{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-IHGu_f3A_Xl",
        "outputId": "110a980a-d6f0-401e-b966-61930fac0781"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# connect to google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmKRag_UBMBR"
      },
      "source": [
        "# Install Necessary Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cluADcYmkzm6",
        "outputId": "81b382b4-c05d-4840-d129-d8a71c3e7a59"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'FlagEmbedding'...\n",
            "remote: Enumerating objects: 1631, done.\u001b[K\n",
            "remote: Counting objects: 100% (514/514), done.\u001b[K\n",
            "remote: Compressing objects: 100% (229/229), done.\u001b[K\n",
            "remote: Total 1631 (delta 333), reused 430 (delta 285), pack-reused 1117\u001b[K\n",
            "Receiving objects: 100% (1631/1631), 5.80 MiB | 23.11 MiB/s, done.\n",
            "Resolving deltas: 100% (961/961), done.\n",
            "Obtaining file:///content\n",
            "\u001b[31mERROR: file:///content does not appear to be a Python project: neither 'setup.py' nor 'pyproject.toml' found.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/FlagOpen/FlagEmbedding.git\n",
        "!pip install -e ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ckj6S0SyIKYy"
      },
      "outputs": [],
      "source": [
        "!pip install huggingface_hub\n",
        "!pip install datasets\n",
        "!pip install transformers\n",
        "!pip install loguru -qU\n",
        "!pip install tokenizers\n",
        "!pip install langchain -qU\n",
        "!pip install bitsandbytes -qU\n",
        "!pip install accelerate==0.21.0\n",
        "!pip install peft==0.4.0\n",
        "!pip install trl==0.4.7\n",
        "!pip install guardrail-ml==0.0.12\n",
        "!pip install flash-attn --no-build-isolation\n",
        "!pip install -U FlagEmbedding\n",
        "!pip install bert-score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "co7qOUaohj_m"
      },
      "outputs": [],
      "source": [
        "class PromptTemplate:\n",
        "      system_prompt = None\n",
        "\n",
        "\n",
        "      def __init__(self, system_prompt=None):\n",
        "          self.system_prompt = system_prompt\n",
        "          self.user_messages = []\n",
        "          self.model_replies = []\n",
        "\n",
        "      def add_user_message(self, message: str, return_prompt=True):\n",
        "          self.user_messages.append(message)\n",
        "          if return_prompt:\n",
        "              return self.build_prompt()\n",
        "\n",
        "      def add_model_reply(self, reply: str, includes_history=True, return_reply=True):\n",
        "          reply_ = reply.replace(self.build_prompt(), \"\") if includes_history else reply\n",
        "          self.model_replies.append(reply_)\n",
        "          if len(self.user_messages) != len(self.model_replies):\n",
        "              raise ValueError(\n",
        "                  \"Number of user messages does not equal number of system replies.\"\n",
        "              )\n",
        "          if return_reply:\n",
        "              return reply_\n",
        "\n",
        "      def get_user_messages(self, strip=True):\n",
        "          return [x.strip() for x in self.user_messages] if strip else self.user_messages\n",
        "\n",
        "      def get_model_replies(self, strip=True):\n",
        "          return [x.strip() for x in self.model_replies] if strip else self.model_replies\n",
        "\n",
        "      def build_prompt(self):\n",
        "          if len(self.user_messages) != len(self.model_replies) + 1:\n",
        "              raise ValueError(\n",
        "                  \"Error: Expected len(user_messages) = len(model_replies) + 1. Add a new user message!\"\n",
        "              )\n",
        "\n",
        "          if self.system_prompt is not None:\n",
        "              SYS = f\"[INST] <<SYS>>\\n{self.system_prompt}\\n<</SYS>>\"\n",
        "          else:\n",
        "              SYS = \"\"\n",
        "\n",
        "          CONVO = \"\"\n",
        "          SYS = \"<s>\" + SYS\n",
        "          for i in range(len(self.user_messages) - 1):\n",
        "              user_message, model_reply = self.user_messages[i], self.model_replies[i]\n",
        "              conversation_ = f\"{user_message} [/INST] {model_reply} </s>\"\n",
        "              if i != 0:\n",
        "                  conversation_ = \"[INST] \" + conversation_\n",
        "              CONVO += conversation_\n",
        "\n",
        "          CONVO += f\"[INST] {self.user_messages[-1]} [/INST]\"\n",
        "\n",
        "          return SYS + CONVO"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTLTwidDBTW9"
      },
      "source": [
        "# Import Necessary Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "4HNma0zBBZVi"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from glob import glob\n",
        "import pandas as pd\n",
        "import json\n",
        "import time\n",
        "import requests\n",
        "import random\n",
        "from loguru import logger\n",
        "import re\n",
        "import numpy as np\n",
        "#from huggingface_hub import HfApi, HfFolder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "J_adAiUrHpi2"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "dpDvwV99H--G"
      },
      "outputs": [],
      "source": [
        "from transformers import(AutoTokenizer,\n",
        "                         AutoModelForMultipleChoice,\n",
        "                         AutoModelForCausalLM,\n",
        "                         AutoTokenizer,\n",
        "\n",
        "                         GenerationConfig,\n",
        "                         BitsAndBytesConfig,\n",
        "\n",
        "                         pipeline,\n",
        "                         Conversation,\n",
        "                         logging,\n",
        "                         )\n",
        "from datasets import load_dataset\n",
        "from tokenizers import Tokenizer\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cGurosW9sIqq"
      },
      "source": [
        "### HELPER FUNCTION for Multi-Agents Debate ###"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "c55kM8BPsHpz"
      },
      "outputs": [],
      "source": [
        "# this part is used to gen_mmlu\n",
        "\n",
        "def construct_message(agents, question, idx):\n",
        "    if len(agents) == 0:\n",
        "        return {\"role\": \"user\", \"content\": \"Can you double check that your answer is correct. Put your final answer in the form (X) at the end of your response.\"}\n",
        "\n",
        "    prefix_string = \"These are the solutions to the problem from other agents: \"\n",
        "\n",
        "    for agent in agents:\n",
        "        agent_response = agent[idx][\"content\"]\n",
        "        response = \"\\n\\n One agent solution: ```{}```\".format(agent_response)\n",
        "\n",
        "        prefix_string = prefix_string + response\n",
        "\n",
        "    prefix_string = prefix_string + \"\"\"\\n\\n Using the reasoning from other agents as additional advice, can you give an updated answer? Examine your solution and that other agents step by step. /n/n Here is the original question: {}. \"\"\".format(question)\n",
        "    return {\"role\": \"user\", \"content\": prefix_string}\n",
        "\n",
        "\n",
        "def construct_assistant_message(completion):\n",
        "    # just construct the assistant_message directly.\n",
        "\n",
        "    return {\"role\": \"assistant\", \"content\": completion}\n",
        "\n",
        "\n",
        "def generate_answer(answer_context):\n",
        "    \"\"\"\n",
        "    input: list of dict, answer_context\n",
        "    output: str, content\n",
        "    \"\"\"\n",
        "    try:\n",
        "        # Generate a prompt\n",
        "        messages = answer_context\n",
        "\n",
        "        tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\")\n",
        "\n",
        "        # Generate a response\n",
        "        outputs = base_model.generate(\n",
        "                                tokenized_chat,\n",
        "                                max_new_tokens=400,\n",
        "                                max_time=90, # control the generation time\n",
        "\n",
        "                                do_sample = True,\n",
        "\n",
        "                                top_k = 50, # both top_k and top_p combined to help me control the quality of logit\n",
        "                                top_p = 0.9,\n",
        "\n",
        "                                temperature= 0.1,\n",
        "                                #num_return_sequences= 1, # control the num of returned sequence, to less the recall api time\n",
        "\n",
        "                                repetition_penalty= 1.5,\n",
        "                                )\n",
        "\n",
        "        # parse output_text\n",
        "        output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "        # Removing the query content and everything before it\n",
        "        # Find the index where the query content ends in the output_text\n",
        "        end_idx_of_query = output_text.find(messages[0]['content']) + len(messages[0]['content'])\n",
        "        cleaned_output_text = output_text[end_idx_of_query:].strip()\n",
        "\n",
        "        # Further clean up if needed\n",
        "        pattern = r\"\\[.*?\\]|\\(.*?\\)|\\{.*?\\}\"\n",
        "        cleaned_output_text = re.sub(pattern, \"\", cleaned_output_text)\n",
        "    except:\n",
        "        print(\"retrying due to an error......\")\n",
        "        time.sleep(20)\n",
        "        return generate_answer(answer_context)\n",
        "\n",
        "    return  cleaned_output_text\n",
        "\n",
        "\n",
        "def parse_question_answer(df, ix):\n",
        "    question = df.iloc[ix, 0]\n",
        "    a = df.iloc[ix, 1]\n",
        "    b = df.iloc[ix, 2]\n",
        "    c = df.iloc[ix, 3]\n",
        "    d = df.iloc[ix, 4]\n",
        "\n",
        "    question = \"Can you answer the following question as accurately as possible? {}: /n/n A) {}, /n B) {}, /n C) {}, /n D) {}. /n Explain your answer, putting the answer in the form (X) at the end of your response.\".format(question, a, b, c, d)\n",
        "\n",
        "    answer = df.iloc[ix, 5]\n",
        "\n",
        "    return question, answer\n",
        "\n",
        "\n",
        "\n",
        "# an alternative way to implement generating the text answer\n",
        "def sample_model(prompt):\n",
        "    conversation_pipeline = pipeline(\n",
        "                                    'conversational',\n",
        "                                    model=model,\n",
        "                                    tokenizer=tokenizer,\n",
        "                                    max_new_tokens=300,\n",
        "                                    max_time=90, # control the generation time\n",
        "\n",
        "                                    do_sample = True,\n",
        "\n",
        "                                    top_k = 75, # both top_k and top_p combined to help me control the quality of logit\n",
        "                                    top_p = 0.9,\n",
        "\n",
        "                                    temperature= 0.9,\n",
        "                                    #num_return_sequences= 1, # control the num of returned sequence, to less the recall api time\n",
        "\n",
        "                                    repetition_penalty= 1.2,\n",
        "                                    eos_token_id= tokenizer.eos_token_id,\n",
        "                                    pad_token_id= tokenizer.eos_token_id,\n",
        "                                    bos_token_id= tokenizer.eos_token_id,\n",
        "                                    )\n",
        "    conversation = Conversation(prompt)\n",
        "    conversation_pipeline([conversation])\n",
        "    return conversation.generated_responses[-1]\n",
        "\n",
        "#-------------------------------------------------------- RAG HELPER FUNCTION -----------------------------------------------------------------------#\n",
        "\n",
        "# Helper function for printing docs\n",
        "\n",
        "def pretty_print_docs(docs):\n",
        "    print(f\"\\n{'-' * 100}\\n\".join([f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]))\n",
        "\n",
        "def reciprocal_rank_fusion(document_scores, k=60):\n",
        "    fused_scores = {}\n",
        "    for doc, score in document_scores.items():\n",
        "        rank = 1 / score  # Assuming a lower score means a better match\n",
        "        fused_scores[doc] = 1 / (rank + k)\n",
        "\n",
        "    reranked_results = {doc: score for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)}\n",
        "    return reranked_results\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XnQ4dGcIDVCH"
      },
      "source": [
        "# 1. Set up the model"
      ]
    },
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        },
        "id": "B1brHjJGwAkP",
        "outputId": "1690ba26-c87f-42b3-81e7-2557f4b8f7dd"
      },
      "outputs": [
        {
          "data": {
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          },
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        {
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              "model_id": "0d262f434fe047d3833b627526520445",
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "#base_model_path = '/content/drive/MyDrive/Hallucination/Llama2_7b_base/Llama2_7b_Finance_FT_3/Llama2-7b_Finance_FT_3_with_lora'\n",
        "base_model_path = 'NousResearch/Llama-2-7b-hf'\n",
        "\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained('NousResearch/Llama-2-7b-hf',\n",
        "                                          use_fast=True,\n",
        "                                          #chat_template = set_template\n",
        "                                          )\n",
        "\n",
        "\n",
        "# Load the trained model\n",
        "base_model = AutoModelForCausalLM.from_pretrained(base_model_path,\n",
        "                                             #quantization_config=bnb_config,\n",
        "                                             trust_remote_code=True,\n",
        "                                             load_in_8bit=True,\n",
        "                                             device_map=\"auto\",\n",
        "                                             #use_flash_attention_2=True,\n",
        "                                             )\n",
        "\n",
        "base_model.config.use_cache = False # Because, we just take the performance of single turn into consideration,\n",
        "\n",
        "#model.push_to_hub(\"Llama2-7b_Finance_lora_3\")\n",
        "\n",
        "# If you're using a GPU, move the model to GPU\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "#base_llm = model#.to(device) # int8,int can not put into .to()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AkYIVI0qG9sf",
        "outputId": "31521ee1-5967-4001-b202-7e675a9c6c7b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using sep_token, but it is not set yet.\n",
            "Using mask_token, but it is not set yet.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "BOS Token: <s>\n",
            "EOS Token: </s>\n",
            "PAD Token: <unk>\n",
            "SEP Token: None\n",
            "MASK Token: None\n"
          ]
        }
      ],
      "source": [
        "# Print the BOS and EOS tokens\n",
        "print(\"BOS Token:\", tokenizer.bos_token)\n",
        "print(\"EOS Token:\", tokenizer.eos_token)\n",
        "print(\"PAD Token:\", tokenizer.pad_token)\n",
        "print(\"SEP Token:\", tokenizer.sep_token)\n",
        "print(\"MASK Token:\", tokenizer.mask_token)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSSZHKakJqXa",
        "outputId": "08221074-202f-488b-bc58-76abac9beebd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "BOS Token id: 1\n",
            "EOS Token id: 2\n",
            "PAD Token id: 0\n",
            "SEP Token id: None\n",
            "MASK Token id: None\n"
          ]
        }
      ],
      "source": [
        "print(\"BOS Token id:\", tokenizer.bos_token_id)\n",
        "print(\"EOS Token id:\", tokenizer.eos_token_id)\n",
        "print(\"PAD Token id:\", tokenizer.pad_token_id)\n",
        "print(\"SEP Token id:\", tokenizer.sep_token_id)\n",
        "print(\"MASK Token id:\", tokenizer.mask_token_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "RzRC-731h7aZ",
        "outputId": "f70bb5fe-d496-4d51-fd98-ce37f260a81f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\\\\n\\\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don\\\\'t know the answer to a question, please don\\\\'t share false information.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}\""
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.default_chat_template"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ju5tPqDIDa4i"
      },
      "source": [
        "# 2. Retrieval Augmention Generation(RAG)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LUCsdyKsD6Ce"
      },
      "outputs": [],
      "source": [
        "!pip install yfinance\n",
        "!pip install wikipedia\n",
        "!pip install faiss-GPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "vfJFPMJQkAGA"
      },
      "outputs": [],
      "source": [
        "from langchain.agents import AgentType, initialize_agent\n",
        "\n",
        "\n",
        "#tools\n",
        "from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool\n",
        "\n",
        "#retrievers\n",
        "from langchain.retrievers import WikipediaRetriever\n",
        "\n",
        "from langchain.retrievers import ContextualCompressionRetriever\n",
        "from langchain.retrievers.document_compressors import LLMChainExtractor\n",
        "\n",
        "from langchain.retrievers.document_compressors import EmbeddingsFilter\n",
        "\n",
        "from FlagEmbedding import (FlagReranker, FlagModel, LLMEmbedder)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCNNeNKf5UVg"
      },
      "source": [
        "# 2.1 tools \\[   YahooFinanceNewsTool,\\]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "CNacgxXakAIZ"
      },
      "outputs": [],
      "source": [
        "# this tool-- YahooFinanceNewsTool only be used for financial test\n",
        "tools = [YahooFinanceNewsTool()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJOdQnmHN-Hm"
      },
      "source": [
        "# 2.2 wikipedia_retirevers as a import documents source"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "YZ7ONlJ6kAM9"
      },
      "outputs": [],
      "source": [
        "# because we are dealing with MMLU problem, which is discrimination evaluation, so wikipediaretriever\n",
        "wiki_retriever = WikipediaRetriever()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJ9HcIEwVdA-"
      },
      "source": [
        "# 2.3 Embedding Model, Reranker, ContextualCompressionRetriever"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbl8sJch55Dh"
      },
      "source": [
        "#####2.3.1 Reranker"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
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        },
        "id": "U2J0U4GFVbTS",
        "outputId": "cdb9cf14-3a86-4384-940b-45c3d6b3e714"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "77ef2076e48f497a8e5b02a528649711",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading tokenizer_config.json:   0%|          | 0.00/443 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e107c4b857e040c3b4c726d8d4e6e0d9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3801ac81f5ad42c78d095965b1201bbd",
              "version_major": 2,
              "version_minor": 0
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              "Downloading tokenizer.json:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0f6e349870354da7985f54e9b16f6f68",
              "version_major": 2,
              "version_minor": 0
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "5f0af8ecfc01491f843e60f874b7580e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/801 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d55e90525b0244b0a466788090d32168",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/2.24G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-1.529296875\n",
            "[-5.60546875, 5.76171875]\n"
          ]
        }
      ],
      "source": [
        "reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16= True) # Setting use_fp16 to True speeds up computation with a slight performance degradation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "am4yc93P59Mi"
      },
      "source": [
        "##### 2.3.2 Embedding Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433,
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        },
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          },
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        {
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          },
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        },
        {
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              "model_id": "dc0844a207fa45098174762761163583",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from langchain.embeddings import HuggingFaceBgeEmbeddings\n",
        "model_name = \"BAAI/bge-large-en-v1.5\"\n",
        "model_kwargs = {'device': 'cuda'}\n",
        "encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity\n",
        "embed_model = HuggingFaceBgeEmbeddings(\n",
        "    model_name=model_name,\n",
        "    model_kwargs=model_kwargs,\n",
        "    encode_kwargs=encode_kwargs,\n",
        "    query_instruction=\"Generate a representation for this sentence to retrieve relevant articles: \"\n",
        ")\n",
        "embed_model.query_instruction = \"Generate a representation for this sentence to retrieve relevant articles: \""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-_UDrmxtrwR9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "DhNu4SL0rxhT"
      },
      "outputs": [],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.document_loaders import TextLoader\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.retrievers.document_compressors import EmbeddingsFilter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9oB96mqLStIG"
      },
      "source": [
        "# 2.5 Build up the RAG part"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "OyFsL_5aSsau"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# this module useful and helpful to track the latest and every day's news and information\n",
        "def RAG(answer_context, embed_model, reranker):\n",
        "\n",
        "    \"\"\"\n",
        "    input: str, query to ask question\n",
        "    output: str, a bunch of documents as the background context\n",
        "    \"\"\"\n",
        "\n",
        "    # query for retirever\n",
        "    messages = answer_context\n",
        "    query = messages[0]['content']\n",
        "\n",
        "    # raw documents from search_engine as the source external knowledge\n",
        "    raw_documents = wiki_retriever.get_relevant_documents(query = query) # this line is able to become any search engine to import necessary updated external knowledge\n",
        "    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 100)\n",
        "    texts = text_splitter.split_documents(raw_documents)\n",
        "    base_retriever = FAISS.from_documents(texts, embed_model).as_retriever()\n",
        "\n",
        "    # Contextual_compression_retriever_EmbeddingsFilter for contextual compressed documents\n",
        "    embeddings = embed_model\n",
        "    embeddings_filter = EmbeddingsFilter(embeddings= embeddings, similarity_threshold = 0.6)\n",
        "    compression_retriever = ContextualCompressionRetriever(base_compressor = embeddings_filter, base_retriever = base_retriever)\n",
        "    compressed_docs = compression_retriever.get_relevant_documents(query)\n",
        "\n",
        "    document_scores = {}\n",
        "\n",
        "    # Reranker module\n",
        "    for doc in compressed_docs:\n",
        "        score = reranker.compute_score([query, doc.page_content])\n",
        "        document_scores[doc.page_content] = score\n",
        "\n",
        "    reranked_docs  = reciprocal_rank_fusion(document_scores) # implement the actual rerank part\n",
        "\n",
        "    documents = \"\\n\\n\".join([f\"Document {i}: {doc}\" for i, doc in enumerate(reranked_docs)] )\n",
        "\n",
        "\n",
        "    return documents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTRv0XqmSs-5"
      },
      "source": [
        "# RAG TEST"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "9g7QLNtofnGl"
      },
      "outputs": [],
      "source": [
        "query_2 = [{\n",
        "            'role': 'user',\n",
        "            'content': \"<s> can you tell me who is kobe bryant? </s>\"\n",
        "}]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "-vEpTSr-SLrv"
      },
      "outputs": [],
      "source": [
        "tem = RAG(query_2, embed_model, reranker)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "vnxYGJu3SLyd",
        "outputId": "f0ea98cb-dba2-44b5-afa5-2d07dbb1ca18"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"Document 0: === 1997–1998 ===\\nBryant was assigned by the Lakers to play in the 1997 NBA Summer League to improve as a team player and learn where to send the ball when he drew double teams. The following season, he was voted as a starter in the 1998 All-Star Game though he was a reserve on the Lakers. The team struggled after the All-Star break, losing seven of their first twelve games, and Bryant had a stretch where he made only 30 of 100 shots. O'Neal wanted a championship immediately, and he did not want to wait for Bryant to mature as a player. Harris thought the NBA and its television broadcaster, NBC, were overexposing Bryant and that he became more of a one-on-one player after the break. Bryant's playing time became reduced. The Lakers were eliminated in the 1998 playoffs in the conference finals after they were swept by the Jazz, 4–0.\""
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oj7KDBeLFb27"
      },
      "source": [
        "# Generation on MMLU data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "g2eBMgpWSKmO"
      },
      "outputs": [],
      "source": [
        "from bert_score import score as bs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXf8mck_VjJ4",
        "outputId": "924c2f46-2571-417a-d40d-0dd38e412484"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precisions: tensor([0.9614])\n",
            "Recalls: tensor([0.9690])\n",
            "F1 scores: tensor([0.9652])\n"
          ]
        }
      ],
      "source": [
        "candidates = [\"This is a test sentence for BERTscore.\"]\n",
        "\n",
        "# Your reference sentence (e.g., a human translation)\n",
        "references = [\"This sentence is a test for BERTscore.\"]\n",
        "\n",
        "# Calculating the BERTscore\n",
        "P, R, F1 = bs(candidates, references, lang='en')\n",
        "\n",
        "# P, R, and F1 are lists of scores corresponding to Precision, Recall, and F1 score.\n",
        "print(f\"Precisions: {P}\")\n",
        "print(f\"Recalls: {R}\")\n",
        "print(f\"F1 scores: {F1}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_96QzJ1PkQ2",
        "outputId": "33ce873c-6f5a-4452-c30c-d81ed2f3b3f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Question 12-06543679 - You're looking through an old set...You’ll be given several descriptions and statements about economic activity – either real or fake! Choose from one fo…\n",
            "The best answers show that they know what all those terms mean and have studied this material well enough so one can be confident he knows something about Economics or will never forget it even if doesn't seem necessary now - which means doing really good work here :) This isn’s just any old test either because most companies paying their own employees would expect people like us who write exams under these circumstances every day…but there aren”t too many places left where someone else could get some nice things without feeling guilty after having done nothing meaningful themselves! It takes time & effort--or maybe not...you need patience instead 🙂 But then again sometimes we do learn from failure right away ;-p So yes please go ahead try out my services by clicking on ____________, I promise no regrets afterwards!!! If anything goes wrong though don\"™ want hear me say \"¯\\__/\".....this makes sense until next month when everyone returns home tired with backpack full contents......well worth getting started ;)Exam Prep – Practice Question Solver Exercise #4This exercise deals mainly only questions that require knowledge gained through studying materials related topic before answering them correctly since otherwise result may look bad but still helpful anyway : ) Remember rule number three = ?___ _____ ?_____ ?? = 1+2=3 etceter\n",
            " Question 12-06543679 - You're looking through an old set...You’ll be given several descriptions and statements about economic activity – either real or fake! Choose from one fo…<s> These are the solutions to the problem from other agents:One agent solution: ```The best answers show that they know what all those terms mean and have studied this material well enough so one can be confident he knows something about Economics or will never forget it even if doesn't seem necessary now - which means doing really good work here :) This isn’s just any old test either because most companies paying their own employees would expect people like us who write exams under these circumstances every day…but there aren”t too many places left where someone else could get some nice things without feeling guilty after having done nothing meaningful themselves! It takes time & effort--or maybe not...you need patience instead 🙂 But then again sometimes we do learn from failure right away ;-p So yes please go ahead try out my services by clicking on ____________, I promise no regrets afterwards!!! If anything goes wrong though don\"™ want hear me say \"¯\\__/\".....this makes sense until next month when everyone returns home tired with backpack full contents......well worth getting started ;)Exam Prep – Practice Question Solver Exercise #4This exercise deals mainly only questions that require knowledge gained through studying materials related topic before answering them correctly since otherwise result may look bad but still helpful anyway : ) Remember rule number three = ?___ _____ ?_____ ?? = 1+2=3 etceter``` Using the reasoning from other agents as additional advice, can you give an updated answer? Examine your solution and that other agents step by step. /n/n Here is the original question: .Note regarding language: Language and punctuation errors lead readers astray; consequently poor grammar might ruin arguments made over complex topics such trying understand economical systems better than another country does:) In addition incorrect use words often create confusion rather clear sentences while misuse capitalization letters causes problems interpreting texts written English style also known “ELL” spoken dialect taught schools world wide today due high percentages students whose native tongue includes letter usage different rules followed standardized writing languages employed countries everywhere . /n There has been discussion whether using British spelling versus American variation word was appropriate choice based upon academic standards used collegiate campuses throughout America Canada Europe Japan China Australia New Zealand India Indonesia South Africa United Arab Emirates Middle East Niger Republic Ethiopa Kenya Tanzania Uganda Zimbabwe Malawi Mozambique Mauritius Egypt Sudan Libyan Demokratik Republik Yemen Saudiarabaid Sultanate\n",
            " The best answers show that they know what all those terms mean and have studied this material well enough so one can be confident he knows something about Economics or will never forget it even if doesn't seem necessary now - which means doing really good work here :) This isn’s just any old test either because most companies paying their own employees would expect people like us who write exams under these circumstances every day…but there aren”t too many places left where someone else could get some nice things without feeling guilty after having done nothing meaningful themselves! It takes time & effort--or maybe not...you need patience instead 🙂 But then again sometimes we do learn from failure right away ;-p So yes please go ahead try out my services by clicking on ____________, I promise no regrets afterwards!!! If anything goes wrong though don\"™ want hear me say \"¯\\__/\".....this makes sense until next month when everyone returns home tired with backpack full contents......well worth getting started ;)Exam Prep – Practice Question Solver Exercise #4This exercise deals mainly only questions that require knowledge gained through studying materials related topic before answering them correctly since otherwise result may look bad but still helpful anyway : ) Remember rule number three = ?___ _____ ?_____ ?? = 1+2=3 etceter<s> These are the solutions to the problem from other agents:One agent solution: ```Question 12-06543679 - You're looking through an old set...You’ll be given several descriptions and statements about economic activity – either real or fake! Choose from one fo…``` Using the reasoning from other agents as additional advice, can you give an updated answer? Examine your solution and that other agents step by step. /n/n Here is the original question: .<u>#</u><b>/solution//?keywords=[Amonopolyyou%E8rLlookingthroughana)?q=%2Finst&amp;is=&lt;&gt;</br>&nbsp;Answered By Anonymous\n",
            "I'm guessing it would have something do with cross product between vector representation or using matrix... Is this correct ? I can never tell when a hint is required :D Help !! = )\n",
            ":'s are all wrong..I just used a calculator on these two...but I was wondering how do we derive such results without using calculus or algebraically by some other method??Thanks!! :)\n",
            " I'm guessing it would have something do with cross product between vector representation or using matrix... Is this correct ? I can never tell when a hint is required :D Help !! = )<s> These are the solutions to the problem from other agents:One agent solution: ```:'s are all wrong..I just used a calculator on these two...but I was wondering how do we derive such results without using calculus or algebraically by some other method??Thanks!! :)``` Using the reasoning from other agents as additional advice, can you give an updated answer? Examine your solution and that other agents step by step. /n/n Here is the original question: .`* _ *_`*_ *_ *__*_*_ **_** __**_\\*** **\\**** \\\\/// ***\\\\\\/__// ________/ * \\*\\*/**/**\\\\ **/*________ ///////\\////////*.________________.__._/************************|/ /\\ /// |\\// +-----+----------+----+----o-----oo---*--*++-|----O------|-------------\\------++-------+--------+\\-.=============./^^^@@@^-^\\ ^.^*..../.\\.............\\.....`.'````''\"\"\"..'.\"'\".'.`*.'\".'\",`.'\\ ..`''''.\\\"\"'-.\\.\\..\\'.,_.;,.;\"',\\\",,\\\";,,_,.-.`';.`.`\\,.*..*.\\.;\\. ... .......\" ,,-~.................. ~-,...................,...~,' \"~`-*.-.,.-~..;::.!.... ;:.........:\"*`..............-*,.*.:... .:-~,--,\"`,/-~~-\n",
            " :'s are all wrong..I just used a calculator on these two...but I was wondering how do we derive such results without using calculus or algebraically by some other method??Thanks!! :)<s> These are the solutions to the problem from other agents:One agent solution: ```I'm guessing it would have something do with cross product between vector representation or using matrix... Is this correct ? I can never tell when a hint is required :D Help !! = )``` Using the reasoning from other agents as additional advice, can you give an updated answer? Examine your solution and that other agents step by step. /n/n Here is the original question: .*NOTE*: You must provide feedback for every individual participant! For instance if there were eight participants then include notes below each one explaining what they did correctly AND incorrect.* NOTES TO SELF FOR FUTURE PROBLEMS** 89747502792</>\n",
            "A1: Yes or No 20%\n",
            " 1276943Greetings everyone! For my next assignment I have to determine which virus out four can be correctly paired with a correct mode or methodology employed: Viral Entry via Receptor-Mediated Endocytosis: Coronavirus Ebola Zika Parainfluenza Herpes Viridae Paramyxoviridae Picornvir...\n",
            " A1: Yes or No 20%<s> These are the solutions to the problem from other agents:One agent solution: ``` 1276943Greetings everyone! For my next assignment I have to determine which virus out four can be correctly paired with a correct mode or methodology employed: Viral Entry via Receptor-Mediated Endocytosis: Coronavirus Ebola Zika Parainfluenza Herpes Viridae Paramyxoviridae Picornvir...``` Using the reasoning from other agents as additional advice, can you give an updated answer? Examine your solution and that other agents step by step. /n/n Here is the original question: . </u> <i class=\"fa fa-comments\"></i></p>````}}},} |text=Dear Agents we need all help since this task seems harder than it should due to its simple logic behind it but let us look on what other think first., so go ahead and find their answers after them if not like me just guess because i do feel confident about myself today ,} }}`, title=`Answers &#xa; Instinctively Code Contemst 🎉`, metaTags:[, {\"content\"=>{\"@context\"> https//www instagr am\\/photoshare/videos https www facebook com profile page fb id //www instagram com brooke lewis page like brookie instagram posts photos videos bio follower analytics insights eng\n",
            "\"\"\"solving the puzzle using basic python\"\"\" def solvePuzzles:global nRNASequences ## function name defined here only ; this is another module ##not importing so it becomes independent c=]*ctpBOT\n",
            "\n",
            "1890: How did William Seward change New York state politics with his alliance building tactics throughout a political career spanning four decades?: ,,,,,.. ]The following is an excerpt from Michael Burlingame's The Inner World Of Abraham Lincoln: What specific events or activities happened on these dates during 27th through April.: Students can use any books including online books such as \"Abraham lincoln : portrait\",etc...and Wikipedia....or else they may be provided additional research material . Students need to find answers by using available materials not outside sources unless it was required , or if instructors provide some special rules then students must follow those .. 160+ words essay about corruption according social issues like education pover\n",
            " <s> These are the solutions to the problem from other agents:One agent solution: ```1890: How did William Seward change New York state politics with his alliance building tactics throughout a political career spanning four decades?: ,,,,,.. ]The following is an excerpt from Michael Burlingame's The Inner World Of Abraham Lincoln: What specific events or activities happened on these dates during 27th through April.: Students can use any books including online books such as \"Abraham lincoln : portrait\",etc...and Wikipedia....or else they may be provided additional research material . Students need to find answers by using available materials not outside sources unless it was required , or if instructors provide some special rules then students must follow those .. 160+ words essay about corruption according social issues like education pover``` Using the reasoning from other agents as additional advice, can you give an updated answer? Examine your solution and that other agents step by step. /n/n Here is the original question: .``.`>```</s> <a href=\"https://www.redditstorygamebookwiki.com/\" rel =\"nofollow\"> https//reddittorybhqiw wiki </></p><form id='comment_page'_no javascript > comment textarea name=comment textarea autocapitalize offautofocus falseautocompleteoff dirltr maxlength =35kminwidth=4x> (<https//twitter.com/></p><div class 'userlink userimg'> <a data href '/r/#r#/u/#i-00zNYmLQEtDV2W0yHJgjAoGqBvKZSZPbTUQF5Q=='> r\\/Inequality </title/> </td> </table>' ></li>&nbsp;&nbsp;</ul>\" target =\"blank\"\" title=\"\" &gt;\" width=\"1%\"></\n",
            "console && console ? window.push :- document instanceof XMLHttpRequest;window', '').slice.split;}}}.open('/api/v4/?action='+\"&title:\"+'poster'+\":\"+'answer'+':'+\"question\":\"'+strTitle\n",
            "\n",
            "\n",
            " <s> These are the solutions to the problem from other agents:One agent solution: `````` Using the reasoning from other agents as additional advice, can you give an updated answer? Examine your solution and that other agents step by step. /n/n Here is the original question: .</>}### Question answered successfully!</p></div><script src=\"https://www.mathjaxjsx2cdnhostingerappab3cdwz5fggg.cloudfrontconsole.net//MathJax.min_9c3ebd0a_.js\"></script>>@endsection <style>.question}/* */body::after , body ::before #answerLabel,.solutionText label#footertabletd th p olli\n",
            "}}@if)) <?php // print_r; ?> {{ Form :: open(['route' => ['agents','\n"
          ]
        }
      ],
      "source": [
        "agents = 2\n",
        "rounds = 2\n",
        "\n",
        "tasks = glob(\"/content/drive/MyDrive/Hallucination/Parse_Data/data/test/*.csv\")\n",
        "\n",
        "dfs = [pd.read_csv(task) for task in tasks]\n",
        "\n",
        "random.seed(123)\n",
        "response_dict = {}\n",
        "\n",
        "for i in range(100):\n",
        "    df = random.choice(dfs)\n",
        "    ix = len(df)\n",
        "    idx = random.randint(0, ix-1)\n",
        "\n",
        "    question, answer = parse_question_answer(df, idx)\n",
        "\n",
        "    agent_contexts = [[{\"role\": \"user\", \"content\": question}] for agent in range(agents)]\n",
        "\n",
        "    # initilized debate rounds\n",
        "    for round in range(rounds):\n",
        "        for i, agent_context in enumerate(agent_contexts):\n",
        "\n",
        "            if round != 0:\n",
        "                agent_contexts_other = agent_contexts[:i] + agent_contexts[i+1:]\n",
        "                message = construct_message(agent_contexts_other, question, 2 * round - 1)\n",
        "                agent_context.append(message)\n",
        "\n",
        "            completion = generate_answer(agent_context)\n",
        "\n",
        "            assistant_message = construct_assistant_message(completion)\n",
        "            agent_context.append(assistant_message)\n",
        "            print(completion)\n",
        "\n",
        "\n",
        "    # BERTscore determine whether consistent\n",
        "\n",
        "    main_expert_sentence = [agent_contexts[0][-1]['content']]\n",
        "    auxiliary_expert_sentence = [agent_contexts[1][-1]['content']]\n",
        "\n",
        "    P, R, F1 = bs(auxiliary_expert_sentence, main_expert_sentence)\n",
        "\n",
        "    if F1 <= 0.8:\n",
        "        #if not consistent, RAG part provides relevant documents for more information\n",
        "\n",
        "        background_context = RAG(agent_contexts, embed_model, reranker)\n",
        "\n",
        "        new_template = \"{ } /n/n these are extra CONTEXT DOCUMENTS provided to help you reason question below: /n { }\".format(background_context,question)\n",
        "\n",
        "        new_chat_template = [{\n",
        "            'role' : \"user\",\n",
        "            'content' : new_template,\n",
        "        }]\n",
        "\n",
        "        completion_rag = generate_answer(new_chat_template)\n",
        "\n",
        "        assistant_message_rag = construct_assistant_message(completion_rag)\n",
        "\n",
        "        agent_contexts[0][-1] = assistant_message_rag\n",
        "        agent_contexts[1][-1] = assistant_message_rag\n",
        "\n",
        "        response_dict[question] = (agent_contexts, answer)\n",
        "\n",
        "\n",
        "    else:\n",
        "        response_dict[question] = (agent_contexts, answer)\n",
        "\n",
        "\n",
        "json.dump(response_dict, open(\"mmlu_{}_{}.json\".format(agents, rounds), \"w\"))\n",
        "\n",
        "#wandb.finish()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Iql5g82viriN",
        "outputId": "7ce48ffe-382d-455e-8a45-6a3f28e6804f"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_681c0629-ac61-4d68-91d3-7b518e4d442b\", \"mmlu_2_2.json\", 44806)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download(file_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KTVZvw2mH08"
      },
      "source": [
        "# Evaluation on MMLU test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "0UQDLJRzX-wM"
      },
      "outputs": [],
      "source": [
        "\n",
        "def parse_bullets(sentence):\n",
        "    bullets_preprocess = sentence.split(\"\\n\")\n",
        "    bullets = []\n",
        "\n",
        "    for bullet in bullets_preprocess:\n",
        "        try:\n",
        "            idx = bullet.find(next(filter(str.isalpha, bullet)))\n",
        "        except:\n",
        "            continue\n",
        "\n",
        "        bullet = bullet[idx:]\n",
        "\n",
        "        if len(bullet) != 0:\n",
        "            bullets.append(bullet)\n",
        "\n",
        "    return bullets\n",
        "\n",
        "\n",
        "def parse_yes_no(string):\n",
        "    \"\"\"\n",
        "    Parses a string containing \"yes\" or \"no\" and returns a boolean value.\n",
        "\n",
        "    Args:\n",
        "        string (str): The string to parse.\n",
        "\n",
        "    Returns:\n",
        "        bool: True if the string contains \"yes\", False if the string contains \"no\".\n",
        "\n",
        "    Raises:\n",
        "        ValueError: If the input string does not contain \"yes\" or \"no\".\n",
        "    \"\"\"\n",
        "    if \"yes\" in string.lower():\n",
        "        return True\n",
        "    elif \"no\" in string.lower():\n",
        "        return False\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "\n",
        "def solve_math_problems(input_str):\n",
        "    pattern = r\"\\d+\\.?\\d*\"\n",
        "\n",
        "    matches = re.findall(pattern, input_str)\n",
        "    if matches:\n",
        "        return matches[-1]\n",
        "\n",
        "    return None\n",
        "\n",
        "def parse_answer(input_str):\n",
        "    pattern = r'\\((\\w)\\)'\n",
        "    matches = re.findall(pattern, input_str)\n",
        "\n",
        "    solution = None\n",
        "    # print(\"predicted solution\")\n",
        "    # print(input_str)\n",
        "    # print(\"matches\")\n",
        "    # print(matches)\n",
        "\n",
        "    for match_str in matches[::-1]:\n",
        "        solution = match_str.upper()\n",
        "        if solution:\n",
        "            break\n",
        "\n",
        "    return solution\n",
        "\n",
        "\n",
        "def compute_accuracy(gt, pred_solutions):\n",
        "    if type(pred_solutions) == list:\n",
        "        pred_answers = []\n",
        "\n",
        "        for pred_solution in pred_solutions:\n",
        "            pred_answer = parse_answer(pred_solution)\n",
        "\n",
        "            if pred_answer is None:\n",
        "                pred_answer = solve_math_problems(pred_solution)\n",
        "\n",
        "            if pred_answer is not None:\n",
        "                pred_answers.append(pred_answer)\n",
        "\n",
        "        if pred_answer is None:\n",
        "            return 0\n",
        "        pred_answer = most_frequent(pred_answers)\n",
        "        # pred_answer = pred_answers[0]\n",
        "    else:\n",
        "        pred_answer = parse_answer(pred_solutions)\n",
        "        if pred_answer is None:\n",
        "            pred_answer = solve_math_problems(pred_solutions)\n",
        "\n",
        "    if gt == pred_answer:\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "\n",
        "def most_frequent(List):\n",
        "    counter = 0\n",
        "    num = List[0]\n",
        "\n",
        "    for i in List:\n",
        "        current_frequency = List.count(i)\n",
        "        if current_frequency > counter:\n",
        "            counter = current_frequency\n",
        "            num = i\n",
        "\n",
        "    return num\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCf0N3BgYro_",
        "outputId": "d4dbfaa3-64f5-4bab-b045-043e8aea5a25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "accuracies: 0.0 0.0\n",
            "accuracies: 0.0 0.0\n",
            "accuracies: 0.0 0.0\n",
            "accuracies: 0.0 0.0\n",
            "accuracies: 0.0 0.0\n"
          ]
        }
      ],
      "source": [
        "response_dict = json.load(open(\"/content/drive/MyDrive/Hallucination/Generated_data/mmlu/mmlu_2_2.json\", \"r\"))\n",
        "questions = list(response_dict.keys())\n",
        "\n",
        "accuracies = []\n",
        "\n",
        "for question in questions:\n",
        "    responses, gt = response_dict[question]\n",
        "\n",
        "    pred_solutions = []\n",
        "    for response in responses:\n",
        "        pred_solution = response[-1]['content']\n",
        "\n",
        "        pred_solutions.append(pred_solution)\n",
        "        # break\n",
        "\n",
        "    # pred_solutions = pred_solutions[:1]\n",
        "\n",
        "    accurate = compute_accuracy(gt, pred_solutions)\n",
        "\n",
        "\n",
        "    if accurate is not None:\n",
        "        accuracies.append(float(accurate))\n",
        "    else:\n",
        "        import pdb\n",
        "        pdb.set_trace()\n",
        "        print(gt)\n",
        "\n",
        "    print(\"accuracies:\", np.mean(accuracies), np.std(accuracies) / (len(accuracies) ** 0.5))"
      ]
    }
  ],
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    "accelerator": "GPU",
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      "gpuType": "V100",
      "machine_shape": "hm",
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