recipe_rag.ipynb

{
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKJJuGNrhMZt",
        "outputId": "8daad8dc-64ac-4557-d46e-bdd9748543ae"
      },
      "outputs": [],
      "source": [
        "!pip install -qqq datasets pandas pymongo sentence_transformers accelerate gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KWU2leC4jGzo"
      },
      "outputs": [],
      "source": [
        "import gradio as gr\n",
        "import pandas as pd\n",
        "import pymongo\n",
        "\n",
        "from google.colab import userdata\n",
        "from huggingface_hub import login\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PMg92_HXqdSb",
        "outputId": "8eaab76d-f53d-4696-beed-0aa4aa9a6cf1"
      },
      "outputs": [],
      "source": [
        "# https://huggingface.co/datasets/recipe_nlg\n",
        "# Download from https://recipenlg.cs.put.poznan.pl/ and upload to drive\n",
        "# NOTE: the following works in Google Drive or in Linux. Adapt according to your environment.\n",
        "\n",
        "!unzip /content/drive/MyDrive/data/recipe-rag/dataset.zip -d /content\n",
        "!head -n3 /content/dataset/full_dataset.csv"
      ]
    },
    {
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        "id": "q-vz2k0Lv3J8",
        "outputId": "2ff4d446-8dcd-407a-c31b-8c612853085f"
      },
      "outputs": [],
      "source": [
        "# https://huggingface.co/thenlper/gte-large\n",
        "embedding_model = SentenceTransformer(\"thenlper/gte-large\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8gJ9zpq1xBE9"
      },
      "outputs": [],
      "source": [
        "def get_embedding(text: str) -> list[float]:\n",
        "    if not text.strip():\n",
        "        print(\"Attempted to get embedding for empty text.\")\n",
        "        return []\n",
        "\n",
        "    embedding = embedding_model.encode(text)\n",
        "\n",
        "    return embedding.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yUP61M6Q7mVn",
        "outputId": "3dedd981-f0bb-4fc4-f50f-c29e75e7b5f2"
      },
      "outputs": [],
      "source": [
        "def get_mongo_client(mongo_uri):\n",
        "    \"\"\"Establish connection to the MongoDB.\"\"\"\n",
        "    try:\n",
        "        client = pymongo.MongoClient(mongo_uri)\n",
        "        print(\"Connection to MongoDB successful\")\n",
        "        return client\n",
        "    except pymongo.errors.ConnectionFailure as e:\n",
        "        print(f\"Connection failed: {e}\")\n",
        "        return None\n",
        "\n",
        "\n",
        "mongo_uri = userdata.get(\"MONGO_URI\")\n",
        "if not mongo_uri:\n",
        "    print(\"MONGO_URI not set in environment variables\")\n",
        "\n",
        "mongo_client = get_mongo_client(mongo_uri)\n",
        "\n",
        "# Ingest data into MongoDB\n",
        "db = mongo_client[\"recipe\"]\n",
        "collection = db[\"recipe_collection\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5QPtWzKBpY3o"
      },
      "outputs": [],
      "source": [
        "def unlistify_string(input):\n",
        "  return input.replace('\" , \"', \" \").translate({ord(c): None for c in '[]\"'}).replace(\".,\", \".\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J3Td_TdP71MT"
      },
      "outputs": [],
      "source": [
        "def populate_dataset(path=\"/content/dataset/full_dataset.csv\", n=1000):\n",
        "  dataset_df = pd.read_csv(path, nrows=n, index_col=0)\n",
        "  dataset_df.drop(columns=[\"link\", \"source\", \"NER\"], inplace=True)\n",
        "  dataset_df.dropna(inplace=True)\n",
        "  dataset_df[\"ingredients\"] = dataset_df[\"ingredients\"].apply(unlistify_string)\n",
        "  dataset_df[\"directions\"] = dataset_df[\"directions\"].apply(unlistify_string)\n",
        "  dataset_df[\"embedding\"] = dataset_df[\"directions\"].apply(get_embedding)\n",
        "  documents = dataset_df.to_dict(\"records\")\n",
        "  # Optionally delete if collection is already populated\n",
        "  collection.delete_many({})\n",
        "  collection.insert_many(documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1EqKvIRqasne"
      },
      "outputs": [],
      "source": [
        "populate_dataset()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C_tAkk38WeAI"
      },
      "outputs": [],
      "source": [
        "def vector_search(user_query, collection):\n",
        "\n",
        "    # Generate embedding for the user query\n",
        "    query_embedding = get_embedding(user_query)\n",
        "\n",
        "    if query_embedding is None:\n",
        "        return \"Invalid query or embedding generation failed.\"\n",
        "\n",
        "    # Define the vector search pipeline\n",
        "    pipeline = [\n",
        "        {\n",
        "            \"$vectorSearch\": {\n",
        "                \"index\": \"vector_index\",\n",
        "                \"queryVector\": query_embedding,\n",
        "                \"path\": \"embedding\",\n",
        "                \"numCandidates\": 150,  # Number of candidate matches to consider\n",
        "                \"limit\": 4,  # Return top 4 matches\n",
        "            }\n",
        "        },\n",
        "        {\n",
        "            \"$project\": {\n",
        "                \"_id\": 0,\n",
        "                \"title\": 1,\n",
        "                \"ingredients\": 1,\n",
        "                \"directions\": 1,\n",
        "                \"score\": {\"$meta\": \"vectorSearchScore\"},  # Include the search score\n",
        "            }\n",
        "        },\n",
        "    ]\n",
        "\n",
        "    # Execute the search\n",
        "    results = collection.aggregate(pipeline)\n",
        "    return list(results)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aCuxoKxkXM9b"
      },
      "outputs": [],
      "source": [
        "def get_search_result(query, collection):\n",
        "\n",
        "    get_knowledge = vector_search(query, collection)\n",
        "\n",
        "    search_result = \"\"\n",
        "    for result in get_knowledge:\n",
        "        search_result += f\"Recipe Name: {result.get('title', 'N/A')}, Ingredients: {result.get('ingredients', 'N/A')}, Directions: {result.get('directions', 'N/A')}\\n\"\n",
        "\n",
        "    return search_result, get_knowledge"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUSUOKpofJ6k",
        "outputId": "06db7342-db2a-4afe-db15-7861108e541e"
      },
      "outputs": [],
      "source": [
        "login(token=userdata.get(\"HF_TOKEN\"))"
      ]
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        "id": "eg4QSOXHdwIV",
        "outputId": "f6aec052-2613-4342-ce09-cbbd792e725a"
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      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2b-it\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"google/gemma-2b-it\", device_map=\"auto\")"
      ]
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        "def process_response(message, history):\n",
        "  source_information, matches = get_search_result(message, collection)\n",
        "  recipe_dict = {}\n",
        "  for x in matches:\n",
        "    name = x.pop(\"title\")\n",
        "    recipe_dict[name] = x\n",
        "\n",
        "  combined_information = f\"Query: {message}\\nContinue to answer the query by using the Search Results:\\n{source_information}.\"\n",
        "  input_ids = tokenizer(combined_information, return_tensors=\"pt\").to(\"cuda\")\n",
        "  response = model.generate(**input_ids, max_new_tokens=500)\n",
        "  response_text = tokenizer.decode(response[0]).split(\"\\n.\\n\")[-1].split(\"<eos>\")[0].strip()\n",
        "\n",
        "  matched_recipe = \"\"\n",
        "  for title in recipe_dict.keys():\n",
        "    if title in response_text:\n",
        "      matched_recipe = title\n",
        "      break\n",
        "  if not matched_recipe:\n",
        "    matched_recipe = next(iter(recipe_dict))\n",
        "  recipe = recipe_dict[matched_recipe]\n",
        "\n",
        "  response_text += f\"\\n\\nRecipe for **{matched_recipe}**:\"\n",
        "  response_text += \"\\n### List of ingredients:\\n- {0}\".format(\"\\n- \".join(recipe[\"ingredients\"].split(\", \")))\n",
        "  response_text += \"\\n### Directions:\\n- {0}\".format(\".\\n- \".join(recipe[\"directions\"].split(\". \")))\n",
        "\n",
        "  return response_text"
      ]
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        "colab": {
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        "outputId": "7bcd28fd-5c8e-4948-832c-86291f676599"
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      "outputs": [],
      "source": [
        "print(process_response(\"What is the best recipe for making a dish using milk?\", []))"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "colab": {
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