From 946d73dba1716fe6075512684028a4bf1ec7abbc Mon Sep 17 00:00:00 2001
From: Jeff Tang <jeff.x.tang@gmail.com>
Date: Tue, 26 Nov 2024 14:57:10 -0800
Subject: [PATCH] replaced the nba playoff example with The paper Attention Is
 All You Need in knowledge_graphs_with_structured_outputs

---
 ...ledge_graphs_with_structured_outputs.ipynb | 779 +++++++++---------
 1 file changed, 390 insertions(+), 389 deletions(-)

diff --git a/recipes/3p_integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb b/recipes/3p_integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb
index 87a3561c..fb153fab 100644
--- a/recipes/3p_integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb
+++ b/recipes/3p_integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb
@@ -1,405 +1,406 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Generating Knowledge Graphs with LLMs and Structured Outputs\n",
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/togethercomputer/together-cookbook/blob/main/Knowledge_Graphs_with_Structured_Outputs.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "In this notebook we show how to generate and visualize knowledge graphs using Large Language Models (LLMs), JSON mode for structured output generation. We will use [Together AI JSON mode](https://docs.together.ai/docs/json-mode) to access models that will generate JSON structures that represent a knowledge graph (including nodes, edges, labels) for us. \n",
-        "\n",
-        "We will then pass these knowledge graph components to [GraphViz](https://graphviz.org/) that will help us generate and visualize these graphs.\n",
-        "\n",
-        "<img src=\"images/cluster.png\" height=\"500\">"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Install relevant libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "DFAjay1FZVrn",
-        "outputId": "d4b17b31-c125-4de5-ad54-6d4d08d81eaa"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Collecting together\n",
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-            "\u001b[?25hInstalling collected packages: together\n",
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-            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (0.20.3)\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install together\n",
-        "!pip install graphviz"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "id": "5L-bdWL8Zz2B"
-      },
-      "outputs": [],
-      "source": [
-        "from together import Together\n",
-        "import together\n",
-        "import os, json\n",
-        "\n",
-        "# Paste in your Together AI API Key or load it\n",
-        "TOGETHER_API_KEY = os.environ.get(\"TOGETHER_API_KEY\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Define Knowledge Graph Schema in Pydantic\n",
-        "\n",
-        "We need a way of telling the LLM what structure a knowledge graph has - including what information the nodes, edges must have and how they come together to create the overall graph structure. We will do this using `pydantic` models.\n",
-        "\n",
-        "Below we define the required classes. \n",
-        "\n",
-        "- Each node in our graph needs to have an `id` and a `label`. The `Node` class specifies this.\n",
-        "- Each edge in our graph needs to have a `label` and must also connect two nodes from the `source` to `destination` direction. The `Edge` class specified this.\n",
-        "- Each Knowledge graph is a combination of multiple `Node` and `Edge` elements. The `KnowledgeGraph` class specifies this. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "metadata": {
-        "id": "ycvtkJdhZ5Zo"
-      },
-      "outputs": [],
-      "source": [
-        "from pydantic import BaseModel, Field\n",
-        "from typing import List\n",
-        "\n",
-        "class Node(BaseModel, frozen=True):\n",
-        "    id: int\n",
-        "    label: str\n",
-        "\n",
-        "class Edge(BaseModel, frozen=True):\n",
-        "    source: int\n",
-        "    target: int\n",
-        "    label: str\n",
-        "\n",
-        "class KnowledgeGraph(BaseModel):\n",
-        "    nodes: List[Node] = Field(..., default_factory=list)\n",
-        "    edges: List[Edge] = Field(..., default_factory=list)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 39,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5auwyteuadki",
-        "outputId": "38fecb4a-5c11-4755-e311-a376bc308bee"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'$defs': {'Edge': {'properties': {'source': {'title': 'Source',\n",
-              "     'type': 'integer'},\n",
-              "    'target': {'title': 'Target', 'type': 'integer'},\n",
-              "    'label': {'title': 'Label', 'type': 'string'}},\n",
-              "   'required': ['source', 'target', 'label'],\n",
-              "   'title': 'Edge',\n",
-              "   'type': 'object'},\n",
-              "  'Node': {'properties': {'id': {'title': 'Id', 'type': 'integer'},\n",
-              "    'label': {'title': 'Label', 'type': 'string'}},\n",
-              "   'required': ['id', 'label'],\n",
-              "   'title': 'Node',\n",
-              "   'type': 'object'}},\n",
-              " 'properties': {'nodes': {'items': {'$ref': '#/$defs/Node'},\n",
-              "   'title': 'Nodes',\n",
-              "   'type': 'array'},\n",
-              "  'edges': {'items': {'$ref': '#/$defs/Edge'},\n",
-              "   'title': 'Edges',\n",
-              "   'type': 'array'}},\n",
-              " 'title': 'KnowledgeGraph',\n",
-              " 'type': 'object'}"
-            ]
-          },
-          "execution_count": 39,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Print out the JSON Schema for our knowledge graph\n",
-        "KnowledgeGraph.model_json_schema()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Function to Generate a Knowledge Graph using LLMs\n",
-        "\n",
-        "Here we use Together AI to access `Meta-Llama-3.1-70B` in strict JSON mode. We pass in instructions to get the LLM to generate a knowledge that captures the information about a passed in `input` string. \n",
-        "\n",
-        "We pass in the above `KnowledgeGraph` JSON schema to guide the structured output generation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 64,
-      "metadata": {
-        "id": "GHwlq8vuZ9QM"
-      },
-      "outputs": [],
-      "source": [
-        "# Call the LLM with the JSON schema\n",
-        "def generate_graph(input) -> KnowledgeGraph:\n",
-        "    together = Together(api_key = TOGETHER_API_KEY)\n",
-        "\n",
-        "    extract = together.chat.completions.create(\n",
-        "        messages=[\n",
-        "            {\n",
-        "                \"role\": \"user\",\n",
-        "                \"content\": f\"Build a knowledge graph to explain: {input}. Only answer in JSON.\",\n",
-        "            }\n",
-        "        ],\n",
-        "        model=\"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\",\n",
-        "        response_format={\n",
-        "            \"type\": \"json_object\",\n",
-        "            \"schema\": KnowledgeGraph.model_json_schema(),\n",
-        "        },\n",
-        "    )\n",
-        "\n",
-        "    output = json.loads(extract.choices[0].message.content)\n",
-        "    return output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Lets use the function above to generate a knowledge graph about the 2019 NBA playoffs\n",
-        "# For context in the 2019 NBA playoffs, the Toronto Raptors won the NBA championship facing the Golden State Warriors.\n",
-        "\n",
-        "graph = generate_graph(\"NBA 2019 playoffs\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'nodes': [{'id': 1, 'label': 'NBA 2019 playoffs'},\n",
-              "  {'id': 2, 'label': 'Eastern Conference'},\n",
-              "  {'id': 3, 'label': 'Western Conference'},\n",
-              "  {'id': 4, 'label': 'Milwaukee Bucks'},\n",
-              "  {'id': 5, 'label': 'Toronto Raptors'},\n",
-              "  {'id': 6, 'label': 'Golden State Warriors'},\n",
-              "  {'id': 7, 'label': 'Portland Trail Blazers'},\n",
-              "  {'id': 8, 'label': 'NBA Finals'},\n",
-              "  {'id': 9, 'label': 'Conference Finals'},\n",
-              "  {'id': 10, 'label': 'Semifinals'},\n",
-              "  {'id': 11, 'label': 'First Round'}],\n",
-              " 'edges': [{'source': 1, 'target': 2, 'label': 'conference'},\n",
-              "  {'source': 1, 'target': 3, 'label': 'conference'},\n",
-              "  {'source': 2, 'target': 4, 'label': 'participant'},\n",
-              "  {'source': 2, 'target': 5, 'label': 'participant'},\n",
-              "  {'source': 3, 'target': 6, 'label': 'participant'},\n",
-              "  {'source': 3, 'target': 7, 'label': 'participant'},\n",
-              "  {'source': 4, 'target': 9, 'label': 'qualified for'},\n",
-              "  {'source': 5, 'target': 9, 'label': 'qualified for'},\n",
-              "  {'source': 6, 'target': 8, 'label': 'qualified for'},\n",
-              "  {'source': 7, 'target': 9, 'label': 'qualified for'},\n",
-              "  {'source': 5, 'target': 8, 'label': 'qualified for'},\n",
-              "  {'source': 6, 'target': 9, 'label': 'qualified for'},\n",
-              "  {'source': 9, 'target': 8, 'label': 'preceded by'},\n",
-              "  {'source': 10, 'target': 9, 'label': 'preceded by'},\n",
-              "  {'source': 11, 'target': 10, 'label': 'preceded by'}]}"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Lets see the knowlege graph components generated!\n",
-        "graph"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use GraphViz to Create and Visualize the Graph\n",
-        "\n",
-        "Below we write a function that takes the knowledge graph JSON structure above and creates a GraphViz Directed Graph(`Digraph`) and generates a picture of the graph."
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generating Knowledge Graphs with LLMs and Structured Outputs\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "In this notebook we show how to generate and visualize knowledge graphs using Large Language Models (LLMs), and how to use JSON mode for structured output generation. We will use [Together AI JSON mode](https://docs.together.ai/docs/json-mode) to access models that will generate JSON structures that represent a knowledge graph (including nodes, edges, labels) for us. \n",
+    "\n",
+    "We will then pass these knowledge graph components to [GraphViz](https://graphviz.org/) that will help us generate and visualize these graphs.\n",
+    "\n",
+    "<img src=\"images/cluster.png\" height=\"500\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "DFAjay1FZVrn",
+    "outputId": "d4b17b31-c125-4de5-ad54-6d4d08d81eaa"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 65,
-      "metadata": {
-        "id": "dso_caYbapzO"
-      },
-      "outputs": [],
-      "source": [
-        "from graphviz import Digraph\n",
-        "\n",
-        "def visualize_knowledge_graph(kg):\n",
-        "    dot = Digraph(comment=\"Knowledge Graph\", format='png')\n",
-        "\n",
-        "    # Add nodes\n",
-        "    for node in kg['nodes']:\n",
-        "        dot.node(str(node['id']), node['label'])\n",
-        "\n",
-        "    # Add edges\n",
-        "    for edge in kg['edges']:\n",
-        "        dot.edge(str(edge['source']), str(edge['target']), label=edge['label'])\n",
-        "\n",
-        "    # Render the graph to a file and open it\n",
-        "    output_path = dot.render(\"knowledge_graph\", view=True)\n",
-        "    print(f\"Graph rendered and saved to {output_path}\")"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Requirement already satisfied: click<9.0.0,>=8.1.7 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (8.1.7)\n",
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+      "Requirement already satisfied: numpy>=1.23.5 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (1.26.4)\n",
+      "Requirement already satisfied: pillow<11.0.0,>=10.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (10.4.0)\n",
+      "Requirement already satisfied: pyarrow>=10.0.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (18.0.0)\n",
+      "Requirement already satisfied: pydantic<3.0.0,>=2.6.3 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (2.9.2)\n",
+      "Requirement already satisfied: requests<3.0.0,>=2.31.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (2.32.3)\n",
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+      "Requirement already satisfied: tabulate<0.10.0,>=0.9.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (0.9.0)\n",
+      "Requirement already satisfied: tqdm<5.0.0,>=4.66.2 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (4.66.6)\n",
+      "Requirement already satisfied: typer<0.13,>=0.9 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (0.12.5)\n",
+      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (2.4.3)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (1.3.1)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (24.2.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (1.5.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (6.1.0)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.12.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (1.17.1)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (4.0.3)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.6.3->together) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.23.4 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.6.3->together) (2.23.4)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.6.3->together) (4.12.2)\n",
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+      "Requirement already satisfied: idna<4,>=2.5 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from requests<3.0.0,>=2.31.0->together) (3.10)\n",
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+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from requests<3.0.0,>=2.31.0->together) (2024.8.30)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from rich<14.0.0,>=13.8.1->together) (3.0.0)\n",
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+      "Requirement already satisfied: shellingham>=1.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from typer<0.13,>=0.9->together) (1.5.4)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=13.8.1->together) (0.1.2)\n",
+      "Requirement already satisfied: propcache>=0.2.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from yarl<2.0,>=1.12.0->aiohttp<4.0.0,>=3.9.3->together) (0.2.0)\n",
+      "Collecting graphviz\n",
+      "  Using cached graphviz-0.20.3-py3-none-any.whl.metadata (12 kB)\n",
+      "Using cached graphviz-0.20.3-py3-none-any.whl (47 kB)\n",
+      "Installing collected packages: graphviz\n",
+      "Successfully installed graphviz-0.20.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install together\n",
+    "!pip install graphviz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "5L-bdWL8Zz2B"
+   },
+   "outputs": [],
+   "source": [
+    "from together import Together\n",
+    "import together\n",
+    "import json\n",
+    "\n",
+    "# Paste in your Together AI API Key\n",
+    "TOGETHER_API_KEY = \"YOUR_TOGETHER_API_KEY\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Knowledge Graph Schema in Pydantic\n",
+    "\n",
+    "We need a way of telling the LLM what structure a knowledge graph has - including what information the nodes, edges must have and how they come together to create the overall graph structure. We will do this using `pydantic` models.\n",
+    "\n",
+    "Below we define the required classes. \n",
+    "\n",
+    "- Each node in our graph needs to have an `id` and a `label`. The `Node` class specifies this.\n",
+    "- Each edge in our graph needs to have a `label` and must also connect two nodes from the `source` to `destination` direction. The `Edge` class specified this.\n",
+    "- Each Knowledge graph is a combination of multiple `Node` and `Edge` elements. The `KnowledgeGraph` class specifies this. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "ycvtkJdhZ5Zo"
+   },
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "from typing import List\n",
+    "\n",
+    "class Node(BaseModel, frozen=True):\n",
+    "    id: int\n",
+    "    label: str\n",
+    "\n",
+    "class Edge(BaseModel, frozen=True):\n",
+    "    source: int\n",
+    "    target: int\n",
+    "    label: str\n",
+    "\n",
+    "class KnowledgeGraph(BaseModel):\n",
+    "    nodes: List[Node] = Field(..., default_factory=list)\n",
+    "    edges: List[Edge] = Field(..., default_factory=list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "5auwyteuadki",
+    "outputId": "38fecb4a-5c11-4755-e311-a376bc308bee"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 68,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "q9v0fnk9dgLY",
-        "outputId": "668c6e7a-4460-4248-e7af-ed6c5eb54d2b"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Graph rendered and saved to knowledge_graph.png\n"
-          ]
-        }
-      ],
-      "source": [
-        "visualize_knowledge_graph(graph)"
+     "data": {
+      "text/plain": [
+       "{'$defs': {'Edge': {'properties': {'source': {'title': 'Source',\n",
+       "     'type': 'integer'},\n",
+       "    'target': {'title': 'Target', 'type': 'integer'},\n",
+       "    'label': {'title': 'Label', 'type': 'string'}},\n",
+       "   'required': ['source', 'target', 'label'],\n",
+       "   'title': 'Edge',\n",
+       "   'type': 'object'},\n",
+       "  'Node': {'properties': {'id': {'title': 'Id', 'type': 'integer'},\n",
+       "    'label': {'title': 'Label', 'type': 'string'}},\n",
+       "   'required': ['id', 'label'],\n",
+       "   'title': 'Node',\n",
+       "   'type': 'object'}},\n",
+       " 'properties': {'nodes': {'items': {'$ref': '#/$defs/Node'},\n",
+       "   'title': 'Nodes',\n",
+       "   'type': 'array'},\n",
+       "  'edges': {'items': {'$ref': '#/$defs/Edge'},\n",
+       "   'title': 'Edges',\n",
+       "   'type': 'array'}},\n",
+       " 'title': 'KnowledgeGraph',\n",
+       " 'type': 'object'}"
       ]
-    },
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Print out the JSON Schema for our knowledge graph\n",
+    "KnowledgeGraph.model_json_schema()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Function to Generate a Knowledge Graph using LLMs\n",
+    "\n",
+    "Here we use Together AI to access `Meta-Llama-3.1-70B` in strict JSON mode. We pass in instructions to get the LLM to generate a knowledge that captures the information about a passed in `input` string. \n",
+    "\n",
+    "We pass in the above `KnowledgeGraph` JSON schema to guide the structured output generation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "GHwlq8vuZ9QM"
+   },
+   "outputs": [],
+   "source": [
+    "# Call the LLM with the JSON schema\n",
+    "def generate_graph(input) -> KnowledgeGraph:\n",
+    "    together = Together(api_key = TOGETHER_API_KEY)\n",
+    "\n",
+    "    extract = together.chat.completions.create(\n",
+    "        messages=[\n",
+    "            {\n",
+    "                \"role\": \"user\",\n",
+    "                \"content\": f\"Build a knowledge graph to explain: {input}. Only answer in JSON.\",\n",
+    "            }\n",
+    "        ],\n",
+    "        model=\"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\",\n",
+    "        response_format={\n",
+    "            \"type\": \"json_object\",\n",
+    "            \"schema\": KnowledgeGraph.model_json_schema(),\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    output = json.loads(extract.choices[0].message.content)\n",
+    "    return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's use the function above to generate a knowledge graph about the paper Attention Is All You Need,\n",
+    "# a landmark in AI which introduced the Transformer architecture, enabling breakthroughs in LLMs.\n",
+    "\n",
+    "graph = generate_graph(\"The paper Attention Is All You Need\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 69,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 540
-        },
-        "id": "w8JnsjGaifAx",
-        "outputId": "d08dabeb-5777-4aa0-a5a3-9f32917b1dc1"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from IPython.display import Image, display\n",
-        "\n",
-        "# Display the image of the knowledge graph\n",
-        "display(Image(filename='/content/knowledge_graph.png'))"
+     "data": {
+      "text/plain": [
+       "{'nodes': [{'id': 1, 'label': 'Transformer'},\n",
+       "  {'id': 2, 'label': 'Attention Mechanism'},\n",
+       "  {'id': 3, 'label': 'Encoder-Decoder Architecture'},\n",
+       "  {'id': 4, 'label': 'Self-Attention'},\n",
+       "  {'id': 5, 'label': 'Vaswani et al.'},\n",
+       "  {'id': 6, 'label': 'Sequence-to-Sequence Models'},\n",
+       "  {'id': 7, 'label': 'Recurrent Neural Networks (RNNs)'},\n",
+       "  {'id': 8, 'label': 'Convolutional Neural Networks (CNNs)'},\n",
+       "  {'id': 9, 'label': 'Parallelization'}],\n",
+       " 'edges': [{'source': 1, 'target': 2, 'label': 'utilizes'},\n",
+       "  {'source': 1, 'target': 3, 'label': 'employs'},\n",
+       "  {'source': 2, 'target': 4, 'label': 'includes'},\n",
+       "  {'source': 3, 'target': 6, 'label': 'enhances'},\n",
+       "  {'source': 5, 'target': 1, 'label': 'proposes'},\n",
+       "  {'source': 6, 'target': 7, 'label': 'replaces'},\n",
+       "  {'source': 6, 'target': 8, 'label': 'outperforms'},\n",
+       "  {'source': 1, 'target': 9, 'label': 'enables'}]}"
       ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets see the knowlege graph components generated!\n",
+    "graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use GraphViz to Create and Visualize the Graph\n",
+    "\n",
+    "Below we write a function that takes the knowledge graph JSON structure above and creates a GraphViz Directed Graph(`Digraph`) and generates a picture of the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "dso_caYbapzO"
+   },
+   "outputs": [],
+   "source": [
+    "from graphviz import Digraph\n",
+    "\n",
+    "def visualize_knowledge_graph(kg):\n",
+    "    dot = Digraph(comment=\"Knowledge Graph\", format='png')\n",
+    "\n",
+    "    # Add nodes\n",
+    "    for node in kg['nodes']:\n",
+    "        dot.node(str(node['id']), node['label'])\n",
+    "\n",
+    "    # Add edges\n",
+    "    for edge in kg['edges']:\n",
+    "        dot.edge(str(edge['source']), str(edge['target']), label=edge['label'])\n",
+    "\n",
+    "    # Render the graph to a file and open it\n",
+    "    output_path = dot.render(\"knowledge_graph\", view=True)\n",
+    "    print(f\"Graph rendered and saved to {output_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "q9v0fnk9dgLY",
+    "outputId": "668c6e7a-4460-4248-e7af-ed6c5eb54d2b"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We were able to get a LLM to generate a knowledge graph by telling is the structure that a knowledge graph library, GraphViz, expects the output to be in. We then used GraphViz to create and visualize the knowledge graph above!\n",
-        "\n",
-        "Learn more about how to use JSON mode in the [docs](https://docs.together.ai/docs/json-mode) here!"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Graph rendered and saved to knowledge_graph.png\n"
+     ]
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "visualize_knowledge_graph(graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
     "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
+     "base_uri": "https://localhost:8080/",
+     "height": 540
     },
-    "language_info": {
-      "name": "python"
+    "id": "w8JnsjGaifAx",
+    "outputId": "d08dabeb-5777-4aa0-a5a3-9f32917b1dc1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "# Display the image of the knowledge graph\n",
+    "display(Image(filename='knowledge_graph.png'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We were able to get a LLM to generate a knowledge graph by telling is the structure that a knowledge graph library, GraphViz, expects the output to be in. We then used GraphViz to create and visualize the knowledge graph above!\n",
+    "\n",
+    "Learn more about how to use JSON mode in the [docs](https://docs.together.ai/docs/json-mode) here!"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
-- 
GitLab