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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3iLZu0wfFirH"
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   "source": [
    "!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 --upgrade\n",
    "!pip install langchain einops accelerate transformers bitsandbytes scipy\n",
    "!pip install xformers sentencepiece\n",
    "!pip install llama-index llama_hub\n",
    "!pip install sentence-transformers pypdf openai glob2"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#Loading Model and tokenizer"
   ],
   "metadata": {
    "id": "ywXH0FunfuCW"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, GenerationConfig\n",
    "import huggingface_hub\n",
    "from langchain import HuggingFacePipeline\n",
    "\n",
    "\n",
    "# Loading model and tokenizer from huggingface\n",
    "def load_model(device_type, model_id):\n",
    "    print(f\"Loading Model: {model_id}, on: {device_type}\")\n",
    "\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=\"./model/\")\n",
    "\n",
    "    model = AutoModelForCausalLM.from_pretrained(\n",
    "        model_id,\n",
    "        cache_dir=\"./model/\",\n",
    "        torch_dtype=torch.float16,\n",
    "        trust_remote_code=True,\n",
    "        load_in_8bit=True,\n",
    "    )\n",
    "\n",
    "    generation_config = GenerationConfig.from_pretrained(model_id)\n",
    "\n",
    "    # Create a pipeline for text generation\n",
    "    pipe = pipeline(\n",
    "        \"text-generation\",\n",
    "        model=model,\n",
    "        tokenizer=tokenizer,\n",
    "        max_length=4096,\n",
    "        top_p=0.95,\n",
    "        repetition_penalty=1.15,\n",
    "        generation_config=generation_config,\n",
    "    )\n",
    "\n",
    "    local_llm = HuggingFacePipeline(pipeline=pipe, model_kwargs={\"temperature\": 0})\n",
    "    print(\"Local LLM Loaded\")\n",
    "\n",
    "    return tokenizer, local_llm"
   ],
   "metadata": {
    "id": "MqIluQ1xOFx4"
   },
   "execution_count": 2,
   "outputs": []
  },
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    },
    "id": "P9bQ54soO9rq",
    "outputId": "1c4ba968-4231-49fa-be9a-a01fe2144f50"
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Loading Model: krthk/llama-2-7b-hs-tuned, on: cuda\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "output_type": "display_data",
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      ],
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Local LLM Loaded\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import glob\n",
    "import re\n",
    "import pandas as pd\n",
    "import string\n",
    "from langchain.document_loaders import PyPDFLoader\n",
    "\n",
    "\n",
    "def clean_document(text):  # remove unnecessary characters reducing amibuity\n",
    "    text = re.sub(\"\\s+\", \" \", text)\n",
    "    text = re.sub(r\"https?://\\S+\", \"\", text)\n",
    "    text = re.sub(r\"\\.{2,}\", \".\", text)\n",
    "    text = \"\".join([i for i in text if i not in string.punctuation or i == \".\"])\n",
    "    text = re.sub(r\"\\d+\", \"\", text)\n",
    "    return \" \".join([word for word in text.split() if len(word) > 1])\n",
    "\n",
    "\n",
    "def load_documents_and_chunks(\n",
    "    directory,\n",
    "):  # Given a directory retrive the documents in it\n",
    "    documents = []\n",
    "    dataframe = {\"Document\": [], \"Content\": []}\n",
    "    full_string = \"\"\n",
    "\n",
    "    for item_path in glob.glob(directory + \"*.pdf\"):\n",
    "        loader = PyPDFLoader(item_path)\n",
    "        documents.extend(loader.load())\n",
    "\n",
    "    for i, doc in enumerate(documents):\n",
    "        cleaned_string = clean_document(doc.page_content)\n",
    "        doc.page_content = cleaned_string\n",
    "        full_string += cleaned_string\n",
    "        dataframe[\"Document\"].append(f\"Document_{i}\")\n",
    "        dataframe[\"Content\"].append(cleaned_string)\n",
    "\n",
    "    dataframe = pd.DataFrame(dataframe)\n",
    "\n",
    "    # Documents, Documents in a dataframe, full concatinated string of all documents\n",
    "    return documents, dataframe, full_string"
   ],
   "metadata": {
    "id": "Dwg6pzehFoNL"
   },
   "execution_count": 1,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "documents, dataframe, full_String = load_documents_and_chunks(\"/content/documents/\")"
   ],
   "metadata": {
    "id": "oHOMPvU3GCLm"
   },
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(dataframe)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mbMfPBJUIOF4",
    "outputId": "7475e78e-6197-41f1-eaed-3173933d667c"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "       Document                                            Content\n",
      "0    Document_0  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "1    Document_1  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "2    Document_2  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "3    Document_3  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "4    Document_4  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "5    Document_5  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "6    Document_6  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "7    Document_7  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "8    Document_8  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "9    Document_9  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "10  Document_10  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "11  Document_11  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "12  Document_12  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "13  Document_13  HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR...\n",
      "14  Document_14  Open Domain Multidocument Summarization Compre...\n",
      "15  Document_15  datasets retrievers and summarizers •We evalua...\n",
      "16  Document_16  amples and evaluate several of the topperformi...\n",
      "17  Document_17  Table Results of the opendomain MDS experiment...\n",
      "18  Document_18  Table Results of the opendomain MDS experiment...\n",
      "19  Document_19  Table Human evaluation on MultiNews. bino mial...\n",
      "20  Document_20  ROUGEAvg retrieved groundtruthMultiNews WCEP M...\n",
      "21  Document_21  PRIMERA MultiNews LEDBase Cochrane ROUGEAvg ra...\n",
      "22  Document_22  tions in summarization performance are observe...\n",
      "23  Document_23  ences can be several points below the baseline...\n",
      "24  Document_24  Human Language Technologies pages Seattle Unit...\n",
      "25  Document_25  Kong China. Association for Computational Ling...\n",
      "26  Document_26  Milad Moradi and Matthias Samwald. Evaluating ...\n",
      "27  Document_27  Zejiang Shen Kyle Lo Lauren Yu Nathan Dahlberg...\n",
      "28  Document_28  Dataset Details All datasets were managed in t...\n",
      "29  Document_29  Table Dataset statistics counting whitespace t...\n",
      "30  Document_30  Table Reported versus reproduced ROUGEL scores...\n",
      "31  Document_31  Table Results of the opendomain MDS experiment...\n",
      "32  Document_32  MultiNews WCEP MultiXScience MS sparseCochrane...\n",
      "33  Document_33  Figure Graphical depiction of the backtranslat...\n",
      "34  Document_34  Table Examples of degradation of summarization...\n",
      "35  Document_35  ROUGEAvg random oracleaddition duplication del...\n",
      "36  Document_36  ROUGEAvg random oracleaddition duplication del...\n",
      "37  Document_37  Corpora Evaluation and System Bias Detection i...\n",
      "38  Document_38  Q. Why do systems show bias on different met r...\n",
      "39  Document_39  contains. Pyramid score is deﬁned as the ratio...\n",
      "40  Document_40  Figure Heatmap depicting the corpus metric Int...\n",
      "41  Document_41  thread as separate document and the best answe...\n",
      "42  Document_42  Figure Abstractness across datasets. Redundanc...\n",
      "43  Document_43  Table Various metrics Met showing ROUGE Scores...\n",
      "44  Document_44  systems we feel that the objective metrics ave...\n",
      "45  Document_45  for MDS systems to achieve double the ROUGE sc...\n",
      "46  Document_46  Sergey Brin and Lawrence Page. The anatomy of ...\n",
      "47  Document_47  Linguistics HLTNAACL pages Boston Massachusett...\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from bertopic import BERTopic\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "\n",
    "# Maximal Marginal Relevance to remove redundancy in the documents\n",
    "def mmr(documents, dataframe, full_string, top_n=5, lambda_param=0.5):\n",
    "    vectorizer = CountVectorizer().fit_transform(dataframe[\"Content\"])\n",
    "    similarity_matrix = cosine_similarity(vectorizer, vectorizer)\n",
    "\n",
    "    def mmr_score(selected, candidate, lambda_param):\n",
    "        sim_selected = cosine_similarity(\n",
    "            vectorizer[selected, :], vectorizer[candidate, :]\n",
    "        )\n",
    "        sim_candidate = cosine_similarity(\n",
    "            vectorizer[candidate, :], vectorizer[candidate, :]\n",
    "        )\n",
    "\n",
    "        return lambda_param * sim_candidate - (1 - lambda_param) * sim_selected.mean()\n",
    "\n",
    "    selected_docs = []\n",
    "    redundant_docs = []\n",
    "    remaining_docs = list(range(len(documents)))\n",
    "\n",
    "    while len(selected_docs) < top_n:\n",
    "        mmr_scores = []\n",
    "        for candidate in remaining_docs:\n",
    "            if not selected_docs:\n",
    "                mmr_scores.append((candidate, 0.0))\n",
    "            else:\n",
    "                score = mmr_score(selected_docs, candidate, lambda_param)\n",
    "                mmr_scores.append((candidate, score))\n",
    "\n",
    "        mmr_scores.sort(key=lambda x: x[1], reverse=True)\n",
    "        selected_doc_index = mmr_scores[0][0]\n",
    "\n",
    "        selected_docs.append(selected_doc_index)\n",
    "        remaining_docs.remove(selected_doc_index)\n",
    "\n",
    "    cleaned_documents = []\n",
    "\n",
    "    for i, doc_index in enumerate(selected_docs):\n",
    "        selected_content = dataframe.loc[doc_index, \"Content\"]\n",
    "        redundant_content = \"\"\n",
    "\n",
    "        for redundant_index in redundant_docs:\n",
    "            redundant_content += dataframe.loc[redundant_index, \"Content\"]\n",
    "\n",
    "        cleaned_content = selected_content.replace(redundant_content, \"\")\n",
    "        cleaned_documents.append(cleaned_content)\n",
    "\n",
    "    return cleaned_documents"
   ],
   "metadata": {
    "id": "eEiyAjn3IV5L"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "cleaned_documents = mmr(documents, dataframe, full_String, top_n=len(documents) / 1.5)\n",
    "\n",
    "len(cleaned_documents)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "whznyVDkJXqB",
    "outputId": "5abe7d95-4f20-4962-ad2a-2b02b5f4dc5c"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "cleaned_documents[0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 661
    },
    "id": "kXNhbpLvJjbY",
    "outputId": "e4f5d7c6-4d21-4a29-825c-ea387a9c8661"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'HYBRID LONG DOCUMENT SUMMARIZATION USING CFFAR AND CHATGPT RACTICAL STUDY Guang Lu Sylvia B. Larcher Institute of Communication and Marketing Lucerne University of Applied Sciences and Arts Zentralstrasse Lucerne Switzerland guang.luhslu.ch sylvia.bendelhslu.chTu Tran getAbstract AG Alpenquai Lucerne Switzerland tu.trangetAbstract.com June ABSTRACT Text summarization is an important downstream natural language processing NLP task that chal lenges both the understanding and generation capabilities of language models. Thanks to large language models LLMs and techniques for finetuning models in machine learning significant progress has been made in automatically summarizing short texts such as news articles often leading to very satisfactory machinegenerated results. In contrast summarizing long documents still remains major challenge. This is partly due to the complex nature of contextual information in long texts but also due to the lack of opensource benchmarking datasets and the corresponding evaluation frameworks that can be used to develop and test model performance. In this work we use ChatGPT the latest breakthrough in the field of NLP and LLMs together with the extractive summarization model CFFAR CoarsetoFine FacetAware Ranking to propose hybrid extraction and sum marization pipeline for long documents such as business articles and books. We work with the worldrenowned company getAbstract AG and leverage their expertise and experience in professional book summarization. practical study has shown that machinegenerated summaries can perform at least as well as humanwritten summaries when evaluated using current automated evaluation metrics. However closer examination of the texts generated by ChatGPT through human evaluations has shown that there are still critical issues in terms of text coherence faithfulness and style. Overall our results show that the use of ChatGPT is very promising but not yet mature approach for summarizing long documents and can at best serve as an inspiration for human editors. We anticipate that our work will inform NLP researchers about the extent to which ChatGPT’s capabilities for summarizing long documents overlap with practitioners’ needs. Further work is needed to test the proposed hybrid summarization pipeline in particular involving GPT and to propose new evaluation framework tailored to the task of summarizing long documents. Keywords Long document summarization ·Large language models LLMs ·ChatGPT ·GPT ·Natural language processing NLP ·Machine learning Introduction Text summarization is longstanding task in the field of natural language processing NLP and the corresponding techniques can be distinguished as extractive and abstractive Extractive summarization identifies key sentences that can convey the most important information in the original text and uses these as summaries while abstractive summarization captures the semantic meanings of document by first understanding the text and then constructing new sentences as summaries. Both approaches have their merits and are sometimes even combined into hybrid extraction and summarization pipeline to achieve better model performance Automatic summarization of short texts such as news articles about words per document has made significant progress thanks to the rapid development of large language models LLMs the large benchmarking datasets and aarXiv.v cs.CL Jun'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "\n",
    "embedding_model = SentenceTransformer(\n",
    "    \"all-MiniLM-L12-v2\"\n",
    ")  # Model to create vector embeddings\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=2048,\n",
    "    chunk_overlap=128,  # similar token len in overlap of text between chunks\n",
    "    is_separator_regex=False,\n",
    ")"
   ],
   "metadata": {
    "id": "XxqDTIb-K13_",
    "outputId": "c81e52e7-6af1-4990-dd80-5a668480d88b",
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   ]
  },
  {
   "cell_type": "code",
   "source": [
    "chunks = text_splitter.split_text(\n",
    "    \" \".join(cleaned_documents)\n",
    ")  # Create chunks of size 2046\n",
    "\n",
    "len(chunks)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bI5o5_RbM9yC",
    "outputId": "9a540e04-8128-4609-804d-982bf3478011"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "\n",
    "# AgglomerativeClustering for vector clustering\n",
    "def cluster_sentences(sentences, embedding_model, distance_threshold=1.5):\n",
    "    sentence_embeddings = embedding_model.encode(sentences)\n",
    "\n",
    "    clustering_model = AgglomerativeClustering(\n",
    "        distance_threshold=distance_threshold, n_clusters=None, linkage=\"ward\"\n",
    "    )\n",
    "    clustering_model.fit(sentence_embeddings)\n",
    "\n",
    "    clustered_sentences = {}\n",
    "    for sentence_id, cluster_id in enumerate(clustering_model.labels_):\n",
    "        if cluster_id not in clustered_sentences:\n",
    "            clustered_sentences[cluster_id] = []\n",
    "        clustered_sentences[cluster_id].append(sentences[sentence_id])\n",
    "\n",
    "    return [cluster for cluster in clustered_sentences.values()]"
   ],
   "metadata": {
    "id": "oIv0UBHlNWYi"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "clusters = cluster_sentences(\n",
    "    chunks, embedding_model\n",
    ")  # Cluster sentences based on similarity score\n",
    "\n",
    "for i in clusters:\n",
    "    print(len(i), \"chunks\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sDbVJt4kNc1K",
    "outputId": "97cb889a-dbb6-4484-b3d4-a7add67bb8aa"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "7 chunks\n",
      "16 chunks\n",
      "9 chunks\n",
      "4 chunks\n",
      "7 chunks\n",
      "10 chunks\n",
      "4 chunks\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from transformers import AutoTokenizer\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "\n",
    "def calc_tokens(text, tokenizer):  # Calculates number of tokens using llama2 tokenizer\n",
    "    return len(tokenizer.tokenize(text))\n",
    "\n",
    "\n",
    "def redistribution(\n",
    "    listoflistofchunks, tokenizer\n",
    "):  # Limit the clusters maximum of 3000 tokens\n",
    "    limit = 3072\n",
    "    redistributed = []\n",
    "\n",
    "    for index, chunks in enumerate(listoflistofchunks):\n",
    "        tokens = calc_tokens(\"\\n\".join(chunks), tokenizer)\n",
    "\n",
    "        if tokens < limit:  # Token size of cluster is lower than the limit\n",
    "            print(f\"chunk {index} of tokens {tokens} is fine\")\n",
    "            redistributed.extend([chunks])\n",
    "        else:  # If limit exceeded divide into suitable parts creating new cluster\n",
    "            print(\n",
    "                f\"chunk {len(chunks)} of tokens {tokens} is splitted into {math.ceil(tokens/limit)} parts\"\n",
    "            )\n",
    "            partitionlist = np.array_split(chunks, math.ceil(tokens / limit))\n",
    "            redistributed.extend([list(x) for x in partitionlist])\n",
    "    return redistributed"
   ],
   "metadata": {
    "id": "WtuBKb_dNuez"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "limitclusters = redistribution(clusters, tokenizer)\n",
    "\n",
    "print(\"------------------------------------------\")\n",
    "\n",
    "for i in limitclusters:\n",
    "    print(\"chunk:\", len(i), \"- tokens:\", calc_tokens(\"\\.\".join(i), tokenizer))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TEeddoUQOGE0",
    "outputId": "f1272e74-d4bf-4573-ba40-7216d2c8ed51"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "chunk 7 of tokens 3428 is splitted into 2 parts\n",
      "chunk 16 of tokens 8378 is splitted into 3 parts\n",
      "chunk 9 of tokens 4134 is splitted into 2 parts\n",
      "chunk 3 of tokens 1755 is fine\n",
      "chunk 7 of tokens 3157 is splitted into 2 parts\n",
      "chunk 10 of tokens 4567 is splitted into 2 parts\n",
      "chunk 6 of tokens 1863 is fine\n",
      "------------------------------------------\n",
      "chunk: 4 - tokens: 2122\n",
      "chunk: 3 - tokens: 1304\n",
      "chunk: 6 - tokens: 3350\n",
      "chunk: 5 - tokens: 2607\n",
      "chunk: 5 - tokens: 2418\n",
      "chunk: 5 - tokens: 2333\n",
      "chunk: 4 - tokens: 1800\n",
      "chunk: 4 - tokens: 1755\n",
      "chunk: 4 - tokens: 1755\n",
      "chunk: 3 - tokens: 1401\n",
      "chunk: 5 - tokens: 2294\n",
      "chunk: 5 - tokens: 2272\n",
      "chunk: 4 - tokens: 1863\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from summa.summarizer import summarize\n",
    "\n",
    "key_information_extracted = []\n",
    "for cluster in limitclusters:\n",
    "    key_information_extracted.append(summarize(\" \".join(cluster), ratio=0.5))\n",
    "\n",
    "print(\n",
    "    \"length:\",\n",
    "    len(\" \".join(key_information_extracted)),\n",
    "    \"tokens:\",\n",
    "    calc_tokens(\" \".join(key_information_extracted), tokenizer),\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wslXgaKpOGCe",
    "outputId": "c058a902-52e5-40e3-c596-002727735cf8"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "length: 75652 tokens: 18028\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from langchain import PromptTemplate, LLMChain\n",
    "\n",
    "\n",
    "def cluster_summaries(\n",
    "    text_chunks_list,\n",
    "):  # Takes in chunks for recursive summary (Map Reduce)\n",
    "    template = \"\"\"Summarize as a abstract summary: \\\n",
    "    {text}\"\"\"\n",
    "    prompt = PromptTemplate(template=template, input_variables=[\"text\"])\n",
    "    llm_chain = LLMChain(prompt=prompt, llm=LLM)\n",
    "\n",
    "    ip = [{\"text\": i} for i in text_chunks_list]\n",
    "    summary = llm_chain.apply(ip)\n",
    "\n",
    "    return [i[\"text\"] for i in summary]\n",
    "\n",
    "\n",
    "def generate_final_summary(\n",
    "    text_chunk,\n",
    "):  # Takes the final text and generates the final summary\n",
    "    template = \"\"\"Summarize as a abstract summary: \\\n",
    "    {text}\"\"\"\n",
    "\n",
    "    prompt = PromptTemplate(template=template, input_variables=[\"text\"])\n",
    "    llm_chain = LLMChain(prompt=prompt, llm=LLM)\n",
    "\n",
    "    summary = llm_chain.run(text_chunk)\n",
    "\n",
    "    return summary"
   ],
   "metadata": {
    "id": "9bzF8y2bOF_3"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def recursive_summary(chunks):\n",
    "    tokens = calc_tokens(\" \".join(chunks))\n",
    "    if tokens < 4000:\n",
    "        return generate_final_summary(\" \".join(chunks))\n",
    "    else:\n",
    "        listofsummaries = cluster_summaries(chunks)\n",
    "        return recursive_summary(listofsummaries)"
   ],
   "metadata": {
    "id": "qdshqSFKySMx"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "final_summary = recursive_summary(key_information_extracted)\n",
    "\n",
    "final_summary"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CTNm0vMOWmYt",
    "outputId": "1880a0c5-97a6-4d1f-f089-7dfed0ccf067"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "This comprehensive study delves into the intricate task of summarizing lengthy documents, leveraging a hybrid approach combining ChatGPT and CFFAR (Coarse-to-Fine Facet-Aware Ranking), an extractive summarization model. While ChatGPT displays promise, persisting challenges in text coherence, faithfulness, and style prompt the integration of CFFAR to augment summarization quality. The study underlines the preliminary nature of ChatGPT for long document summarization, suggesting it as an inspirational tool for human editors.\n",
      "The authors anticipate that their work will serve as a guide for Natural Language Processing (NLP) researchers, aligning ChatGPT's capabilities with practitioners' needs. Stressing the necessity for ongoing research, the study emphasizes the need to test the hybrid summarization pipeline and construct specialized evaluation frameworks for long document summarization. The provided text delves into an array of topics related to multidocument summarization (MDS) datasets and systems, introducing frameworks for future datasets, exploring correlations between abstractness in summaries and references, and questioning the completeness of the MDS task.\n",
      "\n",
      "Addressing nuances in ROUGE-based rankings across corpora, the study attributes disparities to biases within datasets. Notably, summarization systems designed for news corpora exploit layout bias, concentrating on initial tokens. Correlations between abstractness in generated summaries and references reveal varying system behaviors. The study probes the status of MDS, demonstrating that even top-performing systems fall significantly below the extractive oracle upper bounds on diverse corpora.\n",
      "Novel metrics like Inter Document Distribution, Redundancy, Layout Bias, and InversePyramid Score are introduced to evaluate candidate document contributions, redundancy, layout bias, and bias in reference summaries. Opendomain MDS challenges are discussed, emphasizing the sensitivity of summarizers to document retrieval errors and proposing practical guidelines for enhancement.\n",
      "The text further explores experiments, including training summarizers in the opendomain setting, simulating retrieval errors, and analyzing the robustness of large language models (LLMs) in opendomain MDS. The study concludes by presenting results from experiments utilizing CFFAR and ChatGPT for long document summarization. Evaluation metrics encompass ROUGE, BERTScore, BLANC, and ESTIME, with human evaluations scrutinizing coherence, faithfulness, and style in generated summaries.\n",
      "\n",
      "The text traverses a broad spectrum of multidocument summarization topics, encompassing dataset metrics, system performance, biases, challenges in opendomain settings, and detailed evaluations of summarization models, providing a comprehensive overview of the complexities within this domain.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "5tJYj7T2Ic7g"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "0gCYjtp2PUP_"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "from llama_index.prompts.prompts import SimpleInputPrompt\n",
    "from llama_index import set_global_service_context\n",
    "from llama_index import ServiceContext\n",
    "from llama_index import VectorStoreIndex\n",
    "from llama_index import download_loader\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from llama_index.embeddings import LangchainEmbedding\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from llama_index.llms import HuggingFaceLLM\n",
    "\n",
    "system_prompt = \"\"\"<s>[INST] <<SYS>>\n",
    "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.\n",
    "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.\n",
    "Please ensure that your responses are socially unbiased and positive in nature.\n",
    "\n",
    "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct.\n",
    "If you don't know the answer to a question, please don't share false information. <</SYS>>\n",
    "\"\"\"  # Llama2's official system prompt\n",
    "\n",
    "\n",
    "def model_tokenizer_embedder(model_name, auth_token):\n",
    "    tokenizer = AutoTokenizer.from_pretrained(\n",
    "        model_name, cache_dir=\"./model/\", use_auth_token=auth_token\n",
    "    )\n",
    "    model = AutoModelForCausalLM.from_pretrained(\n",
    "        model_name,\n",
    "        cache_dir=\"./model/\",\n",
    "        use_auth_token=auth_token,\n",
    "        torch_dtype=torch.float16,\n",
    "        load_in_8bit=True,\n",
    "    )\n",
    "\n",
    "    embedding_llm = LangchainEmbedding(\n",
    "        HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
    "    )\n",
    "\n",
    "    return tokenizer, model, embedding_llm\n",
    "\n",
    "\n",
    "tokenizer, model, embedding_llm = model_tokenizer_embedder(\n",
    "    \"krthk/llama-2-7b-hs-tuned\", \"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\"\n",
    ")\n",
    "\n",
    "query_wrapper_prompt = SimpleInputPrompt(\"{query_str} [/INST]\")\n",
    "\n",
    "llm = HuggingFaceLLM(\n",
    "    context_window=4096,\n",
    "    max_new_tokens=256,\n",
    "    system_prompt=system_prompt,\n",
    "    query_wrapper_prompt=query_wrapper_prompt,\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    ")\n",
    "\n",
    "service_context = ServiceContext.from_defaults(\n",
    "    chunk_size=1024, llm=llm, embed_model=embedding_llm\n",
    ")\n",
    "\n",
    "set_global_service_context(service_context)\n",
    "\n",
    "index = VectorStoreIndex.from_documents(documents)\n",
    "query_engine = index.as_query_engine()"
   ],
   "metadata": {
    "id": "T8FRUbiHbgQc"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "PyMuPDFReader = download_loader(\"PyMuPDFReader\")\n",
    "loader = PyMuPDFReader()\n",
    "\n",
    "\n",
    "def load_documents(directory):\n",
    "    documents = []\n",
    "    for item_path in glob(directory + \"*.pdf\"):\n",
    "        documents.extend(loader.load(file_path=item_path, metadata=True))\n",
    "    return documents"
   ],
   "metadata": {
    "id": "PfipeOW4Olfk"
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "documents = load_documents(\"/content/documents/\")"
   ],
   "metadata": {
    "id": "deLETlwzOess"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "index = VectorStoreIndex.from_documents(documents)\n",
    "\n",
    "query_engine = index.as_query_engine()"
   ],
   "metadata": {
    "id": "wuK92CcRbgOJ"
   },
   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "response = query_engine.query(\"What is C2F_FAR?\")\n",
    "\n",
    "print(response)"
   ],
   "metadata": {
    "id": "lup1f32Ub27x",
    "outputId": "9d04fe88-d00c-43e3-f108-e00fc9d1dee1",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " As a responsible and ethical AI language model, I must first emphasize that C2F-FAR is not a widely recognized or established term in the field of natural language processing (NLP). However, based on the context and the information provided, I can provide some insights.\n",
      "\n",
      "C2F-FAR seems to be a variant of the popular open-source library, C2F (Convolutional and Recurrent Feedforward Networks), which is designed for extractive summarization tasks. The \"FAR\" part of the name could potentially stand for \"Far-Reaching Accuracy,\" implying that the model is capable of producing high-quality summaries that cover a wide scope of information.\n",
      "\n",
      "Without access to the original source code or detailed documentation, it's difficult to provide a comprehensive analysis of C2F-FAR's performance or features. However, based on the information provided in the context, here are some general insights:\n",
      "\n",
      "1. C2F-FAR appears to be an extension of the C2F library that incorporates farming techniques to improve the quality of the generated summaries.\n",
      "2. The model uses a combination of convolutional\n"
     ]
    }
   ]
  }
 ]
}