VectorDatabasesTutorial.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Qdtrant"
      ],
      "metadata": {
        "id": "1C4wQirS2EuY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- https://cloud.qdrant.io/accounts/24954147-9bd5-4343-9143-289157379601/overview\n",
        "- https://qdrant.tech/documentation/tutorials/search-beginners/"
      ],
      "metadata": {
        "id": "z_rpmpiZ2aXB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Simple Semantic Search"
      ],
      "metadata": {
        "id": "oWQBr5Y33QJp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install qdrant-client transformers\n",
        "!pip install sentence_transformers\n",
        "! pip install kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "A9UGusuv2GqY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d32fb84e-c3ef-4bcf-8ec7-1a55ce585180"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: tokenizers, safetensors, urllib3, protobuf, portalocker, hyperframe, hpack, h11, grpcio, httpcore, h2, grpcio-tools, huggingface-hub, httpx, transformers, qdrant-client\n",
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            "Building wheels for collected packages: sentence_transformers\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125924 sha256=21a9d79c7680c3e7cc03f90c1067ea1f15eb12c53d2a259e96ff438c0f25a767\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence_transformers\n",
            "Installing collected packages: sentencepiece, sentence_transformers\n",
            "Successfully installed sentence_transformers-2.2.2 sentencepiece-0.1.99\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n",
            "cp: cannot stat 'kaggle.json': No such file or directory\n",
            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Lets first get the data from kaggle\n",
        "- Our data is a news data with thier class\n",
        "- But our case will be using it for similarity search"
      ],
      "metadata": {
        "id": "i-DQdSUPVFq0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import zipfile\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "DwtDZljcMhFK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "### get your kaggle token from kaggle.com\n",
        "api_token = {\"username\":\"\",\"key\":\"\"}\n",
        "\n",
        "\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsjbri4BMhL-",
        "outputId": "6fe94e29-503e-4175-ce37-dfaec88d562d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle datasets download savasy/ttc4900"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYazS86kMf8O",
        "outputId": "5950d1f2-8084-45eb-a2f7-a2a3b3d2e435"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading ttc4900.zip to /content\n",
            " 60% 2.00M/3.34M [00:00<00:00, 2.96MB/s]\n",
            "100% 3.34M/3.34M [00:00<00:00, 3.91MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_zip = \"/content/ttc4900.zip\"\n",
        "with zipfile.ZipFile(dataset_zip, \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"/content\")"
      ],
      "metadata": {
        "id": "6pHM2JMTM1Po"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/7allV03.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6695
        },
        "id": "GsNVabb9NT6p",
        "outputId": "2af5c046-452a-4313-86bb-769e361a3d9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "        category                                               text\n",
              "0       siyaset    3 milyon ile ön seçim vaadi mhp nin 10 olağan...\n",
              "1       siyaset    mesut_yılmaz yüce_divan da ceza alabilirdi pr...\n",
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              "4       siyaset    erdoğan idamın bir haklılık sebebi var demek ...\n",
              "...          ...                                                ...\n",
              "4895  teknoloji    iphone lara geri dönüyor ios 6 sürümüyle tele...\n",
              "4896  teknoloji    muslukta devrim sadece elimizi yıkadığımız mu...\n",
              "4897  teknoloji    halka iyi anlatılmalı bilgi_teknolojileri ile...\n",
              "4898  teknoloji    çöpe gidiyorlar apple 775 bin uygulamayla app...\n",
              "4899  teknoloji    google bu kez edward_gorey dedi ! google bu k...\n",
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            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-29d87267-c7f0-4511-af95-62f67c18ae5f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-29d87267-c7f0-4511-af95-62f67c18ae5f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Since Dataset is in Turkish Language, we are going to use an embedding model for Turkish language."
      ],
      "metadata": {
        "id": "tEVbjbQaVbaI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "encoder = SentenceTransformer(\"emrecan/bert-base-turkish-cased-mean-nli-stsb-tr\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401,
          "referenced_widgets": [
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      },
      "execution_count": null,
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        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qdrant_client import models, QdrantClient"
      ],
      "metadata": {
        "id": "XcICy0yr2GtN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Before connecting, you need to go `[Qdrant](https://qdrant.tech/)` and open an account\n",
        "- Once you open account you can go to cloud and get your credentials for connecting"
      ],
      "metadata": {
        "id": "fvZYHhycVxf-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qdrant_client = QdrantClient(\n",
        "    url='',\n",
        "    api_key=''\n",
        "     )"
      ],
      "metadata": {
        "id": "mdtX3gvZJH0V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a collection is a simple task, here we name our collection news_reccomender"
      ],
      "metadata": {
        "id": "wkxBTf-6Vs1-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qdrant_client.recreate_collection(\n",
        "\tcollection_name=\"news_reccomender\",\n",
        "\tvectors_config=models.VectorParams(\n",
        "\t\tsize=encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model\n",
        "\t\tdistance=models.Distance.COSINE,\n",
        "\t\ton_disk=True\n",
        "\t)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NUZbREAWVrv7",
        "outputId": "b40e245f-0643-4025-85c9-1a1b008dd946"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Once you created you collection, you can use following code to upload your records\n",
        "- You can also verify your collection with followng code"
      ],
      "metadata": {
        "id": "oAVvhMkeWQz-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "collection_info = qdrant_client.get_collection(collection_name='news_reccomender')\n",
        "list(collection_info)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQSWhgSRXC7O",
        "outputId": "db96ab64-5234-4a2d-a071-7f955491b919"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('status', <CollectionStatus.GREEN: 'green'>),\n",
              " ('optimizer_status', <OptimizersStatusOneOf.OK: 'ok'>),\n",
              " ('vectors_count', 0),\n",
              " ('indexed_vectors_count', 0),\n",
              " ('points_count', 0),\n",
              " ('segments_count', 2),\n",
              " ('config',\n",
              "  CollectionConfig(params=CollectionParams(vectors=VectorParams(size=768, distance=<Distance.COSINE: 'Cosine'>, hnsw_config=None, quantization_config=None, on_disk=True), shard_number=1, replication_factor=1, write_consistency_factor=1, on_disk_payload=True), hnsw_config=HnswConfig(m=16, ef_construct=100, full_scan_threshold=10000, max_indexing_threads=0, on_disk=False, payload_m=None), optimizer_config=OptimizersConfig(deleted_threshold=0.2, vacuum_min_vector_number=1000, default_segment_number=0, max_segment_size=None, memmap_threshold=None, indexing_threshold=20000, flush_interval_sec=5, max_optimization_threads=1), wal_config=WalConfig(wal_capacity_mb=32, wal_segments_ahead=0), quantization_config=None)),\n",
              " ('payload_schema', {})]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qdrant_client.upload_records(\n",
        "\tcollection_name=\"news_reccomender\",\n",
        "\trecords=[\n",
        "\t\tmodels.Record(\n",
        "\t\t\tid=idx,\n",
        "\t\t\tvector=encoder.encode(doc[\"text\"]).tolist(),\n",
        "\t\t\tpayload=doc\n",
        "\t\t) for idx, doc in enumerate(df.to_dict(orient='records'))\n",
        "\t]\n",
        ")"
      ],
      "metadata": {
        "id": "Jd58lRy4aLtS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "collection_info = qdrant_client.get_collection(collection_name='news_reccomender')\n",
        "list(collection_info)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvFMm_PwXTPH",
        "outputId": "e642ab6f-b72b-4125-a695-5f1797e4d568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('status', <CollectionStatus.GREEN: 'green'>),\n",
              " ('optimizer_status', <OptimizersStatusOneOf.OK: 'ok'>),\n",
              " ('vectors_count', 4900),\n",
              " ('indexed_vectors_count', 0),\n",
              " ('points_count', 4900),\n",
              " ('segments_count', 2),\n",
              " ('config',\n",
              "  CollectionConfig(params=CollectionParams(vectors=VectorParams(size=768, distance=<Distance.COSINE: 'Cosine'>, hnsw_config=None, quantization_config=None, on_disk=True), shard_number=1, replication_factor=1, write_consistency_factor=1, on_disk_payload=True), hnsw_config=HnswConfig(m=16, ef_construct=100, full_scan_threshold=10000, max_indexing_threads=0, on_disk=False, payload_m=None), optimizer_config=OptimizersConfig(deleted_threshold=0.2, vacuum_min_vector_number=1000, default_segment_number=0, max_segment_size=None, memmap_threshold=None, indexing_threshold=20000, flush_interval_sec=5, max_optimization_threads=1), wal_config=WalConfig(wal_capacity_mb=32, wal_segments_ahead=0), quantization_config=None)),\n",
              " ('payload_schema', {})]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets recheck our collection"
      ],
      "metadata": {
        "id": "qlGUjzvAXUvR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hits = qdrant_client.search(\n",
        "\tcollection_name=\"news_reccomender\",\n",
        "\tquery_vector=encoder.encode(\"Cumhurbaskani yurtdisina\").tolist(),\n",
        "\tlimit=3\n",
        ")\n",
        "for hit in hits:\n",
        "\tprint(hit.payload, \"score:\", hit.score)"
      ],
      "metadata": {
        "id": "r0FGezO_2ZZW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cfb911c-af27-42b3-e5db-03a74ab2fb38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'category': 'ekonomi ', 'text': ' erdoğan dünyayı dört kez turladı başbakan recep_tayyip_erdoğan 2012 \\x92 de 22 ülkeye 24 ziyaret gerçekleştirdi başbakan_erdoğan \\x92 ın 2012 \\x92 de ziyaret ettiği ülkeler şöyle güney_kore çin azerbaycan 2 pakistan 2 meksika brezilya endonezya brunei bosna_hersek ukrayna slovenya almanya ispanya italya ingiltere bulgaristan katar mısır suudi_arabistan rusya kazakistan ve iran yurt dışı temasları için toplam 165 bin 120 kilometre yol kateden erdoğan \\x92 ın en uzun yolculuğu g 20 zirvesi \\x92 ne katılmak için 12 bin kilometre yol katederek ulaştığı meksika \\x92 nın los cabos kentine oldu erdoğan \\x92 ın yurt dışı ziyaretlerinde katettiği toplam 165 bin 120 kilometre dünya çevresinde yapılacak 4 tura denk geliyor'} score: 0.43037367\n",
            "{'category': 'siyaset ', 'text': ' emniyet_müdürü nü hemen görevden alın mhp_grup_başkanvekili_oktay_vural diyarbakır emniyet_müdürü recep güven in hemen görevden alınması gerektiğini iddia etti tbmm de basın toplantısı düzenleyen vural genel_kurul da yarın irak ın kuzeyine sınırötesi operasyon yapma konusunda hükümete yetki veren başbakanlık tezkeresi nin görüşüleceğini hatırlattı mhp olarak tezkereye bir daha olumlu oy vereceklerini dile getiren vural 7 kez getirilen tezkereye rağmen türkiye nin pkk nın güvenli bölgelerini bertaraf edemediğini söyledi vural tezkerenin pkk terör örgütünün faaliyetlerine son vermek amacıyla mı tbmm ye getirildiğini yoksa milletin gazını almak terörle mücadele ediyor gözükmek için mi getirildiğini milletimizin takdirine bırakıyorum kullanmıyorsun bunları yazıyoruz gün gelecek devran dönecek bu yetkileri kullanmayanlar kullanmakta gaflet gösterenler hukuk önünde hesap verecek bu milletsize bir yetki vermişse o yetkiyi kullanacaksın diye konuştu diyarbakır emniyet_müdürü recep güven in sözlerini eleştiren vural bu konu hafife alınacak bir konu değildir dedi bugün rehabilitasyon ve bakım merkezini ziyaret ettiğini orada elleri ve kolları olmayan mehmetçiklerin bulunduğuna dikkati çeken vural şöyle devam etti bunlar kimin için ayaklarını kollarını verdi bebelere kurşun sıkanlara arkadan vuranlar için ağlayana söylüyorum ağlamayanlar insan değilmiş değil mi gazetelerde bir takım köşe yazarları manşetler atılıyor utanmalısınız be insanlığı öldürenler için ağlıyorsun önce o ölen insanlar için ağlamayı bileceksin bu ne hadsizliktir empati yapalım şu zihniyete bakın sen kim oluyorsun benim insanlığımı sen sorgulayamazsın dil uzatamazsın bu yurdun ekmeğini yiyorsanız suyunu içiyorsanız havasını kokluyorsanız köşenizi yazıyorsanız program yapıyorsanız bilin ki o şehitlerin sayesindedir terör örgütünü makul empati kurulması gereken insanlar olarak göstererek terörle mücadeleyi dışlayan anlayışı kabul edemeyiz kan akıyor kan gazeteci ahmet taner kışlalı yı öldürenlere de mi empati yapılacak teröristle empati yapacakmışız yok öyle bir şey türkiye hukuk devleti ise cezası bellidir şahin görünümlü güvercin vural başbakan_recep_tayyip_erdoğan ın grup toplantısında başbakan yardımcısı bülent_arınç ın bakanlar_kurulu toplantısı sonrasında ak_parti grup başkanvekili ahmet_aydın ın da yaptığı açıklamaları gazetecilere dinleterek başbakan_yardımcısı takdirle karşılıyorum diyor grup başkanvekili insani ve vicdani diyor başbakan ağlamayız diyor hangisine inanacaksınız diye konuştu erdoğan ın cumhurbaşkanı olmak istediğini ileri süren vural başbakan akp nin yapacaklarını demeyecek başbakan ın diyemediklerini akp yapacak bu ikircikli yaklaşım iliklerine kadar akp ye yapışmıştır dedi vural ak_parti nin terörle mücadele politikasını eleştirerek şahin görünümlü güvercin politikası uyguluyorlar ifadesini kullandı mhp li vural başbakan çok kritik bir yerde görev verilen bir kimsenin terör örgütüne empati beslenmesini gerektiğini söyleyen zihniyet karşısında siyasal duruşunuz varsa bu siyasal duruşunuza samimiyetle inanmamızı da arzu ediyorsanız diyarbakır emniyet_müdürü nü hemen görevden alın o zaman grup başkanvekiline ve başbakan yardımcına da bu konuda ne yapılması gerekiyorsa yap da inanalım yoksa laftan başka bir anlam taşımıyor dedi oslo daki görüşmelerde terör örgütü temsilcilerinden adem uzun adında birinin bulunduğunu bu kişinin daha sonra tank savar pazarlığı yaparken yakalandığını ileri süren vural bir elleri silahta bir elleri masada pkk ya tanksavar almak isteyen insanlarla masa kurmuşlar pazarlık ediyorlar belki de yeni oslo larla tanksavar ve uçaksavar almak için de bir görüşme planlayabilirler diye konuştu tasarının amacı siyasi vural gazetecilerin sorularını da cevaplandırdı büyükşehir_belediyesi kanunu tasarısı na ilişkin soru üzerine vural bu konuda grubu acil toplantıya çağırdıklarını söyledi bir oldu bitti ve dayatmayla karşı karşıya olduklarını ifade eden vural ak_parti nin demokrasinin köklerini kurutmak istediğini öne sürdü vural köyleri belde belediyeleri kaldırıyorlar bunun amacı siyasidir açık ve net bunlar insanların seçme hakkına saygı göstermiyorlar diye konuştu vural tasarıya göre mhp li 231 belediyenin kapatılacağını vurguladı vural seçilme yaşının 18 e düşürülmesine ilişkin önerinin de düşünülmeden ulu orta yapıldığını iddia ederek 18 yaşın seçme hakkı veriyorsun askerlik hizmetini yapma mecburiyeti var erlere seçme hakkı veriyorsun nasıl propaganda yapacaklar bize somut olarak gelmiş bir teklif yok dedi aa'} score: 0.36531544\n",
            "{'category': 'dunya ', 'text': ' cemil_çiçek bakü de türkiye_büyük_millet_meclisi tbmm başkanı cemil_çiçek azerbaycan ın başkenti bakü ye geldi çiçek karadeniz ekonomik işbirliği parlamenter asamblesi keipa genel_kurul toplantısı na katılacak özel uçak dap ile bakü ye gelen çiçek ve beraberindeki heyeti azerbaycan meclis başkan_yardımcısı ziyafet askerov ve türkiye nin büyükelçiliği yetkilileri karşıladı çiçek bakü de çeşitli iş çevrelerinin toplantıların da katılacak azerbaycan cumhurbaşkanı ilham aliye in çiçek i kabul etmesi bekleniyor cihan'} score: 0.36443597\n"
          ]
        }
      ]
    }
  ]
}