configuration_guide.ipynb

{
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        "id": "D5CcPpK-14w-"
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      "source": [
        "## Structure of Our Milvus Vector Index\n",
        "\n",
        "The structure of Vector Index is as follows:\n",
        "* The collection is named `Chunk_Vector_Index`\n",
        "* It has a default partition named `_default` created automatically at the time of creation\n",
        "* For every pdf file uploaded, we create a new partition with a unique, randomly chosen name. refer to `modules.py` for code\n",
        "* the chunks for that pdf file are uploaded in its own unique partition\n",
        "\n",
        "\n",
        "\n",
        "Note that a particular partition is dependent on following things:\n",
        "1. Schema:\n",
        "    + Our Schema right now is:\n",
        "        * ID(`id`)\n",
        "        * chunk_vector(`vector`)\n",
        "        * chunk_text(`text`)\n",
        "        * source document(`source`)\n",
        "        * page number of the chunk in document(`page`)\n",
        "2. Embedding dimension: embedding dimension will be input at the time of creating the collection. So incase we are choosing a different embedding model, we will need to create a new collection. For now the embedding model is opensource `HuggingFaceEmbeddings()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qMS5iRh14xA",
        "outputId": "6b4ea453-235b-4a2a-f1ca-9c0d02689fe2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# %pip install langchain_huggingface\n",
        "%pip install --quiet sentence-transformers pymilvus langchain_huggingface"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install grpcio>=1.49.1"
      ],
      "metadata": {
        "id": "M2wAg5pe3HkW"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --force-reinstall \"grpcio<=1.63.0,>=1.49.1\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D00lMwB23ifi",
        "outputId": "c51d4f50-8bc8-4662-d705-e6f0866241ff"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting grpcio<=1.63.0,>=1.49.1\n",
            "  Using cached grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB)\n",
            "Installing collected packages: grpcio\n",
            "  Attempting uninstall: grpcio\n",
            "    Found existing installation: grpcio 1.49.1\n",
            "    Uninstalling grpcio-1.49.1:\n",
            "      Successfully uninstalled grpcio-1.49.1\n",
            "Successfully installed grpcio-1.63.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ffdNW6_14xC",
        "outputId": "7800d278-9db4-4532-f0c2-4f60400cd63a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DEBUG:pymilvus.milvus_client.milvus_client:Created new connection using: 80c0ad1b7dce468780de2445f9b7317b\n"
          ]
        }
      ],
      "source": [
        "from pymilvus import MilvusClient, connections,Collection\n",
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "MILVUS_URI = \"http://4.213.193.212:19530\"\n",
        "\n",
        "milvus_client = MilvusClient(MILVUS_URI)\n"
      ]
    },
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        },
        "id": "gsOsShVa14xC",
        "outputId": "0df08cd0-9410-4a2e-d55e-b70ffa02053e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/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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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
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          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
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          "metadata": {}
        },
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          "output_type": "display_data",
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              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "2040cf4e6af6433fa4d0c3d0c310c8ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "c45fc5e7c44944158bc9b55642e3446b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbc05a42969c4285bb6ea6c6c28ada10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "101eea2d4d2d42f09c35c49317456c85"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "embedding_model = HuggingFaceEmbeddings()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "5uvepYP614xC"
      },
      "outputs": [],
      "source": [
        "def embed_text(text):\n",
        "    return embedding_model.embed_query(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8UTf_-l14xC",
        "outputId": "af2ac720-c6c5-484b-9742-90c4ef59d455"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "768\n"
          ]
        }
      ],
      "source": [
        "test_embedding = embed_text(\"This is a test\")\n",
        "embedding_dim = len(test_embedding)\n",
        "print(embedding_dim)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res = milvus_client.list_collections()\n",
        "\n",
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csaBSjjR4dlp",
        "outputId": "b5976708-c2e1-4200-e568-e5f721692d89"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Q5IDzOJv14xD"
      },
      "outputs": [],
      "source": [
        "collection_name = \"Chunk_Vector_Index\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "_NTs_UJL14xD"
      },
      "outputs": [],
      "source": [
        "if milvus_client.has_collection(collection_name):\n",
        "    milvus_client.drop_collection(collection_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVgkG1CK14xD",
        "outputId": "9680827c-5a69-4ace-c87a-cc0be9365951"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DEBUG:pymilvus.milvus_client.milvus_client:Successfully created collection: Chunk_Vector_Index\n",
            "DEBUG:pymilvus.milvus_client.milvus_client:Successfully created an index on collection: Chunk_Vector_Index\n"
          ]
        }
      ],
      "source": [
        "milvus_client.create_collection(\n",
        "    collection_name=collection_name,\n",
        "    dimension=embedding_dim,\n",
        "    metric_type=\"COSINE\",  # Cosine similarity\n",
        "    consistency_level=\"Strong\",  # Strong consistency level\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "res"
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        "# milvus_client.release_partitions(collection_name,partition_names=['owais'])\n",
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