Semantic_Search_exploration_using_BERT_GH.ipynb

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      "source": [
        "#  **Exploring the use of sentence-BERT**\n",
        "\n",
        "[Tutorial](https://towardsdatascience.com/quick-semantic-search-using-siamese-bert-networks-1052e7b4df1)\n"
      ],
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        "# installing packages needed\n",
        "!pip install sentence-transformers\n",
        "!pip install scipy"
      ],
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        "colab": {
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        },
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      },
      "execution_count": null,
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.30.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.65.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.0.1+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.15.2+cu118)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.10.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (3.8.1)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.1.99)\n",
            "Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.15.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (3.12.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (2.27.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (23.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence-transformers) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence-transformers) (16.0.5)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2022.10.31)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.3.1)\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "                                                                                                                                                                                        # load the BERT model\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model = SentenceTransformer('bert-base-nli-mean-tokens')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433,
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      },
      "execution_count": null,
      "outputs": [
        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set up an example corpus\n",
        "\n",
        "sentences = ['Absence of sanity',\n",
        "             'Lack of saneness',\n",
        "             'A man is eating food.',\n",
        "             'A man is eating a piece of bread.',\n",
        "             'The girl is carrying a baby.',\n",
        "             'A man is riding a horse.',\n",
        "             'A woman is playing violin.',\n",
        "             'Two men pushed carts through the woods.',\n",
        "             'A man is riding a white horse on an enclosed ground.',\n",
        "             'A monkey is playing drums.',\n",
        "             'A cheetah is running behind its prey.']\n",
        "\n",
        "# creat embeddings\n",
        "sentence_embeddings = model.encode(sentences)\n",
        "\n",
        "print('Sample BERT embedding vector - length', len(sentence_embeddings[0]))\n",
        "print('Sample BERT embedding vector - note includes negative values', sentence_embeddings[0].tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_lR93iVNxPi",
        "outputId": "379ae93f-fd1b-4019-c1ad-83517f887f1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample BERT embedding vector - length 768\n",
            "Sample BERT embedding vector - note includes negative values [-0.05245405435562134, 0.026297958567738533, 0.02364524081349373, -0.046886514872312546, 0.09920787066221237, -0.016844285652041435, 0.02325245551764965, 0.010967695154249668, 0.03190600499510765, -0.04975314810872078, 0.07217402011156082, -0.02231350913643837, 0.015832798555493355, -0.013539413921535015, 0.02234629914164543, -0.043928712606430054, -0.07782064378261566, -0.009699025191366673, -0.018518369644880295, 0.0025585137773305178, -0.023588215932250023, 0.06260788440704346, -0.02184705249965191, 0.023285064846277237, 0.011254267767071724, 0.005217744503170252, 0.060471609234809875, -0.01357501931488514, 0.006760442163795233, 0.0357733815908432, -0.030896849930286407, 0.0076118772849440575, 0.012363860383629799, 0.016074014827609062, -0.030300596728920937, -0.02721293829381466, 0.0050917877815663815, -0.022153057157993317, -0.0003695595369208604, -0.02154984138906002, -0.07233229279518127, 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-0.01727590337395668, 0.014977504499256611, -0.03712025284767151, -0.015571714378893375, -0.04248885437846184, -0.030252819880843163, 0.0843881294131279, 0.012615067884325981, 0.042986929416656494, -0.01702149212360382, -0.02851620875298977, 0.01948757842183113, -0.01951741613447666, 0.01818685606122017, 0.015817802399396896, -0.021326690912246704, 0.022997131571173668, 0.04104962572455406, -0.05097359046339989, 6.33648014627397e-05, 0.03391147032380104, 0.07299627363681793, -0.013246795162558556, -0.05119486525654793, -0.0724201500415802, -0.012504871934652328, 0.024877281859517097, 0.0025875428691506386, -0.0417623333632946, -0.02930341474711895, 0.016555238515138626, 0.04718557000160217, 0.023908473551273346, -0.013648763298988342, 0.040163781493902206, -0.021926095709204674, 0.0003180938947480172, -0.03088979981839657, 0.05933557450771332, 0.04659655690193176, -0.01557699404656887, -0.016103852540254593, -0.03550700470805168, 0.018698669970035553, -0.02715740166604519, 0.03449045121669769, -0.010733252391219139, -0.00996775459498167, -0.0490788072347641, -0.030346855521202087, 0.07318180054426193, 0.05997239425778389, 0.0013543858658522367, 0.015115270391106606]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# perform semantic search\n",
        "import scipy\n",
        "\n",
        "query = 'Nobody has sane thoughts' #@param {type: \"string\"}\n",
        "\n",
        "queries = [query]\n",
        "query_embeddings = model.encode(queries)\n",
        "\n",
        "# Find the closest 3 sentences of the corpus for each query sentence based on cosine similarity\n",
        "number_top_matches = 3 #@param {type: \"number\"}\n",
        "\n",
        "print(\"Semantic Search Results\")\n",
        "\n",
        "for query, query_embedding in zip(queries, query_embeddings):\n",
        "    distances = scipy.spatial.distance.cdist([query_embedding], sentence_embeddings, \"cosine\")[0]\n",
        "\n",
        "    results = zip(range(len(distances)), distances)\n",
        "    results = sorted(results, key=lambda x: x[1])\n",
        "\n",
        "    print(\"\\n\\n======================\\n\\n\")\n",
        "    print(\"Query:\", query)\n",
        "    print(\"\\nTop 5 most similar sentences in corpus:\")\n",
        "\n",
        "    for idx, distance in results[0:number_top_matches]:\n",
        "        print(sentences[idx].strip(), \"(Cosine Score: %.4f)\" % (1-distance))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pf-RUXcyOEGl",
        "outputId": "1cc725b9-f5f8-4c78-e5b3-a03fb4a5d7d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic Search Results\n",
            "\n",
            "\n",
            "======================\n",
            "\n",
            "\n",
            "Query: Nobody has sane thoughts\n",
            "\n",
            "Top 5 most similar sentences in corpus:\n",
            "Lack of saneness (Cosine Score: 0.8958)\n",
            "Absence of sanity (Cosine Score: 0.8744)\n",
            "A man is riding a horse. (Cosine Score: 0.1705)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Semantic search using Quora dataset**\n",
        "\n",
        "Utillising larger dataset and vector database\n",
        "\n",
        "[Tutorial 1](https://www.youtube.com/watch?v=7RF03_WQJpQ), [Tutorial 2](https://docs.pinecone.io/docs/semantic-text-search), [Tutorial 3](https://github.com/pinecone-io/examples/blob/master/search/semantic-search/semantic-search-fast.ipynb)"
      ],
      "metadata": {
        "id": "P5WRDCs3XiTQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check where running\n",
        "!curl ipinfo.io"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bc01wH2uclkM",
        "outputId": "63cf8384-5274-43c2-ca87-15f57f0d7e3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"ip\": \"35.199.0.35\",\n",
            "  \"hostname\": \"35.0.199.35.bc.googleusercontent.com\",\n",
            "  \"city\": \"Washington\",\n",
            "  \"region\": \"Washington, D.C.\",\n",
            "  \"country\": \"US\",\n",
            "  \"loc\": \"38.8951,-77.0364\",\n",
            "  \"org\": \"AS396982 Google LLC\",\n",
            "  \"postal\": \"20004\",\n",
            "  \"timezone\": \"America/New_York\",\n",
            "  \"readme\": \"https://ipinfo.io/missingauth\"\n",
            "}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install dependencies\n",
        "!pip install sentence-transformers torch\n",
        "!pip install pinecone-client\n",
        "!pip install datasets # quora data"
      ],
      "metadata": {
        "id": "HkYCLGgveDdm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get a sample of the dataset**\n",
        "\n",
        "From the datasets library (provided by HuggingFace)"
      ],
      "metadata": {
        "id": "eqGN2n0If5XM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get sample of the Quora dataset\n",
        "import datasets\n",
        "quora = datasets.load_dataset('quora', split='train[:100]') # first 100 samples"
      ],
      "metadata": {
        "id": "V00JC6NeeUMI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create dictionary with all the sentences\n",
        "id2text = {}\n",
        "for item in quora:\n",
        "    question_ids = item['questions']['id']\n",
        "    question_texts = item['questions']['text']\n",
        "    for q_id, q_text in zip(question_ids, question_texts):\n",
        "        id2text[str(q_id)] = q_text"
      ],
      "metadata": {
        "id": "EcYHe4Jhq1Kf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploring the dataset**\n",
        "\n",
        "The 5th row of the dataset gives us a pair of sentences (IDs 11 and 12). These questions are a duplicate of one another - semantically these are the same sentence. The 30th row is an example where the 2 sentences are semantically different."
      ],
      "metadata": {
        "id": "za3oRnnegANl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# exploring the data\n",
        "# the 5th row of the dataset gives\n",
        "quora[5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OODSOpQTfln8",
        "outputId": "010cb33c-6d0a-4626-b2c9-a28adf53637f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'questions': {'id': [11, 12],\n",
              "  'text': ['Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?',\n",
              "   \"I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?\"]},\n",
              " 'is_duplicate': True}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quora[30]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5QT1SDzghq2",
        "outputId": "bfcc1a20-1385-4eb2-c2cf-ae37f7b72291"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'questions': {'id': [61, 62],\n",
              "  'text': [\"What's one thing you would like to do better?\",\n",
              "   \"What's one thing you do despite knowing better?\"]},\n",
              " 'is_duplicate': False}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# exploring the dictionary imported\n",
        "# all the sentences are contained in the dictionary\n",
        "id2text['62']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "laQr_V0cgoxU",
        "outputId": "31d1494a-346e-4534-e9b2-2c61789858dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"What's one thing you do despite knowing better?\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(id2text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltrEfZ20ZjTD",
        "outputId": "94705f65-6a55-4c71-b302-ef49cfe85a59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'dict'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get the model**\n",
        "\n",
        "all-distilroberta-v1. From Hugging Face, intended to be used as a sentence and short paragraph encoder."
      ],
      "metadata": {
        "id": "FiU3kImV7IFQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "model = SentenceTransformer('all-distilroberta-v1')\n",
        "model"
      ],
      "metadata": {
        "id": "XulBRWjP7nlA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80410846-3b4f-4b5b-df93-cf04198b54d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SentenceTransformer(\n",
              "  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel \n",
              "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
              "  (2): Normalize()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Generate embeddings and then upsert**\n",
        "\n",
        "Use BERT model to generate sentence embeddings for your corpus. Upload these embeddings to Pinecone (vector storage). Encode the query so it is embedding format, then search Pinecone database for similar sentences (through comparing the sentence embeddings that were generated by BERT). Using cosine similarity.\n",
        "\n",
        "Generate vectors and organise into tuples:\n",
        "* `id`: a string ID for the vector\n",
        "* `values`: the embedding values\n",
        "* `metadata`: additional json data\n",
        "\n"
      ],
      "metadata": {
        "id": "MrmQ9L7R7y6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# example embedding\n",
        "text = \"What's one thing you do despite knowing better?\"\n",
        "embedding = model.encode(text).tolist() # using to.list() so it prints out horizontally (rather than as an array)\n",
        "print(embedding)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgPZlSfL82ZJ",
        "outputId": "5f02c3bb-fd08-407f-e796-e0c697c2bd37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "code below demonstrates that encoding a single sentences leaves us with a `768` dimensional sentence embedding (aligned to the `word_embedding_dimension` above)"
      ],
      "metadata": {
        "id": "eq3auyhzWgHo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embedding = model.encode(text)\n",
        "embedding.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qx2ZCM26WZba",
        "outputId": "7eec12ae-9a24-4dc9-f0fb-f99dac84d6fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768,)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# organise into vector - this needs to be done so that the vectors can later be upserted to Pinecone\n",
        "id = \"vec62\"\n",
        "values = embedding\n",
        "metadata = {\n",
        "    'is_duplicate': False,\n",
        "    'char_length':len(text)\n",
        "}\n",
        "\n",
        "vector = (id, values, metadata)\n",
        "print(vector)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0RA4e6bm9XLM",
        "outputId": "f47d20be-854d-46ec-ac1e-ecea4841b8c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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0.013830896466970444, -0.06036902591586113, -0.05752295255661011, 0.03838696703314781, 0.02715929225087166, -0.031084271147847176, -0.03431834280490875, -0.05067810043692589, -0.014562911354005337, 0.06257825344800949, 0.048308514058589935, -0.03987188637256622, -0.007939576171338558, 0.029378186911344528, 0.008442157879471779, -0.05648082122206688, -0.02727653831243515, 0.04531067609786987, -0.018105344846844673, 0.0038712224923074245, -0.019261173903942108, -0.04735127463936806, 0.0917111337184906, -0.025891510769724846, -0.012585746124386787, -0.004876425489783287, 0.011693257838487625, 0.0436219647526741, 0.025246795266866684, 0.028159288689494133, 0.02896413579583168, 0.0117745166644454, -0.0221566092222929, 0.054523590952157974, 0.009000147692859173, -0.001864030258730054, 0.0064399573020637035, 0.023814868181943893, 0.05009695515036583, 0.02551007643342018, 0.003031270345672965, -0.03181612491607666, 0.02771048992872238, 0.02408747933804989, -0.006035802885890007, -0.03667934611439705, 0.007570243440568447, -0.008388875052332878, -0.011425645090639591, 0.0025985310785472393, 0.04989565908908844], {'is_duplicate': False, 'char_length': 47})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we connect to **Pinecone**.\n",
        "\n",
        "tutorial for using Pinecone can be found [here](https://towardsdatascience.com/crud-with-pinecone-ee6b6f8b54e8)."
      ],
      "metadata": {
        "id": "wuoLEFGF-PZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Connect to Pinecone\n",
        "!pip install --upgrade pinecone-client\n"
      ],
      "metadata": {
        "id": "VeKg48zD-NBU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pinecone\n",
        "from google.colab import userdata\n",
        "\n",
        "key = userdata.get('pinecone_api_key')\n",
        "env = userdata.get('pinecone_environment')"
      ],
      "metadata": {
        "id": "QEYG70hkW8Vk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize connection to Pinecone\n",
        "pinecone.init(api_key=key, environment=env)"
      ],
      "metadata": {
        "id": "vPjHxO_SWin6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create pinecone index\n",
        "index_name = 'quora-semantic-search-example'\n",
        "\n",
        "# only create index if it doesn't exist\n",
        "if index_name not in pinecone.list_indexes():\n",
        "    pinecone.create_index(\n",
        "        name=index_name,\n",
        "        dimension=768, # vector space for BERT is 768\n",
        "        metric='cosine'\n",
        "    )\n",
        "    # wait a moment for the index to be fully initialized\n",
        "    time.sleep(1)\n",
        "\n",
        "index = pinecone.Index(index_name)"
      ],
      "metadata": {
        "id": "y2gKqDqYEfei"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Upsert vector to pinecone\n",
        "index.upsert(vectors=[vector])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LB7vmIPtTVdD",
        "outputId": "5d8d158a-5f4d-4ea5-e870-9a9e0eebea97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'upserted_count': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check index\n",
        "index.describe_index_stats()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdRBvCaQUOCY",
        "outputId": "e93b78c0-8710-4983-bef9-4c4fd9d4125f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'dimension': 768,\n",
              " 'index_fullness': 0.0,\n",
              " 'namespaces': {'': {'vector_count': 1}},\n",
              " 'total_vector_count': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Upserting more data to pinecone**\n",
        "\n",
        "Need to first transform the data to a suitable format."
      ],
      "metadata": {
        "id": "KIndJfEcYewx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# transforming the data\n",
        "\n",
        "questions = []\n",
        "\n",
        "for record in quora['questions']:\n",
        "    questions.extend(record['text'])\n",
        "\n",
        "# remove duplicates\n",
        "questions = list(set(questions))\n",
        "print('\\n'.join(questions[:5]))\n",
        "print(len(questions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8hmcs8ocMjC",
        "outputId": "efcd7f80-116c-4891-cb54-c694022410e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nd she is always sad?\n",
            "Does Quora have a character limit for profile descriptions?\n",
            "How do I prevent breast cancer?\n",
            "How is the average speed of gas molecules determined?\n",
            "Are there any people who genuinely enjoy salad with no dressing?\n",
            "200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When uploading to pinecone, the records objects needs to be a list of `(id, embedding, metadata)` tuples"
      ],
      "metadata": {
        "id": "w_Y8B11keL8p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "batch_size = 128\n",
        "\n",
        "for i in tqdm(range(0, len(questions), batch_size)):\n",
        "    # find end of batch\n",
        "    i_end = min(i+batch_size, len(questions))\n",
        "    # create IDs batch\n",
        "    ids = [str(x) for x in range(i, i_end)]\n",
        "    # create metadata batch\n",
        "    metadatas = [{'text': text} for text in questions[i:i_end]]\n",
        "    # create embeddings\n",
        "    xc = model.encode(questions[i:i_end]).tolist()\n",
        "    # create records list for upsert\n",
        "    records = list(zip(ids, xc, metadatas))\n",
        "    print(type(records))\n",
        "    # upsert to Pinecone\n",
        "    index.upsert(vectors=records)\n",
        "\n",
        "# check number of records in the index\n",
        "index.describe_index_stats()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "5096c7933a6948b9b939bba2beba0e46",
            "98ae3496dc35487e888f1200cf92d9af",
            "25640bfd7531484c8448bf5365cd0417",
            "b14e65bee3c1480dbc14a589156da22b",
            "fc53c5a6df014752a121911bebb95f1d",
            "2da9aa4e61714c6d93583cd3bc743498",
            "78cf08dfdf7a4cc4aa06cbe0914dce70",
            "cd0abe4206fe433ab108f811fc3d26ff",
            "ec2b1fda9ae34ee3b071c38d9a4a8d43",
            "60f7c1f1413c46da99c6b36431e44c23",
            "3f87129b5e0244819a4cdebcfeecd16b"
          ]
        },
        "id": "whIxzqpzYwQw",
        "outputId": "a71599cc-4413-44be-d6c6-373728ae7880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5096c7933a6948b9b939bba2beba0e46"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n",
            "<class 'list'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'dimension': 768,\n",
              " 'index_fullness': 0.0,\n",
              " 'namespaces': {'': {'vector_count': 201}},\n",
              " 'total_vector_count': 201}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making queries !!**\n",
        "\n",
        "this example is performing a semantic search for similar questions, so need to embed and search with another question\n"
      ],
      "metadata": {
        "id": "URgg9muGYLr1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"which city has the highest population in the world?\"\n",
        "\n",
        "# create the query vector\n",
        "xq = model.encode([query]).tolist()\n",
        "\n",
        "# now query\n",
        "xc = index.query(xq, top_k=5, include_metadata=True)\n",
        "xc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHlIllhRYNQ8",
        "outputId": "9b3fc492-cdb4-43ac-879f-2f34d2f45be8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'matches': [{'id': '82',\n",
              "              'metadata': {'text': 'What are some of the high salary income '\n",
              "                                   'jobs in the field of biotechnology?'},\n",
              "              'score': 0.239463449,\n",
              "              'values': []},\n",
              "             {'id': '110',\n",
              "              'metadata': {'text': 'Who is the richest gambler of all time and '\n",
              "                                   'how can I reach his level?'},\n",
              "              'score': 0.214109302,\n",
              "              'values': []},\n",
              "             {'id': '22',\n",
              "              'metadata': {'text': 'Who is the richest gambler of all time and '\n",
              "                                   'how can I reach his level as a gambler?'},\n",
              "              'score': 0.192162454,\n",
              "              'values': []},\n",
              "             {'id': '133',\n",
              "              'metadata': {'text': 'What is the best travel website in spain?'},\n",
              "              'score': 0.188128248,\n",
              "              'values': []},\n",
              "             {'id': '16',\n",
              "              'metadata': {'text': 'What are some high paying jobs for a '\n",
              "                                   'fresher with an M.Tech in biotechnology?'},\n",
              "              'score': 0.173057854,\n",
              "              'values': []}],\n",
              " 'namespace': ''}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reformat the response so it is easier to read:\n",
        "\n",
        "\n",
        "**note:** *these aren't that similar as it is a very small dataset.*"
      ],
      "metadata": {
        "id": "Q98Ca67IfVZw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for result in xc['matches']:\n",
        "    print(f\"{round(result['score'], 2)}: {result['metadata']['text']}\")"
      ],
      "metadata": {
        "id": "o890dPrNa2-c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b4c80cb-0560-4985-c60c-7ce9d252a4e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.24: What are some of the high salary income jobs in the field of biotechnology?\n",
            "0.21: Who is the richest gambler of all time and how can I reach his level?\n",
            "0.19: Who is the richest gambler of all time and how can I reach his level as a gambler?\n",
            "0.19: What is the best travel website in spain?\n",
            "0.17: What are some high paying jobs for a fresher with an M.Tech in biotechnology?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Creating a semantic search engine with Transformers and Faiss**\n",
        "\n",
        "will use sentence transformers model (same as above)\n",
        "\n",
        "**Faiss** is an alternative vector database provided by Facebook."
      ],
      "metadata": {
        "id": "EUPIGwxbkByd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Need to change the runtime to GPU and then clone this repository to access the data used in this tutorial (which is similar to the data that we would need for an app ourselves).\n"
      ],
      "metadata": {
        "id": "c-kwSYsbASgY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/kstathou/vector_engine"
      ],
      "metadata": {
        "id": "r8U2KrkOBGvd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd vector_engine"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJHTtdmiBMil",
        "outputId": "33ea544d-1752-40b6-ef7a-2779aae6fb38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vector_engine\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "guL60dkQBPSp",
        "outputId": "05723e63-c42d-4db4-9993-5a2700228da6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Obtaining file:///content/vector_engine (from -r requirements.txt (line 9))\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement torch==1.8.1 (from versions: 1.11.0, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 2.0.0, 2.0.1)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for torch==1.8.1\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# installing torch as it didn't work above - and additional packages being imported below\n",
        "!pip install torch\n",
        "!pip install sentence-transformers\n",
        "!pip install faiss-gpu\n",
        "!pip install faiss-cpu\n",
        "!pip install pathlib\n",
        "!pip install vector"
      ],
      "metadata": {
        "id": "N4qE7LDTBWDc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext autoreload"
      ],
      "metadata": {
        "id": "F-pTOWvfBm_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%autoreload 2\n",
        "# Used to import data from local.\n",
        "import pandas as pd\n",
        "\n",
        "# Used to create the dense document vectors.\n",
        "import torch\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# Used to create and store the Faiss index.\n",
        "import faiss\n",
        "import numpy as np\n",
        "import pickle\n",
        "from pathlib import Path\n",
        "\n",
        "# Used to do vector searches and display the results - these are functions specifically in the cloned git directory\n",
        "# written out in full later in the code\n",
        "from vector_engine.utils import vector_search, id2details"
      ],
      "metadata": {
        "id": "z7VZYRWRBrt0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**get data**\n",
        "\n",
        "Data has been pre-processed to get it into this dataframe. Will need to do something similar for NAO reports."
      ],
      "metadata": {
        "id": "5lSh7IeREcui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# stored and processed data in s3\n",
        "df = pd.read_csv('data/misinformation_papers.csv')"
      ],
      "metadata": {
        "id": "Yqfbr14LDTft"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "TlPiokzgDjr0",
        "outputId": "622dd4bb-4b62-4147-dbcd-142f1c015f64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      original_title  \\\n",
              "0  When Corrections Fail: The Persistence of Poli...   \n",
              "1  A postmodern Pandora's box: anti-vaccination m...   \n",
              "2      Spread of (Mis)Information in Social Networks   \n",
              "3  Mandatory Influenza Vaccination of Health Care...   \n",
              "4  Intrauterine contraception in Saint Louis: a s...   \n",
              "5     Tracking the source of glacier misinformation.   \n",
              "6  Cost Overruns in Large-Scale Transportation In...   \n",
              "7  The Role of the Social Network in Contraceptiv...   \n",
              "8  Persistent high fertility in Uganda: young peo...   \n",
              "9  How to Disappear: Erase Your Digital Footprint...   \n",
              "\n",
              "                                            abstract  year  citations  \\\n",
              "0  An extensive literature addresses citizen igno...  2010        901   \n",
              "1  The Internet plays a large role in disseminati...  2010        440   \n",
              "2  We provide a model to investigate the tension ...  2010        278   \n",
              "3  BACKGROUND Influenza vaccination of health car...  2010        232   \n",
              "4  Abstract Background Many obstacles to intraute...  2010         99   \n",
              "5  A recent News of the Week story on Himalayan g...  2010         83   \n",
              "6  Managing large-scale transportation infrastruc...  2010        122   \n",
              "7  Abstract Purpose Understanding reasons for con...  2010        106   \n",
              "8  Background\\r\\nHigh fertility among young peopl...  2010        100   \n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Misinformation, disinformation and fake news papers: {df.id.unique().shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ErRMYPZDuQs",
        "outputId": "44910dd3-4098-4427-ca0d-a398fef00aa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Misinformation, disinformation and fake news papers: 8430\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the `distilbert-base-nli-stsb-mean-tokens` model which has the best performance on Semantic Textual Similarity tasks among the DistilBERT versions."
      ],
      "metadata": {
        "id": "4pxx0ZRdD486"
      }
    },
    {
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        "# Instantiate the sentence-level DistilBERT\n",
        "model = SentenceTransformer('distilbert-base-nli-stsb-mean-tokens')\n",
        "# Check if GPU is available and use it\n",
        "if torch.cuda.is_available():\n",
        "    model = model.to(torch.device(\"cuda\"))\n",
        "print(model.device)"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert abstracts to embeddings\n",
        "embeddings = model.encode(df.abstract.to_list(), show_progress_bar=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4da4959f88b842d39a91a53f44fc8c80",
            "ce1d596e67cc434e974f4b86311137fe",
            "985ac310012a42a19433f10b65a26932",
            "5deef9ff42854a3fa33d786dfba805fa",
            "480c2af17d6b49fab19a1f2597c708c4",
            "d9ee37dd3eb04e228af7c20c27756944",
            "34a58f0a99cd4e13bb2d57897d354789",
            "d9c3bd7859fa47a492889940e7200699",
            "fdc459c032574dd1a37e669ffa72d7c2",
            "2b8eb847906d49a5ab269fb0a0e73b83",
            "0d0312c6ec4f437e97d49e5ec83469ba"
          ]
        },
        "id": "sCoG6DyUEFKd",
        "outputId": "3897516c-b13e-49ab-c7a3-827c2916645d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/264 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "4da4959f88b842d39a91a53f44fc8c80"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Shape of the vectorised abstract: {embeddings[0].shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_i-srFQEOxT",
        "outputId": "1b059795-497e-41e6-df5d-2202dd9bf74d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the vectorised abstract: (768,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**vector similarity search with Faiss**\n",
        "\n",
        "Faiss is built around the `Index` object which contains, and sometimes preprocesses, the searchable vectors.\n",
        "\n",
        "**Note:** Faiss uses only 32-bit floating point matrices. This means that you will have to change the data type of the input before building the index.\n",
        "\n",
        "Use `IndexFlatL2` index: simple index that performs a brute-force L2 distance search. Scales linearly.\n",
        "\n",
        "To create the index:\n",
        "\n",
        "1. Change the data type of the abstract vectors to float32.\n",
        "2. Build an index and pass it the dimension of the vectors it will operate on.\n",
        "3. Pass the index to `IndexIDMap`, an object that enables us to provide a custom list of IDs for the indexed vectors.\n",
        "4. Add the abstract vectors and their ID mapping to the index. In our case, we will map vectors to their paper IDs from MAG."
      ],
      "metadata": {
        "id": "jkC12QdOEX9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Change data type\n",
        "embeddings = np.array([embedding for embedding in embeddings]).astype(\"float32\")\n",
        "\n",
        "# Step 2: Instantiate the index\n",
        "index = faiss.IndexFlatL2(embeddings.shape[1])\n",
        "\n",
        "# Step 3: Pass the index to IndexIDMap\n",
        "index = faiss.IndexIDMap(index)\n",
        "\n",
        "# Step 4: Add vectors and their IDs\n",
        "index.add_with_ids(embeddings, df.id.values)\n",
        "\n",
        "print(f\"Number of vectors in the Faiss index: {index.ntotal}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmmNTnXMFIrT",
        "outputId": "351fcdc7-59a0-4a00-b8b1-3f996c88877e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of vectors in the Faiss index: 8430\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Searching the index:\n",
        "\n",
        "The index we built will perform a k-nearest-neighbour search. Provide the number of neighbours to be returned.\n",
        "\n",
        "Query the index with an abstract from the dataset. First document has to be the query."
      ],
      "metadata": {
        "id": "1W_d-0A4FRUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Paper abstract\n",
        "df.iloc[5415, 1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "id": "xaF8WxSBFZss",
        "outputId": "53ca5d51-619c-44c5-d40c-a2c78ac8cadd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve the 10 nearest neighbours\n",
        "D, I = index.search(np.array([embeddings[5415]]), k=10)\n",
        "print(f'L2 distance: {D.flatten().tolist()}\\n\\nMAG paper IDs: {I.flatten().tolist()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebhH3ivhFe-g",
        "outputId": "d9fc4d49-bcef-4ca7-da4e-7739e2dbdd12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L2 distance: [0.0, 1.3749969005584717, 55.55474853515625, 65.87544250488281, 67.96585083007812, 69.05718994140625, 69.70634460449219, 70.40652465820312, 70.57284545898438, 71.0338134765625]\n",
            "\n",
            "MAG paper IDs: [3092618151, 3011345566, 3012936764, 3011186656, 3092128270, 3048848247, 3044429417, 3055557295, 3024620668, 3044097955]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetch the paper titles based on their index\n",
        "id2details(df, I, 'original_title')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6yG9UZd_Fin6",
        "outputId": "57aadecb-0b23-4d67-ac2e-6b73733392ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['The COVID-19 social media infodemic.'],\n",
              " ['The COVID-19 Social Media Infodemic'],\n",
              " ['Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset'],\n",
              " ['Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter'],\n",
              " ['Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy'],\n",
              " ['COVID-19-Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis.'],\n",
              " ['Effects of misinformation on COVID-19 individual responses and recommendations for resilience of disastrous consequences of misinformation'],\n",
              " ['Covid-19 infodemic reveals new tipping point epidemiology and a revised R formula.'],\n",
              " ['Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning'],\n",
              " ['Coronavirus-related online web search desire amidst the rising novel coronavirus incidence in Ethiopia: Google Trends-based infodemiology']]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetch the paper abstracts based on their index\n",
        "id2details(df, I, 'abstract')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Suje9ukLFsK2",
        "outputId": "2900a22d-d73a-4732-b864-2262e6f2a7b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[\"We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.\"],\n",
              " [\"We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction numbers $R_0$ for each social media platform. Moreover, we characterize information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.\"],\n",
              " ['The objective of this work is to explore popular discourse about the COVID-19 pandemic and policies implemented to manage it. Using Natural Language Processing, Text Mining, and Network Analysis to analyze corpus of tweets that relate to the COVID-19 pandemic, we identify common responses to the pandemic and how these responses differ across time. Moreover, insights as to how information and misinformation were transmitted via Twitter, starting at the early stages of this pandemic, are presented. Finally, this work introduces a dataset of tweets collected from all over the world, in multiple languages, dating back to January 22nd, when the total cases of reported COVID-19 were below 600 worldwide. The insights presented in this work could help inform decision makers in the face of future pandemics, and the dataset introduced can be used to acquire valuable knowledge to help mitigate the COVID-19 pandemic.'],\n",
              " ['Background Since the beginning of the coronavirus disease 2019 (COVID-19) epidemic, misinformation has been spreading\\xa0uninhibited\\xa0over traditional and social media at a rapid pace. We sought to analyze the magnitude of misinformation that is being spread on Twitter\\xa0(Twitter, Inc., San Francisco, CA) regarding the coronavirus epidemic.\\xa0 Materials and methods We conducted a search on Twitter using 14 different trending hashtags and keywords related to the COVID-19 epidemic. We then summarized and assessed individual tweets for misinformation in comparison to verified and peer-reviewed resources. Descriptive statistics were used to compare\\xa0terms and hashtags, and to identify individual tweets and account characteristics. Results The study included 673 tweets. Most tweets were posted by informal individuals/groups (66%), and 129 (19.2%) belonged to verified Twitter accounts. The majority of included tweets contained serious content (91.2%); 548 tweets (81.4%) included genuine information pertaining to the COVID-19 epidemic. Around 70% of the tweets tackled medical/public health information, while the others were pertaining to sociopolitical and financial factors. In total, 153 tweets (24.8%) included misinformation, and 107 (17.4%) included unverifiable information regarding the COVID-19 epidemic. The rate of misinformation was higher among informal individual/group accounts (33.8%, p: <0.001). Tweets from unverified Twitter accounts contained more misinformation (31.0% vs 12.6% for verified accounts, p: <0.001). Tweets from healthcare/public health accounts had the lowest rate of unverifiable information (12.3%, p: 0.04). The number of likes and retweets per tweet was not associated with a difference in either false or unverifiable content. The keyword \"COVID-19\" had the lowest rate of misinformation and unverifiable information, while the keywords \"#2019_ncov\" and \"Corona\" were associated with the highest amount of misinformation and unverifiable content respectively. Conclusions Medical misinformation and unverifiable content pertaining to the global COVID-19 epidemic are being propagated at an alarming rate on social media. We provide an early quantification of the magnitude of misinformation spread and highlight the importance of early interventions in order to curb this phenomenon that endangers public safety at a time when awareness and appropriate preventive actions are paramount.'],\n",
              " ['During the Covid-19 pandemics, we also experience another dangerous pandemics based on misinformation. Narratives disconnected from fact-checking on the origin and cure of the disease intertwined with pre-existing political fights. We collect a database on Twitter posts and analyse the topology of the networks of retweeters (users broadcasting again the same elementary piece of information, or tweet) and validate its structure with methods of statistical physics of networks. Furthermore, by using commonly available fact checking software, we assess the reputation of the pieces of news exchanged. By using a combination of theoretical and practical weapons, we are able to track down the flow of misinformation in a snapshot of the Twitter ecosystem. Thanks to the presence of verified users, we can also assign a polarization to the network nodes (users) and see the impact of low-quality information producers and spreaders in the Twitter ecosystem.'],\n",
              " ['Infodemics, often including rumors, stigma, and conspiracy theories, have been common during the COVID-19 pandemic. Monitoring social media data has been identified as the best method for tracking rumors in real time and as a possible way to dispel misinformation and reduce stigma. However, the detection, assessment, and response to rumors, stigma, and conspiracy theories in real time are a challenge. Therefore, we followed and examined COVID-19-related rumors, stigma, and conspiracy theories circulating on online platforms, including fact-checking agency websites, Facebook, Twitter, and online newspapers, and their impacts on public health. Information was extracted between December 31, 2019 and April 5, 2020, and descriptively analyzed. We performed a content analysis of the news articles to compare and contrast data collected from other sources. We identified 2,311 reports of rumors, stigma, and conspiracy theories in 25 languages from 87 countries. Claims were related to illness, transmission and mortality (24%), control measures (21%), treatment and cure (19%), cause of disease including the origin (15%), violence (1%), and miscellaneous (20%). Of the 2,276 reports for which text ratings were available, 1,856 claims were false (82%). Misinformation fueled by rumors, stigma, and conspiracy theories can have potentially serious implications on the individual and community if prioritized over evidence-based guidelines. Health agencies must track misinformation associated with the COVID-19 in real time, and engage local communities and government stakeholders to debunk misinformation.'],\n",
              " ['Abstract The proliferation of misinformation on social media platforms is faster than the spread of Corona Virus Diseases (COVID-19) and it can generate hefty deleterious consequences on health amid a disaster like COVID-19. Drawing upon research on the stimulus-response theory (hypodermic needle theory) and the resilience theory, this study tested a conceptual framework considering general misinformation belief, conspiracy belief, and religious misinformation belief as the stimulus; and credibility evaluations as resilience strategy; and their effects on COVID-19 individual responses. Using a self-administered online survey during the COVID-19 pandemic, the study obtained 483 useable responses and after test, finds that all-inclusive, the propagation of misinformation on social media undermines the COVID-19 individual responses. Particularly, credibility evaluation of misinformation strongly predicts the COVID-19 individual responses with positive influences and religious misinformation beliefs as well as conspiracy beliefs and general misinformation beliefs come next and influence negatively. The findings and general recommendations will help the public, in general, to be cautious about misinformation, and the respective authority of a country, in particular, for initiating proper safety measures about disastrous misinformation to protect the public health from being exploited.'],\n",
              " [\"Many governments have managed to control their COVID-19 outbreak with a simple message: keep the effective '$R$ number' $R<1$ to prevent widespread contagion and flatten the curve. This raises the question whether a similar policy could control dangerous online 'infodemics' of information, misinformation and disinformation. Here we show, using multi-platform data from the COVID-19 infodemic, that its online spreading instead encompasses a different dynamical regime where communities and users within and across independent platforms, sporadically form temporary active links on similar timescales to the viral spreading. This allows material that might have died out, to evolve and even mutate. This has enabled niche networks that were already successfully spreading hate and anti-vaccination material, to rapidly become global super-spreaders of narratives featuring fake COVID-19 treatments, anti-Asian sentiment and conspiracy theories. We derive new tools that incorporate these coupled social-viral dynamics, including an online $R$, to help prevent infodemic spreading at all scales: from spreading across platforms (e.g. Facebook, 4Chan) to spreading within a given subpopulation, or community, or topic. By accounting for similar social and viral timescales, the same mathematical theory also offers a quantitative description of other unconventional infection profiles such as rumors spreading in financial markets and colds spreading in schools.\"],\n",
              " ['A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations (“anti-vax”). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination (“pro-vax”) community. However, the anti-vax community exhibits a broader range of “flavors” of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation.'],\n",
              " ['Abstract\\nBackground: During outbreaks, like novel coronavirus (COVID 19) disease pandemic, social communication and behaviors are very important. Under such circumstances, individual activities in social media and other online platforms will increase tremendously resulting in the circulation of information in the community. These pieces of information can be infodemic monikers misleading the community. Thus exploring the trends of public interest in searching COVID 19 related issues and checking information circulating in the public is highly crucial. \\nObjective: This study aimed to explore users9 concerns towards coronavirus related online web search and to investigate the extent of infodemic monikers (misinformation) adopted for identifying the virus in the early stage of COVID 19 spread in Ethiopia.\\nMethods: Google Trends was employed in exploring the tendency towards coronavirus related web search activities in Ethiopia from March 13 to May 8, 2020. Keywords of the different names of COVID 19 and health related issues were used to investigate the trends of public interest in searching from Google over time. Relative search volume (RSV), Average peak comparison (APC), and associated graphs were used to compare the trends of online search interests. Pearson correlation coefficient was calculated to check for the presence of correlation.\\nResult: During the study period, the top names users adopted to identify the virus in Ethiopia were corona, virus, coronavirus, corona virus, China coronavirus, and COVID 19. In almost all search activities, the users employed infodemic monikers to identify the virus (overall relative search percentage of 99%). Updates related issues (APC=60, 95% CI, 55, 66) were the most commonly trending health related searches on Google followed by mortality (APC=27, 95% CI, 24, 30) and symptoms (APC=55, 95% CI, 50, 60) related issues. The regional comparison showed the highest cumulative peak for the Oromia region (cumulative total RSVs, 536) followed by the Tigray region (cumulative total RSVs, 512) on querying health related information from Google. No significant correlation was detected between COVID 19 cases per region/city and the maximum peaks of health related searches.\\nConclusion: This study revealed an initial increase in the public interest of COVID 19 related Google search, but this interest was declined over time. Tremendous circulation of infodemic monikers for the identification of the virus was also noticed in the country. The authors recommend for the government and other stakeholders to work immensely to keep the people alert on coronavirus-related issues and to promote the official names of the virus, like COVID 19, SARS CoV 2 in the public to decrease the circulation of misleading and misinformation amid the outbreak.\\nKeywords: Coronavirus, infodemiology, infodemic monikers; Google Trends, Ethiopia']]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Putting it all together**\n",
        "\n",
        "To query the index with an unseen query and retrieve its most relevant documents:\n",
        "\n",
        "1. Encode the query with the same sentence-DistilBERT model we used for the rest of the abstract vectors\n",
        "2. Change its data type to float32\n",
        "3. Search the index with the encoded query"
      ],
      "metadata": {
        "id": "zDbPwAgEFy0u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_query = \"\"\"\n",
        "WhatsApp was alleged to have been widely used to spread misinformation and propaganda\n",
        "during the 2018 elections in Brazil and the 2019 elections in India. Due to the\n",
        "private encrypted nature of the messages on WhatsApp, it is hard to track the dissemination\n",
        "of misinformation at scale. In this work, using public WhatsApp data from Brazil and India, we\n",
        "observe that misinformation has been largely shared on WhatsApp public groups even after they\n",
        "were already fact-checked by popular fact-checking agencies. This represents a significant portion\n",
        "of misinformation spread in both Brazil and India in the groups analyzed. We posit that such\n",
        "misinformation content could be prevented if WhatsApp had a means to flag already fact-checked\n",
        "content. To this end, we propose an architecture that could be implemented by WhatsApp to counter\n",
        "such misinformation. Our proposal respects the current end-to-end encryption architecture on WhatsApp,\n",
        "thus protecting users’ privacy while providing an approach to detect the misinformation that benefits\n",
        "from fact-checking efforts.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "6F_96eW_FuKV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def vector_search(query, model, index, num_results=10):\n",
        "    \"\"\"Tranforms query to vector using a pretrained, sentence-level\n",
        "    DistilBERT model and finds similar vectors using FAISS.\n",
        "\n",
        "    Args:\n",
        "        query (str): User query that should be more than a sentence long.\n",
        "        model (sentence_transformers.SentenceTransformer.SentenceTransformer)\n",
        "        index (`numpy.ndarray`): FAISS index that needs to be deserialized.\n",
        "        num_results (int): Number of results to return.\n",
        "\n",
        "    Returns:\n",
        "        D (:obj:`numpy.array` of `float`): Distance between results and query.\n",
        "        I (:obj:`numpy.array` of `int`): Paper ID of the results.\n",
        "\n",
        "    \"\"\"\n",
        "    vector = model.encode(list(query))\n",
        "    D, I = index.search(np.array(vector).astype(\"float32\"), k=num_results)\n",
        "    return D, I\n",
        "\n",
        "\n",
        "def id2details(df, I, column):\n",
        "    \"\"\"Returns the paper titles based on the paper index.\"\"\"\n",
        "    return [list(df[df.id == idx][column]) for idx in I[0]]\n"
      ],
      "metadata": {
        "id": "PKJucF3wHhWD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# query the index\n",
        "D, I = vector_search([user_query], model, index, num_results=10)\n",
        "print(f'L2 distance: {D.flatten().tolist()}\\n\\nMAG paper IDs: {I.flatten().tolist()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFcwSJaqGg4M",
        "outputId": "f6cf014d-99ef-43cf-b9c2-e65f6d917725"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L2 distance: [7.636616230010986, 58.327415466308594, 58.327415466308594, 70.91807556152344, 73.32894897460938, 81.48760986328125, 85.36540985107422, 85.85223388671875, 87.20014953613281, 92.07552337646484]\n",
            "\n",
            "MAG paper IDs: [3047438096, 3037966274, 3021927925, 2889959140, 2791045616, 2943077655, 2990343632, 2974128076, 3014380170, 3028584171]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetching the paper titles based on their index\n",
        "id2details(df, I, 'original_title')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36Grl7_NGkEN",
        "outputId": "baab60a9-04bc-48c9-b532-30b66c2aa580"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['Can WhatsApp Benefit from Debunked Fact-Checked Stories to Reduce Misinformation?'],\n",
              " ['A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections'],\n",
              " ['A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections'],\n",
              " ['A System for Monitoring Public Political Groups in WhatsApp'],\n",
              " ['Politics of Fake News: How WhatsApp Became a Potent Propaganda Tool in India'],\n",
              " ['Characterizing Attention Cascades in WhatsApp Groups'],\n",
              " ['Can WhatsApp Counter Misinformation by Limiting Message Forwarding'],\n",
              " ['Can WhatsApp Counter Misinformation by Limiting Message Forwarding'],\n",
              " ['OS IMPACTOS JURÍDICOS E SOCIAIS DAS FAKE NEWS EM TERRITÓRIO BRASILEIRO'],\n",
              " ['Images and Misinformation in Political Groups: Evidence from WhatsApp in India']]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetching the paper titles based on their index\n",
        "id2details(df, I, 'original_title')"
      ],
      "metadata": {
        "id": "F9fsLcBSGraq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rbxFKF-DZxg0",
        "outputId": "2d38157f-07ec-4b7e-da92-6e8bab476b7e"
      },
      "source": [
        "# Define project base directory\n",
        "project_dir = Path('notebooks').resolve().parents[0]\n",
        "print(project_dir)\n",
        "\n",
        "# Serialise index and store it as a pickle\n",
        "with open(f\"{project_dir}/models/faiss_index.pickle\", \"wb\") as h:\n",
        "    pickle.dump(faiss.serialize_index(index), h)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vector_engine\n"
          ]
        }
      ]
    }
  ]
}