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      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/gangadharnitw/RAG_Finance_LLaMa/blob/main/LLaMA_3p2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install faiss-cpu sentence-transformers transformers torch pandas"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "hpdIT9vI0N4q",
        "outputId": "8c0a55ec-1e50-4489-ba29-82819290f669"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting faiss-cpu\n",
            "  Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence_transformers-3.1.1-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.4.1+cu121)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (1.26.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (24.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.5.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.13.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.24.7)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (10.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.6.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading sentence_transformers-3.1.1-py3-none-any.whl (245 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.3/245.3 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: faiss-cpu, sentence-transformers\n",
            "Successfully installed faiss-cpu-1.8.0.post1 sentence-transformers-3.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import faiss\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from transformers import LlamaForCausalLM, AutoTokenizer\n",
        "import torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "OKzQSRIE0N0j",
        "outputId": "f9c64645-b5dd-4fd5-b52d-7e60b99f2e62"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'faiss'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-611cd6f5115e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mfaiss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msentence_transformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSentenceTransformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLlamaForCausalLM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAutoTokenizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'faiss'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = [\n",
        "    {\"id\": 1, \"title\": \"Artificial Intelligence\", \"text\": \"Artificial intelligence refers to the development of systems capable of performing tasks that normally require human intelligence.\"},\n",
        "    {\"id\": 2, \"title\": \"World War II\", \"text\": \"World War II was a global conflict that spanned from 1939 to 1945, involving many of the world’s nations.\"},\n",
        "    {\"id\": 3, \"title\": \"Quantum Computing\", \"text\": \"Quantum computing uses quantum-mechanical phenomena, such as superposition and entanglement, to perform computations.\"},\n",
        "    {\"id\": 4, \"title\": \"Climate Change\", \"text\": \"Climate change is caused primarily by the increased levels of carbon dioxide produced by the use of fossil fuels.\"},\n",
        "    {\"id\": 5, \"title\": \"Blockchain Technology\", \"text\": \"Blockchain is a decentralized ledger technology that ensures secure transactions without the need for intermediaries.\"},\n",
        "    {\"id\": 6, \"title\": \"The Renaissance\", \"text\": \"The Renaissance was a period in European history marking the transition from the Middle Ages to modernity.\"}\n",
        "]\n"
      ],
      "metadata": {
        "id": "td2_8HqS0aNC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data)\n",
        "documents = df['text'].tolist()\n",
        "titles = df['title'].tolist()"
      ],
      "metadata": {
        "id": "7W1qter-0a3N"
      },
      "execution_count": null,
      "outputs": []
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        "# Load Sentence-BERT model to generate embeddings\n",
        "embedding_model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "# Generate embeddings for the documents\n",
        "embeddings = embedding_model.encode(documents, convert_to_tensor=False)\n",
        "\n",
        "# Convert embeddings to numpy array for FAISS\n",
        "embeddings = np.array(embeddings)"
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          ]
        },
        "id": "JKPQp4Ii0prQ",
        "outputId": "5bc3f857-6252-4962-b818-7de455adca3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
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          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "68897bb4e98b44f78c6666c785bb8d4f"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ae3d33a8ee424c4abbadd66d42361f29"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3e50d7c1bc14e17b0a1baff17e8e86e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the FAISS index with L2 distance\n",
        "dimension = embeddings.shape[1]\n",
        "faiss_index = faiss.IndexFlatL2(dimension)\n",
        "\n",
        "# Add the embeddings to the FAISS index\n",
        "faiss_index.add(embeddings)\n"
      ],
      "metadata": {
        "id": "UB7wgBUL0sip"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "# Login with your token\n",
        "login(\"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\")\n",
        "\n",
        "# Load the LLaMA model and tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-3.2-1B-Instruct')\n",
        "model = LlamaForCausalLM.from_pretrained('meta-llama/Llama-3.2-1B-Instruct')\n",
        "\n",
        "# Move the model to GPU if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204,
          "referenced_widgets": [
            "585003b8551a46db9ef72fe359c5cf54",
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            "fe931f0184eb4f77a1e775aa2e9c820d",
            "678457d173a84d33b36b6f1cf7e824bf",
            "fb9357e404c0474a9528992eaa33a2ed",
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            "bfa87ff357844aff83ea6b3119f2595c",
            "f9b99149edaf40b0b49587c245a5f2f1",
            "76eab7be43294c17ab3b7a7b16da12c4",
            "30137b9fd3f3464592f6bb68653b0811"
          ]
        },
        "id": "YzOMq68UU_FX",
        "outputId": "7521c371-4732-4d64-fd3b-26d250dfebee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/877 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "585003b8551a46db9ef72fe359c5cf54"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/2.47G [00:00<?, ?B/s]"
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/189 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def search_faiss(query, k=3):\n",
        "    \"\"\"\n",
        "    Search for the most relevant documents using FAISS.\n",
        "\n",
        "    :param query: The query text to search for.\n",
        "    :param k: Number of nearest neighbors to retrieve.\n",
        "    :return: List of top k document texts.\n",
        "    \"\"\"\n",
        "    # Generate embeddings for the query\n",
        "    query_embedding = embedding_model.encode([query], convert_to_tensor=False)\n",
        "\n",
        "    # Perform the search on the FAISS index\n",
        "    distances, indices = faiss_index.search(np.array(query_embedding), k)\n",
        "\n",
        "    # Retrieve the corresponding document texts\n",
        "    retrieved_docs = [documents[i] for i in indices[0]]\n",
        "    return retrieved_docs\n",
        "\n",
        "def generate_response(query, retrieved_docs):\n",
        "    \"\"\"\n",
        "    Generate a response using the LLaMA model, augmented with retrieved documents.\n",
        "\n",
        "    :param query: The input query.\n",
        "    :param retrieved_docs: The retrieved document chunks.\n",
        "    :return: Generated response from LLaMA.\n",
        "    \"\"\"\n",
        "    # Augment the query with retrieved document chunks as context\n",
        "    context = \" \".join(retrieved_docs)\n",
        "    augmented_query = f\"Context: {context} \\n\\nQuery: {query}\"\n",
        "\n",
        "    # Tokenize the input\n",
        "    inputs = tokenizer(augmented_query, return_tensors=\"pt\").to(device)\n",
        "\n",
        "    # Generate the response\n",
        "    outputs = model.generate(\n",
        "        inputs[\"input_ids\"],\n",
        "        max_length=100,\n",
        "        num_beams=5,\n",
        "        early_stopping=True\n",
        "    )\n",
        "\n",
        "    # Decode the output tokens to a string\n",
        "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    return response"
      ],
      "metadata": {
        "id": "lzfiyNJirErx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_queries = [\n",
        "    \"How does blockchain technology work?\",\n",
        "    \"What were the main causes of World War II?\",\n",
        "    \"What is quantum computing?\",\n",
        "    \"Explain the impact of climate change on the environment.\"\n",
        "]\n",
        "\n",
        "# Run retrieval and generate responses for each query\n",
        "for query in sample_queries:\n",
        "    print(f\"Query: {query}\")\n",
        "\n",
        "    # Retrieve relevant documents\n",
        "    retrieved_docs = search_faiss(query, k=3)\n",
        "\n",
        "    # Generate the final response using LLaMA\n",
        "    response = generate_response(query, retrieved_docs)\n",
        "\n",
        "    print(f\"Response: {response}\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UlK7UH5P05bN",
        "outputId": "88e666a5-7f88-40d4-e56b-e500a80e3d93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query: How does blockchain technology work?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Response: Context: Blockchain is a decentralized ledger technology that ensures secure transactions without the need for intermediaries. Quantum computing uses quantum-mechanical phenomena, such as superposition and entanglement, to perform computations. Artificial intelligence refers to the development of systems capable of performing tasks that normally require human intelligence. \n",
            "\n",
            "Query: How does blockchain technology work? \n",
            "\n",
            "Answer: Blockchain is a decentralized, digital ledger that records transactions across a network of computers in a secure and transparent manner. Each block in the chain contains\n",
            "\n",
            "Query: What were the main causes of World War II?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Response: Context: World War II was a global conflict that spanned from 1939 to 1945, involving many of the world’s nations. Climate change is caused primarily by the increased levels of carbon dioxide produced by the use of fossil fuels. The Renaissance was a period in European history marking the transition from the Middle Ages to modernity. \n",
            "\n",
            "Query: What were the main causes of World War II? \n",
            "\n",
            "Answer: The main causes of World War II were the aggressive expansion of Nazi Germany,\n",
            "\n",
            "Query: What is quantum computing?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Response: Context: Quantum computing uses quantum-mechanical phenomena, such as superposition and entanglement, to perform computations. Artificial intelligence refers to the development of systems capable of performing tasks that normally require human intelligence. Blockchain is a decentralized ledger technology that ensures secure transactions without the need for intermediaries. \n",
            "\n",
            "Query: What is quantum computing? \n",
            "\n",
            "Answer: Quantum computing is a type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform computations. It\n",
            "\n",
            "Query: Explain the impact of climate change on the environment.\n",
            "Response: Context: Climate change is caused primarily by the increased levels of carbon dioxide produced by the use of fossil fuels. World War II was a global conflict that spanned from 1939 to 1945, involving many of the world’s nations. Quantum computing uses quantum-mechanical phenomena, such as superposition and entanglement, to perform computations. \n",
            "\n",
            "Query: Explain the impact of climate change on the environment. \n",
            "\n",
            "Answer: Climate change is having a profound impact on the environment, with\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8O8oDRr63okU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}