diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index f98bb50..4c087f4 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -15,27 +15,29 @@ 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 - runs-on: ubuntu-latest + - 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 ${{ matrix.chapter.name }} steps: - name: Checkout repository @@ -46,6 +48,17 @@ jobs: run: echo "branch=${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}" >> $GITHUB_OUTPUT id: extract_branch + - name: (GitHub hosted) Free up disk space + shell: bash + run: | + printf '\nDisk usage before cleanup\n' + df --human-readable + # Based on https://github.com/actions/runner-images/issues/2840#issuecomment-790492173 + rm -r /usr/share/dotnet + rm -r /opt/hostedtoolcache/ + printf '\nDisk usage after cleanup\n' + df --human-readable + - name: Build Image id: build run: | @@ -80,6 +93,28 @@ jobs: docker network connect my-network neo4j fi + # Start Neo4j if we are testing chapter 13 + if [ "${{ matrix.chapter.name }}" == "chap12" ]; + 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='["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 \ + 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 +124,7 @@ 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/Chapter12/LLM_and_Graphs.ipynb b/Chapter12/LLM_and_Graphs.ipynb new file mode 100644 index 0000000..90dac8f --- /dev/null +++ b/Chapter12/LLM_and_Graphs.ipynb @@ -0,0 +1,622 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LMM and Graphs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aH3Q56MGDARI" + }, + "source": [ + "Let's create a toy dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "lDLHAhIlDEvm" + }, + "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", + "\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), # 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": 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/", + "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": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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, num_classes):\n", + " super(GNN, self).__init__()\n", + " self.conv1 = GCNConv(input_dim, hidden_dim)\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)\n", + " x = F.relu(x)\n", + " x = self.dropout(x)\n", + " x = self.conv2(x, edge_index)\n", + " return x # Return logits (not softmax)\n", + "\n", + "# 2. Define the Text Encoder (BERT-based)\n", + "class TextEncoder(torch.nn.Module):\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", + " # 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, 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", + " # 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", + " # 4.1 Train GNN using LLM pseudo-labels from previous iteration\n", + " gnn.train()\n", + " optimizer_gnn.zero_grad()\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", + " # 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_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", + " # 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", + "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": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AwmGzCmH_669", + "outputId": "2577a628-8f1c-4efb-c732-1dfbbade06bf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + } + ], + "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": 6, + "metadata": { + "id": "JKEORJfWwI7w" + }, + "outputs": [], + "source": [ + "import os\n", + "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 = f\"http://{os.environ.get('OLLAMA_HOST', 'localhost')}:11434/v1\"\n", + " api_key = \"ollama\"\n", + " # llm_model = \"minicpm-v\"\n", + " llm_model = \"phi4\"\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": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ProgressResponse(status='success', completed=None, total=None, digest=None)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import ollama\n", + "\n", + "# ollama.pull(llm_model)\n", + "# ollama.pull(\"nomic-embed-text\")\n", + "ollama.pull(\"phi4\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ollama.list()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bK17PDF7KxCv" + }, + "source": [ + "# Neo4j" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OUWxS0p0-d-e", + "outputId": "527d26fc-0dfc-479b-f1c8-330ec8b7d52b" + }, + "outputs": [], + "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 = f\"bolt://{host}: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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100644 index 0000000..dc5bcc3 --- /dev/null +++ b/Chapter12/pyproject.toml @@ -0,0 +1,35 @@ +[tool.poetry] +name = "Graph Machine Learning (2nd Edition) - Chapter 13" +version = "1.0.0" +description = "" +authors = ["Enrico Deusebio "] +packages = [] +package-mode = false + +[tool.setuptools] +py-modules = [] + +[tool.poetry.dependencies] +python = "~3.10" +ipykernel = ">=6.0.0" +matplotlib = "==3.2.2" +numpy = ">=1.26,<2.0" +# 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/Chapter12/requirements.txt b/Chapter12/requirements.txt new file mode 100644 index 0000000..5db35d6 --- /dev/null +++ b/Chapter12/requirements.txt @@ -0,0 +1,137 @@ +accelerate==1.8.1 ; python_version == "3.10" +aiohappyeyeballs==2.6.1 ; python_version == "3.10" +aiohttp==3.12.13 ; 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==2025.6.15 ; python_version == "3.10" +cffi==1.17.1 ; (implementation_name == "pypy" or platform_python_implementation == "PyPy") and python_version == "3.10" +charset-normalizer==3.4.2 ; 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==1.8.14 ; python_version == "3.10" +decorator==5.2.1 ; python_version == "3.10" +distro==1.9.0 ; python_version == "3.10" +exceptiongroup==1.3.0 ; python_version == "3.10" +executing==2.2.0 ; python_version == "3.10" +filelock==3.18.0 ; python_version == "3.10" +frozenlist==1.7.0 ; python_version == "3.10" +fsspec==2024.12.0 ; python_version == "3.10" +greenlet==3.2.3 ; 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==0.16.0 ; python_version == "3.10" +hf-xet==1.1.5 ; (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64") and python_version == "3.10" +httpcore==1.0.9 ; python_version == "3.10" +httpx-sse==0.4.0 ; python_version == "3.10" +httpx==0.28.1 ; python_version == "3.10" +huggingface-hub==0.33.0 ; python_version == "3.10" +idna==3.10 ; python_version == "3.10" +ipykernel==6.29.5 ; python_version == "3.10" +ipython==8.37.0 ; python_version == "3.10" +jedi==0.19.2 ; python_version == "3.10" +jinja2==3.1.6 ; 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==5.8.1 ; python_version == "3.10" +kiwisolver==1.4.8 ; 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==0.3.24 ; python_version == "3.10" +langchain-text-splitters==0.3.8 ; 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==6.5.0 ; 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==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" +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==12.9.86 ; 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==0.4.9 ; 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==11.2.1 ; 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==2.33.2 ; 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==5.6.0 ; 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==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==2.32.4 ; python_version == "3.10" +safetensors==0.5.3 ; 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==2.0.41 ; python_version == "3.10" +stack-data==0.6.3 ; 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" +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==1.7.3 ; python_version == "3.10" +torchvision==0.16.2 ; 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]==4.52.4 ; python_version == "3.10" +triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10" +types-pyyaml==6.0.12.20250516 ; python_version == "3.10" +typing-extensions==4.14.0 ; python_version == "3.10" +typing-inspect==0.9.0 ; python_version == "3.10" +typing-inspection==0.4.1 ; python_version == "3.10" +urllib3==2.5.0 ; python_version == "3.10" +wcwidth==0.2.13 ; python_version == "3.10" +yarl==1.20.1 ; python_version == "3.10" +zstandard==0.23.0 ; python_version == "3.10" diff --git a/Chapter12/setup_ollama.sh b/Chapter12/setup_ollama.sh new file mode 100755 index 0000000..1571c39 --- /dev/null +++ b/Chapter12/setup_ollama.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +curl -fsSL https://ollama.com/install.sh | sudo sh + +ollama -v + +ollama pull minicpm-v +ollama pull nomic-embed-text + diff --git a/docker/Dockerfile b/docker/Dockerfile index 3530d5e..14655ee 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 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