12.llm-evaluation.ipynb

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 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dql87ef6cxW3",
    "outputId": "a499175d-c4c0-4b81-ccaf-68521f09ae9d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 4.25.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip -qqq install pip --progress-bar off\n",
    "!pip -qqq install groq==0.9.0 --progress-bar off\n",
    "!pip -qqq install datasets==2.19.2 --progress-bar off\n",
    "!pip -qqq install deepeval==0.21.55 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import json\n",
    "import os\n",
    "import pickle\n",
    "import random\n",
    "import time\n",
    "import typing as t\n",
    "from dataclasses import dataclass, field\n",
    "from pathlib import Path\n",
    "from textwrap import dedent\n",
    "from typing import Any, Dict, List, Optional\n",
    "\n",
    "import groq\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datasets import Dataset, load_dataset\n",
    "from deepeval.metrics import (\n",
    "    AnswerRelevancyMetric,\n",
    "    FaithfulnessMetric,\n",
    "    HallucinationMetric,\n",
    ")\n",
    "from deepeval.models.base_model import DeepEvalBaseLLM\n",
    "from deepeval.test_case import LLMTestCase\n",
    "from google.colab import userdata\n",
    "from groq import Groq\n",
    "from IPython.display import display\n",
    "from tqdm import tqdm\n",
    "\n",
    "os.environ[\"GROQ_API_KEY\"] = userdata.get(\"GROQ_API_KEY\")\n",
    "os.environ[\"DEEPEVAL_TELEMETRY_OPT_OUT\"] = \"YES\"\n",
    "os.environ[\"ERROR_REPORTING\"] = \"NO\"\n",
    "\n",
    "N_ITEMS = 20\n",
    "CRITIC_MODEL = \"llama3-70b-8192\"\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "\n",
    "def save_list(items, file_path):\n",
    "    try:\n",
    "        with Path(file_path).open(\"wb\") as f:\n",
    "            pickle.dump(items, f)\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred while saving the list of objects to file: {e}\")\n",
    "\n",
    "\n",
    "def load_list(file_path):\n",
    "    try:\n",
    "        with Path(file_path).open(\"rb\") as file:\n",
    "            items = pickle.load(file)\n",
    "        return items\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred while loading the list of objects from file: {e}\")\n",
    "        return None"
   ],
   "metadata": {
    "id": "BcsdfDeufduP"
   },
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Dataset"
   ],
   "metadata": {
    "id": "iLn0whcK8g2H"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "dataset = load_dataset(\"virattt/financial-qa-10K\")\n",
    "dataset"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220,
     "referenced_widgets": [
      "c00b0f0755d6471ab99f76a2df33a1cb",
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      "b1fd70412e69427c97d0e2d6329523d0",
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      "94d3aabcc485408db846ec56b6254026",
      "b7b7782df1a24890afe974b144910d6d",
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      "610b8cddd8834d42904510cbd8195a7c",
      "ba0b643014e445fbafb6e94d5ce1c65a",
      "bcca122aef1749f78dcaa3fc9e5c8364",
      "b25d5114ff9f4c058081d32abebc2440",
      "a7940b51b0af49fdbc9f3201e35eb44e",
      "c1ced276ed3d4bba9b715cdae6486be7",
      "44061b7734ac45e7b3c95704314004d0",
      "20450a0b9b1a48ad9f3b21ce29eff7a8",
      "b371017189b044b1976078746d7d4bb9",
      "045a7195d7784014a5f357af0ad0cc0b",
      "7bc2a83bbdf74caaa60bf03d50816bdb",
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      "34ecbf1bec6a40aa9366454ea138b408",
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      "5a88088528774f1683659b3a140300a7",
      "ccee5313aa444bbabf254c07e60ee18d",
      "5e890bd220fb4aa2a8b666868f520b07"
     ]
    },
    "id": "shcgfHAIf1ci",
    "outputId": "bdb76c08-ad0e-4f58-d9f4-3f6767fc0b53"
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/419 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c00b0f0755d6471ab99f76a2df33a1cb"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading data:   0%|          | 0.00/1.59M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b1fd70412e69427c97d0e2d6329523d0"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating train split:   0%|          | 0/7000 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "44061b7734ac45e7b3c95704314004d0"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['question', 'answer', 'context', 'ticker', 'filing'],\n",
       "        num_rows: 7000\n",
       "    })\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "dataset[\"train\"][0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f9YCU3qkgL2X",
    "outputId": "53fa9372-a36c-4b0c-f3b2-fc542a15692e"
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'question': 'What area did NVIDIA initially focus on before expanding to other computationally intensive fields?',\n",
       " 'answer': 'NVIDIA initially focused on PC graphics.',\n",
       " 'context': 'Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.',\n",
       " 'ticker': 'NVDA',\n",
       " 'filing': '2023_10K'}"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sample = dataset[\"train\"].shuffle().select(range(N_ITEMS))"
   ],
   "metadata": {
    "id": "VwfwfpDAgqRk"
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "client = Groq(api_key=os.environ.get(\"GROQ_API_KEY\"))\n",
    "\n",
    "\n",
    "def predict(prompt: str, model: str, client: Groq = client):\n",
    "    try:\n",
    "        chat_completion = client.chat.completions.create(\n",
    "            messages=[\n",
    "                {\n",
    "                    \"role\": \"user\",\n",
    "                    \"content\": prompt,\n",
    "                }\n",
    "            ],\n",
    "            model=model,\n",
    "        )\n",
    "        return chat_completion.choices[0].message.content\n",
    "    except groq.APIConnectionError as e:\n",
    "        print(\"The server could not be reached\")\n",
    "        print(e.__cause__)\n",
    "    except groq.RateLimitError as e:\n",
    "        print(\"A 429 status code was received; we should back off a bit.\")\n",
    "    except groq.APIStatusError as e:\n",
    "        print(\"Another non-200-range status code was received\")\n",
    "        print(e.status_code)\n",
    "        print(e.response)"
   ],
   "metadata": {
    "id": "DCDS0K2_jrOF"
   },
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def format_prompt(question: str, context: str):\n",
    "    return dedent(\n",
    "        f\"\"\"\n",
    "        Use the following context:\n",
    "        ```\n",
    "        {context}\n",
    "        ```\n",
    "        to answer the question:\n",
    "        ```\n",
    "        {question}\n",
    "        ```\n",
    "\n",
    "        Your answer must be succinct!\n",
    "        Answer:\n",
    "    \"\"\"\n",
    "    )"
   ],
   "metadata": {
    "id": "7370G2u7me-i"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "@dataclass\n",
    "class QuestionAnswer:\n",
    "    question: str\n",
    "    answer: str\n",
    "    true_answer: str\n",
    "    context: str"
   ],
   "metadata": {
    "id": "mcy3zu2q-MUA"
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def extract_predictions(dataset: Dataset, model: str) -> List[QuestionAnswer]:\n",
    "    return [\n",
    "        QuestionAnswer(\n",
    "            question=item[\"question\"],\n",
    "            answer=predict(format_prompt(item[\"question\"], item[\"context\"]), model),\n",
    "            true_answer=item[\"answer\"],\n",
    "            context=item[\"context\"],\n",
    "        )\n",
    "        for item in tqdm(dataset)\n",
    "    ]"
   ],
   "metadata": {
    "id": "1H6XfLP0_iLQ"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "models = [\"gemma-7b-it\", \"llama3-8b-8192\", \"mixtral-8x7b-32768\"]\n",
    "\n",
    "predictions = {}\n",
    "for model in models:\n",
    "    predictions[model] = extract_predictions(sample, model=model)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "as8aNTwBAcsU",
    "outputId": "dbb8f2dd-af70-4591-d56b-644a87f44bda"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 20/20 [00:06<00:00,  2.97it/s]\n",
      "100%|██████████| 20/20 [00:05<00:00,  3.80it/s]\n",
      "100%|██████████| 20/20 [00:06<00:00,  3.10it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "save_list(predictions, \"predictions.pkl\")"
   ],
   "metadata": {
    "id": "Bb0khkyIVRA8"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Simple Evaluation"
   ],
   "metadata": {
    "id": "ARZfxSc8nqEV"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "eval_prompt = \"\"\"\n",
    "\n",
    "Consider the question: {question}\n",
    "and answer: {answer}\n",
    "based on the context: {context}\n",
    "compare with the true answer: {true_answer}\n",
    "\n",
    "Score how correct the response is on a scale from 0 to 10.\n",
    "Respond with the integer number only.\n",
    "\"\"\""
   ],
   "metadata": {
    "id": "Urmwy4L1laQA"
   },
   "execution_count": 11,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prediction = predictions[\"gemma-7b-it\"][10]\n",
    "prediction.__dict__"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PbnmaN46nL6V",
    "outputId": "3eaecb67-1bb4-434e-c3f7-ea0c5b4bceb0"
   },
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'question': 'What are the interest rate ranges for the unsecured notes due between 2024 to 2029?',\n",
       " 'answer': 'Interest rates for unsecured notes due between 2024 and 2029 range from 2.90% to 7.38%.',\n",
       " 'true_answer': 'The interest rates for the unsecured notes due between 2024 to 2029 range from 2.90% to 7.38%.',\n",
       " 'context': 'Unsecured notes due between 2024 and 2029 have interest rates ranging from 2.90% to 7.38%.'}"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "predict(\n",
    "    eval_prompt.format(\n",
    "        question=prediction.question,\n",
    "        answer=prediction.answer,\n",
    "        context=prediction.context,\n",
    "        true_answer=prediction.true_answer,\n",
    "    ),\n",
    "    CRITIC_MODEL,\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "jc7g_fp_m7Sj",
    "outputId": "662ca653-7c69-4305-a3b9-124bb5ab141d"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'9'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {},
     "execution_count": 15
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "scores = {}\n",
    "for model, model_predictions in predictions.items():\n",
    "    model_scores = []\n",
    "    for prediction in tqdm(model_predictions):\n",
    "        score = predict(\n",
    "            eval_prompt.format(\n",
    "                question=prediction.question,\n",
    "                answer=prediction.answer,\n",
    "                context=prediction.context,\n",
    "                true_answer=prediction.true_answer,\n",
    "            ),\n",
    "            CRITIC_MODEL,\n",
    "        )\n",
    "        model_scores.append(int(score))\n",
    "\n",
    "        sleep_time = random.uniform(0.5, 2.0)\n",
    "        time.sleep(sleep_time)\n",
    "    scores[model] = model_scores"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vJPRmq6Load2",
    "outputId": "e6267596-4721-4385-8aa2-ac7c0a42b4b9"
   },
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 20/20 [00:31<00:00,  1.59s/it]\n",
      "100%|██████████| 20/20 [00:43<00:00,  2.16s/it]\n",
      "100%|██████████| 20/20 [00:54<00:00,  2.72s/it]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "rows = []\n",
    "for model, model_scores in scores.items():\n",
    "    rows.append({\"model\": model, \"score\": np.mean(model_scores)})\n",
    "pd.DataFrame(rows)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "T9fl3OBVphG1",
    "outputId": "46802030-5818-4fb5-c249-4a6491770873"
   },
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                model  score\n",
       "0         gemma-7b-it   8.95\n",
       "1      llama3-8b-8192   8.85\n",
       "2  mixtral-8x7b-32768   9.15"
      ],
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       "      <th>0</th>\n",
       "      <td>gemma-7b-it</td>\n",
       "      <td>8.95</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>llama3-8b-8192</td>\n",
       "      <td>8.85</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mixtral-8x7b-32768</td>\n",
       "      <td>9.15</td>\n",
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       "        document.querySelector('#df-fe5be34c-7359-439a-8559-3952fcd770b2 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-fe5be34c-7359-439a-8559-3952fcd770b2');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
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       "\n",
       "<style>\n",
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       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
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       "      --hover-bg-color: #434B5C;\n",
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       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
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       "    height: 32px;\n",
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       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-b061e85f-d1d3-4821-9a21-2aa3c00ce36c button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"gemma-7b-it\",\n          \"llama3-8b-8192\",\n          \"mixtral-8x7b-32768\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15275252316519508,\n        \"min\": 8.85,\n        \"max\": 9.15,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.95,\n          8.85,\n          9.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {},
     "execution_count": 19
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Critic Model"
   ],
   "metadata": {
    "id": "8gSoXRnSx3SF"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class GroqCriticModel(DeepEvalBaseLLM):\n",
    "    def __init__(self, model: str):\n",
    "        self.model = model\n",
    "\n",
    "    def load_model(self):\n",
    "        pass\n",
    "\n",
    "    def generate(self, prompt: str) -> str:\n",
    "        return predict(prompt, self.model)\n",
    "\n",
    "    async def a_generate(self, prompt: str) -> str:\n",
    "        return self.generate(prompt)\n",
    "\n",
    "    def get_model_name(self):\n",
    "        return self.model"
   ],
   "metadata": {
    "id": "FlLAatQtDE7c"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "critic_model = GroqCriticModel(CRITIC_MODEL)"
   ],
   "metadata": {
    "id": "goYaADoBEYnp"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Metrics"
   ],
   "metadata": {
    "id": "cf6UZfmjRBbR"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "test_case = LLMTestCase(\n",
    "    input=prediction.question,\n",
    "    actual_output=prediction.answer,\n",
    "    context=[prediction.context],\n",
    "    retrieval_context=[prediction.context],\n",
    ")"
   ],
   "metadata": {
    "id": "fEPjC-z6QRQP"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "relevancy_metric = AnswerRelevancyMetric(\n",
    "    threshold=0.7, model=critic_model, include_reason=True\n",
    ")\n",
    "\n",
    "relevancy_metric.measure(test_case)\n",
    "print(relevancy_metric.score, relevancy_metric.reason)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51,
     "referenced_widgets": [
      "68c3cce660ef435c891e45d5e50b2aed",
      "aa88d43cf852423197faf6b2f1af0421"
     ]
    },
    "id": "SrvajS7oEYD4",
    "outputId": "e3c6fec5-2282-4cf7-d86a-ff344ceb7f5a"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Output()"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "68c3cce660ef435c891e45d5e50b2aed"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Event loop is already running. Applying nest_asyncio patch to allow async execution...\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Event loop is already running. Applying nest_asyncio patch to allow async execution...\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1.0 The score is 1.00 because the output perfectly addresses the question and provides accurate information without any irrelevant statements.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "faithfulness_metric = FaithfulnessMetric(\n",
    "    threshold=0.7, model=critic_model, include_reason=True\n",
    ")\n",
    "\n",
    "faithfulness_metric.measure(test_case)\n",
    "print(faithfulness_metric.score, faithfulness_metric.reason)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51,
     "referenced_widgets": [
      "f1ab28e31f934d5591fb0f0ca8ff03c7",
      "e82536b2a42143309f1a3da770b4e2f3"
     ]
    },
    "id": "V539HR0qIZJ3",
    "outputId": "04512ffc-de54-45fb-a263-f1d2aea7730f"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Output()"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f1ab28e31f934d5591fb0f0ca8ff03c7"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Event loop is already running. Applying nest_asyncio patch to allow async execution...\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Event loop is already running. Applying nest_asyncio patch to allow async execution...\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1.0 The score is 1.00 because there are no contradictions found, meaning the actual output perfectly aligns with the retrieval context - great job!\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "hallucination_metric = HallucinationMetric(\n",
    "    threshold=0.5, model=critic_model, include_reason=True\n",
    ")\n",
    "\n",
    "hallucination_metric.measure(test_case)\n",
    "print(hallucination_metric.score, hallucination_metric.reason)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35,
     "referenced_widgets": [
      "30ca68b0c53d44e7aef7cbea0e63245d",
      "d11b5bdf9ee74cf58fb00125e5adf612"
     ]
    },
    "id": "26XKJoBWQWYl",
    "outputId": "dc2b433b-aa9f-48f6-f013-23431343e997"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Output()"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "30ca68b0c53d44e7aef7cbea0e63245d"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.0 The score is 0.00 because the actual output does not contradict the context and aligns with the provided information.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Evaluation"
   ],
   "metadata": {
    "id": "9OgFek0R8u-D"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "@dataclass\n",
    "class ModelEvaluation:\n",
    "    model: str\n",
    "    relevancy: List[float] = field(default_factory=list)\n",
    "    faithfulness: List[float] = field(default_factory=list)\n",
    "    hallucination: List[float] = field(default_factory=list)\n",
    "\n",
    "    no_relevancy_reasons: List[str] = field(default_factory=list)\n",
    "    no_faithfulness_reasons: List[str] = field(default_factory=list)\n",
    "    hallucination_reasons: List[str] = field(default_factory=list)"
   ],
   "metadata": {
    "id": "wEHvAezARrUJ"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "evaluations = []\n",
    "for model, model_predictions in predictions.items():\n",
    "    evaluation = ModelEvaluation(model)\n",
    "    for prediction in tqdm(model_predictions):\n",
    "        test_case = LLMTestCase(\n",
    "            input=prediction.question,\n",
    "            actual_output=prediction.answer,\n",
    "            context=[prediction.context],\n",
    "            retrieval_context=[prediction.context],\n",
    "        )\n",
    "        try:\n",
    "            relevancy_metric.measure(test_case)\n",
    "            faithfulness_metric.measure(test_case)\n",
    "            hallucination_metric.measure(test_case)\n",
    "\n",
    "            evaluation.relevancy.append(relevancy_metric.score)\n",
    "            evaluation.faithfulness.append(faithfulness_metric.score)\n",
    "            evaluation.hallucination.append(hallucination_metric.score)\n",
    "\n",
    "            if relevancy_metric.score < 0.5:\n",
    "                evaluation.no_relevancy_reasons.append(relevancy_metric.reason)\n",
    "            if faithfulness_metric.score < 0.5:\n",
    "                evaluation.no_faithfulness_reasons.append(faithfulness_metric.reason)\n",
    "            if hallucination_metric.score > 0.5:\n",
    "                evaluation.hallucination_reasons.append(hallucination_metric.reason)\n",
    "        except:\n",
    "            continue\n",
    "\n",
    "        sleep_time = random.uniform(0.5, 2.0)\n",
    "        time.sleep(sleep_time)\n",
    "    evaluations.append(evaluation)"
   ],
   "metadata": {
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     "height": 1000,
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  {
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    "evaluations[1]"
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    },
    "id": "V7SJEuntV1KN",
    "outputId": "25cb0734-1e78-4031-b531-3cb062883e96"
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   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "ModelEvaluation(model='llama3-8b-8192', relevancy=[1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], faithfulness=[1.0, 1.0, 1.0, 1.0, 1.0, 1, 0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], hallucination=[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0], no_relevancy_reasons=[\"The score is 0.00 because the actual output did not provide any relevant information about the company's monetization strategy for its film and television content, and instead included an unclear statement that did not address the input question.\"], no_faithfulness_reasons=[], hallucination_reasons=['The score is 1.00 because the actual output lacks crucial information about DJS-002 and only partially mentions its uses, resulting in significant hallucinations.', 'The score is 1.00 because the actual output provides incomplete information without mentioning the total future lease payments, which is a crucial context.', 'The score is 1.00 because the actual output lacks specific details and information that is present in the context, resulting in a complete mismatch.', 'The score is 1.00 because the actual output lacks essential information and only provides a partial answer, while the context provides more detailed and specific information.', 'The score is 1.00 because the actual output omits crucial details mentioned in the context, specifically the information on legal proceedings.', 'The score is 1.00 because the actual output omits crucial context and only provides a numerical value, leading to a complete lack of alignment with the context.', 'The score is 1.00 because the actual output lacks essential information present in the context, causing a complete mismatch.', 'The score is 1.00 because the actual output lacks significant information (decrease of $28.6 million) mentioned in the context, indicating a high level of hallucination.'])"
      ]
     },
     "metadata": {},
     "execution_count": 29
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    "save_list(evaluations, \"evaluations.pkl\")"
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   "outputs": []
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    "## Report"
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    "id": "wTTA881pMDdJ"
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   "source": [
    "rows = []\n",
    "for evaluation in evaluations:\n",
    "    rows.append(\n",
    "        {\n",
    "            \"model\": evaluation.model,\n",
    "            \"relevancy\": np.mean(evaluation.relevancy),\n",
    "            \"faithfulness\": np.mean(evaluation.faithfulness),\n",
    "            \"hallucination\": np.mean(evaluation.hallucination),\n",
    "        }\n",
    "    )"
   ],
   "metadata": {
    "id": "rwBYYlT-qJeX"
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   "execution_count": null,
   "outputs": []
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    "pd.DataFrame(rows)"
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    "id": "nd7ixrajqhdS",
    "outputId": "0f34b97a-12c4-4c09-f070-8e5bbd271c0d"
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   "execution_count": null,
   "outputs": [
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       "                model  relevancy  faithfulness  hallucination\n",
       "0         gemma-7b-it       1.00      1.000000            0.0\n",
       "1      llama3-8b-8192       0.95      0.975000            0.4\n",
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       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-7e929e4b-0bcf-4fb3-a2b6-febf9dbe02e6 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"gemma-7b-it\",\n          \"llama3-8b-8192\",\n          \"mixtral-8x7b-32768\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.028867513459481315,\n        \"min\": 0.95,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.95,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012834725294554764,\n        \"min\": 0.975,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hallucination\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23094010767585035,\n        \"min\": 0.0,\n        \"max\": 0.4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.4,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {},
     "execution_count": 33
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "report_prompt = \"\"\"\n",
    "Summarize the most common reasons in a paragraph based on the texts:\n",
    "\n",
    "{relevancy}\n",
    "\n",
    "{faithfulness}\n",
    "\n",
    "{hallucination}\n",
    "\n",
    "If the reasons are empty - conclude that the model did great.\n",
    "\"\"\""
   ],
   "metadata": {
    "id": "W2Bx9RhbrWVZ"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "reports = {}\n",
    "for evaluation in tqdm(evaluations):\n",
    "    reports[evaluation.model] = predict(\n",
    "        report_prompt.format(\n",
    "            relevancy=\"\\n\".join(evaluation.no_relevancy_reasons),\n",
    "            faithfulness=\"\\n\".join(evaluation.no_faithfulness_reasons),\n",
    "            hallucination=\"\\n\".join(evaluation.hallucination_reasons),\n",
    "        ),\n",
    "        CRITIC_MODEL,\n",
    "    )"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Smd1lBNNtHsb",
    "outputId": "bf692311-9bf0-4caf-92a6-f2a754e0d5ee"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 3/3 [00:01<00:00,  2.57it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "Buoo-HWwwFjt"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for model, report in reports.items():\n",
    "    print(model)\n",
    "    print()\n",
    "    display(report)\n",
    "    print()\n",
    "    print(\"-\" * 50)\n",
    "    print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "id": "2BBX_pCAuCtj",
    "outputId": "5b7da8a7-e07b-45fa-fe44-6b2c8fc700ec"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "gemma-7b-it\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "'Since there are no texts provided, I conclude that the model did great!'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "llama3-8b-8192\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "'The most common reasons for low scores are the lack of relevant information, incomplete answers, omission of crucial details, and hallucinations. The models often failed to provide specific details, ignored important context, and omitted essential information, leading to incomplete or misleading responses. Additionally, the models sometimes provided unclear or partial answers, failed to address the input question, or included irrelevant information, resulting in a significant mismatch with the context.'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "mixtral-8x7b-32768\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "'There are no texts provided, so I assume the reasons are empty. Therefore, I conclude that the model did great!'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## References\n",
    "\n",
    "- [DeepEval](https://github.com/confident-ai/deepeval)\n",
    "- [ragas](https://github.com/explodinggradients/ragas/)\n",
    "- [Giskard](https://github.com/Giskard-AI/giskard)\n",
    "- [financial-qa-10K Dataset](https://huggingface.co/datasets/virattt/financial-qa-10K)"
   ],
   "metadata": {
    "id": "JUgMKBz1bOlP"
   }
  }
 ]
}