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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", "3c2a1f70bdec4e9f94311711599cb1f0", "ed809f10ce1b491093de908c816132ce", 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