notebooks/Agents_with_OpenAI_Assistants.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/towardsai/ai-tutor-rag-system/blob/main/notebooks/Agents_with_OpenAI_Assistants.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPKtF57twVp8",
        "outputId": "0af0655d-d8a0-478b-d7f3-8ce55292454a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m328.5/328.5 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q openai==1.37.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "gf1VoYD-Y7TL"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# Set the \"OPENAI_API_KEY\" in the Python environment. Will be used by OpenAI client later.\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"[OPENAI_API_KEY]\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLTbUTtthHGG"
      },
      "source": [
        "# Math Tutor\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "QxYu2uw9YoG8"
      },
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "client = OpenAI()\n",
        "\n",
        "assistant = client.beta.assistants.create(\n",
        "    name=\"Math Tutor\",\n",
        "    instructions=\"You are a personal math tutor. Write and run code to answer math questions.\",\n",
        "    model=\"gpt-4o\",\n",
        "    tools=[{\"type\": \"code_interpreter\"}],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "zdAu65wDY43T"
      },
      "outputs": [],
      "source": [
        "thread = client.beta.threads.create()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "AeiK-j7NZIJI"
      },
      "outputs": [],
      "source": [
        "message = client.beta.threads.messages.create(\n",
        "    thread_id=thread.id,\n",
        "    role=\"user\",\n",
        "    content=\"I need to solve the equation `3x + 11 = 14`. Can you help me?\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "-PWEekBTZJSR"
      },
      "outputs": [],
      "source": [
        "run = client.beta.threads.runs.create_and_poll(\n",
        "    thread_id=thread.id, assistant_id=assistant.id\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "SKcOwN2XZKTy"
      },
      "outputs": [],
      "source": [
        "messages = list(client.beta.threads.messages.list(thread_id=thread.id, run_id=run.id))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndRo014JZSLo",
        "outputId": "7186ef9a-7fb9-4e4b-c1cf-365f4d0d3bdc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Of course! To solve the equation \\(3x + 11 = 14\\), follow these steps:\n",
            "\n",
            "1. **Isolate the variable term**:\n",
            "   Subtract 11 from both sides of the equation.\n",
            "   \\[\n",
            "   3x + 11 - 11 = 14 - 11\n",
            "   \\]\n",
            "   Simplifies to:\n",
            "   \\[\n",
            "   3x = 3\n",
            "   \\]\n",
            "\n",
            "2. **Solve for \\( x \\)**:\n",
            "   Divide both sides of the equation by 3.\n",
            "   \\[\n",
            "   x = \\frac{3}{3}\n",
            "   \\]\n",
            "   Simplifies to:\n",
            "   \\[\n",
            "   x = 1\n",
            "   \\]\n",
            "\n",
            "So, the solution to the equation is \\( x = 1 \\).\n",
            "\n",
            "Let's verify this by substituting \\( x = 1 \\) back into the original equation to confirm that both sides are equal.\n",
            "\n",
            "\\[\n",
            "3(1) + 11 = 14\n",
            "\\]\n",
            "\\[\n",
            "3 + 11 = 14\n",
            "\\]\n",
            "\\[\n",
            "14 = 14\n",
            "\\]\n",
            "\n",
            "The left and right sides are equal, so the solution \\( x = 1 \\) is correct.\n"
          ]
        }
      ],
      "source": [
        "print(messages[0].content[0].text.value)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cgE3EEaHhFEh"
      },
      "source": [
        "# Customer Support\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-zDilXchGGU",
        "outputId": "9e9e306a-61fb-4617-f5a2-99eedd8f6bd2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-07-23 17:09:57--  https://personales.unican.es/corcuerp/linux/resources/LinuxCommandLineCheatSheet_1.pdf\n",
            "Resolving personales.unican.es (personales.unican.es)... 193.144.193.111\n",
            "Connecting to personales.unican.es (personales.unican.es)|193.144.193.111|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 267816 (262K) [application/pdf]\n",
            "Saving to: ‘LinuxCommandLineCheatSheet_1.pdf’\n",
            "\n",
            "LinuxCommandLineChe 100%[===================>] 261.54K   314KB/s    in 0.8s    \n",
            "\n",
            "2024-07-23 17:09:58 (314 KB/s) - ‘LinuxCommandLineCheatSheet_1.pdf’ saved [267816/267816]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://personales.unican.es/corcuerp/linux/resources/LinuxCommandLineCheatSheet_1.pdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "IqLR9ss9lKrz"
      },
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "client = OpenAI()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VevcGLDCjdUi",
        "outputId": "049f6306-84f6-434d-f3c7-dc0741bbbfb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "completed\n",
            "FileCounts(cancelled=0, completed=1, failed=0, in_progress=0, total=1)\n"
          ]
        }
      ],
      "source": [
        "# Create a vector store caled \"Financial Statements\"\n",
        "vector_store = client.beta.vector_stores.create(name=\"Tech Support\")\n",
        "\n",
        "# Ready the files for upload to OpenAI\n",
        "file_streams = [open(\"LinuxCommandLineCheatSheet_1.pdf\", \"rb\")]\n",
        "\n",
        "# Use the upload and poll SDK helper to upload the files, add them to the vector store,\n",
        "# and poll the status of the file batch for completion.\n",
        "file_batch = client.beta.vector_stores.file_batches.upload_and_poll(\n",
        "    vector_store_id=vector_store.id, files=file_streams\n",
        ")\n",
        "\n",
        "# You can print the status and the file counts of the batch to see the result of this operation.\n",
        "print(file_batch.status)\n",
        "print(file_batch.file_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "pTzfL1XCjdXT"
      },
      "outputs": [],
      "source": [
        "assistant = client.beta.assistants.create(\n",
        "    name=\"Tech Support\",\n",
        "    instructions=\"You are a tech support chatbot. Use the product manual to respond accurately to customer inquiries.\",\n",
        "    model=\"gpt-4o\",\n",
        "    tools=[{\"type\": \"file_search\"}],\n",
        "    tool_resources={\"file_search\": {\"vector_store_ids\": [vector_store.id]}},\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "FSTCsotRjdPj"
      },
      "outputs": [],
      "source": [
        "# Create a thread and attach the file to the message\n",
        "thread = client.beta.threads.create(\n",
        "    messages=[\n",
        "        {\n",
        "            \"role\": \"user\",\n",
        "            \"content\": \"What 'ls' command do?\",\n",
        "        }\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "jdD5yJK2jdMu"
      },
      "outputs": [],
      "source": [
        "run = client.beta.threads.runs.create_and_poll(\n",
        "    thread_id=thread.id, assistant_id=assistant.id\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0w3ts1DjdKW",
        "outputId": "a1720556-12df-42ff-bfd4-a001f3bb2565"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The `ls` command in Linux is used to list the contents of a directory. The common usage of `ls` can be extended with options to display detailed information about files and directories. For example:\n",
            "\n",
            "- `ls -al` lists all files, including hidden ones, in a long listing format that provides detailed information such as permissions, number of links, owner, group, size, and timestamp【4:0†source】【4:1†source】.\n"
          ]
        }
      ],
      "source": [
        "messages = list(client.beta.threads.messages.list(thread_id=thread.id, run_id=run.id))\n",
        "\n",
        "print(messages[0].content[0].text.value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1KafLldjdFI",
        "outputId": "0f1f388f-c04a-4eda-fe6b-9a00d83f0070"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[FileCitationAnnotation(end_index=394, file_citation=FileCitation(file_id='file-EMNwQYbq7rGni9Ct4V7B8XTR'), start_index=382, text='【4:0†source】', type='file_citation'),\n",
              " FileCitationAnnotation(end_index=406, file_citation=FileCitation(file_id='file-EMNwQYbq7rGni9Ct4V7B8XTR'), start_index=394, text='【4:1†source】', type='file_citation')]"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "messages[0].content[0].text.annotations"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyOyF/2q5TUS6bkOmPxn67kV",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.12.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}