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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5w6kJSjaAPl",
        "outputId": "692ee691-1e38-4614-e1f4-beaf41771a76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.8/244.8 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.6/117.6 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.6/67.6 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade --quiet pinecone-client pinecone-text pinecone-notebooks"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "\n",
        "api_key = userdata.get('PINECONE_API')"
      ],
      "metadata": {
        "id": "GpDnq1ZCavZJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-community -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pECTUfY3bdB7",
        "outputId": "3f7bdc2c-51a7-4d12-f0d8-2c17b20db23c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m990.0/990.0 kB\u001b[0m \u001b[31m54.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m373.5/373.5 kB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.1/141.1 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.retrievers import PineconeHybridSearchRetriever"
      ],
      "metadata": {
        "id": "7ZIaj6Uma7G4"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from pinecone import Pinecone, ServerlessSpec\n",
        "index_name = \"hybrid-search-langchain-pinecone\"\n",
        "\n",
        "## Initialize the Pinecone client\n",
        "pc = Pinecone(api_key=api_key)\n",
        "\n",
        "# Create the index\n",
        "if index_name not in pc.list_indexes().names():\n",
        "    pc.create_index(\n",
        "        name=index_name,\n",
        "        dimension=384,\n",
        "        metric=\"dotproduct\",\n",
        "        spec=ServerlessSpec(cloud='aws',region=\"us-east-1\")\n",
        "        )"
      ],
      "metadata": {
        "id": "JjODGvdxb9S1"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index=pc.Index(index_name)\n",
        "index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HM-5pg-wLKdU",
        "outputId": "7f909beb-a348-47b7-82bb-09affc37026d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pinecone.data.index.Index at 0x7dc79d735000>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-huggingface -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfKMZc0AOyN7",
        "outputId": "458a0f7c-4765-4441-f341-57cf60a7201f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m35.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Vector embedding\n",
        "import os\n",
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
        "embeddings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
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        "id": "oYzQvtcLOPtX",
        "outputId": "f2a5a4a9-7bc2-46f4-b728-297a68ab17b1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
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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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              "model_id": "17ccb69388dd4f1cbfcc619c42e3e9f9"
            }
          },
          "metadata": {}
        },
        {
          "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,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [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": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea105513a59d436eaae51dc88cf5b8fa"
            }
          },
          "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,
              "model_id": "ff2f2cd3987945a6b45e3c9d7f9f8f9c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a349c92849c4ddabdf0e71348640ebd"
            }
          },
          "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": "1375a9ed425f4a4284cda08eccfbed5b"
            }
          },
          "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": "0d1415137fd5401ab6e0e14958abb757"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HuggingFaceEmbeddings(client=SentenceTransformer(\n",
              "  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel \n",
              "  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})\n",
              "  (2): Normalize()\n",
              "), model_name='all-MiniLM-L6-v2', cache_folder=None, model_kwargs={}, encode_kwargs={}, multi_process=False, show_progress=False)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sparse\n",
        "\n",
        "from pinecone_text.sparse import BM25Encoder\n",
        "\n",
        "bm25_encoder = BM25Encoder().default()\n",
        "bm25_encoder"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IqF8kCpzPg-3",
        "outputId": "f0a51d6f-92b7-4a12-f068-1aa2ab96f904"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pinecone_text.sparse.bm25_encoder.BM25Encoder at 0x7dc7b18f8b80>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentences=[\n",
        "    \"In 2023, I visited Paris\",\n",
        "    \"In 2022, I visited New York\",\n",
        "    \"In 2021, I visited London\",\n",
        "    \"In 2020, I visited Rome\"\n",
        "]\n",
        "\n",
        "# tf-idf values on the sentences\n",
        "bm25_encoder.fit(sentences)\n",
        "\n",
        "# Store values to json file\n",
        "bm25_encoder.dump(\"bm25_values.json\")\n",
        "\n",
        "# load to BM25Encoder object\n",
        "bm25_encoder = BM25Encoder().load(\"bm25_values.json\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "735bbe869cf942a8bd7cc735051e24d9",
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          ]
        },
        "id": "VU1ER0TpRn2d",
        "outputId": "cc172fc5-db8e-420b-ec0c-64886914fcfa"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "735bbe869cf942a8bd7cc735051e24d9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = PineconeHybridSearchRetriever(embeddings=embeddings, sparse_encoder=bm25_encoder,index=index)"
      ],
      "metadata": {
        "id": "Pl3Jlg5QP6mg"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retriever.add_texts(\n",
        "    [\n",
        "    \"In 2023, I visited Paris\",\n",
        "    \"In 2022, I visited New York\",\n",
        "    \"In 2021, I visited London\",\n",
        "    \"In 2020, I visited Rome\"\n",
        "]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "82a9b6f8de8442408ec6bdcca2589b7c",
            "54733610b1664526ba0a51c2bfd5b200",
            "3a5115bcb034406c8aadc5bb60b57948",
            "3ca57326bbb54a75bca72309deb99676",
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            "1ec481e49bf14b01b094ef714a5ef359",
            "fba9c67e00c947df8e0b402b9225f0cf"
          ]
        },
        "id": "8Vn1yXBkRHCz",
        "outputId": "d6d66f78-801b-4478-9fe0-3247f4bfb92e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82a9b6f8de8442408ec6bdcca2589b7c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retriever.invoke(\"What city did i visit in 2022?\")[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWyKcFJRS4fC",
        "outputId": "31d5182c-a2fc-44f3-e9ef-12831366ca7c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content='In 2022, I visited New York')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    }
  ]
}