Index_claims_from_abstracts_for_searches.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aaalexlit/omdena_climate_change_challenge_notebooks/blob/main/Index_claims_from_abstracts_for_searches.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Index claims from abstracts for searches"
      ],
      "metadata": {
        "id": "GJYx4ScK-MDj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download csv with the abstracts retrieved from OpenAlex using keyword search\n",
        "\n",
        "This is a short version that has only about 10300 abstracts\n",
        "\n",
        "The csv was obtained using [this code](https://github.com/mcallaghan/NLP-climate-science-tutorial-CCAI/blob/main/A_obtaining_data.ipynb) with some minor modifications from https://openalex.org/ "
      ],
      "metadata": {
        "id": "QBxT3TvE-7e_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown https://drive.google.com/uc?id=14hqOpm4HsNarlWiGnakz3sPsP8g24XWC -O 'openalex_abstracts.csv'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAkwaVAc_Aew",
        "outputId": "62223c24-4369-4e47-f96d-57f167a518a1"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=14hqOpm4HsNarlWiGnakz3sPsP8g24XWC\n",
            "To: /content/openalex_abstracts.csv\n",
            "100% 10.8M/10.8M [00:00<00:00, 22.5MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check if GPU is available to install gpu version of faiss"
      ],
      "metadata": {
        "id": "p4baR2Ek_Fs8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import os\n",
        "faiss_to_install = \"faiss-gpu\"\n",
        "if not torch.cuda.is_available():\n",
        "  faiss_to_install = \"faiss\"\n",
        "\n",
        "ret_code = os.system(f\"pip install farm-haystack[{faiss_to_install}]\")\n",
        "if not ret_code:\n",
        "  print(f\"Installed {faiss_to_install}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Px85qQdv_VEj",
        "outputId": "2b0a9f73-cc5d-499f-eae5-6bd2af3431ca"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installed faiss-gpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Index documents "
      ],
      "metadata": {
        "id": "GukCM1zq_WIu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from haystack.document_stores import FAISSDocumentStore\n",
        "from haystack.nodes import EmbeddingRetriever\n",
        "import logging\n",
        "from timeit import default_timer as timer\n",
        "from haystack import Document\n",
        "import pandas as pd\n",
        "import torch\n",
        "from nltk.tokenize import sent_tokenize\n",
        "from transformers import AutoTokenizer, pipeline, \\\n",
        "RobertaForSequenceClassification, AutoModelForSequenceClassification\n",
        "import gc\n",
        "import itertools\n",
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcVaNmAq_awl",
        "outputId": "1b4ce3a6-0d6d-400a-d1f2-9af3ccecb2ed"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model to use for semantic embeddings"
      ],
      "metadata": {
        "id": "F8dht6jkYl9R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = 'sentence-transformers/all-MiniLM-L6-v2'\n",
        "# embedding size used by all-MiniLM-L6-v2\n",
        "EMBEDDING_DIM = 384\n",
        "\n",
        "chunk_size = 1800\n",
        "start_from_row = 0 * chunk_size\n",
        "\n",
        "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
        "pd.options.mode.chained_assignment = None\n"
      ],
      "metadata": {
        "id": "lQhrqXJcCtPk"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Claim detection"
      ],
      "metadata": {
        "id": "4wnd0Bq9FYWF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "claimbuster_tokenizer = AutoTokenizer.from_pretrained(\"lucafrost/ClaimBuster-DeBERTaV2\")\n",
        "claimbuster_model = AutoModelForSequenceClassification.from_pretrained(\"lucafrost/ClaimBuster-DeBERTaV2\")"
      ],
      "metadata": {
        "id": "XAnzlSF7MeYw"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def is_claim(sentences,\n",
        "             model=claimbuster_model,\n",
        "             tokenizer=claimbuster_tokenizer,\n",
        "             debug=False):\n",
        "    if torch.cuda.is_available():\n",
        "      device = 0\n",
        "      batch_size = 64\n",
        "    else:\n",
        "      device = -1\n",
        "      batch_size = 1\n",
        "    pipe = pipeline(\"text-classification\", model=model,\n",
        "                    tokenizer=tokenizer, device=device)\n",
        "    labels, probs = [], []\n",
        "    for out in pipe(sentences, batch_size=batch_size):\n",
        "        labels.append(out['label'])\n",
        "        probs.append(out['score'])\n",
        "    if debug:\n",
        "        for sentence, label, prob in zip(sentences, labels, probs):\n",
        "            print(f\"{label}({prob:.3f})\")\n",
        "            print(sentence)\n",
        "    return list(map(lambda l: model.config.label2id[l], labels)), probs"
      ],
      "metadata": {
        "id": "X53elVbBFuwi"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_claims_from_texts(df, id_col='id', text_col='abstract', threshold=0.5, debug=False):\n",
        "    df[text_col] = df[text_col].map(sent_tokenize)\n",
        "    df = df.explode(text_col)\n",
        "    sentences = df[text_col].tolist()\n",
        "    ids = df[id_col].tolist()\n",
        "    predicted_class_ids, probs = is_claim(sentences, debug=debug)\n",
        "    claims_df = pd.DataFrame(\n",
        "        [(doi, sentence) for doi, sentence, label, prob in zip(ids, sentences, predicted_class_ids, probs) if\n",
        "         label in [1, 2] and prob > threshold], columns=[id_col, 'claims'])\n",
        "    claims_df = claims_df.groupby(id_col).agg({'claims': lambda x: x.tolist()})\n",
        "    return claims_df"
      ],
      "metadata": {
        "id": "iJjyMWpQE3B7"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Class that deals with initiating and writing to faiss store"
      ],
      "metadata": {
        "id": "0ncIw2pnCbIR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class FAISSIndexer():\n",
        "    def __init__(self, path_to_index_dir,\n",
        "                 model_name, embedding_dim\n",
        "                 , path_to_postgres=None) -> None:\n",
        "        self.path_to_index_dir = path_to_index_dir\n",
        "        # our db is postgres, only need to set path to faiss index\n",
        "        if path_to_postgres:\n",
        "            self.path_to_db = path_to_postgres\n",
        "            self._set_path_to_index()\n",
        "        # our db is SQLLite\n",
        "        else:\n",
        "            self._set_path_to_index_and_db()\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.model_name = model_name\n",
        "        self.document_store = self._init_document_store()\n",
        "        self.retriever = self._init_retriever()\n",
        "\n",
        "    def _set_path_to_index(self):\n",
        "        if not os.path.exists(self.path_to_index_dir):\n",
        "            os.makedirs(self.path_to_index_dir)\n",
        "        self.path_to_index = os.path.join(self.path_to_index_dir, \"faiss_index\")\n",
        "\n",
        "    def _set_path_to_index_and_db(self):\n",
        "        self._set_path_to_index()\n",
        "        self.path_to_db = f\"sqlite:///{os.path.join(self.path_to_index_dir, 'faiss_document_store.db')}\"\n",
        "\n",
        "    def _init_document_store(self):\n",
        "        if os.path.exists(self.path_to_index):\n",
        "            return FAISSDocumentStore.load(index_path=self.path_to_index)\n",
        "        else:\n",
        "            return FAISSDocumentStore(\n",
        "                sql_url=self.path_to_db,\n",
        "                return_embedding=True,\n",
        "                similarity='cosine',\n",
        "                embedding_dim=self.embedding_dim,\n",
        "                duplicate_documents='skip'\n",
        "            )\n",
        "\n",
        "    def _init_retriever(self, progress_bar=True):\n",
        "        return EmbeddingRetriever(\n",
        "            document_store=self.document_store,\n",
        "            embedding_model=self.model_name,\n",
        "            model_format='sentence_transformers',\n",
        "            # include article title into the embedding\n",
        "            embed_meta_fields=[\"title\"],\n",
        "            progress_bar=progress_bar\n",
        "        )\n",
        "\n",
        "    def write_documents(self, docs):\n",
        "        self.document_store.write_documents(docs)\n",
        "\n",
        "        print('Updating embeddings ...')\n",
        "\n",
        "        self.document_store.update_embeddings(\n",
        "            retriever=self.retriever,\n",
        "            update_existing_embeddings=False\n",
        "        )\n",
        "\n",
        "        print(f'current embedding count is {self.document_store.get_embedding_count()}')\n",
        "        self.document_store.save(index_path=self.path_to_index)\n",
        "\n",
        "    def retrieve_matches_for_a_phrase(self, phrase, top_k=10):\n",
        "        return self.retriever.retrieve(phrase, top_k=top_k)\n",
        "\n",
        "    def retrieve_matches_for_phrases(self, phrases, top_k=10):\n",
        "        return self.retriever.retrieve_batch(phrases, top_k=top_k)"
      ],
      "metadata": {
        "id": "fY2ZTK9XCcvA"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Functions for indexing"
      ],
      "metadata": {
        "id": "IVMmoax5CtsY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_openalex_claims_to_haystack_document(row):\n",
        "    return [{'content': claim,\n",
        "             'meta': {\n",
        "                 'title': row['title'],\n",
        "                 'publication_year': row['publication_year'],\n",
        "                 'authors': row['authors'],\n",
        "                 'doi': row['doi'],\n",
        "                 'open_alex_id': row['id']\n",
        "             }} for claim in row['claims']]"
      ],
      "metadata": {
        "id": "eMfPUGkvH4ul"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_openalex_rows_to_haystack_document(row):\n",
        "    return [{'content': sent,\n",
        "             'meta': {\n",
        "                 'title': row['title'],\n",
        "                 'publication_year': row['publication_year'],\n",
        "                 'authors': row['authors'],\n",
        "                 'doi': row['doi'],\n",
        "                 'open_alex_id': row['id']\n",
        "             }} for sent in sent_tokenize(row['abstract'])]"
      ],
      "metadata": {
        "id": "t9AVDBvQIiU_"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_abstracts_from_openalex_to_haystack_docs(filename, chunk_size, start_from_row,detect_claims=False):\n",
        "    id_col = 'id'\n",
        "    chunk_number = 1\n",
        "    for df in pd.read_csv(filename, chunksize=chunk_size, skiprows=range(1, start_from_row)):\n",
        "        print(f'starting to index chunk number {chunk_number}')\n",
        "        df.fillna(\"\", inplace=True)\n",
        "        if detect_claims:\n",
        "          claims_df = get_claims_from_texts(df[[id_col, 'abstract']])\n",
        "          print('Finished extracting claims')\n",
        "          df = df.merge(claims_df, on=id_col)\n",
        "          del claims_df\n",
        "          gc.collect()\n",
        "          df.drop(columns=['abstract'], inplace=True)\n",
        "          row_dict = df.to_dict('records')\n",
        "          yield list(itertools.chain(*[convert_openalex_claims_to_haystack_document(row) for row in row_dict]))\n",
        "        else:\n",
        "          row_dict = df.to_dict('records')\n",
        "          yield list(itertools.chain(*[convert_openalex_rows_to_haystack_document(row) for row in row_dict]))\n",
        "        chunk_number += 1\n"
      ],
      "metadata": {
        "id": "avvMlbaREbjL"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def index_docs_from_csv(filename, docs_extractor,\n",
        "                        indexer,\n",
        "                        chunk_size, start_from_row):\n",
        "  for docs in docs_extractor(filename, chunk_size, start_from_row):\n",
        "    indexer.write_documents(docs)"
      ],
      "metadata": {
        "id": "nw_I_VUbCvXK"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Launch indexing process"
      ],
      "metadata": {
        "id": "SzNw3SQdC-um"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start = timer()\n",
        "STORE_PATH = '/content/data/faiss/'\n",
        "csv_path = '/content/openalex_abstracts.csv'\n",
        "\n",
        "faiss_indexer = FAISSIndexer(STORE_PATH, MODEL_NAME, EMBEDDING_DIM)\n",
        "\n",
        "index_docs_from_csv(csv_path,\n",
        "                    convert_abstracts_from_openalex_to_haystack_docs,\n",
        "                    faiss_indexer,\n",
        "                    chunk_size,\n",
        "                    start_from_row\n",
        "                    )\n",
        "\n",
        "end = timer()\n",
        "print(end - start)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 903,
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      },
      "execution_count": 30,
      "outputs": [
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          "text": [
            "starting to index chunk number 1\n"
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        {
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              "Writing Documents:   0%|          | 0/14067 [00:00<?, ?it/s]"
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          "text": [
            "Updating embeddings ...\n"
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        {
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          "data": {
            "text/plain": [
              "Updating Embedding:   0%|          | 0/14067 [00:00<?, ? docs/s]"
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              "Batches:   0%|          | 0/128 [00:00<?, ?it/s]"
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          "text": [
            "current embedding count is 14067\n",
            "starting to index chunk number 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing Documents:   0%|          | 0/13893 [00:00<?, ?it/s]"
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          "name": "stdout",
          "text": [
            "Updating embeddings ...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Updating Embedding:   0%|          | 0/27960 [00:00<?, ? docs/s]"
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              "Batches:   0%|          | 0/122 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
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          "text": [
            "current embedding count is 27960\n",
            "starting to index chunk number 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing Documents:   0%|          | 0/14135 [00:00<?, ?it/s]"
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          "text": [
            "Updating embeddings ...\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Updating Embedding:   0%|          | 0/42095 [00:00<?, ? docs/s]"
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        {
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              "Batches:   0%|          | 0/313 [00:00<?, ?it/s]"
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          "data": {
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              "Batches:   0%|          | 0/130 [00:00<?, ?it/s]"
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          "text": [
            "current embedding count is 42095\n",
            "starting to index chunk number 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing Documents:   0%|          | 0/13600 [00:00<?, ?it/s]"
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          "text": [
            "Updating embeddings ...\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Updating Embedding:   0%|          | 0/55695 [00:00<?, ? docs/s]"
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              "Batches:   0%|          | 0/313 [00:00<?, ?it/s]"
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              "Batches:   0%|          | 0/113 [00:00<?, ?it/s]"
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          "text": [
            "current embedding count is 55695\n",
            "starting to index chunk number 5\n"
          ]
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        {
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          "data": {
            "text/plain": [
              "Writing Documents:   0%|          | 0/6685 [00:00<?, ?it/s]"
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          "text": [
            "Updating embeddings ...\n"
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        {
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          "data": {
            "text/plain": [
              "Updating Embedding:   0%|          | 0/62380 [00:00<?, ? docs/s]"
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          "text": [
            "current embedding count is 62380\n",
            "230.66624144699995\n"
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}