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"https://localhost:8080/" + }, + "id": "MK2lNerYcJd6", + "outputId": "102e6810-1cf8-4e68-a644-d6bb7ecbd73b" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting faiss-cpu\n", + " Downloading faiss_cpu-1.11.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (4.8 kB)\n", + "Requirement already satisfied: numpy<3.0,>=1.25.0 in /usr/local/lib/python3.11/dist-packages (from faiss-cpu) (2.0.2)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from faiss-cpu) (24.2)\n", + "Downloading faiss_cpu-1.11.0-cp311-cp311-manylinux_2_28_x86_64.whl (31.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m31.3/31.3 MB\u001b[0m \u001b[31m59.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: faiss-cpu\n", + "Successfully installed faiss-cpu-1.11.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import openai\n", + "df=pd.read_csv(\"/content/faq_data.csv\")\n", + "print(df)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l7FdlomXVC-8", + "outputId": "7f7aaac1-3649-4b74-97cd-a7def5b5c83e" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " topic question \\\n", + "0 Airport Services What types of baggage services are available a... \n", + "1 Airport Services How can I check in for my flight online or at ... \n", + "2 Airport Services What are the airport's security procedures and... \n", + "3 Airport Services Where can I find information about airport tra... \n", + "4 Airport Services Are there any services available for passenger... \n", + "5 Airport Services What amenities and facilities are available at... \n", + "6 Airport Services How can I report lost and found items at the a... \n", + "7 Airport Services What are the airport's policies regarding pets? \n", + "8 Airport Services How can I contact airport customer service or ... \n", + "\n", + " answer \n", + "0 Airline X offers several baggage services at t... \n", + "1 **Online Check-in:**\\n\\nTo check in online, vi... \n", + "2 Airline X does not control airport security pr... \n", + "3 Airline X does not provide airport transportat... \n", + "4 Airline X is committed to providing a safe and... \n", + "5 Airline X does not operate airport lounges or ... \n", + "6 To report a lost item at Airline X, please pro... \n", + "7 Airline X accepts cats and dogs in the cabin o... \n", + "8 Airline X does not provide direct customer ser... \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from openai import OpenAI\n", + "from google.colab import userdata\n", + "#Feel free to use chatgpt or other models - Google offere free gemini api hence I have used this\n", + "openai = OpenAI(\n", + " base_url=\"https://generativelanguage.googleapis.com/v1beta/\",\n", + " api_key=userdata.get('gemini_api'),\n", + ")" + ], + "metadata": { + "id": "Yp_qP7qEDV6h" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kWajuc5iDk04", + "outputId": "25562866-8e34-4d89-c62f-09240095a92b" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1045" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "import numpy as np\n", + "\n", + "\n", + "# Optional: chunk answers into smaller pieces (e.g. 200 tokens) for better retrieval\n", + "def chunk_text(text, max_tokens=200):\n", + " words = text.split()\n", + " for i in range(0, len(words), max_tokens):\n", + " yield \" \".join(words[i:i+max_tokens])\n", + "\n", + "chunks = []\n", + "metadata = []\n", + "for idx, row in df.iterrows():\n", + " for c in chunk_text(row['answer']):\n", + " chunks.append(c)\n", + " metadata.append({\n", + " \"topic\": row['topic'],\n", + " \"question\": row['question'],\n", + " \"answer_chunk\": c\n", + " })\n" + ], + "metadata": { + "id": "KPvOt3bXXvff" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sentence_transformers import SentenceTransformer\n", + "import numpy as np\n", + "#I am using the huggingface model to generate embeddings , If the data was large once can try & explore other embeddings\n", + "embedder = SentenceTransformer('all-MiniLM-L6-v2')\n", + "\n", + "# Embed chunks\n", + "chunk_vectors = embedder.encode(chunks, convert_to_numpy=True)\n", + "\n" + ], + "metadata": { + "id": "XqOBuEmrZQs5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 404, + "referenced_widgets": [ + "0a96e35ec8ad408b96c2c49f3b3939e9", + "6ebf22d974e24a8eaa0f12425c7b572d", + 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