", + " Number of nodes: 3\n", + " Node feature dimension: 10\n", + " Number of edges: 2\n", + " Number of classes: 3\n", + " True labels: [0, 1, 2]\n" + ] + } + ], + "source": [ + "num_classes = len(torch.unique(data.y)) # Number of unique classes\n", + "\n", + "print(f\"Dataset info:\")\n", + "print(f\" Number of nodes: {data.x.size(0)}\")\n", + "print(f\" Node feature dimension: {data.x.size(1)}\")\n", + "print(f\" Number of edges: {data.edge_index.size(1)}\")\n", + "print(f\" Number of classes: {num_classes}\")\n", + "print(f\" True labels: {data.y.tolist()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -107,89 +147,125 @@ "outputId": "f823051a-2dab-49df-b472-5373cde384a1" }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: GNN Loss = 1.122875690460205, LLM Loss = 5.0661540031433105\n", - "Iteration 2: GNN Loss = 1.0656100511550903, LLM Loss = 3.393122911453247\n", - "Iteration 3: GNN Loss = 1.0160475969314575, LLM Loss = 2.1740071773529053\n", - "Iteration 4: GNN Loss = 0.9782276153564453, LLM Loss = 3.1693878173828125\n", - "Iteration 5: GNN Loss = 0.9556815028190613, LLM Loss = 3.8063628673553467\n" + "Starting training...\n", + "Iteration 1: GNN Loss = 1.1190, LLM Loss = 0.9824\n", + " GNN predictions: [1, 1, 1]\n", + " LLM predictions: [1, 1, 1]\n", + "Iteration 2: GNN Loss = 0.8503, LLM Loss = 1.0020\n", + " GNN predictions: [1, 1, 1]\n", + " LLM predictions: [2, 2, 1]\n", + "Iteration 3: GNN Loss = 0.9946, LLM Loss = 0.9380\n", + " GNN predictions: [1, 1, 1]\n", + " LLM predictions: [1, 2, 1]\n", + "Iteration 4: GNN Loss = 0.8984, LLM Loss = 0.9884\n", + " GNN predictions: [1, 1, 1]\n", + " LLM predictions: [0, 1, 1]\n", + "Iteration 5: GNN Loss = 0.9916, LLM Loss = 0.7875\n", + " GNN predictions: [1, 1, 1]\n", + " LLM predictions: [1, 1, 1]\n" ] } ], "source": [ "from transformers import AutoTokenizer, AutoModel\n", "from torch_geometric.nn import GCNConv\n", + "import torch.nn.functional as F\n", "\n", "# 1. Define the Graph Neural Network (GNN)\n", "class GNN(torch.nn.Module):\n", - " def __init__(self, input_dim, hidden_dim, output_dim):\n", + " def __init__(self, input_dim, hidden_dim, num_classes):\n", " super(GNN, self).__init__()\n", " self.conv1 = GCNConv(input_dim, hidden_dim)\n", - " self.conv2 = GCNConv(hidden_dim, output_dim)\n", - "\n", + " self.conv2 = GCNConv(hidden_dim, num_classes) # Output num_classes\n", + " self.dropout = torch.nn.Dropout(0.2)\n", + " \n", " def forward(self, x, edge_index):\n", - " x = self.conv1(x, edge_index).relu()\n", + " x = self.conv1(x, edge_index)\n", + " x = F.relu(x)\n", + " x = self.dropout(x)\n", " x = self.conv2(x, edge_index)\n", - " return x\n", + " return x # Return logits (not softmax)\n", "\n", - "# 2. Define the LLM (e.g., BERT for text encoding)\n", + "# 2. Define the Text Encoder (BERT-based)\n", "class TextEncoder(torch.nn.Module):\n", - " def __init__(self, model_name=\"bert-base-uncased\", output_dim=128):\n", + " def __init__(self, model_name=\"bert-base-uncased\", num_classes=3):\n", " super(TextEncoder, self).__init__()\n", " self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " self.model = AutoModel.from_pretrained(model_name)\n", - " self.fc = torch.nn.Linear(self.model.config.hidden_size, output_dim)\n", - "\n", + " # Project from BERT's hidden size to number of classes\n", + " self.classifier = torch.nn.Linear(self.model.config.hidden_size, num_classes)\n", + " self.dropout = torch.nn.Dropout(0.1)\n", + " \n", " def forward(self, texts):\n", " # Tokenize and encode text data\n", - " inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True)\n", - " outputs = self.model(**inputs)\n", - " cls_embedding = outputs.last_hidden_state[:, 0, :] # [CLS] token embedding\n", - " return self.fc(cls_embedding)\n", - "\n", - "# 3. Training Loop with Pseudo-label Exchange\n", + " inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n", + " \n", + " with torch.no_grad(): # Freeze BERT parameters during training\n", + " outputs = self.model(**inputs)\n", + " \n", + " # Use [CLS] token embedding\n", + " cls_embedding = outputs.last_hidden_state[:, 0, :]\n", + " cls_embedding = self.dropout(cls_embedding)\n", + " logits = self.classifier(cls_embedding)\n", + " return logits # Return logits (not softmax)\n", + "\n", + "\n", + "# 4. Training Loop with Bidirectional Pseudo-label Exchange\n", "def train_prediction_alignment(data, gnn, text_encoder, num_iterations=5):\n", " optimizer_gnn = torch.optim.Adam(gnn.parameters(), lr=0.01)\n", " optimizer_text = torch.optim.Adam(text_encoder.parameters(), lr=0.0001)\n", - "\n", + " \n", + " # Initialize with true labels for first iteration\n", + " gnn_pseudo_labels = data.y.clone()\n", + " llm_pseudo_labels = data.y.clone()\n", + " \n", " for iteration in range(num_iterations):\n", - " # 3.1 Train GNN\n", + " # 4.1 Train GNN using LLM pseudo-labels from previous iteration\n", " gnn.train()\n", " optimizer_gnn.zero_grad()\n", - " gnn_labels = gnn(data.x, data.edge_index) # GNN generates pseudo-labels\n", - " gnn_loss = torch.nn.CrossEntropyLoss()(gnn_labels, data.y) # Assume `data.y` is the target\n", + " gnn_logits = gnn(data.x, data.edge_index)\n", + " gnn_loss = torch.nn.CrossEntropyLoss()(gnn_logits, llm_pseudo_labels)\n", " gnn_loss.backward()\n", " optimizer_gnn.step()\n", - "\n", - " # 3.2 Train LLM with GNN-generated pseudo-labels\n", + " \n", + " # Generate new GNN pseudo-labels\n", + " with torch.no_grad():\n", + " gnn_pseudo_labels = torch.argmax(gnn_logits, dim=1)\n", + " \n", + " # 4.2 Train Text Encoder using GNN pseudo-labels\n", " text_encoder.train()\n", " optimizer_text.zero_grad()\n", - " text_features = text_encoder(data.text) # Text encoding\n", - " llm_loss = torch.nn.CrossEntropyLoss()(text_features, torch.argmax(gnn_labels, dim=1))\n", + " text_logits = text_encoder(data.text)\n", + " llm_loss = torch.nn.CrossEntropyLoss()(text_logits, gnn_pseudo_labels)\n", " llm_loss.backward()\n", " optimizer_text.step()\n", - "\n", - " print(f\"Iteration {iteration+1}: GNN Loss = {gnn_loss.item()}, LLM Loss = {llm_loss.item()}\")\n", + " \n", + " # Generate new LLM pseudo-labels for next iteration\n", + " with torch.no_grad():\n", + " llm_pseudo_labels = torch.argmax(text_logits, dim=1)\n", + " \n", + " print(f\"Iteration {iteration+1}: GNN Loss = {gnn_loss.item():.4f}, LLM Loss = {llm_loss.item():.4f}\")\n", + " print(f\" GNN predictions: {gnn_pseudo_labels.tolist()}\")\n", + " print(f\" LLM predictions: {llm_pseudo_labels.tolist()}\")\n", "\n", "# Initialize models and train\n", - "gnn = GNN(input_dim=10, hidden_dim=16, output_dim=3)\n", - "text_encoder = TextEncoder()\n", - "train_prediction_alignment(data, gnn, text_encoder)" + "input_dim = data.x.size(1) # Node feature dimension\n", + "hidden_dim = 64\n", + "\n", + "gnn = GNN(input_dim=input_dim, hidden_dim=hidden_dim, num_classes=num_classes)\n", + "text_encoder = TextEncoder(num_classes=num_classes)\n", + "\n", + "print(\"Starting training...\")\n", + "train_prediction_alignment(data, gnn, text_encoder, num_iterations=5)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -202,16 +278,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1: Loss = 1.059374213218689\n", - "Epoch 2: Loss = 1.0176868438720703\n", - "Epoch 3: Loss = 1.0710476636886597\n", - "Epoch 4: Loss = 1.0982847213745117\n", - "Epoch 5: Loss = 1.098282814025879\n", - "Epoch 6: Loss = 1.0987071990966797\n", - "Epoch 7: Loss = 1.0966845750808716\n", - "Epoch 8: Loss = 1.0122456550598145\n", - "Epoch 9: Loss = 1.0971144437789917\n", - "Epoch 10: Loss = 1.0901302099227905\n" + "Epoch 1: Loss = 1.1974958181381226\n", + "Epoch 2: Loss = 0.9338013529777527\n", + "Epoch 3: Loss = 1.0983785390853882\n", + "Epoch 4: Loss = 1.0986661911010742\n", + "Epoch 5: Loss = 1.0841368436813354\n", + "Epoch 6: Loss = 1.0974050760269165\n", + "Epoch 7: Loss = 0.621391773223877\n", + "Epoch 8: Loss = 0.5264388918876648\n", + "Epoch 9: Loss = 1.1055184602737427\n", + "Epoch 10: Loss = 1.0985291004180908\n" ] } ], @@ -316,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 6, "metadata": { "id": "JKEORJfWwI7w" }, @@ -352,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -361,7 +437,7 @@ "ProgressResponse(status='success', completed=None, total=None, digest=None)" ] }, - "execution_count": 39, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -376,16 +452,16 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ListResponse(models=[Model(model='phi4:latest', modified_at=datetime.datetime(2025, 4, 5, 21, 8, 26, 809068, tzinfo=TzInfo(UTC)), digest='ac896e5b8b34a1f4efa7b14d7520725140d5512484457fab45d2a4ea14c69dba', size=9053116391, details=ModelDetails(parent_model='', format='gguf', family='phi3', families=['phi3'], parameter_size='14.7B', quantization_level='Q4_K_M')), Model(model='minicpm-v:latest', modified_at=datetime.datetime(2025, 4, 5, 21, 4, 17, 111987, tzinfo=TzInfo(UTC)), digest='c92bfad0120556eda311984f1ac2f0d0a589b8d68c4053c13486b526276aa205', size=5473838466, details=ModelDetails(parent_model='', format='gguf', family='qwen2', families=['qwen2', 'clip'], parameter_size='7.6B', quantization_level='Q4_0'))])" + "ListResponse(models=[Model(model='phi4:latest', modified_at=datetime.datetime(2025, 6, 21, 20, 30, 17, 738327, tzinfo=TzInfo(UTC)), digest='ac896e5b8b34a1f4efa7b14d7520725140d5512484457fab45d2a4ea14c69dba', size=9053116391, details=ModelDetails(parent_model='', format='gguf', family='phi3', families=['phi3'], parameter_size='14.7B', quantization_level='Q4_K_M'))])" ] }, - "execution_count": 40, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -405,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -413,48 +489,7 @@ "id": "OUWxS0p0-d-e", "outputId": "527d26fc-0dfc-479b-f1c8-330ec8b7d52b" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='1867', type='Date', properties={}), Node(id='Polish And Naturalised-French', type='Nationality', properties={}), Node(id='Physicist', type='Profession', properties={}), Node(id='Chemist', type='Profession', properties={}), Node(id='Radioactivity', type='Concept', properties={}), Node(id='Nobel Prize', type='Award', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]\n", - "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='1867', type='Date', properties={}), type='BORN_IN_YEAR', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Polish And Naturalised-French', type='Nationality', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Physicist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Chemist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Radioactivity', type='Concept', properties={}), type='RESEARCHED_ON', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARDED', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Marie Curie', type='Person', properties={}), type='SPOUSE_OF', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='CO-WINNER_OF', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='PROFESSOR_AT', properties={})]\n", - "\n", - "Testing queries...\n", - "\n", - "Question: Who married a Nobel Prize?\n", - "\n", - "\n", - "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.UnknownPropertyKeyWarning} {category: UNRECOGNIZED} {title: The provided property key is not in the database} {description: One of the property names in your query is not available in the database, make sure you didn't misspell it or that the label is available when you run this statement in your application (the missing property name is: name)} {position: line: 2, column: 54, offset: 60} for query: 'cypher\\nMATCH (person:Person)-[:RECIPIENT_OF]->(award:Award {name: \"Nobel Prize\"})\\nMATCH (spouse:Person)-[:MARRIED_TO]->(person)\\nRETURN DISTINCT spouse.id AS marriedToNobelPrizeWinner\\n'\n", - "Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.UnknownRelationshipTypeWarning} {category: UNRECOGNIZED} {title: The provided relationship type is not in the database.} {description: One of the relationship types in your query is not available in the database, make sure you didn't misspell it or that the label is available when you run this statement in your application (the missing relationship type is: MARRIED_TO)} {position: line: 3, column: 25, offset: 106} for query: 'cypher\\nMATCH (person:Person)-[:RECIPIENT_OF]->(award:Award {name: \"Nobel Prize\"})\\nMATCH (spouse:Person)-[:MARRIED_TO]->(person)\\nRETURN DISTINCT spouse.id AS marriedToNobelPrizeWinner\\n'\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generated Cypher:\n", - "\u001b[32;1m\u001b[1;3mcypher\n", - "MATCH (person:Person)-[:RECIPIENT_OF]->(award:Award {name: \"Nobel Prize\"})\n", - "MATCH (spouse:Person)-[:MARRIED_TO]->(person)\n", - "RETURN DISTINCT spouse.id AS marriedToNobelPrizeWinner\n", - "\u001b[0m\n", - "Full Context:\n", - "\u001b[32;1m\u001b[1;3m[]\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n", - "Response: I don't know the answer.\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -579,1723 +614,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.18" }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "000c7c5876a04721ac8ccc89d9e6163e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - 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}, + } + }, "nbformat": 4, "nbformat_minor": 4 }