LPI_QA_Generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/neomatrix369/learning-path-index/blob/advanced-rag/LPI_QA_Generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tXF13RVnbxeb"
   },
   "source": [
    "# This notebook demonstrates\n",
    "* Colab notebook authenticating to a GCP account (with specific project_id and region )\n",
    "* Tests prompting Gemini models via VertexAI\n",
    "* Test QA on a TEXT sent with prompt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2wktWDwzZiGM"
   },
   "source": [
    "# Install required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SihfvvEAP7f5",
    "outputId": "eab122bf-f333-4b49-e0fe-0c12fd82cc1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCPU times: user 180 ms, sys: 24.8 ms, total: 205 ms\n",
      "Wall time: 22.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "!pip install --upgrade -q google-cloud-aiplatform\n",
    "!pip install -q google-colab\n",
    "!pip install -q  loguru"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v2aLFzmlZlYG"
   },
   "source": [
    "# Import required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "waBqkphJUIDx"
   },
   "outputs": [],
   "source": [
    "from google.colab import userdata\n",
    "from google.colab import auth\n",
    "from google.cloud import aiplatform\n",
    "from pathlib import Path\n",
    "import os\n",
    "import base64\n",
    "import vertexai\n",
    "from vertexai.generative_models import GenerativeModel, Part\n",
    "from loguru import logger\n",
    "import json\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YMBFrbENZqi5"
   },
   "source": [
    "# Authenticate to GCP account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y9_YccRaTpk1",
    "outputId": "4c0fa132-3e77-4777-df45-38895014a451"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hirejorgecampos@gmail.com\n",
      "generated-mote-434518-u9\n",
      "\u001b[1;33mWARNING:\u001b[0m Property validation for compute/region was skipped.\n",
      "Updated property [compute/region].\n",
      "us-central1\n",
      "CPU times: user 405 ms, sys: 50.5 ms, total: 455 ms\n",
      "Wall time: 29.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "PROJECT_ID = \"generated-mote-434518-u9\" # Set this to a valid project id in your google cloud account\n",
    "REGION = 'us-central1' # Set this to the GCP region you want to use\n",
    "auth.authenticate_user(project_id=PROJECT_ID) # First time, this might prompt you to login to your google cloud account\n",
    "!gcloud config get core/account # This will validate if your authentication was successful and echo the login email\n",
    "!gcloud config get-value project # Verify by echo'ing the project config\n",
    "!gcloud config set compute/region {REGION} # Set the region\n",
    "!gcloud config get-value compute/region # Verify by echo'ing the region config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SfXKN6paa79H"
   },
   "source": [
    "# **Send a prompt and Text File To Gemini 1.5**\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IzN41MPGjNUN"
   },
   "source": [
    "## Creating the Prompt and system instructions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BudAD9FAiEPQ"
   },
   "outputs": [],
   "source": [
    "prompt = \"\"\"Using the following list of course files, generate a dataset of 10 question-answer pairs in JSON format. Each question should reflect a student\\'s perspective, seeking courses to learn machine learning and AI concepts at various proficiency levels. Include questions from beginners, intermediates, and advanced learners interested in areas like LLMs, NLP, RAG pipelines, fine-tuning models, and vector databases. Ensure the answers provide a tailored set of recommended courses that match the student\\'s learning objectives and skill level.\n",
    "The format for each question-answer pair should be as follows:\n",
    "{\"Question\":\"I am a beginner and want to learn about NLP. Which courses should I take to understand the fundamentals?\",\"Answer\":\"Given your beginner level and interest in NLP, I recommend the following courses and modules:\\\\n\\\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\\\n|---------------|--------|-------|----------------------|----------|--------|\\\\n| **NLP Basics** | Coursera | Beginner | 4-6 weeks | NLP, text preprocessing, tokenization, embeddings | Covers foundational NLP techniques for building text processing models. |\\\\n| **Python for Data Science** | DataCamp | Beginner | 4-8 weeks | Python, data manipulation, basic ML | Essential for understanding coding basics required in NLP workflows. |\\\"}\"\"\"\n",
    "\n",
    "LLM_instructions = \"\"\"You task is to create a syntenic conversational dataset from a dataset containing Machine Learning courses from various sites.\n",
    "Diversity in Questions:\n",
    "Include questions at varying skill levels (beginner, intermediate, advanced).\n",
    "Address specific interests (e.g., NLP, fine-tuning LLMs, vector databases, retrieval techniques).\n",
    "Incorporate scenarios like wanting to focus on a specific machine learning concept or pipeline.\n",
    "Answer Structure:\n",
    "Use a table to list multiple recommended courses/modules.\n",
    "For each course, include:\n",
    "Course/Module Name\n",
    "Source (e.g., Coursera, edX, official documentation)\n",
    "Level (Beginner, Intermediate, Advanced)\n",
    "Duration Estimate (e.g., 4-6 weeks, 10 hours)\n",
    "Keywords (relevant topics covered)\n",
    "Reason (brief explanation of why the course fits the student’s need)\n",
    "Ensure Answer Completeness:\n",
    "For each answer, list at least 3-5 relevant courses that progressively cover the required concepts.\n",
    "Each course/module should be aligned with the student’s stated goals and background level.\n",
    "JSON Output Format:\n",
    "The final output should be a JSON array containing \\'x\\' amount of qa pairs asked by the user\"\"\"\n",
    "\n",
    "generation_config = {\n",
    "    \"max_output_tokens\": 8192,\n",
    "    \"temperature\": 0.5,\n",
    "    \"top_p\": 1,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GFJFUKe3F60P"
   },
   "source": [
    "## Creating Function to check dataset size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NI_rQZvyhr1C"
   },
   "outputs": [],
   "source": [
    "def size_of_file(file: str) -> int:\n",
    "    \"\"\"\n",
    "    This function assumes a JSONL file and returns how many rows are in the file.\n",
    "    \"\"\"\n",
    "\n",
    "        # Check if the file exists\n",
    "    if not os.path.exists(file):\n",
    "        print(f\"File '{file}' not found.\")\n",
    "        return 0\n",
    "\n",
    "    # Reopen the file in read mode to check the size\n",
    "    with open(file, \"r\") as f:\n",
    "        lines = f.readlines()\n",
    "    return len(lines)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5YvUuIZpGDso"
   },
   "source": [
    "## Main Function script and call to vertex ai\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6F_VI2JGQYhb",
    "outputId": "f9b6b8a7-cc42-4082-dcc2-ed9ca41df44b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2024-10-27 17:21:20.342\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<cell line: 125>\u001b[0m:\u001b[36m127\u001b[0m - \u001b[1mProcessing file: LPI_index.txt\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.908\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 1, Amount of q-a sets generated: 43 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.911\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.913\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.915\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.919\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.920\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.923\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.924\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.925\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.928\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.929\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.930\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.934\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.935\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.936\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.938\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.940\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.941\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.942\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.943\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.945\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.946\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.948\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.949\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.951\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.952\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.956\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.961\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.963\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.965\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.967\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.970\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.972\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.974\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.976\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.979\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.981\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.983\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.987\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.988\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.990\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.992\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.993\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.995\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.997\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:22:51.999\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 0\n",
      " time elapse: 91.64716744422913\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.258\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 2, Amount of q-a sets generated: 13 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.260\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.262\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.265\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.266\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I am a complete beginner with no coding experience. Where should I start my machine learning journey?', 'Answer': \"Given your background, it's best to start with introductory courses focusing on both basic coding and machine learning concepts. Here are some recommendations:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Introduction to Machine Learning** | Google Developers | Beginner | ~1 hour | machine learning, supervised learning | Provides a high-level overview of ML without requiring coding. |\\n| **Python for Everybody** | Coursera | Beginner | 4-6 weeks | Python, programming basics | Equips you with fundamental Python skills necessary for ML. |\\n| **Machine Learning Crash Course (Foundation)** | Google Developers | Intermediate | ~6 hours | machine learning, TensorFlow | A more hands-on introduction to ML with practical exercises using TensorFlow. |\"}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.270\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I have some Python knowledge and want to learn about building NLP applications. What courses would you suggest?', 'Answer': 'Since you have Python basics covered, you can dive into NLP-specific courses. Here are some recommendations:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Natural Language Processing Specialization** | DeepLearning.AI | Intermediate | 4 months | NLP, embeddings, transformers, chatbots | Comprehensive specialization covering various NLP techniques and applications. |\\n| **ChatGPT Prompt Engineering for Developers** | DeepLearning.AI | Beginner | 1 hour | GenAI applications, prompt engineering, transformers | Focuses on using LLMs for text generation and building conversational AI. |\\n| **Practical Deep Learning for Coders** | Fast.ai | Advanced | ~40 hours | Deep learning, NLP, data ethics | Hands-on course covering deep learning techniques for NLP, including ethical considerations. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.272\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm an intermediate learner and want to understand how to fine-tune large language models for specific tasks. Any recommendations?\", 'Answer': 'Fine-tuning LLMs requires a good understanding of deep learning. Here are some courses to help you:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Deep Learning Specialization** | DeepLearning.AI | Intermediate | 6 months | Computer vision, deep learning, NLP, supervised learning | Provides a solid foundation in deep learning concepts and techniques. |\\n| **Finetuning Large Language Models** | DeepLearning.AI | Intermediate | 1 hour | Deep learning, fine-tuning, transformers | Focuses specifically on fine-tuning techniques for LLMs. |\\n| **Reinforcement Learning from Human Feedback** | DeepLearning.AI | Intermediate | Variable | Fine-tuning, generative models, LLMOps, transformers | Covers advanced techniques for aligning LLMs with human preferences. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.273\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to learn about building retrieval augmented generation (RAG) pipelines. Which courses cover this topic?', 'Answer': 'RAG pipelines combine information retrieval with LLM generation. These courses can help you understand this concept:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **LangChain for LLM Application Development** | DeepLearning.AI | Beginner | 1 hour | AI frameworks, agents, generative models, prompt engineering, RAG | Introduces LangChain, a framework for building LLM applications, including RAG. |\\n| **LangChain: Chat with Your Data** | DeepLearning.AI | Beginner | 1 hour | Embeddings, RAG, vector databases | Focuses on using LangChain to build conversational AI that retrieves information from your data. |\\n| **Building and Evaluating Advanced RAG Applications** | DeepLearning.AI | Beginner | Variable | AI frameworks, RAG, search and retrieval | Covers advanced techniques for building and evaluating RAG pipelines. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.277\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in learning about vector databases and their use in machine learning. What are some good resources?\", 'Answer': \"Vector databases are essential for efficient similarity search in ML applications. Here's where you can learn about them:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Vector Databases: from Embeddings to Applications** | DeepLearning.AI | Intermediate | Variable | Embeddings, RAG, search and retrieval, vector databases | A comprehensive course covering vector databases and their applications in ML. |\\n| **Large Language Models with Semantic Search** | DeepLearning.AI | Beginner | 1 hour | Embeddings, NLP, RAG, search and retrieval, vector databases | Focuses on using vector databases for semantic search with LLMs. |\\n| **Building Applications with Vector Databases** | DeepLearning.AI | Beginner | Variable | Anomaly detection, embeddings, multimodal, vector databases | Explores various applications of vector databases in ML, including anomaly detection and multimodal search. |\"}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.279\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm a data scientist familiar with traditional ML algorithms. How can I transition to deep learning?\", 'Answer': 'Transitioning to deep learning requires understanding neural networks. Here are some courses to help you:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Deep Learning Specialization** | DeepLearning.AI | Intermediate | 6 months | Computer vision, deep learning, NLP, supervised learning | A comprehensive deep learning specialization covering fundamentals and advanced concepts. |\\n| **Practical Deep Learning for Coders** | Fast.ai | Advanced | ~40 hours | Deep learning, machine learning, NLP, data ethics | Hands-on deep learning course with a practical approach and real-world applications. |\\n| **TensorFlow Developer Professional Certificate** | DeepLearning.AI | Intermediate | 2-4 weeks | AI frameworks, computer vision, deep learning, NLP | Focuses on using TensorFlow for building and deploying deep learning models. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.281\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to learn about deploying machine learning models in production. What resources are available?', 'Answer': 'Deploying ML models requires understanding MLOps principles. Here are some relevant courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Machine Learning in Production** | DeepLearning.AI | Intermediate | 4 months | Data engineering, deep learning, MLOps | Covers the entire ML lifecycle, including deployment and monitoring in production. |\\n| **TensorFlow: Data and Deployment Specialization** | DeepLearning.AI | Intermediate | 2-6 months | Data processing, MLOps, on-device AI | Focuses on data handling and deployment strategies for TensorFlow models. |\\n| **LLMOps** | DeepLearning.AI | Beginner | Variable | AI safety, data processing, evaluation and monitoring, fine-tuning, LLMOps | Introduces LLMOps principles and best practices for managing LLMs in production. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.282\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm an advanced learner and want to explore cutting-edge topics like generative adversarial networks (GANs). What courses do you recommend?\", 'Answer': 'For advanced learners interested in GANs, these courses are a good fit:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Generative Adversarial Networks (GANs) Specialization** | DeepLearning.AI | Intermediate | 3 months | Computer vision, deep learning, generative models | Comprehensive specialization covering various GAN architectures and applications. |\\n| **How Diffusion Models Work** | DeepLearning.AI | Intermediate | 1 hour | Deep learning, diffusion models, GenAI applications, generative models | Focuses on diffusion models, a powerful type of generative model used for image synthesis. |\\n| **Building Generative AI Applications with Gradio** | DeepLearning.AI | Beginner | 1 hour | Chatbots, diffusion models, GenAI applications, generative models | Teaches how to build and deploy generative AI applications using Gradio, a user interface library. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.289\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm concerned about the ethical implications of AI. Are there any courses that address responsible AI?\", 'Answer': 'Responsible AI is crucial. These courses can help you understand ethical considerations:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **AI for Everyone** | DeepLearning.AI | Beginner | 6 hours | Deep learning, machine learning, NLP, data ethics | Includes a module on the societal impacts and ethical considerations of AI. |\\n| **What is AI Ethics?** | IBM | Intermediate | ~10 minutes | AI ethics, artificial intelligence, responsible AI | Provides a brief but informative overview of AI ethics principles. |\\n| **Introduction to Generative AI** | Google Cloud Skill Boost | Beginner | ~2 hours | Responsible AI, ethics, AI ethics, fairness, bias, transparency | Discusses responsible AI principles in the context of generative AI. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.292\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Unterminated string starting at: line 1 column 119 (char 118). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.294\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:24:01.297\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 9\n",
      " time elapse: 160.9457724094391\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.372\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 3, Amount of q-a sets generated: 43 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.375\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.376\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.378\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.381\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.385\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.386\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.391\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.392\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.396\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.398\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.400\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.402\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.404\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.406\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.411\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.414\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.415\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.419\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.421\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.422\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.424\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.426\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.428\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.430\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.432\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.434\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.436\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.438\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.440\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.442\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.444\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.447\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.449\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.451\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.453\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.455\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.458\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.460\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.462\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.464\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.466\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.468\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.470\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.472\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:25:16.476\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 9\n",
      " time elapse: 236.12454080581665\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.459\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 4, Amount of q-a sets generated: 13 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.462\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.463\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.465\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.466\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I am a complete beginner with no prior knowledge of machine learning. Where should I start?', 'Answer': 'Given your beginner status, I recommend starting with introductory courses that cover the fundamentals of machine learning:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Introduction to Machine Learning | Google Developers | Beginner | ~1 hour | machine learning, supervised learning | Provides a gentle introduction to core ML concepts and terminology. |\\n| Machine Learning Crash Course (Foundation) | Google Developers | Intermediate | ~4 hours | machine learning, framing, terminology, linear regression | Covers essential ML terminology, problem framing, and basic algorithms like linear regression. |\\n| AI for Everyone | DeepLearning.AI | Beginner | 6 hours | Deep Learning, Machine Learning | This course will provide you with a high-level understanding of AI concepts and applications. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.469\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I have some basic understanding of ML. I want to learn more about neural networks. What courses would you suggest?', 'Answer': 'Since you have a basic understanding of ML, you can dive into neural networks with these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Machine Learning Crash Course (Foundation) | Google Developers | Intermediate | ~4 hours | neural networks, structure, training | Introduces the structure of neural networks and basic training concepts. |\\n| Deep Learning Specialization | DeepLearning.AI | Intermediate | ~6 months | Computer Vision, Deep Learning, NLP, Supervised Learning, Transformers | This specialization covers various deep learning architectures, including convolutional and recurrent neural networks, and their applications in computer vision and NLP. |\\n| TensorFlow Developer Professional Certificate | DeepLearning.AI | Intermediate | 2-4 weeks | AI Frameworks, Computer Vision, Deep Learning, NLP, Time Series | This certificate program focuses on using TensorFlow to build and deploy deep learning models. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.471\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in learning about Large Language Models (LLMs) and how they work. Could you recommend some relevant courses?\", 'Answer': 'Given your interest in LLMs, here are some courses that explore their architecture and applications:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Introduction to Generative AI | Google Cloud Skill Boost | Beginner | ~45 minutes | Generative AI, Deep Learning, Artificial Intelligence, Image Generation, Text Generation | Provides an overview of Generative AI and LLMs, their capabilities, and responsible AI considerations. |\\n| ChatGPT Prompt Engineering for Developers | DeepLearning.AI | Beginner | 1 hour | GenAI Applications, Prompt Engineering, Transformers | This course teaches you how to effectively interact with and utilize LLMs like ChatGPT through prompt engineering techniques. |\\n| Large Language Models with Semantic Search | DeepLearning.AI | Beginner | 1 hour | Embeddings, NLP, RAG, Search and Retrieval, Vector Databases | This course explores using LLMs for semantic search applications and introduces concepts like embeddings and vector databases. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.473\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm an intermediate learner and want to focus on Natural Language Processing (NLP). Which courses should I take?\", 'Answer': \"For an intermediate learner interested in NLP, I recommend these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Natural Language Processing Specialization | DeepLearning.AI | Intermediate | ~4 months | Chatbots, Embeddings, NLP, Transformers | Covers a wide range of NLP techniques, including word embeddings, sequence models, and attention mechanisms. |\\n| Natural Language (NLP) | Fast.ai | Advanced | ~100 minutes | Deep Learning, Machine Learning, NLP | Provides a practical approach to NLP using deep learning techniques. |\\n| Natural Language Processing on Google Cloud | Google Cloud Skill Boost | Advanced | ~12 hours | Machine Learning, NLP, Cloud, Dialogflow API | Focuses on applying NLP techniques using Google Cloud's tools and services. |\"}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.475\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to learn about building Retrieval Augmented Generation (RAG) pipelines. What resources can you recommend?', 'Answer': 'To learn about building RAG pipelines, consider these resources:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| LangChain for LLM Application Development | DeepLearning.AI | Beginner | 1 hour | AI Frameworks, Agents, Chatbots, Generative Models, Prompt Engineering, RAG | Introduces LangChain, a framework for building LLM-powered applications, including RAG pipelines. |\\n| LangChain: Chat with Your Data | DeepLearning.AI | Beginner | 1 hour | Computer Vision, Document Processing, Embeddings, RAG, Vector Databases | This course focuses on using LangChain to build a chatbot that can access and retrieve information from your own data. |\\n| Building and Evaluating Advanced RAG Applications | DeepLearning.AI | Beginner | N/A | AI Frameworks, Evaluation and Monitoring, GenAI Applications, LLMOps, RAG, Search and Retrieval | This course delves into building more sophisticated RAG applications and covers evaluation and monitoring techniques. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.477\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I have a good understanding of ML and want to learn about fine-tuning models. Any suggestions?', 'Answer': 'For fine-tuning models, these courses are suitable for advanced learners:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Finetuning Large Language Models | DeepLearning.AI | Intermediate | 1 hour | Deep Learning, Fine-Tuning, Transformers | Focuses specifically on fine-tuning large language models for specific tasks and domains. |\\n| Reinforcement Learning from Human Feedback | DeepLearning.AI | Intermediate | N/A | Fine-Tuning, Generative Models, LLMOps, Transformers | This course explores advanced techniques for fine-tuning LLMs using human feedback to improve performance and alignment. |\\n| Efficiently Serving LLMs | DeepLearning.AI | Intermediate | N/A | Fine-Tuning, Generative Models, LLMOps, LLM Serving, Transformers | This course covers fine-tuning techniques in the context of efficiently deploying and serving LLMs. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.478\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in using vector databases for my AI projects. Which courses should I explore?\", 'Answer': 'To learn about vector databases and their applications, check out these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Large Language Models with Semantic Search | DeepLearning.AI | Beginner | 1 hour | Embeddings, NLP, RAG, Search and Retrieval, Vector Databases | Introduces vector databases in the context of semantic search with LLMs. |\\n| LangChain: Chat with Your Data | DeepLearning.AI | Beginner | 1 hour | Computer Vision, Document Processing, Embeddings, RAG, Vector Databases | This course demonstrates using a vector database to store and retrieve information for a LangChain-powered chatbot. |\\n| Vector Databases: from Embeddings to Applications | DeepLearning.AI | Intermediate | N/A | Embeddings, GenAI Applications, RAG, Search and Retrieval, Vector Databases | This course provides a comprehensive overview of vector databases, their use cases, and how to integrate them into AI applications. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.479\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm a data engineer and need to learn about serverless data processing on Google Cloud. What courses would you recommend?\", 'Answer': 'For serverless data processing on Google Cloud, these courses are relevant for data engineers:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Data Engineer - Serverless Data Processing with Dataflow: Foundations | Google Cloud Skill Boost | Intermediate | ~2 hours | Cloud, Dataflow, Serverless, Data Processing, Data Pipelines | Provides a foundation for understanding serverless data processing concepts and Dataflow. |\\n| Data Engineer - Serverless Data Processing with Dataflow: Develop Pipelines | Google Cloud Skill Boost | Intermediate | ~35-36 hours | Cloud, Dataflow, Serverless, Data Processing, Data Pipelines, Beam | This course dives deeper into developing data pipelines using Dataflow and Apache Beam. |\\n| Dataflow: Qwik Start - Templates & Python | Google Cloud Skill Boost | Beginner | ~1 hour | Machine Learning, Deployment, Cloud, Lab, AI, task, Qwik Start, Command Line, Challenge | Offers a quick introduction to using Dataflow templates and Python for data processing tasks. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.481\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm concerned about AI ethics and bias. Are there any courses that address these issues?\", 'Answer': \"Yes, there are courses that specifically focus on AI ethics and bias:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| AI Ethics Resources | IBM | Intermediate | ~25 minutes | AI Ethics, Artificial Intelligence, AI, Responsible AI | Introduces the concepts of AI ethics, responsible AI principles, and practical considerations. |\\n| Introduction to Generative AI | Google Cloud Skill Boost | Beginner | ~20 minutes | Responsible AI, Ethics, AI Ethics, Fairness, Bias, Transparency | This module within the Generative AI course covers responsible AI principles and their application in developing and using generative AI models. |\\n| Fairness in Perspective API | Google Developers | Advanced | ~90 minutes | Machine Learning, Advance Machine Learning, Neural Network, Fairness, AI, Bias | Explores fairness considerations in machine learning models using Google's Perspective API, a tool for analyzing text for toxicity and bias. |\"}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.482\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Unterminated string starting at: line 1 column 173 (char 172). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.487\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:26:29.497\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 18\n",
      " time elapse: 309.14535188674927\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.923\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 5, Amount of q-a sets generated: 43 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.926\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.927\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.929\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.935\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.936\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.937\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.939\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.940\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.945\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.947\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.948\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.949\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.955\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.956\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.957\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.958\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.964\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.965\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.966\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.972\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.974\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.975\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.976\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.980\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.981\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.982\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.984\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.985\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.988\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.990\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.991\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.992\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.993\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.995\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.998\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:47.999\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.000\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.002\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.003\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.006\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.007\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.008\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.009\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.011\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:27:48.012\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 18\n",
      " time elapse: 387.6609046459198\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.548\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 6, Amount of q-a sets generated: 13 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.550\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.552\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.554\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.555\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I am a complete beginner with no coding experience. I want to start learning about Machine Learning. Where should I begin?', 'Answer': 'Given your background, I recommend starting with these foundational courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Introduction to Machine Learning | Google Developers | Beginner | 20 minutes | machine learning | Provides a basic overview of ML concepts without requiring coding. |\\n| What is Machine Learning | Google Developers | Beginner | 20 minutes | machine learning | Further clarifies fundamental ML concepts in an easy-to-understand way. |\\n| Python for Everybody | Coursera/University of Michigan | Beginner | 4-6 weeks | Python, programming basics | Introduces you to Python, the most popular language for ML. |\\n| AI for Everyone | DeepLearning.AI | Beginner | 6 hours | Deep Learning, Machine Learning | A non-technical course that explains AI broadly, including ML. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.557\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I have some basic understanding of ML and Python. I am interested in learning about Supervised Learning. What courses would you suggest?', 'Answer': 'Since you have a basic understanding of ML and Python, these courses on Supervised Learning would be suitable:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Supervised Learning | Google Developers | Beginner | 20 minutes | supervised learning | Introduces the core concepts of Supervised Learning. |\\n| Machine Learning Crash Course (Foundation) | Google Developers | Intermediate | ~4 hours | machine learning, framing, linear regression | Covers various aspects of ML, including framing problems and linear regression, a key supervised learning algorithm. |\\n| Machine Learning Specialization | DeepLearning.AI, Stanford Online | Beginner | 2.5 Months | Deep Learning, Machine Learning, Supervised Learning, Unsupervised Learning | A comprehensive specialization covering both supervised and unsupervised learning. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.560\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm comfortable with supervised learning and want to explore advanced ML techniques like Decision Trees and Random Forests. What resources can help me?\", 'Answer': \"For diving into Decision Trees and Random Forests, these advanced courses are recommended:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Machine Learning Advance Courses: Decision Forests | Google Developers | Advanced | 2 hours | Decision Trees, Random Forest | An overview of Decision Forests and their application in ML. |\\n| What's in this Course? | Google Developers | Advanced | 5 minutes | Decision Trees, Random Forest, classification, regression |  Introduces the course on Decision Trees and Forests, outlining the topics covered. |\\n| What are Decision Trees? | Google Developers | Advanced | 16 minutes | Decision Trees, Random Forest, Split, Node | Deep dive into the structure and concepts behind Decision Trees. |\\n| What are Random Forests? | Google Developers | Advanced | 22 minutes | Random Forest, Ensemble, Bagging, Bootstrap | Explains the ensemble method of Random Forests and their advantages. |\"}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.561\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to learn about building recommendation systems. Are there any specific courses you would recommend?', 'Answer': 'For learning about recommendation systems, the following courses are highly relevant:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Machine Learning Advance Courses: Recommendation Systems | Google Developers | Advanced | 4 hours | Recommendation System, Neural Network, Deep Learning | Provides a comprehensive overview of recommendation systems. |\\n| Introduction | Google Developers | Advanced | 5 minutes | Recommendation System, Neural Network, Deep Learning | Introduces the course on Recommendation Systems. |\\n| Candidate Generation | Google Developers | Advanced | 20 minutes | Recommendation System, content-based filtering, collaborative filtering | Explains candidate generation, a crucial step in recommendation systems. |\\n| Collaborative Filtering and Matrix Factorization | Google Developers | Advanced | 135 minutes | Recommendation System, collaborative filtering, Embedding | Covers collaborative filtering and matrix factorization, popular techniques for recommendation. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.562\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm fascinated by Generative AI and want to learn how to build applications with it. What are some good starting points?\", 'Answer': 'To get started with Generative AI and build applications, these courses are great options:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Introduction to Generative AI | Google Cloud Skill Boost | Beginner | 45 minutes | Generative AI, Deep Learning, Image Generation, Text Generation | Provides a foundational understanding of Generative AI concepts. |\\n| Generative AI Fundamentals | Google Cloud Skill Boost | Beginner | 120 minutes | Generative AI, Deep Learning, Image Generation, Text Generation | Delves deeper into the fundamentals of Generative AI. |\\n| Generative AI for Developers | Google Cloud Skill Boost | Beginner | 40 minutes | Generative AI, Deep Learning, Image Generation, Text Generation, Generative AI Studio | Focuses on using Generative AI Studio for building applications. |\\n| ChatGPT Prompt Engineering for Developers | DeepLearning.AI | Beginner | 1 hour | GenAI Applications, Prompt Engineering, Transformers | Teaches how to effectively use prompts with ChatGPT for application development. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.564\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm an intermediate learner and want to improve my understanding of NLP, especially transformers and BERT. What courses should I consider?\", 'Answer': 'Given your intermediate level and focus on transformers and BERT, these courses would be beneficial:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Natural Language Processing Specialization | DeepLearning.AI | Intermediate | 4 months | Chatbots, Embeddings, NLP, Transformers | Covers a wide range of NLP topics, including transformers. |\\n| Transformer Model and BERT Models | Google Cloud Skill Boost | Beginner | 42 minutes | Transformer Model, BERT, NLP, Pre-trained Model, Self Attention |  Specifically focuses on the transformer architecture and BERT models. |\\n| ChatGPT Prompt Engineering for Developers | DeepLearning.AI | Beginner | 1 hour | GenAI Applications, Prompt Engineering, Transformers | While focused on ChatGPT, it provides valuable insights into prompt engineering for transformer-based models. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.566\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in learning about Retrieval Augmented Generation (RAG) pipelines. Can you point me to some relevant resources?\", 'Answer': 'To learn about RAG pipelines, consider these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| LangChain for LLM Application Development | DeepLearning.AI | Beginner | 1 hour | AI Frameworks, Agents, Chatbots, Generative Models, Prompt Engineering, RAG | Introduces LangChain, a framework commonly used for building RAG pipelines. |\\n| LangChain: Chat with Your Data | DeepLearning.AI | Beginner | 1 hour | Computer Vision, Document Processing, Embeddings, RAG, Vector Databases | Focuses on using LangChain to build applications that interact with external data sources, a key aspect of RAG. |\\n| Building and Evaluating Advanced RAG Applications | DeepLearning.AI | Beginner |  Beginner | AI Frameworks, Evaluation and Monitoring, GenAI Applications, LLMOps, RAG, Search and Retrieval | Covers advanced RAG concepts and evaluation techniques. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.573\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm an advanced learner and want to explore fine-tuning large language models (LLMs). What are some advanced courses on this topic?\", 'Answer': 'For fine-tuning LLMs, these advanced courses provide in-depth knowledge:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Finetuning Large Language Models | DeepLearning.AI | Intermediate | 1 hour | Deep Learning, Fine-Tuning, Transformers | Provides a focused approach to fine-tuning techniques for LLMs. |\\n| Reinforcement Learning from Human Feedback | DeepLearning.AI | Intermediate | Intermediate | Fine-Tuning, Generative Models, LLMOps, Transformers | Covers advanced concepts like Reinforcement Learning from Human Feedback (RLHF), often used for fine-tuning LLMs. |\\n| Efficiently Serving LLMs | DeepLearning.AI | Intermediate | Intermediate | Fine-Tuning, Generative Models, LLMOps, LLM Serving, Transformers | Discusses efficient serving strategies for fine-tuned LLMs. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.574\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to understand how vector databases work and how they are used in machine learning applications. What resources would you recommend?', 'Answer': 'For understanding vector databases and their applications in ML, these courses are helpful:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| LangChain: Chat with Your Data | DeepLearning.AI | Beginner | 1 hour | Computer Vision, Document Processing, Embeddings, RAG, Vector Databases | Introduces vector databases in the context of building applications with LangChain. |\\n| Vector Databases: from Embeddings to Applications | DeepLearning.AI | Intermediate | Intermediate | Embeddings, GenAI Applications, RAG, Search and Retrieval, Vector Databases | A dedicated course focusing on vector database concepts and their use cases. |\\n| Building Applications with Vector Databases | DeepLearning.AI | Beginner | Beginner | Anomaly Detection, Embeddings, MultiModal, Vector Databases | Provides a practical guide to building applications that leverage vector databases. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.575\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm concerned about the ethical implications of AI and want to learn more about Responsible AI. What courses can guide me?\", 'Answer': 'To learn about Responsible AI and ethical considerations, these courses provide valuable insights:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Introduction to Responsibe AI | Google Cloud Skill Boost | Beginner | 20 minutes | Responsible AI, Ethics, AI Ethics, Fairness, Bias, Transparency | Introduces the core principles of Responsible AI. |\\n| Responsible AI: Applying AI Principles with Google Cloud | Google Cloud Skill Boost | Beginner | 128 minutes | Responsible AI, Ethics, AI Ethics, Fairness, Bias, Transparency |  Provides a practical framework for applying Responsible AI principles in real-world scenarios. |\\n| AI Ethics Resources | IBM | Intermediate | ~25 minutes | AI Ethics, Artificial Intelligence, Responsible AI | Offers a series of modules exploring various aspects of AI ethics. |\\n| What is AI Ethics? | IBM | Intermediate | 6 minutes | AI Ethics, Artificial Intelligence, Responsible AI | Specifically focuses on defining AI ethics and its importance. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.576\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:29:00.578\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 28\n",
      " time elapse: 460.2263379096985\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.689\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 7, Amount of q-a sets generated: 13 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.691\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.694\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.696\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.698\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I am a complete beginner in Machine Learning. Where should I start?', 'Answer': 'Given your beginner status in Machine Learning, I recommend starting with these foundational courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Introduction to Machine Learning | Google Developers | Beginner | ~1 hour | machine learning, supervised learning | Provides a gentle introduction to core ML concepts. |\\n| Machine Learning Crash Course (Foundation) | Google Developers | Intermediate | ~4 hours | machine learning, framing, terminology, linear regression | Covers essential ML terminology and walks you through a basic ML problem. |\\n| AI for Everyone | DeepLearning.AI | Beginner | 6 hours | Deep Learning, Machine Learning | Introduces the basics of AI and its applications. |\\n| Machine Learning Specialization | DeepLearning.AI, Stanford Online | Beginner | 2.5 Months | Anomaly Detection, Deep Learning, Machine Learning, Supervised Learning, Unsupervised Learning | A comprehensive introduction to various machine learning concepts and techniques. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.700\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I have some basic understanding of ML, but I want to dive deeper into Neural Networks.', 'Answer': 'Since you have a basic understanding of ML, these courses on Neural Networks will be beneficial:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Neural Networks | Google Developers | Intermediate | ~1 hour | neural networks, structure, training | Introduces the structure and training of neural networks. |\\n| Deep Learning Specialization | DeepLearning.AI | Intermediate | 6 Months | Computer Vision, Deep Learning, NLP, Supervised Learning, Transformers | Covers deep learning concepts and applications in detail, including neural networks. |\\n| TensorFlow Developer Professional Certificate | DeepLearning.AI | Intermediate | 2-4 Weeks | AI Frameworks, Computer Vision, Deep Learning, NLP, Time Series | Focuses on building and deploying deep learning models using TensorFlow. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.702\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm particularly interested in NLP. What courses do you recommend for someone at an intermediate level?\", 'Answer': 'Given your interest in NLP and intermediate skill level, I suggest these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Natural Language (NLP) | Fast.ai | Advanced | ~2 hours | Deep Learning, Machine Learning, NLP | An advanced course covering NLP using deep learning techniques. |\\n| Natural Language Processing Specialization | DeepLearning.AI | Intermediate | 4 Months | Chatbots, Embeddings, NLP, Transformers | Covers various NLP techniques and applications, including chatbots and transformers. |\\n| ChatGPT Prompt Engineering for Developers | DeepLearning.AI | Beginner | 1 Hour | GenAI Applications, Prompt Engineering, Transformers | Focuses on prompt engineering techniques for ChatGPT and LLMs. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.705\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to learn about building RAG pipelines. Are there any specific courses for that?', 'Answer': 'Building RAG pipelines is a specialized topic. Here are some courses that cover relevant concepts:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| LangChain for LLM Application Development | DeepLearning.AI | Beginner | 1 Hour | AI Frameworks, Agents, Chatbots, Generative Models, Prompt Engineering, RAG | Introduces LangChain, a framework for building LLM-powered applications, including RAG pipelines. |\\n| LangChain: Chat with Your Data | DeepLearning.AI | Beginner | 1 Hour | Computer Vision, Document Processing, Embeddings, RAG, Vector Databases | Focuses on using LangChain to build applications that interact with your data using RAG. |\\n| Building and Evaluating Advanced RAG Applications | DeepLearning.AI | Beginner |  | AI Frameworks, Evaluation and Monitoring, GenAI Applications, LLMOps, RAG, Search and Retrieval | Covers advanced techniques for building and evaluating RAG applications. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.707\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I have a good grasp of ML concepts and want to learn about fine-tuning LLMs for specific tasks.', 'Answer': \"For fine-tuning LLMs, you'll need a solid understanding of deep learning. These courses can help:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Finetuning Large Language Models | DeepLearning.AI | Intermediate | 1 Hour | Deep Learning, Fine-Tuning, Transformers | Focuses specifically on fine-tuning large language models for specific tasks. |\\n| Reinforcement Learning from Human Feedback | DeepLearning.AI | Intermediate |  | Fine-Tuning, Generative Models, LLMOps, Transformers | Covers advanced techniques for fine-tuning LLMs using human feedback. |\\n| Generative AI with LLMs | DeepLearning.AI | Intermediate |  | Fine-Tuning, GenAI Applications, Generative Models, Prompt Engineering, Transformers | Explores generative AI applications using LLMs, including fine-tuning techniques. |\"}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.709\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm an advanced learner and want to explore the use of vector databases in ML applications.\", 'Answer': 'For advanced learners interested in vector databases, these courses are recommended:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Vector Databases: from Embeddings to Applications | DeepLearning.AI | Intermediate |  | Embeddings, GenAI Applications, RAG, Search and Retrieval, Vector Databases | Provides a comprehensive overview of vector databases and their applications in ML. |\\n| Large Language Models with Semantic Search | DeepLearning.AI | Beginner | 1 Hour | Embeddings, NLP, RAG, Search and Retrieval, Vector Databases | Focuses on using LLMs and vector databases for semantic search applications. |\\n| Building Applications with Vector Databases | DeepLearning.AI | Beginner |  | Anomaly Detection, Embeddings, MultiModal, Vector Databases | Covers practical aspects of building applications that leverage vector databases. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.711\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'As a data engineer, I want to improve my skills in building data pipelines for ML workflows. What are some relevant courses?', 'Answer': 'For data engineers looking to build ML data pipelines, these courses are a good fit:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Data Engineer - Serverless Data Processing with Dataflow: Foundations | Google Cloud Skill Boost | Intermediate | ~2 hours | Cloud, Dataflow, Severless, Data Processing, Data Pipelines | Introduces serverless data processing with Dataflow, a key component of ML pipelines on Google Cloud. |\\n| Data Engineer - Serverless Data Processing with Dataflow: Develop Pipelines | Google Cloud Skill Boost | Intermediate | 35-36 hours | Cloud, Dataflow, Severless, Data Processing, Data Pipelines | Covers advanced concepts and techniques for developing data pipelines using Dataflow. |\\n| TensorFlow: Data and Deployment Specialization | DeepLearning.AI | Intermediate | 2-6 Months | Data Processing, MLOps, On-Device AI | Focuses on data processing and deployment techniques for TensorFlow models, including pipeline building. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.712\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in the ethical considerations of AI. Are there any courses that address this?\", 'Answer': 'AI ethics is an important topic. Here are some courses that delve into ethical considerations:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| AI Ethics Resources | IBM | Intermediate | ~20 minutes | AI Ethics, Artificial Intelligence, AI, Responsible AI | Provides a concise overview of AI ethics and its importance. |\\n| Introduction to Generative AI | Google Cloud Skill Boost | Beginner | ~3 hours | Generative AI, Deep Learning, Artificial Intelligence, AI, Image Generation, Text Generation | Includes a module on responsible AI, covering ethical considerations in generative AI. |\\n| Quality and Safety for LLM Applications | DeepLearning.AI | Beginner |  | AI Safety, Embeddings, Evaluation and Monitoring, GenAI Applications, Vector Databases | Focuses on ensuring quality and safety in LLM applications, addressing ethical concerns. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.713\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm looking for a challenging course that covers advanced ML techniques like Generative Adversarial Networks (GANs). What do you suggest?\", 'Answer': 'For advanced learners interested in GANs, this course is highly recommended:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Generative Adversarial Networks (GANs) Specialization | DeepLearning.AI | Intermediate | 3 Months | Computer Vision, Deep Learning, Generative Models | Provides a deep dive into GANs, covering their architecture, training, and applications. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.715\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Unterminated string starting at: line 1 column 140 (char 139). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.716\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:30:12.717\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 37\n",
      " time elapse: 532.3660490512848\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.216\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 8, Amount of q-a sets generated: 43 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.219\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.220\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.223\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.224\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.227\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.229\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.230\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.231\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.232\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.233\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.236\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.238\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.239\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.240\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.242\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.243\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.244\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.246\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.248\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.249\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.251\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.252\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.253\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.255\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.257\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.258\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.259\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.261\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.262\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.264\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.265\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.267\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.268\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.270\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.271\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.272\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.274\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.275\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.276\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.277\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.278\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.280\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.281\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.282\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:31:31.283\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 37\n",
      " time elapse: 610.9317615032196\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.349\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 9, Amount of q-a sets generated: 33 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.353\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.355\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.357\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.358\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 83 (char 82). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.360\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.361\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.362\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 137 (char 136). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.366\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.368\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.369\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 107 (char 106). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.370\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.372\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.373\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 100 (char 99). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.374\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.375\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.377\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 69 (char 68). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.378\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.379\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.381\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 128 (char 127). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.382\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.383\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.385\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 114 (char 113). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.386\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.387\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.388\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 132 (char 131). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.390\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.391\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.392\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 115 (char 114). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.393\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.396\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.397\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting ',' delimiter: line 1 column 123 (char 122). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.399\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.400\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.401\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:32:37.402\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 37\n",
      " time elapse: 677.0509040355682\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.414\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 10, Amount of q-a sets generated: 43 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.417\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.420\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.423\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.428\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.431\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.432\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.434\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.435\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.436\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.438\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.439\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.440\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.441\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.443\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.444\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.445\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.446\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.447\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.448\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.450\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.451\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.452\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.453\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.454\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.456\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.457\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.458\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.459\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.460\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.463\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.464\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.465\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.466\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.467\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.468\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.470\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.471\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.473\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.474\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.475\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.476\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.477\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.479\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.480\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:34:01.481\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 37\n",
      " time elapse: 761.1294586658478\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.665\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 11, Amount of q-a sets generated: 13 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.668\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.669\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.671\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.674\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I am a complete beginner with no coding experience. Where should I start my journey to learn Machine Learning?', 'Answer': 'Given your background, I recommend starting with the following courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Introduction to Machine Learning** | Google Developers | Beginner | 20 minutes | machine learning | Provides a basic overview of what machine learning is and its applications. |\\n| **What is Machine Learning** | Google Developers | Beginner | 20 minutes | machine learning | Further explores the concept of machine learning and its types. |\\n| **AI for Everyone** | DeepLearning.AI | Beginner | 6 hours | Deep Learning, Machine Learning | A non-technical course that explains the fundamentals of AI and its impact. |\\n| **Machine Learning Specialization** | DeepLearning.AI, Stanford Online | Beginner | 2.5 Months | Anomaly Detection, Deep Learning, Machine Learning, Supervised Learning, Unsupervised Learning | A comprehensive introduction to machine learning covering various algorithms and techniques. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.676\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I have some Python knowledge and want to learn about supervised learning. Any recommendations?', 'Answer': 'Since you have Python knowledge and want to learn supervised learning, here are some courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Supervised Learning** | Google Developers | Beginner | 20 minutes | supervised learning | Explains the concept of supervised learning and its algorithms. |\\n| **Machine Learning Crash Course (Foundation)** | Google Developers | Intermediate | Several hours | machine learning, linear regression, logistic regression | A practical crash course covering key supervised learning algorithms and TensorFlow. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.678\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to understand how to build a basic neural network. Which courses are suitable for me?', 'Answer': 'To learn about building basic neural networks, I recommend these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Neural Networks - Video Lecture** | Google Developers | Intermediate | Part of ML Crash Course | neural networks | Introduces the concept of neural networks and their structure. |\\n| **Neural Networks** | Google Developers | Intermediate | Part of ML Crash Course | neural networks | Explores the structure of neural networks and provides practical exercises. |\\n| **3: Neural net foundations** | Fast.ai | Advanced | 90 minutes | Deep Learning, Machine Learning, CNNs, Neural Network Fundamentals | Covers the foundational concepts of neural networks and CNNs. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.681\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in working with LLMs for text generation. What should I study?\", 'Answer': 'To dive into LLMs for text generation, consider these resources:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Introduction to Generative AI** | Google Cloud Skill Boost | Beginner | 45 minutes | Generative AI, Deep Learning, Artificial Intelligence, AI, Image Generation, Text Generation | Provides a foundational understanding of generative AI and its applications. |\\n| **Introduction to Large Language Models** | Google Cloud Skill Boost | Beginner | 45 minutes | Generative AI, Deep Learning, Artificial Intelligence, AI, Text Generation, Natural Language Processing, NLP | Focuses specifically on large language models and their capabilities. |\\n| **ChatGPT Prompt Engineering for Developers** | DeepLearning.AI | Beginner | 1 Hour | GenAI Applications, Prompt Engineering, Transformers | Teaches how to effectively interact with and guide LLMs using prompt engineering techniques. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.682\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to learn about fine-tuning LLMs for specific tasks. Any course suggestions?', 'Answer': 'For fine-tuning LLMs, I recommend the following:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Finetuning Large Language Models** | DeepLearning.AI | Intermediate | 1 Hour | Deep Learning, Fine-Tuning, Transformers | Provides a comprehensive guide to fine-tuning LLMs for specific downstream tasks. |\\n| **Reinforcement Learning from Human Feedback** | DeepLearning.AI | Intermediate |  | Fine-Tuning, Generative Models, LLMOps, Transformers | Explores advanced techniques for aligning LLMs with human preferences through reinforcement learning. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.684\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm an intermediate learner and want to explore Retrieval Augmented Generation (RAG) pipelines. What are some good resources?\", 'Answer': 'For learning about RAG pipelines, here are some recommendations:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **LangChain for LLM Application Development** | DeepLearning.AI | Beginner | 1 Hour | AI Frameworks, Agents, Chatbots, Generative Models, Prompt Engineering, RAG | Introduces LangChain, a framework for building LLM-powered applications, including RAG pipelines. |\\n| **LangChain: Chat with Your Data** | DeepLearning.AI | Beginner | 1 Hour | Computer Vision, Document Processing, Embeddings, RAG, Vector Databases | Focuses on using LangChain to create conversational AI systems that leverage external data sources. |\\n| **Building and Evaluating Advanced RAG Applications** | DeepLearning.AI | Beginner |  | AI Frameworks, Evaluation and Monitoring, GenAI Applications, LLMOps, RAG, Search and Retrieval | Covers advanced concepts and evaluation techniques for building robust RAG pipelines. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.687\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'Which courses can help me understand vector databases and their use in AI applications?', 'Answer': 'To learn about vector databases and their applications, consider these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Large Language Models with Semantic Search** | DeepLearning.AI | Beginner | 1 Hour | Embeddings, NLP, RAG, Search and Retrieval, Vector Databases | Introduces the concept of semantic search and the use of vector databases for efficient retrieval. |\\n| **Vector Databases: from Embeddings to Applications** | DeepLearning.AI | Intermediate |  | Embeddings, GenAI Applications, RAG, Search and Retrieval, Vector Databases | Provides a deeper dive into vector databases, covering their architecture and various use cases. |\\n| **Building Applications with Vector Databases** | DeepLearning.AI | Beginner |  | Anomaly Detection, Embeddings, MultiModal, Vector Databases | Focuses on practical aspects of building AI applications that leverage vector databases. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.688\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in learning about the ethical considerations surrounding AI. What resources do you suggest?\", 'Answer': 'For learning about AI ethics, I recommend the following:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Introduction to Responsibe AI** | Google Cloud Skill Boost | Beginner | 20 minutes | Responsible AI, Ethics, AI Ethics, Fairness, Bias, Transparency | Provides a foundational understanding of responsible AI principles and practices. |\\n| **Responsible AI: Applying AI Principles with Google Cloud** | Google Cloud Skill Boost | Beginner | 128 minutes | Responsible AI, Ethics, AI Ethics, Fairness, Bias, Transparency | Explores how to apply responsible AI principles in real-world scenarios using Google Cloud tools. |\\n| **What is AI Ethics ?** | IBM | Intermediate | 3 minutes | AI Ethics, Artificial Intelligence, AI, Responsible AI | Introduces the concept of AI ethics and its importance in AI development. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.690\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to improve my skills in debugging and testing machine learning models. Any recommendations?', 'Answer': 'To enhance your debugging and testing skills for machine learning models, consider these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| **Testing and Debugging** | Google Developers | Intermediate | Several hours | debugging, learning, testing, ml, model, code | Provides a comprehensive guide to debugging and testing machine learning models, covering various techniques and best practices. |\\n| **Evaluating and Debugging Generative AI Models Using Weights and Biases** | DeepLearning.AI | Intermediate | 1 Hour | Evaluation and Monitoring, Fine-Tuning, Generative Models, LLMOps, MLOps, MultiModal, Prompt Engineering | Focuses on evaluating and debugging generative AI models, including LLMs, using Weights & Biases tools. |\\n| **Automated Testing for LLMOps** | DeepLearning.AI | Intermediate |  | Evaluation and Monitoring, LLMOps, MLOps, Prompt Engineering | Covers automated testing strategies specifically for LLM-powered applications. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.697\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Unterminated string starting at: line 1 column 139 (char 138). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.698\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:35:05.701\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 46\n",
      " time elapse: 825.3493053913116\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.422\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 12, Amount of q-a sets generated: 43 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.425\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.427\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.428\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.430\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.432\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.435\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.436\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.439\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.441\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.443\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.446\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.448\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.450\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.452\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.453\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.455\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.456\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.458\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.460\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.461\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.463\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.464\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.466\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.467\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.470\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.472\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.476\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.479\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.482\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.484\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.486\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.489\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.491\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.494\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.495\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.497\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.500\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.502\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.503\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.506\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting property name enclosed in double quotes: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.508\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 11 (char 10). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.510\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Extra data: line 1 column 9 (char 8). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.512\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.514\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:36:24.518\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 46\n",
      " time elapse: 904.1669540405273\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.874\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mIteration 13, Amount of q-a sets generated: 13 \u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.877\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m56\u001b[0m - \u001b[34m\u001b[1mAppending data into file.....\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.879\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.881\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 2 (char 1). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.883\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I am a complete beginner with no prior knowledge of machine learning. Where should I start?', 'Answer': 'Given your beginner status, I recommend starting with introductory courses that cover the basics of machine learning:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Introduction to Machine Learning | Google Developers | Beginner | ~1 hour | machine learning, supervised learning | A quick overview of core ML concepts. |\\n| Machine Learning Crash Course (Foundation) | Google Developers | Intermediate | ~4 hours | machine learning, framing, terminology, linear regression, training, loss, TensorFlow |  A more in-depth crash course covering essential ML terminology and techniques. |\\n| AI for Everyone | DeepLearning.AI | Beginner | 6 hours | Deep Learning, Machine Learning | This course provides a broad understanding of AI concepts without delving into technical details. |\\n| Machine Learning Specialization | DeepLearning.AI, Stanford Online | Beginner | 2.5 Months | Anomaly Detection, Deep Learning, Machine Learning, Supervised Learning, Unsupervised Learning | A comprehensive specialization covering various aspects of machine learning. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.885\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I have some basic understanding of ML. I want to learn about Natural Language Processing. What courses would you suggest?', 'Answer': 'Since you have basic ML knowledge, you can dive into NLP with these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Natural Language Processing | Google Developers | Intermediate | ~2 hours | neural networks, embeddings, production ML systems |  Focuses on practical NLP techniques using neural networks. |\\n| Natural Language Processing Specialization | DeepLearning.AI | Intermediate | 4 months | Chatbots, Embeddings, NLP, Transformers |  A specialization covering various NLP concepts, including chatbots and transformers. |\\n| Understanding and Applying Text Embeddings | DeepLearning.AI | Beginner | 1 Hour | Embeddings, GenAI Applications, NLP, RAG, Search and Retrieval | This course focuses on text embeddings, a fundamental concept in NLP. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.887\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in building a Retrieval Augmented Generation (RAG) pipeline. What resources can help me get started?\", 'Answer': 'Building a RAG pipeline requires understanding several components. Here are some courses that can help you:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Understanding and Applying Text Embeddings | DeepLearning.AI | Beginner | 1 Hour | Embeddings, GenAI Applications, NLP, RAG, Search and Retrieval |  This course covers text embeddings, a crucial part of RAG pipelines. |\\n| LangChain for LLM Application Development | DeepLearning.AI | Beginner | 1 Hour | AI Frameworks, Agents, Chatbots, Generative Models, Prompt Engineering, RAG | Introduces LangChain, a framework for building LLM applications, including RAG. |\\n| LangChain: Chat with Your Data | DeepLearning.AI | Beginner | 1 Hour | Computer Vision, Document Processing, Embeddings, RAG, Vector Databases | This course teaches you how to use LangChain to build a chatbot that interacts with your data using RAG. |\\n| Building and Evaluating Advanced RAG Applications | DeepLearning.AI | Beginner | Beginner | AI Frameworks, Evaluation and Monitoring, GenAI Applications, LLMOps, RAG, Search and Retrieval | This course covers advanced RAG concepts and techniques for building and evaluating RAG applications. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.889\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm an intermediate learner and want to explore fine-tuning large language models. What courses would you recommend?\", 'Answer': \"For fine-tuning LLMs, you'll need a good grasp of deep learning. These courses can help:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Deep Learning Specialization | DeepLearning.AI | Intermediate | 6 months | Computer Vision, Deep Learning, NLP, Supervised Learning, Transformers | Covers deep learning fundamentals and advanced architectures like transformers. |\\n| Finetuning Large Language Models | DeepLearning.AI | Intermediate | 1 Hour | Deep Learning, Fine-Tuning, Transformers | This course focuses specifically on fine-tuning large language models. |\\n| Reinforcement Learning from Human Feedback | DeepLearning.AI | Intermediate | Intermediate | Fine-Tuning, Generative Models, LLMOps, Transformers | Explores advanced techniques for fine-tuning LLMs using human feedback. |\"}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.893\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to learn about vector databases and their applications. Which courses are relevant?', 'Answer': 'Vector databases are essential for many AI applications. Here are some courses to learn about them:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| LangChain: Chat with Your Data | DeepLearning.AI | Beginner | 1 Hour | Computer Vision, Document Processing, Embeddings, RAG, Vector Databases | Introduces vector databases in the context of building a chatbot with LangChain. |\\n| Vector Databases: from Embeddings to Applications | DeepLearning.AI | Intermediate | Intermediate | Embeddings, GenAI Applications, RAG, Search and Retrieval, Vector Databases | A comprehensive course covering vector database concepts and their applications. |\\n| Large Language Models with Semantic Search | DeepLearning.AI | Beginner | 1 Hour | Embeddings, NLP, RAG, Search and Retrieval, Vector Databases | Shows how to use vector databases for semantic search with LLMs. |\\n| Building Applications with Vector Databases | DeepLearning.AI | Beginner | Beginner | Anomaly Detection, Embeddings, MultiModal, Vector Databases | This course focuses on practical applications of vector databases. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.895\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm fascinated by Generative AI and want to build applications with it. What courses should I take?\", 'Answer': 'Generative AI is a hot topic! Here are some courses to get you started with building applications:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Introduction to Generative AI | Google Cloud Skill Boost | Beginner | 45 minutes | Generative AI, Deep Learning, Artificial Intelligence, AI, Image Generation, Text Generation | Provides a foundational understanding of Generative AI concepts. |\\n| Generative AI for Developers | Google Cloud Skill Boost | Beginner | ~4 hours | Image Generation, GAN, Generative Adversarial Networks, Deep Learning, Neural Network, Image Synthesis, Computer Vision | Focuses on building image generation applications using Generative Adversarial Networks (GANs). |\\n| ChatGPT Prompt Engineering for Developers | DeepLearning.AI | Beginner | 1 Hour | GenAI Applications, Prompt Engineering, Transformers | This course teaches you how to use prompt engineering to build applications with ChatGPT. |\\n| Building Systems with the ChatGPT API | DeepLearning.AI | Beginner | 1 hour | AI Safety, Chatbots, GenAI Applications, Generative Models, Prompt Engineering | This course focuses on using the ChatGPT API to build more complex applications. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.897\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I am an experienced ML engineer and want to learn about deploying and scaling ML models in production. What resources would you recommend?', 'Answer': 'For experienced ML engineers like you, these resources focus on productionizing ML models:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Machine Learning in Production | DeepLearning.AI | Intermediate | 4 months | Data Engineering, Deep Learning, MLOps | Covers the entire ML lifecycle, including deployment and scaling in production. |\\n| TensorFlow: Data and Deployment Specialization | DeepLearning.AI | Intermediate | 2-6 Months | Data Processing, MLOps, On-Device AI | Focuses on data processing and deployment techniques for TensorFlow models. |\\n| LLMOps | DeepLearning.AI | Beginner | Beginner | AI Safety, Chatbots, Data Processing, Evaluation and Monitoring, Fine-Tuning, LLMOps | This course provides an introduction to LLMOps, covering best practices for deploying and managing LLMs in production. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.898\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': 'I want to learn about the ethical considerations and potential biases in AI. What courses can help me understand these issues?', 'Answer': \"Understanding AI ethics is crucial. These courses can provide valuable insights:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| AI Ethics Resources | IBM | Intermediate | ~30 minutes | AI Ethics, Artificial Intelligence, AI, Responsible AI | Explores fundamental AI ethics principles and concerns. |\\n| Introduction to Responsibe AI | Google Cloud Skill Boost | Beginner | 20 minutes | Responsible AI, Ethics, AI Ethics, Fairness, BIas, Transparency | Introduces responsible AI principles and how to apply them in practice. |\\n| Fairness in Perspective API | Google Developers | Advanced | ~1.5 hours | Fairness, AI, Bias |  Focuses on identifying and mitigating bias in AI systems using Google's Perspective API. |\\n| AI for Good | DeepLearning.AI | Beginner | 2-4 Weeks | Computer Vision, Data Processing, GenAI Applications, NLP, Supervised Learning | This course explores how AI can be used for social good, including ethical considerations. |\"}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.899\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm interested in exploring advanced techniques for optimizing machine learning models. What courses would you suggest?\", 'Answer': 'For advanced model optimization, consider these courses:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Machine Learning Advance Courses | Google Developers | Advanced | Variable | Decision Forests, Recommendation Systems, Clustering, GAN, Image Classification, Fairness | A collection of advanced courses covering specific ML areas like decision forests, recommendation systems, and GANs. |\\n| TensorFlow: Advanced Techniques Specialization | DeepLearning.AI | Intermediate | 5 months | | Delves into advanced TensorFlow techniques for optimizing model performance. |\\n| Optimization | Google Cloud Skill Boost | Advanced | ~1 hour | Optimization, loss function, Gradient descent, confusion Matrix, Model evaluation | This course focuses on optimization techniques for machine learning models. |\\n| Vertex Vizier Hyperparameter Tuning | Google Cloud Skill Boost | Advanced | ~3 hours | Vertex, Hyperparameter tuning | This course teaches you how to use Vertex Vizier for hyperparameter tuning. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.904\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSuccessfully processs Q-A set: {'Question': \"I'm a data scientist and want to improve my skills in data preparation and feature engineering for machine learning. What courses can help me?\", 'Answer': 'Data preparation and feature engineering are essential for successful ML. These courses can enhance your skills:\\n\\n| Course/Module | Source | Level | Duration (Estimate) | Keywords | Reason |\\n|---------------|--------|-------|----------------------|----------|--------|\\n| Data Preparation and Feature Engineering in ML | Google Developers | Intermediate | ~2 hours | data preparation, feature engineering, model, algorithm | Covers key concepts and techniques for preparing data and engineering features. |\\n| Feature Engineering | Google Cloud Skill Boost | Advanced | 7 hours | Tensorflow, Machine learning, Features, Vertex AI |  A comprehensive course on feature engineering, including advanced techniques using TensorFlow and Vertex AI. |\\n| Data Engineering | DeepLearning.AI | Intermediate | 15 weeks | Data Engineering, Data Processing, Deep Learning, Search and Retrieval | This course focuses on data engineering principles and practices. |'}\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.905\u001b[0m | \u001b[31m\u001b[1mERROR   \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m71\u001b[0m - \u001b[31m\u001b[1mError decoding JSON: Expecting value: line 1 column 1 (char 0). Skipping this q-a set\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.907\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmultiturn_generate_content\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mFinal results \n",
      " dataset size: 56\n",
      " time elapse: 977.5552821159363\u001b[0m\n",
      "\u001b[32m2024-10-27 17:37:37.914\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<cell line: 125>\u001b[0m:\u001b[36m143\u001b[0m - \u001b[1m\n",
      "Final results \n",
      " dataset size: 68\n",
      " time elapse: 977.57 Seconds\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import base64\n",
    "import vertexai\n",
    "from vertexai.generative_models import GenerativeModel, Part\n",
    "from loguru import logger\n",
    "import json\n",
    "import time\n",
    "def multiturn_generate_content(\n",
    "    output_path:str,\n",
    "    num_sets:int,\n",
    "    ) -> None:\n",
    "    \"\"\"\n",
    "    This function generates a q-a dataset for a given input file using a gemini variant model and returns the response.\n",
    "    Args:\n",
    "        output_path (str): The path to the output file.\n",
    "        num_sets (int): The number of q-a sets to generate per file.\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    vertexai.init(project=\"generated-mote-434518-u9\", location=\"us-central1\")\n",
    "    model = GenerativeModel(\n",
    "        \"gemini-1.5-pro-001\",\n",
    "        system_instruction=[LLM_instructions]\n",
    "    )\n",
    "\n",
    "    dataset_size = 0\n",
    "    iteration = 0\n",
    "    start_time = time.time()\n",
    "\n",
    "    # Loop until the dataset size is num_sets\n",
    "\n",
    "    while dataset_size < num_sets:\n",
    "\n",
    "      # Sending call to api can i start chat once would that improve speed? I'm  I reinitializing the call?\n",
    "      chat = model.start_chat(response_validation=False)\n",
    "      response = chat.send_message(\n",
    "              [document, prompt],\n",
    "              generation_config=generation_config,\n",
    "      )\n",
    "\n",
    "      ## postprocessing depends on how data is outputted\n",
    "      q_a_data = response.text.replace('jsonl','').replace(\"```\",'').strip().splitlines()\n",
    "\n",
    "      # Increment the iteration\n",
    "      iteration += 1\n",
    "      logger.info(f'Iteration {iteration}, Amount of q-a sets generated: {len(q_a_data)} ')\n",
    "\n",
    "\n",
    "\n",
    "      # Open the file in append mode\n",
    "      with open(output_path, \"a\") as f:\n",
    "\n",
    "          # Iterate over the generator and write each item as a JSON string\n",
    "          logger.debug('Appending data into file.....')\n",
    "\n",
    "          for item in q_a_data:\n",
    "            item = item.strip().rstrip(',')\n",
    "            #error handling\n",
    "            try:\n",
    "              # Convert the item to a JSON string\n",
    "              json_item = json.loads(item)\n",
    "              dataset_size += 1\n",
    "              # Write the JSON string to the file\n",
    "              json.dump(json_item, f)\n",
    "              f.write(\"\\n\")  # Write a newline character to ensure each entry starts on a new line\n",
    "              logger.info(f\"Successfully processs Q-A set: {json_item}\")\n",
    "            except json.JSONDecodeError as e:\n",
    "              logger.error(f\"Error decoding JSON: {e}. Skipping this q-a set: {json.item}\")\n",
    "              continue\n",
    "            except Exception as e:\n",
    "              # Handle any other exceptions that may occur\n",
    "              logger.error(f\"Unexpected error: {str(e)}. Skipping this Q&A set.\")\n",
    "              continue  # Skip to the next Q&A set\n",
    "\n",
    "\n",
    "\n",
    "      # Reopen the file in read mode to check the size\n",
    "      #dataset_size = size_of_file(file = output_path)\n",
    "\n",
    "\n",
    "      # End the timer\n",
    "      end_time = time.time()\n",
    "\n",
    "      # Calculate the elapsed time\n",
    "      time_taken = end_time - start_time\n",
    "\n",
    "      logger.info(f\"Final results \\n dataset size: {dataset_size}\\n time elapse: {time_taken}\")\n",
    "\n",
    "\n",
    "     # Outputting chat metadata?\n",
    "    #logger.info( f\"\"\" \\n MetaData:\\n{response}  \"\"\")\n",
    "\n",
    "    # Outputting Gemini Response\n",
    "    #logger.info(f\"\\n Gemini Ouput:\\n{response.text}\")\n",
    "\n",
    "    # TODO:\n",
    "    #   How do convert string data to json file?\n",
    "    #     - Split on each newline or \"{\" then convert that one line into a jsonl then write to a file\n",
    "    #     - What about the text before the json? Could use replace or strip function\n",
    "    #   How do I get a full 1000 q-a sets\n",
    "    #     - Probelm is output token limit. Must loop over calls and append to json file\n",
    "    #     - Check length of json file or store it in dictionary convert it at end\n",
    "    #   Prompt limit size\n",
    "    #     - Needle probelm llm usually considers top and bottom as important\n",
    "    #     - Chunk the pdf into chapters generate 200 q-a prompts for that chapter\n",
    "    #     - Append it to the json  file  (20 chapters = 4000  Q-A)\n",
    "    #\n",
    "    return response\n",
    "\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "# Specify the folder path\n",
    "folder_path = Path('/content/qa-data')\n",
    "\n",
    "# Specify the file path\n",
    "output_path = \"/content/QA_LPI.jsonl\"\n",
    "\n",
    "# Record the start time\n",
    "start_time = time.time()\n",
    "# Loop through each file in the folder\n",
    "for file_path in folder_path.iterdir():\n",
    "\n",
    "    logger.info(f'Processing file: {file_path.name}')\n",
    "\n",
    "    # Encoding text\n",
    "    encoded_data = base64.b64encode( open(file_path, \"rb\").read() ).decode(\"utf-8\")\n",
    "\n",
    "\n",
    "    # Not sure about this can't find documentation\n",
    "    document = Part.from_data(data=base64.b64decode(encoded_data),\n",
    "      mime_type=\"text/plain\", # change to application/pdf to process pdfs\n",
    "    )\n",
    "    multiturn_generate_content(output_path, num_sets=50)\n",
    "    # Record the end time\n",
    "    end_time = time.time()\n",
    "\n",
    "    # Calculate the elapsed time\n",
    "    elapsed_time = end_time - start_time\n",
    "    logger.info(f\"\\nFinal results \\n dataset size: {size_of_file(output_path)}\\n time elapse: {elapsed_time:.2f} Seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HL9a1IL7GNu_"
   },
   "source": [
    "## Data Postprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tgdECy1VwBBO"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from loguru import logger\n",
    "\n",
    "\n",
    "# Open the original JSONL file and read the contents\n",
    "with open(\"/content/Q_A (1).jsonl\", \"r\") as f:\n",
    "    lines = f.readlines()\n",
    "\n",
    "# Create a new list to store the modified lines\n",
    "modified_lines = []\n",
    "\n",
    "# Loop through each line, parse it as JSON, modify, and store it\n",
    "for line in lines:\n",
    "    try:\n",
    "        # Parse the line into a dictionary\n",
    "        data = json.loads(line)\n",
    "\n",
    "        # Normalize the 'answer' key to account for different cases\n",
    "        answer_key = \"answer\" if \"answer\" in data else \"Answer\"\n",
    "\n",
    "        # Modify the dictionary\n",
    "        new_data = {\n",
    "            \"instruction\": data[\"question\"],  # Change 'question' to 'instruction'\n",
    "            \"context\": \"\",                    # Add a blank 'context' field\n",
    "            \"output\": data[answer_key]        # Use the normalized 'answer' or 'Answer'\n",
    "        }\n",
    "\n",
    "        # Convert the modified dictionary back to a JSON-formatted string\n",
    "        modified_line = json.dumps(new_data)\n",
    "\n",
    "        # Append to the list of modified lines\n",
    "        modified_lines.append(modified_line)\n",
    "\n",
    "    except KeyError:\n",
    "        # Skip this entry if the expected key is missing\n",
    "        logger.warning(f\"Skipping entry due to missing 'answer' or 'Answer' key: {line}\")\n",
    "        continue\n",
    "\n",
    "# Write the modified lines to a new JSONL file\n",
    "with open(\"/content/modified_Q_A.jsonl\", \"w\") as f:\n",
    "    for line in modified_lines:\n",
    "        f.write(line + \"\\n\")\n",
    "\n",
    "logger.info(\"File transformation complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "8HUjUr2Xf-AQ"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# Open the original JSONL file and read the contents\n",
    "with open(\"/content/QA_LPI.jsonl, \"r\") as f:\n",
    "    lines = f.readlines()\n",
    "\n",
    "for line in lines:\n",
    "  try:\n",
    "\n",
    "    data = json.loads(line)\n",
    "    # Normalize the 'answer' key to account for different cases\n",
    "    answer_key = \"answer\" if \"answer\" in data else \"Answer\"\n",
    "\n",
    "    new_data = {\n",
    "        \"question\": data[\"question\"],  # Change 'question' to 'instruction'                   #\n",
    "        \"answer\": data[answer_key]        # Use the normalized 'answer' or 'Answer'\n",
    "    }\n",
    "\n",
    "    with open(\"/content/new_CAD_QA.jsonl\", \"a\") as f:\n",
    "      json.dump(new_data, f)\n",
    "      f.write(\"\\n\")  # Write a newline character to ensure each entry starts on a new line\n",
    "\n",
    "  except json.JSONDecodeError:\n",
    "    logger.warning(f\"Skipping entry due to missing json stuff' {line}\")\n",
    "\n",
    "  except KeyError as e:\n",
    "        logger.warning(f\"Skipping entry due to missing 'answer' or 'Answer' key: {line}\")\n",
    "        continue  # Skip this iteration if a key is missing\n"
   ]
  }
 ],
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  "kernelspec": {
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