diff --git "a/notebooks/Main_Ollama_Notebook.ipynb" "b/notebooks/Main_Ollama_Notebook.ipynb" new file mode 100644--- /dev/null +++ "b/notebooks/Main_Ollama_Notebook.ipynb" @@ -0,0 +1,2897 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "pLleJM1Xd5Dm" + }, + "source": [ + "# **Start Ollama Model on GPU in local of Notebook.**" + ] + }, + { + "cell_type": "code", + "source": [ + "%pip install llama-index-llms-ollama -q\n", + "%pip install llama-index -q\n", + "%pip install jsonpath-ng -q" + ], + "metadata": { + "id": "m8lFYWnCXq6O", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0f99603f-2121-4f76-dc3d-1a0527ab532a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.5/15.5 MB\u001b[0m \u001b[31m63.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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"https://localhost:8080/" + }, + "outputId": "f67b2694-3c3b-4a43-d494-3843b8db0d20" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "debconf: unable to initialize frontend: Dialog\n", + "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 3.)\n", + "debconf: falling back to frontend: Readline\n", + "debconf: unable to initialize frontend: Readline\n", + "debconf: (This frontend requires a controlling tty.)\n", + "debconf: falling back to frontend: Teletype\n", + "dpkg-preconfigure: unable to re-open stdin: \n" + ] + } + ], + "source": [ + "!sudo apt-get install -y pciutils > /dev/null" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8tgYX5dw0DX5" + }, + "outputs": [], + "source": [ + "!nvidia-smi > /dev/null" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B_WpKPo00F4v" + }, + "outputs": [], + "source": [ + "!pip install pyngrok -q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R7WZnRmd0I8_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8a03f523-c93d-4e36-e8c2-30eddf889193" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0>>> Downloading ollama...\n", + "100 11868 0 11868 0 0 34540 0 --:--:-- --:--:-- --:--:-- 34600\n", + "############################################################################################# 100.0%\n", + ">>> Installing ollama to /usr/local/bin...\n", + ">>> Creating ollama user...\n", + ">>> Adding ollama user to video group...\n", + ">>> Adding current user to ollama group...\n", + ">>> Creating ollama systemd service...\n", + ">>> NVIDIA GPU installed.\n", + ">>> The Ollama API is now available at 127.0.0.1:11434.\n", + ">>> Install complete. Run \"ollama\" from the command line.\n" + ] + } + ], + "source": [ + "!curl https://ollama.ai/install.sh | sh > /dev/null" + ] + }, + { + "cell_type": "code", + "source": [ + "!ollama list" + ], + "metadata": { + "id": "ROw_PBxgWAqU", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0abdfcc4-527f-40a6-8856-152dda9d6e35" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Error: could not connect to ollama app, is it running?\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "35j84P8p0igP", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6fe3bfa3-73b5-45d9-956d-0715b714528c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Enter your authtoken, which can be copied from https://dashboard.ngrok.com/auth\n" + ] + } + ], + "source": [ + "from pyngrok import ngrok, conf\n", + "import getpass\n", + "print(\"Enter your authtoken, which can be copied from https://dashboard.ngrok.com/auth\")\n", + "conf.get_default().auth_token = \"2ioaEMeTAs0vYDfDs26K71p9q3G_3nZ2suemrcHgwM9kr7X6w\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-av0IOfx0PuH" + }, + "outputs": [], + "source": [ + "import os\n", + "import threading\n", + "from pyngrok import ngrok\n", + "import subprocess\n", + "import time\n", + "\n", + "def ollama():\n", + " os.environ['OLLAMA_HOST'] = '0.0.0.0:11434'\n", + " os.environ['OLLAMA_ORIGINS'] = '*'\n", + " subprocess.Popen([\"ollama\", \"serve\"])\n", + "\n", + "def ngrok_tunnel():\n", + " # Wait for some time to ensure ollama is fully started\n", + " time.sleep(10)\n", + " port = \"11434\"\n", + " public_url = ngrok.connect(port).public_url\n", + " print(f\" * ngrok tunnel {public_url} -> http://127.0.0.1:{port}\")\n", + "\n", + "def monitor_gpu():\n", + " while True:\n", + " # print(subprocess.check_output([\"nvidia-smi\"]).decode(\"utf-8\"))\n", + " time.sleep(10) # adjust the sleep time to your preference\n", + "\n", + "# Create threads to run ollama, ngrok_tunnel, and monitor_gpu functions in the background\n", + "ollama_thread = threading.Thread(target=ollama)\n", + "ngrok_thread = threading.Thread(target=ngrok_tunnel)\n", + "gpu_monitor_thread = threading.Thread(target=monitor_gpu)\n", + "\n", + "# Start the threads\n", + "ollama_thread.start()\n", + "ngrok_thread.start()\n", + "gpu_monitor_thread.start()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NF1kRljCd5Dt" + }, + "source": [ + "# **Implementation of How to use OLLAMA Model in Code Starts**" + ] + }, + { + "cell_type": "code", + "source": [ + "ollama()" + ], + "metadata": { + "id": "eqEK23vEWTtV" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!ollama pull llama3.1" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "traNZuR88jnc", + "outputId": "f421cfbf-2711-469c-93ae-958d471fa949" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "ERROR:pyngrok.process.ngrok:t=2024-08-05T12:30:07+0000 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session err=\"authentication failed: Your account is limited to 1 simultaneous ngrok agent sessions.\\nYou can run multiple simultaneous tunnels from a single agent session by defining the tunnels in your agent configuration file and starting them with the command `ngrok start --all`.\\nRead more about the agent configuration file: https://ngrok.com/docs/secure-tunnels/ngrok-agent/reference/config\\nYou can view your current agent sessions in the dashboard:\\nhttps://dashboard.ngrok.com/tunnels/agents\\r\\n\\r\\nERR_NGROK_108\\r\\n\"\n", + "ERROR:pyngrok.process.ngrok:t=2024-08-05T12:30:07+0000 lvl=eror msg=\"session closing\" obj=tunnels.session err=\"authentication failed: Your account is limited to 1 simultaneous ngrok agent sessions.\\nYou can run multiple simultaneous tunnels from a single agent session by defining the tunnels in your agent configuration file and starting them with the command `ngrok start --all`.\\nRead more about the agent configuration file: https://ngrok.com/docs/secure-tunnels/ngrok-agent/reference/config\\nYou can view your current agent sessions in the dashboard:\\nhttps://dashboard.ngrok.com/tunnels/agents\\r\\n\\r\\nERR_NGROK_108\\r\\n\"\n", + "ERROR:pyngrok.process.ngrok:t=2024-08-05T12:30:07+0000 lvl=eror msg=\"terminating with error\" obj=app err=\"authentication failed: Your account is limited to 1 simultaneous ngrok agent sessions.\\nYou can run multiple simultaneous tunnels from a single agent session by defining the tunnels in your agent configuration file and starting them with the command `ngrok start --all`.\\nRead more about the agent configuration file: https://ngrok.com/docs/secure-tunnels/ngrok-agent/reference/config\\nYou can view your current agent sessions in the dashboard:\\nhttps://dashboard.ngrok.com/tunnels/agents\\r\\n\\r\\nERR_NGROK_108\\r\\n\"\n", + "CRITICAL:pyngrok.process.ngrok:t=2024-08-05T12:30:07+0000 lvl=crit msg=\"command failed\" err=\"authentication failed: Your account is limited to 1 simultaneous ngrok agent sessions.\\nYou can run multiple simultaneous tunnels from a single agent session by defining the tunnels in your agent configuration file and starting them with the command `ngrok start --all`.\\nRead more about the agent configuration file: https://ngrok.com/docs/secure-tunnels/ngrok-agent/reference/config\\nYou can view your current agent sessions in the dashboard:\\nhttps://dashboard.ngrok.com/tunnels/agents\\r\\n\\r\\nERR_NGROK_108\\r\\n\"\n", + "Exception in thread Thread-11 (ngrok_tunnel):\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n", + " self.run()\n", + " File \"/usr/lib/python3.10/threading.py\", line 953, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"\", line 16, in ngrok_tunnel\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyngrok/ngrok.py\", line 316, in connect\n", + " api_url = get_ngrok_process(pyngrok_config).api_url\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyngrok/ngrok.py\", line 156, in get_ngrok_process\n", + " return process.get_process(pyngrok_config)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyngrok/process.py\", line 235, in get_process\n", + " return _start_process(pyngrok_config)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyngrok/process.py\", line 398, in _start_process\n", + " raise PyngrokNgrokError(f\"The ngrok process errored on start: {ngrok_process.startup_error}.\",\n", + "pyngrok.exception.PyngrokNgrokError: The ngrok process errored on start: authentication failed: Your account is limited to 1 simultaneous ngrok agent sessions.\\nYou can run multiple simultaneous tunnels from a single agent session by defining the tunnels in your agent configuration file and starting them with the command `ngrok start --all`.\\nRead more about the agent configuration file: https://ngrok.com/docs/secure-tunnels/ngrok-agent/reference/config\\nYou can view your current agent sessions in the dashboard:\\nhttps://dashboard.ngrok.com/tunnels/agents\\r\\n\\r\\nERR_NGROK_108\\r\\n.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25lpulling manifest ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠴ 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+ "pulling 87048bcd5521... 10% ▕▏ 476 MB/4.7 GB 211 MB/s 19s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 11% ▕▏ 512 MB/4.7 GB 211 MB/s 19s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 11% ▕▏ 533 MB/4.7 GB 211 MB/s 19s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 12% ▕▏ 545 MB/4.7 GB 211 MB/s 19s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 12% ▕▏ 570 MB/4.7 GB 211 MB/s 19s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 13% ▕▏ 600 MB/4.7 GB 211 MB/s 19s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 13% ▕▏ 615 MB/4.7 GB 211 MB/s 19s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 14% ▕▏ 651 MB/4.7 GB 217 MB/s 18s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 14% ▕▏ 671 MB/4.7 GB 217 MB/s 18s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 15% ▕▏ 680 MB/4.7 GB 217 MB/s 18s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 15% ▕▏ 712 MB/4.7 GB 217 MB/s 18s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 16% ▕▏ 732 MB/4.7 GB 217 MB/s 18s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 16% ▕▏ 760 MB/4.7 GB 217 MB/s 17s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 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87048bcd5521... 42% ▕▏ 2.0 GB/4.7 GB 226 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 42% ▕▏ 2.0 GB/4.7 GB 226 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 43% ▕▏ 2.0 GB/4.7 GB 226 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 43% ▕▏ 2.0 GB/4.7 GB 226 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 43% ▕▏ 2.0 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 43% ▕▏ 2.0 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 43% ▕▏ 2.0 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 44% ▕▏ 2.0 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 44% ▕▏ 2.1 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 44% ▕▏ 2.1 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 222 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 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11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 216 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 216 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 216 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 216 MB/s 11s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 187 MB/s 13s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 45% ▕▏ 2.1 GB/4.7 GB 187 MB/s 13s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 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10s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 67% ▕▏ 3.1 GB/4.7 GB 148 MB/s 10s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 68% ▕▏ 3.2 GB/4.7 GB 148 MB/s 10s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 68% ▕▏ 3.2 GB/4.7 GB 148 MB/s 9s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 69% ▕▏ 3.2 GB/4.7 GB 148 MB/s 9s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 70% ▕▏ 3.3 GB/4.7 GB 148 MB/s 9s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 71% ▕▏ 3.3 GB/4.7 GB 148 MB/s 9s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 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7s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 76% ▕▏ 3.6 GB/4.7 GB 163 MB/s 6s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 77% ▕▏ 3.6 GB/4.7 GB 163 MB/s 6s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 77% ▕▏ 3.6 GB/4.7 GB 163 MB/s 6s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 78% ▕▏ 3.6 GB/4.7 GB 163 MB/s 6s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 78% ▕▏ 3.6 GB/4.7 GB 163 MB/s 6s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 79% ▕▏ 3.7 GB/4.7 GB 163 MB/s 6s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 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"pulling 87048bcd5521... 88% ▕▏ 4.1 GB/4.7 GB 66 MB/s 8s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 88% ▕▏ 4.1 GB/4.7 GB 66 MB/s 8s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 88% ▕▏ 4.1 GB/4.7 GB 66 MB/s 8s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 88% ▕▏ 4.1 GB/4.7 GB 66 MB/s 8s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 88% ▕▏ 4.1 GB/4.7 GB 66 MB/s 8s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 88% ▕▏ 4.1 GB/4.7 GB 66 MB/s 8s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 88% ▕▏ 4.1 GB/4.7 GB 66 MB/s 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"pulling 87048bcd5521... 100% ▕▏ 4.6 GB/4.7 GB 70 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 100% ▕▏ 4.6 GB/4.7 GB 70 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 100% ▕▏ 4.7 GB/4.7 GB 70 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 100% ▕▏ 4.7 GB/4.7 GB 70 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 100% ▕▏ 4.7 GB/4.7 GB 70 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 100% ▕▏ 4.7 GB/4.7 GB 70 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 87048bcd5521... 100% ▕▏ 4.7 GB/4.7 GB 70 MB/s 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second ago\t\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from llama_index.llms.ollama import Ollama" + ], + "metadata": { + "id": "2iIOQXyMUY-X" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import json\n", + "from IPython.display import Markdown, display" + ], + "metadata": { + "id": "So-ks6e2VX-U" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "llm = Ollama(model=\"llama3.1\", request_timeout=120)" + ], + "metadata": { + "id": "d4zJJ3uLVMz2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from llama_index.core.agent import ReActAgent\n", + "from llama_index.core.indices.struct_store import JSONQueryEngine\n", + "from llama_index.core.tools import BaseTool, FunctionTool\n", + "from llama_index.core.llms import ChatMessage\n", + "from llama_index.core import PromptTemplate" + ], + "metadata": { + "id": "54kU5Tj-VMxc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# **Contextualize Route**" + ], + "metadata": { + "id": "ePkEEgnFgoCt" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install ollama" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mGj5QImgg2xT", + "outputId": "9847a739-87f0-4ea2-d7c9-7aa0881461cd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: ollama in /usr/local/lib/python3.10/dist-packages (0.3.1)\n", + "Requirement already satisfied: httpx<0.28.0,>=0.27.0 in /usr/local/lib/python3.10/dist-packages (from ollama) (0.27.0)\n", + "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->ollama) (3.7.1)\n", + "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->ollama) (2024.7.4)\n", + "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->ollama) (1.0.5)\n", + "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->ollama) (3.7)\n", + "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->ollama) (1.3.1)\n", + "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<0.28.0,>=0.27.0->ollama) (0.14.0)\n", + "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<0.28.0,>=0.27.0->ollama) (1.2.2)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import ollama\n", + "client = ollama.Client()" + ], + "metadata": { + "id": "AJmLEV9FigaY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "Rg_hfpLYilfL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# **JSON Schema**" + ], + "metadata": { + "id": "4lVibg0eat3Z" + } + }, + { + "cell_type": "code", + "source": [ + "json_value = {\n", + " \"restuarant_name\": \"Wrap and Dip Cafe\",\n", + " \"number_of_times_item_ordered\": {\n", + " \"Make Your Own Meal\": 23,\n", + " \"Chicken Quesadilla\": 14,\n", + " \"Chicken Wings\": 8,\n", + " \"Spicy Chicken Wrap\": 10,\n", + " \"Beef Tacos\": 15,\n", + " \"Chicken Tacos\": 14,\n", + " \"Fresh Orange Juice\": 7,\n", + " \"Avocado Sauce\": 1,\n", + " \"Classic Sauce\": 1,\n", + " \"Guacamole & Chips\": 6,\n", + " \"Beef Quesadilla\": 2,\n", + " \"Mozzarella Sticks\": 3,\n", + " \"Vegetables Taco\": 11,\n", + " \"Signature Beef Wrap\": 4,\n", + " \"Chicken Avocado Wrap\": 3,\n", + " \"Philadelphia Steak Wrap\": 3,\n", + " \"Crunchy Mustard Wrap\": 2,\n", + " \"Meat Wrap\": 2,\n", + " \"Test123\": 2,\n", + " \"eeeee\": 1,\n", + " \"Onion Ring\": 1,\n", + " \"testing\": 1,\n", + " \"Lemon Mint Juice\": 3,\n", + " \"Blueberry Mojito\": 2,\n", + " \"Strawberry Mojito\": 1,\n", + " },\n", + " \"number_of_order_daywise\": {\n", + " \"2024-03-08\": 3,\n", + " \"2024-03-13\": 5,\n", + " \"2024-03-14\": 4,\n", + " \"2024-03-18\": 2,\n", + " \"2024-03-22\": 2,\n", + " \"2024-04-02\": 6,\n", + " \"2024-04-19\": 7,\n", + " \"2024-04-22\": 14,\n", + " \"2024-04-23\": 2,\n", + " \"2024-04-29\": 6,\n", + " },\n", + " \"number_of_order_canceled\": {\n", + " \"2024-03-08\": 0,\n", + " \"2024-03-13\": 1,\n", + " \"2024-03-14\": 0,\n", + " \"2024-03-18\": 0,\n", + " \"2024-03-22\": 0,\n", + " \"2024-04-02\": 0,\n", + " \"2024-04-19\": 0,\n", + " \"2024-04-22\": 2,\n", + " \"2024-04-23\": 0,\n", + " \"2024-04-29\": 2,\n", + " },\n", + " \"number_of_order_completed\": {\n", + " \"2024-03-08\": 3,\n", + " \"2024-03-13\": 1,\n", + " \"2024-03-14\": 3,\n", + " \"2024-03-18\": 1,\n", + " \"2024-03-22\": 0,\n", + " \"2024-04-02\": 3,\n", + " \"2024-04-19\": 6,\n", + " \"2024-04-22\": 11,\n", + " \"2024-04-23\": 0,\n", + " \"2024-04-29\": 5,\n", + " },\n", + " \"number_of_order_aggregator_wise\": {\n", + " \"SAHL\": 104,\n", + " \"zomato\": 32,\n", + " \"talabat\": 50,\n", + " \"noonfood\": 4,\n", + " \"careem\": 2,\n", + " \"deliveroo\": 3,\n", + " \"cari\": 2,\n", + " \"eateasy\": 1,\n", + " },\n", + " \"total_revenue\": 5460.2,\n", + " \"total_orders\": 51,\n", + " \"revenue_daywise\": {\n", + " \"2024-03-08\": 430.0,\n", + " \"2024-03-13\": 680.0,\n", + " \"2024-03-14\": 329.0,\n", + " \"2024-03-18\": 102.0,\n", + " \"2024-03-22\": 153.0,\n", + " \"2024-04-02\": 500.2,\n", + " \"2024-04-19\": 702.0,\n", + " \"2024-04-22\": 1850.0,\n", + " \"2024-04-23\": 231.0,\n", + " \"2024-04-29\": 483.0,\n", + " },\n", + "}\n" + ], + "metadata": { + "id": "FJ3RiZ7eawx0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "json_schema = {\n", + " \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n", + " \"title\": \"RestaurantOrderData\",\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"restaurant_name\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Name of the restaurant\"\n", + " },\n", + " \"number_of_times_item_ordered\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"Number of times each item has been ordered. Each key is item name and value is number of times it get ordered\",\n", + " \"additionalProperties\": {\n", + " \"type\": \"integer\",\n", + " \"description\": \"Count of times the item was ordered\"\n", + " }\n", + " },\n", + " \"number_of_order_daywise\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"Number of orders received each day. Each key is date and value is number of orders\",\n", + " \"additionalProperties\": {\n", + " \"type\": \"integer\",\n", + " \"description\": \"Count of orders for the specific day\"\n", + " }\n", + " },\n", + " \"number_of_order_canceled\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"Number of orders received each day. Each key is date and value is number of orders get canceled\",\n", + " \"additionalProperties\": {\n", + " \"type\": \"integer\",\n", + " \"description\": \"Count of canceled orders for the specific day\"\n", + " }\n", + " },\n", + " \"number_of_order_completed\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"Number of orders received each day. Each key is date and value is number of orders get completed\",\n", + " \"additionalProperties\": {\n", + " \"type\": \"integer\",\n", + " \"description\": \"Count of completed orders for the specific day\"\n", + " }\n", + " },\n", + " \"number_of_order_aggregator_wise\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"Number of orders received from each aggregator. Each key is aggregator name and value is number of orders get on that specific aggregator\",\n", + " \"additionalProperties\": {\n", + " \"type\": \"integer\",\n", + " \"description\": \"Count of orders for the specific aggregator\"\n", + " }\n", + " },\n", + " \"total_revenue\": {\n", + " \"type\": \"number\",\n", + " \"description\": \"Total revenue generated\"\n", + " },\n", + " \"total_orders\": {\n", + " \"type\": \"integer\",\n", + " \"description\": \"Total number of orders\"\n", + " },\n", + " \"revenue_daywise\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"Revenue generated each day. Each key is date and value is total revenue generated on that specific date\",\n", + " \"additionalProperties\": {\n", + " \"type\": \"number\",\n", + " \"description\": \"Revenue for the specific day\"\n", + " }\n", + " }\n", + " },\n", + " \"required\": [\n", + " \"restaurant_name\",\n", + " \"number_of_times_item_ordered\",\n", + " \"number_of_order_daywise\",\n", + " \"number_of_order_canceled\",\n", + " \"number_of_order_completed\",\n", + " \"number_of_order_aggregator_wise\",\n", + " \"total_revenue\",\n", + " \"total_orders\",\n", + " \"revenue_daywise\"\n", + " ]\n", + "}\n" + ], + "metadata": { + "id": "wwXJ-3nGi2Ia" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def create_dynamic_prompt(query):\n", + " \"\"\"Create a dynamic prompt.\"\"\"\n", + " prompt = f\"\"\"\n", + " The following is a task for an intelligent assistant:\n", + "\n", + " Here is the JSON with order details of a restaurant named \"Wrap and Dip Cafe\":\n", + " {json.dumps(json_value, indent=2)}\n", + "\n", + " Given the JSON schema for reference:\n", + " {json.dumps(json_schema, indent=2)}\n", + "\n", + " You are a JSON analysis engine designed to answer questions based on the given restaurant order data. The data includes various aspects such as the number of times each item has been ordered, the number of orders per day, cancellations, completions, orders by aggregator, total revenue, total orders, and daily revenue.\n", + "\n", + " When asked a question, follow these steps:\n", + "\n", + " 1. Understand the question and identify the relevant parts of the JSON data.\n", + " 2. Extract the necessary information from the JSON data.\n", + " 3. Perform any required calculations or comparisons.\n", + " 4. Provide a concise and accurate answer without including unnecessary details.\n", + " 5. If you encounter more than one answer, provide them in a list.\n", + " 6. Provide accurate, concise, and clear answers based on the JSON data provided.\n", + " 7. I only want the response to be printed after 'So,the answer is' or 'Therefore,the answer is' and not the based on json data line.\n", + "\n", + " Special attention should be given to queries about items ordered the most. These queries require looking into \"number_of_times_item_ordered\" and identifying the item(s) with the highest count.\n", + "\n", + " Here are a few examples of questions you should be able to answer:\n", + "\n", + " - \"Which item is ordered the most?\"\n", + " - Look into \"number_of_times_item_ordered\" and find the item with the highest count.\n", + "\n", + " - \"On which date was the highest revenue collected?\"\n", + " - Look into \"revenue_daywise\" and find the date with the highest revenue.\n", + "\n", + " - \"How many orders were completed on 2024-04-22?\"\n", + " - Look into \"number_of_order_completed\" for the value corresponding to \"2024-04-22\".\n", + "\n", + " - \"What is the total revenue generated?\"\n", + " - Return the value from \"total_revenue\".\n", + "\n", + " - \"How many orders were canceled on 2024-03-13?\"\n", + " - Look into \"number_of_order_canceled\" for the value corresponding to \"2024-03-13\".\n", + "\n", + " - \"Find the item with exactly 3 orders.\"\n", + " - Look into \"number_of_times_item_ordered\" and find the item(s) with a count of 3.\n", + "\n", + " Use these examples to guide your responses to similar questions. If you encounter a new type of question, use the structure and examples to determine how to extract and compute the answer.\n", + "\n", + " Remember, your goal is to provide accurate, concise, and clear answers based on the JSON data provided. Do not generate lengthy responses or include detailed breakdowns unless explicitly asked. Return only the direct answer to the question.\n", + "\n", + " The user's query is as follows: \"{query}\"\n", + " \"\"\"\n", + " messages = [\n", + " ChatMessage(role=\"system\", content=prompt),\n", + " ChatMessage(role=\"user\", content=query),\n", + " ]\n", + " resp = llm.chat(messages)\n", + " return resp.message.content" + ], + "metadata": { + "id": "9emhVsKataIi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "query = \"What revenue was g\"\n", + "simplified_query = create_dynamic_prompt(query)\n", + "print(simplified_query)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CNWNES2WuRDi", + "outputId": "6d773252-eabc-4ab0-f458-ef27aa84fbe0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Based on the JSON data, the revenue generated on 14th March (2024-03-14) is $329.0.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "Cquox6745P6q" + }, + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "collapsed_sections": [ + "pLleJM1Xd5Dm" + ] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file