snowflake_Arctic_Handson.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Snowflake Arctic Tutorial With PCA   \n",
        "@Author: Zoumana KEITA     \n",
        "\n",
        "https://www.linkedin.com/in/zoumana-keita/\n"
      ],
      "metadata": {
        "id": "XkpIZah6_hLo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SaFcvbiXw3ed",
        "outputId": "e9578d70-036a-4a18-86a3-ce05f6a05c1f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri May 17 18:14:52 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA L4                      Off | 00000000:00:03.0 Off |                    0 |\n",
            "| N/A   54C    P8              13W /  72W |      1MiB / 23034MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "gR877OyArqBq"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "pip -qqq install transformers>=4.39.0\n",
        "pip -qqq install torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i7WmO6s3q8gu"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "from torch.nn.functional import cosine_similarity"
      ],
      "metadata": {
        "id": "PrGb5Lc4PfWO"
      },
      "execution_count": 4,
      "outputs": []
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        "outputId": "a299bde2-d0b1-4390-dc55-f6a0b9e71bac"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.43k [00:00<?, ?B/s]"
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              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.3M [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        }
      ],
      "source": [
        "model_checkpoint = \"Snowflake/snowflake-arctic-embed-xs\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
        "model = AutoModel.from_pretrained(model_checkpoint, add_pooling_layer=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_embedding(document):\n",
        "    inputs = tokenizer(document, padding=True, truncation=True,\n",
        "                       return_tensors='pt', max_length=512)\n",
        "\n",
        "    embedding = model(**inputs)[0][:, 0]\n",
        "    return embedding"
      ],
      "metadata": {
        "id": "WginrovyrI0D"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_similar_documents(query_document, document_embeddings, top_n=5):\n",
        "\n",
        "    query_embedding = generate_embedding(query_document)\n",
        "\n",
        "    similarity_scores = [cosine_similarity(query_embedding,\n",
        "                                           doc_embedding).item() for doc_embedding in document_embeddings]\n",
        "\n",
        "    sorted_indices = torch.argsort(torch.tensor(similarity_scores),\n",
        "                                   descending=True)\n",
        "\n",
        "    top_documents = [documents[idx] for idx in sorted_indices[:top_n]]\n",
        "    top_scores = [similarity_scores[idx] for idx in sorted_indices[:top_n]]\n",
        "\n",
        "    return top_documents, top_scores"
      ],
      "metadata": {
        "id": "XeZs--SarLCM"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample documents\n",
        "documents = [\n",
        "    \"This is a tutorial about Snowflake Arctic Embedding models\",\n",
        "    \"Arctic-embed is a state-of-the-art text embedding model.\",\n",
        "    \"Snowflake provides various cloud data warehousing solutions.\",\n",
        "    \"Embedding models are used for representing text as dense vectors.\",\n",
        "    \"The Arctic-embed model is based on the transformer architecture.\"\n",
        "]"
      ],
      "metadata": {
        "id": "iAHa-iCTrV0J"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "document_embeddings = [generate_embedding(doc) for doc in documents]"
      ],
      "metadata": {
        "id": "Mm-cDXOEKSMl"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_document = \"What is the Arctic-embed model?\"\n",
        "top_documents, top_scores = find_similar_documents(query_document, document_embeddings)"
      ],
      "metadata": {
        "id": "CT1_Zm16renO"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Query Document:\")\n",
        "print(query_document)\n",
        "print(\"\\nTop Similar Documents:\")\n",
        "for doc, score in zip(top_documents, top_scores):\n",
        "    print(f\"Document: {doc}\")\n",
        "    print(f\"Similarity Score: {score:.4f}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fmd2wLSyrinv",
        "outputId": "c1b34976-ccaa-4eee-9539-15cabc72eb89"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query Document:\n",
            "What is the Arctic-embed model?\n",
            "\n",
            "Top Similar Documents:\n",
            "Document: Arctic-embed is a state-of-the-art text embedding model.\n",
            "Similarity Score: 0.9499\n",
            "\n",
            "Document: The Arctic-embed model is based on the transformer architecture.\n",
            "Similarity Score: 0.9357\n",
            "\n",
            "Document: This is a tutorial about Snowflake Arctic Embedding models\n",
            "Similarity Score: 0.9015\n",
            "\n",
            "Document: Embedding models are used for representing text as dense vectors.\n",
            "Similarity Score: 0.8377\n",
            "\n",
            "Document: Snowflake provides various cloud data warehousing solutions.\n",
            "Similarity Score: 0.7366\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualiation of the documents"
      ],
      "metadata": {
        "id": "QvfrOnfduV4R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D"
      ],
      "metadata": {
        "id": "kbvU69kFucwi"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zi6PsjjUusNN"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Update the find similar documents function\n",
        "\n",
        "def find_similar_documents(query_document, documents, top_n=5):\n",
        "    query_embedding = generate_embedding(query_document)\n",
        "    document_embeddings = [generate_embedding(doc) for doc in documents]\n",
        "\n",
        "    similarity_scores = [cosine_similarity(query_embedding, doc_embedding).item() for doc_embedding in document_embeddings]\n",
        "    sorted_indices = torch.argsort(torch.tensor(similarity_scores), descending=True)\n",
        "\n",
        "    top_documents = [documents[idx] for idx in sorted_indices[:top_n]]\n",
        "    top_scores = [similarity_scores[idx] for idx in sorted_indices[:top_n]]\n",
        "\n",
        "    return top_documents, top_scores, document_embeddings\n",
        "\n",
        "\n",
        "#query_document = \"What is the Arctic-embed model?\"\n",
        "top_documents, top_scores, document_embeddings = find_similar_documents(query_document, documents)\n",
        "\n",
        "# Reshape the embeddings\n",
        "reshaped_embeddings = torch.stack(document_embeddings).detach().numpy()\n",
        "reshaped_embeddings = reshaped_embeddings.reshape(reshaped_embeddings.shape[0], -1)\n",
        "\n",
        "# Apply PCA to reduce the embeddings to 3 dimensions\n",
        "pca = PCA(n_components=3)\n",
        "reduced_embeddings = pca.fit_transform(reshaped_embeddings)\n",
        "\n",
        "# Create a 3D plot\n",
        "fig = plt.figure(figsize=(8, 6))\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "# Plot the document embeddings\n",
        "for i, doc in enumerate(documents):\n",
        "    ax.scatter(reduced_embeddings[i, 0], reduced_embeddings[i, 1], reduced_embeddings[i, 2], marker='o', label=f\"Document {i+1}\")\n",
        "\n",
        "# Plot the query document embedding\n",
        "query_embedding = generate_embedding(query_document)\n",
        "reshaped_query_embedding = query_embedding.detach().numpy().reshape(1, -1)\n",
        "reduced_query_embedding = pca.transform(reshaped_query_embedding)\n",
        "ax.scatter(reduced_query_embedding[0, 0], reduced_query_embedding[0, 1],\n",
        "           reduced_query_embedding[0, 2], marker='*', s=100, color='red',\n",
        "           label=\"Query Document\")\n",
        "\n",
        "ax.set_xlabel('PC1')\n",
        "ax.set_ylabel('PC2')\n",
        "ax.set_zlabel('PC3')\n",
        "ax.legend()\n",
        "plt.title('Document Embeddings in 3D Space')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "ogi7YAtruZmh",
        "outputId": "cefddc0f-0141-4c25-b594-b1ff6405a5bc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Streamlit application with full code  \n",
        "\n",
        "Make sure to paste the following code in a Python (app.py) file and run with the following command:\n",
        "\n",
        "```\n",
        "python run app.py\n",
        "```"
      ],
      "metadata": {
        "id": "yq-eRCBtESrZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "pip -qqq install streamlit\n",
        "pip -qqq install pyngrok"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Exadc7VTEd6D",
        "outputId": "729356a5-a30e-41bb-df9f-d7ecf4f7c9c4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))': /simple/pyngrok/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import streamlit as st\n",
        "from pyngrok import ngrok"
      ],
      "metadata": {
        "id": "OHj98XkPFZVG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Streamlit app\n",
        "st.title(\"Document Similarity Search Using Snowflake Arctic\")\n",
        "\n",
        "query_document = st.text_input(\"Enter your query document:\")\n",
        "top_k = st.number_input(\"Enter the number of top documents to retrieve (K):\", min_value=1, value=3, step=1)\n",
        "\n",
        "if st.button(\"Search\"):\n",
        "    top_documents, top_scores, document_embeddings = find_similar_documents(query_document, documents, top_n=top_k)\n",
        "\n",
        "    st.subheader(\"Top {} Similar Documents:\".format(top_k))\n",
        "    for i, (doc, score) in enumerate(zip(top_documents, top_scores)):\n",
        "        st.write(\"{}. Document: {}\".format(i+1, doc))\n",
        "        st.write(\"   Similarity Score: {:.4f}\".format(score))\n",
        "\n",
        "    # Reshape the embeddings\n",
        "    reshaped_embeddings = torch.stack(document_embeddings).detach().numpy()\n",
        "    reshaped_embeddings = reshaped_embeddings.reshape(reshaped_embeddings.shape[0], -1)\n",
        "\n",
        "    # Apply PCA to reduce the embeddings to 3 dimensions\n",
        "    pca = PCA(n_components=3)\n",
        "    reduced_embeddings = pca.fit_transform(reshaped_embeddings)\n",
        "\n",
        "    # Create a 3D plot\n",
        "    fig = plt.figure(figsize=(8, 6))\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "    # Plot the document embeddings\n",
        "    for i, doc in enumerate(documents):\n",
        "        ax.scatter(reduced_embeddings[i, 0], reduced_embeddings[i, 1], reduced_embeddings[i, 2], marker='o', label=f\"Document {i+1}\")\n",
        "\n",
        "    # Plot the query document embedding\n",
        "    query_embedding = generate_embedding(query_document)\n",
        "    reshaped_query_embedding = query_embedding.detach().numpy().reshape(1, -1)\n",
        "    reduced_query_embedding = pca.transform(reshaped_query_embedding)\n",
        "    ax.scatter(reduced_query_embedding[0, 0], reduced_query_embedding[0, 1], reduced_query_embedding[0, 2], marker='*', s=100, color='red', label=\"Query Document\")\n",
        "\n",
        "    ax.set_xlabel('PC1')\n",
        "    ax.set_ylabel('PC2')\n",
        "    ax.set_zlabel('PC3')\n",
        "    ax.legend()\n",
        "    plt.title('Document Embeddings in 3D Space')\n",
        "\n",
        "    st.pyplot(fig)"
      ],
      "metadata": {
        "id": "U6Hm7uVXEVl2"
      },
      "execution_count": 6,
      "outputs": []
    }
  ],
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