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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", + "9ca2c22d54a14543a0ccb418f84dfc7a", + "6ef1fa9fc050493a88aa2ba634c36cb4", + "95dde357b3704448bfdb0b3a8818413e", + "918e3e0abaaa43119b27a725d9996622", + "c45bf94c8ae1497bb5093dd459e35868", + "78a75c6a1b0444baa32a8dc0444c6e42", + "efae36a6383c472c8d8fe7f39223a13f", 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"/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": "391fd502-f552-45df-d655-6b1af61a7830" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25lpulling manifest ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling 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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... 77% ▕▏ 3.4 GB/4.4 GB 211 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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"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 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 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% ▕▏ 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 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"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 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... 81% ▕▏ 845 MB/1.0 GB 175 MB/s 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"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 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