Add code_graph_demo notebook

.gitignore

@@ -4,6 +4,7 @@
 .prod.env
 cognee/.data/
 
+*.lance/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

cognee/infrastructure/databases/graph/networkx/adapter.py

@@ -270,7 +270,7 @@ class NetworkXAdapter(GraphDBInterface):
                         except:
                           pass
 
-                        if "updated_at" in node:
+                        if "updated_at" in edge:
                             edge["updated_at"] = datetime.strptime(edge["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z")
 
                     self.graph = nx.readwrite.json_graph.node_link_graph(graph_data)

notebooks/cognee_code_graph_demo.ipynb

@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['GRAPHISTRY_USERNAME'] = input(\"Please enter your graphistry username\")\n",
+    "os.environ['GRAPHISTRY_PASSWORD'] = input(\"Please enter your graphistry password\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cognee.modules.users.methods import get_default_user\n",
+    "\n",
+    "from cognee.modules.data.methods import get_datasets\n",
+    "from cognee.modules.data.methods.get_dataset_data import get_dataset_data\n",
+    "from cognee.modules.data.models import Data\n",
+    "\n",
+    "from cognee.modules.pipelines.tasks.Task import Task\n",
+    "from cognee.tasks.documents import classify_documents, check_permissions_on_documents, extract_chunks_from_documents\n",
+    "from cognee.tasks.graph import extract_graph_from_code\n",
+    "from cognee.tasks.storage import add_data_points\n",
+    "from cognee.shared.SourceCodeGraph import SourceCodeGraph\n",
+    "\n",
+    "from cognee.modules.pipelines import run_tasks\n",
+    "\n",
+    "from cognee.shared.utils import render_graph\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user = await get_default_user()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "existing_datasets = await get_datasets(user.id)\n",
+    "\n",
+    "datasets = {}\n",
+    "for dataset in existing_datasets:\n",
+    "    dataset_name = dataset.name.replace(\".\", \"_\").replace(\" \", \"_\")\n",
+    "    data_documents: list[Data] = await get_dataset_data(dataset_id = dataset.id)\n",
+    "    datasets[dataset_name] = data_documents\n",
+    "print(datasets.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tasks = [\n",
+    "    Task(classify_documents),\n",
+    "    Task(check_permissions_on_documents, user = user, permissions = [\"write\"]),\n",
+    "    Task(extract_chunks_from_documents), # Extract text chunks based on the document type.\n",
+    "    Task(add_data_points, task_config = { \"batch_size\": 10 }),\n",
+    "    Task(extract_graph_from_code, graph_model = SourceCodeGraph, task_config = { \"batch_size\": 10 }), # Generate knowledge graphs from the document chunks.\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "async def run_codegraph_pipeline(tasks, data_documents):\n",
+    "    pipeline = run_tasks(tasks, data_documents, \"code_graph_pipeline\")\n",
+    "    results = []\n",
+    "    async for result in pipeline:\n",
+    "        results.append(result)\n",
+    "    return(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = await run_codegraph_pipeline(tasks, datasets[\"main_dataset\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "await render_graph(None, include_nodes = True, include_labels = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cognee",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}