Multilingual_Embeddings_OpenAI_vs_OpenSource.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sHFlkkCOEgBV"
   },
   "source": [
    "# OpenAI vs open-source multilingual embeddings models\n",
    "\n",
    "This noteboook provides example code to assess which embedding model works best for your data. The example task is a retrieval task (as in RAG - retrieval augmented generation), on multilingual data. See associated Medium article [here](https://medium.com/p/e5ccb7c90f05).\n",
    "\n",
    "The data source is based on the European AI Act, and models cover some of the latest OpenAI and open-source embeddings models (as of 02/2024) to deal with multilingual data:\n",
    "\n",
    "OpenAI released [two models](https://openai.com/blog/new-embedding-models-and-api-updates) in January 2024:\n",
    "\n",
    "- text-embedding-3-small (released 25/01/2024)\n",
    "- text-embedding-3-large (released 25/01/2024)\n",
    "\n",
    "We compare with the following open-source models\n",
    "\n",
    "- [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) (released 04/01/2024)\n",
    "- [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) (released 08/02/2024)\n",
    "- [BGE-M3](https://huggingface.co/BAAI/bge-m3) (released 29/01/2024)\n",
    "- [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) (released 10/02/2024)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2HWmbfN8EgBX"
   },
   "source": [
    "# Install and import libraries\n",
    "\n",
    "LlamaIndex will be used for loading embedding models and documents, and evaluate retrieval accuracy.\n",
    "\n",
    "Seaborn will be used for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UiIlNpJ3KXM4"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "!pip install -q llama_index==0.10.1\n",
    "!pip install -q html2text #Required for loading web pages with Llama index's SimpleWebPageReader\n",
    "!pip install -q llama-index-readers-web #Required for loading web pages with Llama index's SimpleWebPageReader\n",
    "!pip install -q transformers==4.37.2 # Required for open-source embeddings\n",
    "!pip install -q openai # Required for GPT and OpenAI embeddings\n",
    "!pip install -q einops #Required for nomic\n",
    "!pip install -q seaborn #Required for visualization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MRa_JuD--yU8"
   },
   "source": [
    "Let us load the libraries/functions required for running this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5HgrTbzREl0y"
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from tqdm.notebook import tqdm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "079gEGjx_GWt"
   },
   "source": [
    "Put your OpenAI API key here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "12qjBEgB_HKS"
   },
   "outputs": [],
   "source": [
    "OPENAI_API_KEY = \"sk-...\"\n",
    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z6LE69rcEgBZ"
   },
   "source": [
    "# Generate a multilingual Question/Answer dataset\n",
    "\n",
    "We'll use the EU AI Act as the corpus. The text in different languages is available at All multilingual versions at https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206.\n",
    "\n",
    "Available languages: \"BG\", \"ES\", \"CS\", \"DA\", \"DE\", \"ET\", \"EL\", \"EN\", \"FR\", \"GA\", \"HR\", \"IT\", \"LV\", \"LT\", \"HU\", \"MT\", \"NL\", \"PL\", \"PT\", \"RO\", \"SK\", \"SL\", \"FI\", \"SV\".\n",
    "\n",
    "We'll use\n",
    "\n",
    "- EN: English (Germanic)\n",
    "- FR: French (Romance)\n",
    "- CS: Czech (Slavic)\n",
    "- HU: Hungarian (Uralic)\n",
    "\n",
    "\n",
    "Useful documentation from Llama Index:\n",
    "- https://docs.llamaindex.ai/en/latest/examples/data_connectors/WebPageDemo.html\n",
    "- https://docs.llamaindex.ai/en/latest/examples/finetuning/embeddings/finetune_embedding.html#\n",
    "- https://docs.llamaindex.ai/en/stable/api/llama_index.node_parser.SentenceSplitter.html\n"
   ]
  },
  {
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      "text/plain": [
       "Parsing nodes:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "93"
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "from llama_index.readers.web import SimpleWebPageReader\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "\n",
    "language = \"EN\"\n",
    "url_doc = \"https://eur-lex.europa.eu/legal-content/\"+language+\"/TXT/HTML/?uri=CELEX:52021PC0206\"\n",
    "\n",
    "documents = SimpleWebPageReader(html_to_text=True).load_data([url_doc])\n",
    "\n",
    "parser = SentenceSplitter(chunk_size=1000)\n",
    "nodes = parser.get_nodes_from_documents(documents, show_progress=True)\n",
    "\n",
    "len(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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      "text/plain": [
       "'![european flag](./../../../../images/eclogo.jpg)EUROPEAN COMMISSION\\n\\nBrussels, 21.4.2021\\n\\nCOM(2021) 206 final\\n\\n2021/0106(COD)\\n\\nProposal for a\\n\\nREGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL\\n\\nLAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL\\nINTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS\\n\\n{SEC(2021) 167 final} - {SWD(2021) 84 final} - {SWD(2021) 85 final}\\n\\n  \\n\\nEXPLANATORY MEMORANDUM\\n\\n1.CONTEXT OF THE PROPOSAL\\n\\n1.1.Reasons for and objectives of the proposal\\n\\nThis explanatory memorandum accompanies the proposal for a Regulation laying\\ndown harmonised rules on artificial intelligence (Artificial Intelligence\\nAct). Artificial Intelligence (AI) is a fast evolving family of technologies\\nthat can bring a wide array of economic and societal benefits across the\\nentire spectrum of industries and social activities. By improving prediction,\\noptimising operations and resource allocation, and personalising service\\ndelivery, the use of artificial intelligence can support socially and\\nenvironmentally beneficial outcomes and provide key competitive advantages to\\ncompanies and the European economy. Such action is especially needed in high-\\nimpact sectors, including climate change, environment and health, the public\\nsector, finance, mobility, home affairs and agriculture. However, the same\\nelements and techniques that power the socio-economic benefits of AI can also\\nbring about new risks or negative consequences for individuals or the society.\\nIn light of the speed of technological change and possible challenges, the EU\\nis committed to strive for a balanced approach. It is in the Union interest to\\npreserve the EU’s technological leadership and to ensure that Europeans can\\nbenefit from new technologies developed and functioning according to Union\\nvalues, fundamental rights and principles.\\n\\nThis proposal delivers on the political commitment by President von der Leyen,\\nwho announced in her political guidelines for the 2019-2024 Commission “A\\nUnion that strives for more” 1 , that the Commission would put forward\\nlegislation for a coordinated European approach on the human and ethical\\nimplications of AI. Following on that announcement, on 19 February 2020 the\\nCommission published the White Paper on AI - A European approach to excellence\\nand trust 2 . The White Paper sets out policy options on how to achieve the\\ntwin objective of promoting the uptake of AI and of addressing the risks\\nassociated with certain uses of such technology. This proposal aims to\\nimplement the second objective for the development of an ecosystem of trust by\\nproposing a legal framework for trustworthy AI. The proposal is based on EU\\nvalues and fundamental rights and aims to give people and other users the\\nconfidence to embrace AI-based solutions, while encouraging businesses to\\ndevelop them. AI should be a tool for people and be a force for good in\\nsociety with the ultimate aim of increasing human well-being. Rules for AI\\navailable in the Union market or otherwise affecting people in the Union\\nshould therefore be human centric, so that people can trust that the\\ntechnology is used in a way that is safe and compliant with the law, including\\nthe respect of fundamental rights. Following the publication of the White\\nPaper, the Commission launched a broad stakeholder consultation, which was met\\nwith a great interest by a large number of stakeholders who were largely\\nsupportive of regulatory intervention to address the challenges and concerns\\nraised by the increasing use of AI.\\n\\nThe proposal also responds to explicit requests from the European Parliament\\n(EP) and the European Council, which have repeatedly expressed calls for\\nlegislative action to ensure a well-functioning internal market for artificial\\nintelligence systems (‘AI systems’) where both benefits and risks of AI are\\nadequately addressed at Union level. It supports the objective of the Union\\nbeing a global leader in the development of secure, trustworthy and ethical\\nartificial intelligence as stated by the European Council 3 and ensures the\\nprotection of ethical principles as specifically requested by the European\\nParliament 4 .\\n\\nIn 2017, the European Council called for a ‘sense of urgency to address\\nemerging trends’ including ‘issues such as artificial intelligence …, while at\\nthe same time ensuring a high level of data protection, digital rights and\\nethical standards’ 5 .'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "# Example of text for the first chunck\n",
    "nodes[0].text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2knTuTKGOayo"
   },
   "source": [
    "### Prompts for generating questions and answers\n",
    "\n",
    "We use the default prompt at: https://github.com/run-llama/llama_index/blob/c058f2531ea86ee74822cb1421ceaeee7098a99f/llama_index/finetuning/embeddings/common.py#L51\n",
    "\n",
    "The French, Czech and Hungarian prompt were obtained using DeepL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZPk3Fi62LA_3"
   },
   "outputs": [],
   "source": [
    "prompts={}\n",
    "\n",
    "prompts[\"EN\"] = \"\"\"\\\n",
    "Context information is below.\n",
    "\n",
    "---------------------\n",
    "{context_str}\n",
    "---------------------\n",
    "\n",
    "Given the context information and not prior knowledge, generate only questions based on the below query.\n",
    "\n",
    "You are a Teacher/ Professor. Your task is to setup {num_questions_per_chunk} questions for an upcoming quiz/examination.\n",
    "The questions should be diverse in nature across the document. Restrict the questions to the context information provided.\"\n",
    "\"\"\"\n",
    "\n",
    "prompts[\"FR\"] = \"\"\"\\\n",
    "Les informations contextuelles se trouvent ci-dessous.\n",
    "\n",
    "---------------------\n",
    "{context_str}\n",
    "---------------------\n",
    "\n",
    "Compte tenu des informations contextuelles et sans connaissances préalables, générer uniquement des questions basées sur la requête ci-dessous.\n",
    "\n",
    "Vous êtes enseignant/professeur. Votre tâche consiste à mettre en place {num_questions_per_chunk} question(s) par requête pour un quiz à venir.\n",
    "Les questions doivent être de nature variée sur l'ensemble du document. Limitez les questions aux informations contextuelles fournies.\n",
    "Les questions doivent être en français.\n",
    "\"\"\"\n",
    "\n",
    "prompts[\"HU\"] = \"\"\"\\\n",
    "A kontextusra vonatkozó információk alább találhatók.\n",
    "\n",
    "---------------------\n",
    "{context_str}\n",
    "---------------------\n",
    "\n",
    "A kontextusinformáció és az előzetes ismeretek nélkül csak az alábbi lekérdezésen alapuló kérdéseket generáljon.\n",
    "\n",
    "Ön tanár/professzor. Az Ön feladata, hogy {num_questions_per_chunk} kérdéseket állítson össze egy közelgő kvízhez/vizsgához.\n",
    "A kérdéseknek változatos jellegűeknek kell lenniük az egész dokumentumban. Korlátozza a kérdéseket a megadott kontextus információkra.\n",
    "A kérdéseknek magyarul kell megszólalniuk.\n",
    "\"\"\"\n",
    "\n",
    "prompts[\"CS\"] = \"\"\"\\\n",
    "Související informace naleznete níže.\n",
    "\n",
    "---------------------\n",
    "{context_str}\n",
    "---------------------\n",
    "\n",
    "Vzhledem ke kontextovým informacím a bez jakýchkoli předchozích znalostí vygenerujte pouze otázky na základě níže uvedeného dotazu.\n",
    "\n",
    "Jste učitel. Vaším úkolem je sestavit {num_questions_per_chunk} otázek na jeden dotaz pro nadcházející kvíz.\n",
    "Otázky musí být v celém dokumentu různorodé. Omezte otázky na uvedené kontextové informace.\n",
    "Otázky by měly být v češtině.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lR6s8D7KOtfn"
   },
   "source": [
    "### Synthetic question/answer generation using OpenAI GPT3.5\n",
    "\n",
    "We use gpt-3.5-turbo-0125, which is according to OpenAI the flagship model of this family, supports a 16K context window and is optimized for dialog (https://platform.openai.com/docs/models/gpt-3-5-turbo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
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    "id": "cnJU9xWoKP1C",
    "outputId": "85b7ca40-03c6-4c02-9d15-d8c0d0501da0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 19%|█▉        | 36/190 [02:58<04:34,  1.78s/it]"
     ]
    }
   ],
   "source": [
    "#from llama_index.llms import OpenAI\n",
    "from llama_index.llms.openai import OpenAI\n",
    "from llama_index.legacy.finetuning import generate_qa_embedding_pairs\n",
    "\n",
    "qa_dataset = generate_qa_embedding_pairs(\n",
    "    llm=OpenAI(model=\"gpt-3.5-turbo-0125\",additional_kwargs={'seed':42}),\n",
    "    nodes=nodes,\n",
    "    qa_generate_prompt_tmpl = prompts[language],\n",
    "    num_questions_per_chunk=2\n",
    ")\n",
    "\n",
    "file_name=language+\"_dataset.json\"\n",
    "qa_dataset.save_json(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cjzW-McSl8jU"
   },
   "source": [
    "### Load dataset from JSON file\n",
    "\n",
    "Data can be reloaded from their JSON file with the EmbeddingQAFinetuneDataset object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RDhv6wP7LpIg"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.evaluation import EmbeddingQAFinetuneDataset\n",
    "\n",
    "language = \"EN\"\n",
    "file_name=language+\"_dataset.json\"\n",
    "qa_dataset = EmbeddingQAFinetuneDataset.from_json(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3HJGaF5ASz5X",
    "outputId": "ed77a7d9-0f34-41cf-98b9-51c07095d46e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['What are the main objectives of the proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) according to the explanatory memorandum?',\n",
       " 'How does the proposal for a Regulation on artificial intelligence aim to address the risks associated with the use of AI while promoting the uptake of AI in the European Union, as outlined in the context information?']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(qa_dataset.queries.values())[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WP5FtNufEgBb"
   },
   "source": [
    "# Evaluate OpenAI embedding models\n",
    "\n",
    "The code inspired from https://docs.llamaindex.ai/en/stable/examples/finetuning/embeddings/finetune_embedding.html\n",
    "\n",
    "The function `evaluate` assesses the performance of a text embedding model on a given dataset for information retrieval tasks. Assessment is in terms of MRR (Mean Reciprocal Rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5Ssne7NyJ6a1"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "from llama_index.core.schema import TextNode\n",
    "from llama_index.core import VectorStoreIndex\n",
    "\n",
    "def evaluate(dataset, embed_model, insert_batch_size=1000, top_k=5):\n",
    "    # Get corpus, queries, and relevant documents from the qa_dataset object\n",
    "    corpus = dataset.corpus\n",
    "    queries = dataset.queries\n",
    "    relevant_docs = dataset.relevant_docs\n",
    "\n",
    "    # Create TextNode objects for each document in the corpus and create a VectorStoreIndex to efficiently store and retrieve embeddings\n",
    "    nodes = [TextNode(id_=id_, text=text) for id_, text in corpus.items()]\n",
    "    index = VectorStoreIndex(\n",
    "        nodes, embed_model=embed_model, insert_batch_size=insert_batch_size\n",
    "    )\n",
    "    retriever = index.as_retriever(similarity_top_k=top_k)\n",
    "\n",
    "    # Prepare to collect evaluation results\n",
    "    eval_results = []\n",
    "\n",
    "    # Iterate over each query in the dataset to evaluate retrieval performance\n",
    "    for query_id, query in tqdm(queries.items()):\n",
    "        # Retrieve the top_k most similar documents for the current query and extract the IDs of the retrieved documents\n",
    "        retrieved_nodes = retriever.retrieve(query)\n",
    "        retrieved_ids = [node.node.node_id for node in retrieved_nodes]\n",
    "\n",
    "        # Check if the expected document was among the retrieved documents\n",
    "        expected_id = relevant_docs[query_id][0]\n",
    "        is_hit = expected_id in retrieved_ids  # assume 1 relevant doc per query\n",
    "\n",
    "        # Calculate the Mean Reciprocal Rank (MRR) and append to results\n",
    "        if is_hit:\n",
    "            rank = retrieved_ids.index(expected_id) + 1\n",
    "            mrr = 1 / rank\n",
    "        else:\n",
    "            mrr = 0\n",
    "        eval_results.append(mrr)\n",
    "\n",
    "    # Return the average MRR across all queries as the final evaluation metric\n",
    "    return np.average(eval_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BsVeVX2FEgBc"
   },
   "outputs": [],
   "source": [
    "embeddings_model_spec = {\n",
    "}\n",
    "\n",
    "embeddings_model_spec['OAI-Large-256']={'model_name':'text-embedding-3-large','dimensions':256, 'batch_size':512}\n",
    "embeddings_model_spec['OAI-Large-3072']={'model_name':'text-embedding-3-large','dimensions':3072, 'batch_size':512}\n",
    "embeddings_model_spec['OAI-Small']={'model_name':'text-embedding-3-small','dimensions':1536, 'batch_size':512}\n",
    "embeddings_model_spec['OAI-ada-002']={'model_name':'text-embedding-ada-002','dimensions':None, 'batch_size':512}\n"
   ]
  },
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      "6adc49e4eb6a4e2da7d2fdec1f942fc2",
      "6b286dbee51e453ab723bbfc151ef0b1",
      "ff749373126c4f58852bb64be9643308",
      "5444c3eefaf744169bb25bfe17cc964b"
     ]
    },
    "id": "_fYwLk9bEgBc",
    "outputId": "bfa1a430-c027-47ad-ecf0-1e07e8f6bd6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing model : {'provider': 'OpenAI', 'model_name': 'text-embedding-3-large', 'dimensions': 256, 'batch_size': 512}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a03ce1ab1a284196bcb282d6a7194752",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/186 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing model : {'provider': 'OpenAI', 'model_name': 'text-embedding-3-small', 'dimensions': 1536, 'batch_size': 512}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "119b1211cb864396b78dc3c0a51374a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/186 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from llama_index.core.evaluation import EmbeddingQAFinetuneDataset\n",
    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
    "\n",
    "results = []\n",
    "\n",
    "languages = [\"FR\", \"EN\"]\n",
    "languages = [\"EN\"]\n",
    "\n",
    "# Loop through all languages\n",
    "for language in languages:\n",
    "\n",
    "    # Load dataset\n",
    "    file_name=language+\"_dataset.json\"\n",
    "    qa_dataset = EmbeddingQAFinetuneDataset.from_json(file_name)\n",
    "\n",
    "    # Loop through all models\n",
    "    for model_name, model_spec in embeddings_model_spec.items():\n",
    "\n",
    "        print(\"Processing model : \"+str(model_spec))\n",
    "\n",
    "        # Get model\n",
    "        embed_model = OpenAIEmbedding(model=model_spec['model_name'],\n",
    "                                      embed_batch_size=model_spec['batch_size'],\n",
    "                                      dimensions=model_spec['dimensions']\n",
    "                                     )\n",
    "\n",
    "        start_time_assessment=time.time()\n",
    "\n",
    "        # Assess embedding score (in terms of MRR)\n",
    "        score = evaluate(qa_dataset, embed_model)\n",
    "\n",
    "        # Get duration of score assessment\n",
    "        duration_assessment = time.time()-start_time_assessment\n",
    "\n",
    "        results.append([language, model_name, score, duration_assessment])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KywoJ7JsFeoW"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(results, columns = [\"Language\" ,\"Embedding model\", \"MRR\", \"Duration\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gUHBnd8meXwS"
   },
   "outputs": [],
   "source": [
    "df.to_pickle(\"results_openai.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HJYH-4q_eXwS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40meh1xEmNTW"
   },
   "source": [
    "## Plotting\n",
    "\n",
    "Let us plot the results using the Seaborn library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 807
    },
    "id": "CiQXqk2GeXwU",
    "outputId": "e2b91e60-698a-400a-d069-f6149873e674"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a grouped bar chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=\"Embedding model\", y=\"MRR\", hue=\"Language\", data=df, palette=\"viridis\")\n",
    "\n",
    "plt.xlabel('Embedding Model')\n",
    "plt.ylabel('MRR  (Mean Reciprocal Rank)')\n",
    "plt.title('MRR by Model and Language')\n",
    "plt.xticks(rotation=45, ha=\"right\")  # Rotate model names for better readability\n",
    "plt.ylim(0, 0.8)\n",
    "\n",
    "plt.legend(title='Language')\n",
    "plt.tight_layout()  # Adjust layout\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KbGB4BmsEgBe"
   },
   "source": [
    "# Evaluate open-source models\n",
    "\n",
    "## Install and import libraries\n",
    "\n",
    "Let us additionally install\n",
    "\n",
    "- sentence-transformers : For using the util.semantic_search function\n",
    "- bitsandbytes : For loading quantized models\n",
    "- accelerate : For more efficient model loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ioCMVMiRvnsK",
    "outputId": "2305b669-f489-4bc5-a9bb-a579f6f5c453"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.8/132.8 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "\n",
    "!pip install -q sentence-transformers bitsandbytes accelerate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5kx6R8jLvnsK"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torch import Tensor\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "from sentence_transformers import util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ABAbUncdmshe"
   },
   "source": [
    "## Pooling functions\n",
    "\n",
    "Two main types of pooling functions: 'mean' and 'cls' (see https://blog.ml6.eu/the-art-of-pooling-embeddings-c56575114cf8).\n",
    "\n",
    "E5_Mistral additionally relies on 'last_token' pooling, see model card: https://huggingface.co/intfloat/e5-mistral-7b-instruct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Bxl6Kg81vnsK"
   },
   "outputs": [],
   "source": [
    "def mean_pooling(model_output):\n",
    "    return torch.mean(model_output[\"last_hidden_state\"], dim=1)\n",
    "\n",
    "def cls_pooling(model_output):\n",
    "    return model_output[0][:, 0]\n",
    "\n",
    "def last_token_pooling(model_output):\n",
    "    return model_output[0][:, -1]\n",
    "\n",
    "def get_sentence_embedding(text, tokenizer, embed_model, normalize, max_length, pooling_type='cls'):\n",
    "\n",
    "    if pooling_type==\"last_token\":\n",
    "        encoded_input = tokenizer(text, max_length=max_length, return_attention_mask=False, padding=False, truncation=True)\n",
    "        encoded_input['input_ids'] = encoded_input['input_ids'] + [tokenizer.eos_token_id]\n",
    "        encoded_input = tokenizer.pad([encoded_input], padding=True, return_attention_mask=True, return_tensors='pt').to(\"cuda\")\n",
    "    else:\n",
    "        encoded_input = tokenizer(text, return_tensors=\"pt\", max_length=max_length, truncation=True).to(\"cuda\")\n",
    "\n",
    "    with torch.no_grad():\n",
    "        model_output = embed_model(**encoded_input)\n",
    "\n",
    "    if pooling_type==\"cls\":\n",
    "        sentence_embeddings = cls_pooling(model_output)\n",
    "    if pooling_type==\"mean\":\n",
    "        sentence_embeddings = mean_pooling(model_output)\n",
    "    if pooling_type==\"last_token\":\n",
    "        sentence_embeddings = last_token_pooling(model_output)\n",
    "\n",
    "    if normalize:\n",
    "        sentence_embeddings = F.normalize(sentence_embeddings)\n",
    "\n",
    "    return sentence_embeddings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9L-qJ4MOnQgV"
   },
   "source": [
    "## Evaluation function\n",
    "\n",
    "We slightly modify the evaluation function to avoid using the Llama Index wrapper for embedding, which does not support 'last_token' embedding. Top k results are computed using the util.semantic_search function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U7b5v4r6vnsL"
   },
   "outputs": [],
   "source": [
    "def evaluate(qa_dataset, tokenizer, embed_model, normalize, max_length=None, pooling_type=\"cls\", top_k=5, verbose=False):\n",
    "\n",
    "    # Get corpus and queries, and relevant documents from the qa_dataset object\n",
    "    input_texts = list(qa_dataset.corpus.values())\n",
    "    input_text_keys = list(qa_dataset.corpus.keys())\n",
    "    queries = qa_dataset.queries\n",
    "    relevant_docs = qa_dataset.relevant_docs\n",
    "\n",
    "    # Compute embeddings for each document in the corpus\n",
    "    embeddings = [get_sentence_embedding(sentence, tokenizer, embed_model, normalize, max_length, pooling_type)\n",
    "                  for sentence in input_texts]\n",
    "    embeddings = torch.cat(embeddings)\n",
    "\n",
    "    # Prepare to collect evaluation results\n",
    "    eval_results = []\n",
    "\n",
    "    # Iterate over each query in the dataset to evaluate retrieval performance\n",
    "    for query_id, query in tqdm(queries.items()):\n",
    "\n",
    "        # Retrieve the top_k most similar documents for the current query and extract the IDs of the retrieved documents\n",
    "        query_embedding = get_sentence_embedding(query, tokenizer, embed_model, normalize, max_length, pooling_type)\n",
    "        results = util.semantic_search(query_embedding, embeddings, top_k=top_k)[0]\n",
    "        retrieved_ids = [input_text_keys[int(result[\"corpus_id\"])] for result in results]\n",
    "\n",
    "        # Check if the expected document was among the retrieved documents\n",
    "        expected_id = relevant_docs[query_id][0]\n",
    "        is_hit = expected_id in retrieved_ids  # assume 1 relevant doc\n",
    "\n",
    "        # Calculate the Mean Reciprocal Rank (MRR) and append to results\n",
    "        if is_hit:\n",
    "            rank = retrieved_ids.index(expected_id) + 1\n",
    "            mrr = 1 / rank\n",
    "        else:\n",
    "            mrr = 0\n",
    "        eval_results.append(mrr)\n",
    "\n",
    "    # Return the average MRR across all queries as the final evaluation metric\n",
    "    return np.average(eval_results)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VXCP3VZboXOk"
   },
   "source": [
    "The code follows the same logic as for OpenAI embeddings: loop through all models and languages and compute MRR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3Q2sTsTiFeoa"
   },
   "outputs": [],
   "source": [
    "embeddings_model_spec = {\n",
    "}\n",
    "\n",
    "embeddings_model_spec['E5-mistral-7b']={'model_name':'intfloat/e5-mistral-7b-instruct','max_length':32768, 'pooling_type':'last_token',\n",
    "                                        'normalize': True, 'batch_size':1, 'kwargs': {'load_in_4bit':True, 'bnb_4bit_compute_dtype':torch.float16}}\n",
    "embeddings_model_spec['ML-E5-large']={'model_name':'intfloat/multilingual-e5-large','max_length':512, 'pooling_type':'mean',\n",
    "                                      'normalize': True, 'batch_size':1, 'kwargs': {'device_map': 'cuda', 'torch_dtype':torch.float16}}\n",
    "embeddings_model_spec['BGE-M3']={'model_name':'BAAI/bge-m3','max_length':8192, 'pooling_type':'cls',\n",
    "                                 'normalize': True, 'batch_size':1, 'kwargs': {'device_map': 'cuda', 'torch_dtype':torch.float16}}\n",
    "embeddings_model_spec['Nomic-Embed']={'model_name':'nomic-ai/nomic-embed-text-v1','max_length':8192, 'pooling_type':'mean',\n",
    "                                      'normalize': True, 'batch_size':1, 'kwargs': {'device_map': 'cuda', 'trust_remote_code' : True}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "id": "cBPlT8l-vnsN",
    "outputId": "f9263174-58b3-4992-86ca-3f19722686d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing model : {'model_name': 'intfloat/e5-mistral-7b-instruct', 'max_length': 32768, 'pooling_type': 'last_token', 'normalize': True, 'batch_size': 1, 'kwargs': {'load_in_4bit': True, 'bnb_4bit_compute_dtype': torch.float16}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "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"
     ]
    },
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      "text/plain": [
       "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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     },
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      "text/plain": [
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
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    },
    {
     "name": "stdout",
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      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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     "data": {
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       "model_id": "239719db77ca4ee6ada03ca094cdd26e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/418 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing model : {'model_name': 'nomic-ai/nomic-embed-text-v1', 'max_length': 8192, 'pooling_type': 'mean', 'normalize': True, 'batch_size': 1, 'kwargs': {'device_map': 'cuda', 'trust_remote_code': True}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:transformers_modules.nomic-ai.nomic-embed-text-v1.c9cf92b91ed6986f593ba836da7afdb8ed1ca85c.modeling_hf_nomic_bert:<All keys matched successfully>\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "458b101c6c8c46eaa6b98f7ce25a4098",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/418 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from llama_index.core.evaluation import EmbeddingQAFinetuneDataset\n",
    "\n",
    "results = []\n",
    "\n",
    "languages = [\"EN\", \"FR\", \"CS\", \"HU\"]\n",
    "\n",
    "# Loop through all models\n",
    "for model_name, model_spec in embeddings_model_spec.items():\n",
    "\n",
    "    print(\"Processing model : \"+str(model_spec))\n",
    "\n",
    "    # Get model\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_spec['model_name'])\n",
    "    embed_model = AutoModel.from_pretrained(model_spec['model_name'], **model_spec['kwargs'])\n",
    "\n",
    "    if model_name==\"Nomic-Embed\":\n",
    "        embed_model.to('cuda')\n",
    "\n",
    "    # Loop through all languages\n",
    "    for language in languages:\n",
    "\n",
    "        # Load dataset\n",
    "        file_name=language+\"_dataset.json\"\n",
    "        qa_dataset = EmbeddingQAFinetuneDataset.from_json(file_name)\n",
    "\n",
    "        start_time_assessment=time.time()\n",
    "\n",
    "        # Assess embedding score (in terms of hit rate at k=5)\n",
    "        score = evaluate(qa_dataset, tokenizer, embed_model, model_spec['normalize'], model_spec['max_length'], model_spec['pooling_type'])\n",
    "\n",
    "        # Get duration of score assessment\n",
    "        duration_assessment = time.time()-start_time_assessment\n",
    "\n",
    "        results.append([language, model_name, score, duration_assessment])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WuJXB922Feob"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(results, columns = [\"Language\" ,\"Embedding model\", \"MRR\", \"Duration\"])\n",
    "df.to_pickle(\"results_open_source.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZV-d7zsYokyA"
   },
   "source": [
    "## Plotting\n",
    "\n",
    "Let us plot the results using the Seaborn library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "id": "ero6GyG7gF7E",
    "outputId": "49c9693e-437f-4917-bd8a-e475a4a8abbe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a grouped bar chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=\"Embedding model\", y=\"MRR\", hue=\"Language\", data=df, palette=\"viridis\")\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('MRR  (Mean Reciprocal Rank)')\n",
    "plt.title('MRR by Model and Language')\n",
    "plt.xticks(rotation=45, ha=\"right\")  # Rotate model names for better readability\n",
    "plt.ylim(0, 0.8)\n",
    "\n",
    "plt.legend(title='Language')\n",
    "plt.tight_layout()  # Adjust layout\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GkQWLcp7Feob"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lCYXHOZ1om2l"
   },
   "source": [
    "Let us plot the processing time per model for English language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TxsFcWQ_Feob"
   },
   "outputs": [],
   "source": [
    "df_english=df[df['Language']==\"EN\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "id": "abSX1mMvFeoc",
    "outputId": "d4632202-ce65-467b-c016-b84e153e6988"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(df_english[\"Embedding model\"], df_english[\"Duration\"], color='blue', alpha=0.5)\n",
    "plt.title('Processing time per embedding model for English Language')\n",
    "#plt.xlabel('Embedding Model')\n",
    "plt.ylabel('Processing time (s)')\n",
    "plt.xticks(rotation=45, ha=\"right\")  # Rotate model names for better readability\n",
    "\n",
    "plt.grid(True)\n",
    "plt.tight_layout()  # Adjust layout\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zbPVuNMviwJc"
   },
   "source": [
    "# Combine all MRR\n",
    "\n",
    "Let us finally combine the mean reciprocal ranks of all models/languages in a single figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JC1igFBeiyKI"
   },
   "outputs": [],
   "source": [
    "df_open_source = pd.read_pickle(\"results_open_source.pkl\")\n",
    "df_openai = pd.read_pickle(\"results_openai.pkl\")\n",
    "df = pd.concat([df_openai, df_open_source])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "id": "4UWCb3OuiyQR",
    "outputId": "37d1558e-216e-4e6b-8c8e-d5010571a908"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a grouped bar chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=\"Embedding model\", y=\"MRR\", hue=\"Language\", data=df, palette=\"viridis\")\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('MRR  (Mean Reciprocal Rank)')\n",
    "plt.title('MRR by Model and Language')\n",
    "plt.xticks(rotation=45, ha=\"right\")  # Rotate model names for better readability\n",
    "plt.ylim(0, 0.8)\n",
    "\n",
    "plt.legend(title='Language')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()  # Adjust layout\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0QhLmo_JFeoc"
   },
   "source": [
    "# Additional resources\n",
    "\n",
    "- Related Medium post:\n",
    "- [Finetune embedding models with LlamaIndex](https://docs.llamaindex.ai/en/latest/examples/finetuning/embeddings/finetune_embedding.html#)\n",
    "- https://medium.com/towards-data-science/how-to-find-the-best-multilingual-embedding-model-for-your-rag-40325c308ebb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qWogrJ8IFeoc"
   },
   "outputs": [],
   "source": []
  }
 ],
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