1.Chatbot_with_langgraph.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbOMDMmIWOx7",
        "outputId": "18766015-f90b-4dbc-bbe3-7104ab682a21"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langgraph\n",
            "  Downloading langgraph-0.2.18-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting langSmith\n",
            "  Downloading langsmith-0.1.115-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting langchain-core<0.4,>=0.2.38 (from langgraph)\n",
            "  Downloading langchain_core-0.2.38-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting langgraph-checkpoint<2.0.0,>=1.0.2 (from langgraph)\n",
            "  Downloading langgraph_checkpoint-1.0.9-py3-none-any.whl.metadata (4.5 kB)\n",
            "Collecting httpx<1,>=0.23.0 (from langSmith)\n",
            "  Downloading httpx-0.27.2-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting orjson<4.0.0,>=3.9.14 (from langSmith)\n",
            "  Downloading orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (50 kB)\n",
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            "Collecting httpcore==1.* (from httpx<1,>=0.23.0->langSmith)\n",
            "  Downloading httpcore-1.0.5-py3-none-any.whl.metadata (20 kB)\n",
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            "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->langSmith)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4,>=0.2.38->langgraph) (6.0.2)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain-core<0.4,>=0.2.38->langgraph)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl.metadata (3.0 kB)\n",
            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4,>=0.2.38->langgraph) (24.1)\n",
            "Collecting tenacity!=8.4.0,<9.0.0,>=8.1.0 (from langchain-core<0.4,>=0.2.38->langgraph)\n",
            "  Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: typing-extensions>=4.7 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4,>=0.2.38->langgraph) (4.12.2)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langSmith) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.20.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langSmith) (2.20.1)\n",
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            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain-core<0.4,>=0.2.38->langgraph)\n",
            "  Downloading jsonpointer-3.0.0-py2.py3-none-any.whl.metadata (2.3 kB)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<1,>=0.23.0->langSmith) (1.2.2)\n",
            "Downloading langgraph-0.2.18-py3-none-any.whl (96 kB)\n",
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            "\u001b[?25hDownloading langsmith-0.1.115-py3-none-any.whl (290 kB)\n",
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            "\u001b[?25hDownloading httpcore-1.0.5-py3-none-any.whl (77 kB)\n",
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            "\u001b[?25hDownloading langchain_core-0.2.38-py3-none-any.whl (396 kB)\n",
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            "\u001b[?25hDownloading langgraph_checkpoint-1.0.9-py3-none-any.whl (15 kB)\n",
            "Downloading orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Downloading tenacity-8.5.0-py3-none-any.whl (28 kB)\n",
            "Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading jsonpointer-3.0.0-py2.py3-none-any.whl (7.6 kB)\n",
            "Installing collected packages: tenacity, orjson, jsonpointer, h11, jsonpatch, httpcore, httpx, langSmith, langchain-core, langgraph-checkpoint, langgraph\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jsonpatch-1.33 jsonpointer-3.0.0 langSmith-0.1.115 langchain-core-0.2.38 langgraph-0.2.18 langgraph-checkpoint-1.0.9 orjson-3.10.7 tenacity-8.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install langgraph langsmith"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_groq langchain langchain_community"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4OTvapfWY9b",
        "outputId": "92b14c30-3fed-4d63-ed48-5f0601b23fd7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain_groq\n",
            "  Downloading langchain_groq-0.1.9-py3-none-any.whl.metadata (2.9 kB)\n",
            "Collecting langchain\n",
            "  Downloading langchain-0.2.16-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting langchain_community\n",
            "  Downloading langchain_community-0.2.16-py3-none-any.whl.metadata (2.7 kB)\n",
            "Collecting groq<1,>=0.4.1 (from langchain_groq)\n",
            "  Downloading groq-0.11.0-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: langchain-core<0.3.0,>=0.2.26 in /usr/local/lib/python3.10/dist-packages (from langchain_groq) (0.2.38)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.2)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.0.32)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.10.5)\n",
            "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (4.0.3)\n",
            "Collecting langchain-text-splitters<0.3.0,>=0.2.0 (from langchain)\n",
            "  Downloading langchain_text_splitters-0.2.4-py3-none-any.whl.metadata (2.3 kB)\n",
            "Requirement already satisfied: langsmith<0.2.0,>=0.1.17 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.1.115)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.26.4)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.8.2)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.32.3)\n",
            "Requirement already satisfied: tenacity!=8.4.0,<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (8.5.0)\n",
            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain_community)\n",
            "  Downloading dataclasses_json-0.6.7-py3-none-any.whl.metadata (25 kB)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.1)\n",
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            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.4)\n",
            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading marshmallow-3.22.0-py3-none-any.whl.metadata (7.2 kB)\n",
            "Collecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from groq<1,>=0.4.1->langchain_groq) (3.7.1)\n",
            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from groq<1,>=0.4.1->langchain_groq) (1.7.0)\n",
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            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.26->langchain_groq) (1.33)\n",
            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.26->langchain_groq) (24.1)\n",
            "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.17->langchain) (3.10.7)\n",
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            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->groq<1,>=0.4.1->langchain_groq) (0.14.0)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.10/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.3.0,>=0.2.26->langchain_groq) (3.0.0)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl.metadata (1.1 kB)\n",
            "Downloading langchain_groq-0.1.9-py3-none-any.whl (14 kB)\n",
            "Downloading langchain-0.2.16-py3-none-any.whl (1.0 MB)\n",
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            "\u001b[?25hDownloading langchain_community-0.2.16-py3-none-any.whl (2.3 MB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.5/106.5 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading langchain_text_splitters-0.2.4-py3-none-any.whl (25 kB)\n",
            "Downloading marshmallow-3.22.0-py3-none-any.whl (49 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.3/49.3 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: mypy-extensions, marshmallow, typing-inspect, groq, dataclasses-json, langchain-text-splitters, langchain_groq, langchain, langchain_community\n",
            "Successfully installed dataclasses-json-0.6.7 groq-0.11.0 langchain-0.2.16 langchain-text-splitters-0.2.4 langchain_community-0.2.16 langchain_groq-0.1.9 marshmallow-3.22.0 mypy-extensions-1.0.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata"
      ],
      "metadata": {
        "id": "3h5Ys1QeW9Gj"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "groq_api_key = userdata.get('groq_api_key')\n",
        "print(groq_api_key)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_OahiZTXCgb",
        "outputId": "508b9318-fcbf-492e-ea20-e36722cfaced"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "langsmith = userdata.get('LANGSMITH_API_KEY')\n",
        "print(langsmith)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2Dpw0SXYz4U",
        "outputId": "841d92a0-da17-4937-fce8-072d68f8401b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['LANGCHAIN_API_KEY'] = langsmith\n",
        "os.environ['LANGCHAIN_TRACING_V2'] = \"true\"\n",
        "os.environ[\"LANGCHAIN_PROJECT\"] = \"CourseLanggraph\""
      ],
      "metadata": {
        "id": "yluczEXoZL9N"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_groq import ChatGroq"
      ],
      "metadata": {
        "id": "84jeFzmyZWFk"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = ChatGroq(groq_api_key= groq_api_key, model_name=\"Gemma2-9b-It\")"
      ],
      "metadata": {
        "id": "UIdeG_0rZ5DX"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Start building chatbot using Langgraph"
      ],
      "metadata": {
        "id": "4kiIR2TeaJg3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Annotated\n",
        "from typing_extensions import TypedDict\n",
        "from langgraph.graph import StateGraph,START,END\n",
        "from langgraph.graph.message import add_messages"
      ],
      "metadata": {
        "id": "R6EqNJ5WaIOn"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class State(TypedDict):\n",
        "  # Messages have the type \"list\". The `add_messages` function\n",
        "    # in the annotation defines how this state key should be updated\n",
        "    # (in this case, it appends messages to the list, rather than overwriting them)\n",
        "  messages:Annotated[list,add_messages]\n",
        "\n",
        "graph_builder=StateGraph(State)\n"
      ],
      "metadata": {
        "id": "TW6SXF9zaPvV"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph_builder"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S33K2AybapXI",
        "outputId": "b5536592-6303-4bb8-87f9-6c8acad72868"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<langgraph.graph.state.StateGraph at 0x7cfe64649780>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def chatbot(state: State):\n",
        "  return {\"message\": llm.invoke(state['messages'])}"
      ],
      "metadata": {
        "id": "iFg8iwvva_tS"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph_builder.add_node(\"chatbot\",chatbot)"
      ],
      "metadata": {
        "id": "CfSQH3APbQ8z"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph_builder"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmmABAe0bZtJ",
        "outputId": "c7455085-889e-4de3-c69e-738877953939"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<langgraph.graph.state.StateGraph at 0x7cfe64649780>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "graph_builder.add_edge(START,\"chatbot\")\n",
        "graph_builder.add_edge(\"chatbot\", END)\n"
      ],
      "metadata": {
        "id": "xEtgE9Hjbbn2"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph = graph_builder.compile()"
      ],
      "metadata": {
        "id": "key-z7yLbvjY"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image, display\n",
        "try:\n",
        "  display(Image(graph.get_graph().draw_mermaid_png()))\n",
        "except Exception:\n",
        "  pass"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "xw9usEEyb0Ta",
        "outputId": "db7d24ac-4134-4b5d-c91d-83dc86c92960"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "while True:\n",
        "  user_input=input(\"User: \")\n",
        "  if user_input.lower() in [\"quit\",\"q\"]:\n",
        "    print(\"Good Bye\")\n",
        "    break\n",
        "  for event in graph.stream({'messages':(\"user\",user_input)}):\n",
        "    print(event.values())\n",
        "    for value in event.values():\n",
        "      print(value['messages'])\n",
        "      print(\"Assistant:\",value[\"messages\"].content)"
      ],
      "metadata": {
        "id": "PVhQp1W8b4sS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "User: hello\n",
        "dict_values([{'messages': AIMessage(content='Hello! 👋  How can I help you today? 😊\\n', response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 15, 'total_tokens': 30, 'completion_time': 0.028948375, 'prompt_time': 0.00232277, 'queue_time': None, 'total_time': 0.031271145}, 'model_name': 'Gemma2-9b-It', 'system_fingerprint': 'fp_10c08bf97d', 'finish_reason': 'stop', 'logprobs': None}, id='run-7c991b84-1ff6-461a-baa7-08c8d2767d10-0', usage_metadata={'input_tokens': 15, 'output_tokens': 15, 'total_tokens': 30})}])\n",
        "content='Hello! 👋  How can I help you today? 😊\\n' response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 15, 'total_tokens': 30, 'completion_time': 0.028948375, 'prompt_time': 0.00232277, 'queue_time': None, 'total_time': 0.031271145}, 'model_name': 'Gemma2-9b-It', 'system_fingerprint': 'fp_10c08bf97d', 'finish_reason': 'stop', 'logprobs': None} id='run-7c991b84-1ff6-461a-baa7-08c8d2767d10-0' usage_metadata={'input_tokens': 15, 'output_tokens': 15, 'total_tokens': 30}\n",
        "Assistant: Hello! 👋  How can I help you today? 😊\n",
        "\n",
        "User: what is generative ai\n",
        "dict_values([{'messages': AIMessage(content=\"Generative AI is a type of artificial intelligence that focuses on creating new content. \\n\\nThink of it like this: instead of simply analyzing existing data, generative AI learns the underlying patterns and structures within that data and then uses that knowledge to generate entirely new outputs.\\n\\nHere's a breakdown:\\n\\n**What it does:**\\n\\n* **Creates new content:** This can include text, images, audio, video, code, and even 3D models.\\n\\n* **Learns from existing data:**  It's trained on massive datasets to understand the nuances and rules of the content it's designed to generate.\\n\\n* **Generates variations:** It can create different variations of a given prompt or input, offering options and exploring creative possibilities.\\n\\n**Examples:**\\n\\n* **Text Generation:**\\n\\nChatGPT (like me!), writing stories, poems, articles, dialogue\\n\\n* **Image Generation:**\\n\\nDALL-E 2, Midjourney creating realistic or imaginative images from text descriptions\\n\\n* **Audio Generation:**\\n\\nJukebox, Amper Music composing music in various styles\\n\\n* **Code Generation:**\\n\\nGitHub Copilot assisting developers in writing code\\n\\n**How it works:**\\n\\nGenerative AI often relies on deep learning algorithms, particularly a type called generative adversarial networks (GANs). These algorithms consist of two neural networks that work in tandem:\\n\\n* **Generator:** Creates new content.\\n* **Discriminator:** Evaluates the quality of the generated content and tries to distinguish it from real data.\\n\\nThrough this competitive process, the generator improves its ability to produce increasingly realistic and sophisticated outputs.\\n\\n**Applications:**\\n\\nGenerative AI has a wide range of potential applications, including:\\n\\n* **Creative Arts:** Writing, music composition, art generation\\n* **Design:** Creating prototypes, generating marketing materials\\n* **Entertainment:** Developing interactive stories, generating game assets\\n* **Research:** Exploring new scientific concepts, simulating experiments\\n* **Education:** Personalized learning experiences, creating interactive learning materials\\n\\n**Ethical Considerations:**\\n\\nWhile generative AI offers exciting possibilities, it also raises ethical concerns:\\n\\n* **Misinformation:** Potential for creating realistic fake news, deepfakes, and propaganda.\\n* **Bias:** AI models can inherit and amplify biases present in the training data, leading to unfair or discriminatory outputs.\\n* **Copyright:** Questions surrounding the ownership and copyright of AI-generated content.\\n\\n\\n\\n\\nLet me know if you have any more questions about generative AI!\\n\", response_metadata={'token_usage': {'completion_tokens': 501, 'prompt_tokens': 18, 'total_tokens': 519, 'completion_time': 1.044149282, 'prompt_time': 0.002699907, 'queue_time': None, 'total_time': 1.046849189}, 'model_name': 'Gemma2-9b-It', 'system_fingerprint': 'fp_10c08bf97d', 'finish_reason': 'stop', 'logprobs': None}, id='run-6b200b57-a482-45f9-a07c-4fa92c425157-0', usage_metadata={'input_tokens': 18, 'output_tokens': 501, 'total_tokens': 519})}])\n",
        "content=\"Generative AI is a type of artificial intelligence that focuses on creating new content. \\n\\nThink of it like this: instead of simply analyzing existing data, generative AI learns the underlying patterns and structures within that data and then uses that knowledge to generate entirely new outputs.\\n\\nHere's a breakdown:\\n\\n**What it does:**\\n\\n* **Creates new content:** This can include text, images, audio, video, code, and even 3D models.\\n\\n* **Learns from existing data:**  It's trained on massive datasets to understand the nuances and rules of the content it's designed to generate.\\n\\n* **Generates variations:** It can create different variations of a given prompt or input, offering options and exploring creative possibilities.\\n\\n**Examples:**\\n\\n* **Text Generation:**\\n\\nChatGPT (like me!), writing stories, poems, articles, dialogue\\n\\n* **Image Generation:**\\n\\nDALL-E 2, Midjourney creating realistic or imaginative images from text descriptions\\n\\n* **Audio Generation:**\\n\\nJukebox, Amper Music composing music in various styles\\n\\n* **Code Generation:**\\n\\nGitHub Copilot assisting developers in writing code\\n\\n**How it works:**\\n\\nGenerative AI often relies on deep learning algorithms, particularly a type called generative adversarial networks (GANs). These algorithms consist of two neural networks that work in tandem:\\n\\n* **Generator:** Creates new content.\\n* **Discriminator:** Evaluates the quality of the generated content and tries to distinguish it from real data.\\n\\nThrough this competitive process, the generator improves its ability to produce increasingly realistic and sophisticated outputs.\\n\\n**Applications:**\\n\\nGenerative AI has a wide range of potential applications, including:\\n\\n* **Creative Arts:** Writing, music composition, art generation\\n* **Design:** Creating prototypes, generating marketing materials\\n* **Entertainment:** Developing interactive stories, generating game assets\\n* **Research:** Exploring new scientific concepts, simulating experiments\\n* **Education:** Personalized learning experiences, creating interactive learning materials\\n\\n**Ethical Considerations:**\\n\\nWhile generative AI offers exciting possibilities, it also raises ethical concerns:\\n\\n* **Misinformation:** Potential for creating realistic fake news, deepfakes, and propaganda.\\n* **Bias:** AI models can inherit and amplify biases present in the training data, leading to unfair or discriminatory outputs.\\n* **Copyright:** Questions surrounding the ownership and copyright of AI-generated content.\\n\\n\\n\\n\\nLet me know if you have any more questions about generative AI!\\n\" response_metadata={'token_usage': {'completion_tokens': 501, 'prompt_tokens': 18, 'total_tokens': 519, 'completion_time': 1.044149282, 'prompt_time': 0.002699907, 'queue_time': None, 'total_time': 1.046849189}, 'model_name': 'Gemma2-9b-It', 'system_fingerprint': 'fp_10c08bf97d', 'finish_reason': 'stop', 'logprobs': None} id='run-6b200b57-a482-45f9-a07c-4fa92c425157-0' usage_metadata={'input_tokens': 18, 'output_tokens': 501, 'total_tokens': 519}\n",
        "Assistant: Generative AI is a type of artificial intelligence that focuses on creating new content.\n",
        "\n",
        "Think of it like this: instead of simply analyzing existing data, generative AI learns the underlying patterns and structures within that data and then uses that knowledge to generate entirely new outputs.\n",
        "\n",
        "Here's a breakdown:\n",
        "\n",
        "**What it does:**\n",
        "\n",
        "* **Creates new content:** This can include text, images, audio, video, code, and even 3D models.\n",
        "\n",
        "* **Learns from existing data:**  It's trained on massive datasets to understand the nuances and rules of the content it's designed to generate.\n",
        "\n",
        "* **Generates variations:** It can create different variations of a given prompt or input, offering options and exploring creative possibilities.\n",
        "\n",
        "**Examples:**\n",
        "\n",
        "* **Text Generation:**\n",
        "\n",
        "ChatGPT (like me!), writing stories, poems, articles, dialogue\n",
        "\n",
        "* **Image Generation:**\n",
        "\n",
        "DALL-E 2, Midjourney creating realistic or imaginative images from text descriptions\n",
        "\n",
        "* **Audio Generation:**\n",
        "\n",
        "Jukebox, Amper Music composing music in various styles\n",
        "\n",
        "* **Code Generation:**\n",
        "\n",
        "GitHub Copilot assisting developers in writing code\n",
        "\n",
        "**How it works:**\n",
        "\n",
        "Generative AI often relies on deep learning algorithms, particularly a type called generative adversarial networks (GANs). These algorithms consist of two neural networks that work in tandem:\n",
        "\n",
        "* **Generator:** Creates new content.\n",
        "* **Discriminator:** Evaluates the quality of the generated content and tries to distinguish it from real data.\n",
        "\n",
        "Through this competitive process, the generator improves its ability to produce increasingly realistic and sophisticated outputs.\n",
        "\n",
        "**Applications:**\n",
        "\n",
        "Generative AI has a wide range of potential applications, including:\n",
        "\n",
        "* **Creative Arts:** Writing, music composition, art generation\n",
        "* **Design:** Creating prototypes, generating marketing materials\n",
        "* **Entertainment:** Developing interactive stories, generating game assets\n",
        "* **Research:** Exploring new scientific concepts, simulating experiments\n",
        "* **Education:** Personalized learning experiences, creating interactive learning materials\n",
        "\n",
        "**Ethical Considerations:**\n",
        "\n",
        "While generative AI offers exciting possibilities, it also raises ethical concerns:\n",
        "\n",
        "* **Misinformation:** Potential for creating realistic fake news, deepfakes, and propaganda.\n",
        "* **Bias:** AI models can inherit and amplify biases present in the training data, leading to unfair or discriminatory outputs.\n",
        "* **Copyright:** Questions surrounding the ownership and copyright of AI-generated content.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Let me know if you have any more questions about generative AI!\n",
        "\n",
        "User: q\n",
        "Good Bye\n"
      ],
      "metadata": {
        "id": "niQndzdZc3Sm"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OvhgBYCycR__"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}