Upload demo.ipynb

notebook/demo.ipynb

@@ -1,53 +1,74 @@
-import nbformat as nbf
-
-# Create a new notebook
-nb = nbf.v4.new_notebook()
-
-# Add cells
-nb.cells = [
-    nbf.v4.new_markdown_cell("# Demo: AI-Powered Scientific Research Companion\nThis notebook demonstrates how to use the `Dispatcher` to search for papers, retrieve reproducible notebook cells, and fetch a knowledge graph."),
-    nbf.v4.new_code_cell("""\
-from orchestrator.dispatcher import Dispatcher
-
-# Initialize dispatcher
-dispatcher = Dispatcher()
-
-# Example query
-query = "CRISPR delivery"
-
-# 1. Search for papers
-papers = dispatcher.search_papers(query, limit=3)
-print("Papers found:")
-for p in papers:
-    print(f"- {p['title']} (ID: {p['id']})")
-"""),
-    nbf.v4.new_code_cell("""\
-# 2. Retrieve notebook cells for the first paper
-if papers:
-    first_id = papers[0]['id']
-    cells = dispatcher.get_notebook_cells(first_id)
-    print(f"Notebook cells for paper {first_id}:")
-    for i, cell in enumerate(cells, 1):
-        print(f"Cell {i}:")
-        print(cell)
-        print("------")
-"""),
-    nbf.v4.new_code_cell("""\
-# 3. Fetch knowledge graph for the first paper
-if papers:
-    graph = dispatcher.get_graph(first_id)
-    print("Graph nodes:")
-    for node in graph.get("nodes", []):
-        print(node)
-    print("Graph edges:")
-    for edge in graph.get("edges", []):
-        print(edge)
-""")
-]
-
-# Save the notebook
-nb_path = "/mnt/data/demo.ipynb"
-with open(nb_path, "w") as f:
-    nbf.write(nb, f)
-
-nb_path
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dfad5f6b",
+   "metadata": {},
+   "source": [
+    "# Demo: AI-Powered Scientific Research Companion\n",
+    "This notebook demonstrates how to use the `Dispatcher` to search for papers, retrieve reproducible notebook cells, and fetch a knowledge graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "307d78b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from orchestrator.dispatcher import Dispatcher\n",
+    "\n",
+    "# Initialize dispatcher\n",
+    "dispatcher = Dispatcher()\n",
+    "\n",
+    "# Example query\n",
+    "query = \"CRISPR delivery\"\n",
+    "\n",
+    "# 1. Search for papers\n",
+    "papers = dispatcher.search_papers(query, limit=3)\n",
+    "print(\"Papers found:\")\n",
+    "for p in papers:\n",
+    "    print(f\"- {p['title']} (ID: {p['id']})\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8db389c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2. Retrieve notebook cells for the first paper\n",
+    "if papers:\n",
+    "    first_id = papers[0]['id']\n",
+    "    cells = dispatcher.get_notebook_cells(first_id)\n",
+    "    print(f\"Notebook cells for paper {first_id}:\")\n",
+    "    for i, cell in enumerate(cells, 1):\n",
+    "        print(f\"Cell {i}:\")\n",
+    "        print(cell)\n",
+    "        print(\"------\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52666e2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. Fetch knowledge graph for the first paper\n",
+    "if papers:\n",
+    "    graph = dispatcher.get_graph(first_id)\n",
+    "    print(\"Graph nodes:\")\n",
+    "    for node in graph.get(\"nodes\", []):\n",
+    "        print(node)\n",
+    "    print(\"Graph edges:\")\n",
+    "    for edge in graph.get(\"edges\", []):\n",
+    "        print(edge)\n"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}