RAG_chunks_eval_comp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RAGAs Implementation\n",
    "\n",
    "This notebook documents the RAG pipeline using various chunk sizing methods and evaluates these chunks using RAGAs evaluation metrics. \n",
    "\n",
    "### Hallucination\n",
    "RAG (Retrieval Augmented Generation) is an attempt to alleviate LLM hallucinations. LLM hallucination is the concept that LLMs generate wrong answers with the confidence to make the user believe it's true. This has been a major problem since the introduction of the LLMs. These hallucinations lead to incorrect and factually wrong answers. \n",
    "\n",
    "### The RAG Process\n",
    "RAG segments a document text into chunks; based on the segmenter implemented. Then, it creates those chunks as embeddings (numerical representations) and stores them in a vector database. It then retrieves these embeddings to augment an LLM response. By doing this, the LLM uses the chunks as contextual support to generate an answer. In this project we tested the semantic chunker using a sample of 1000 documents of the MSMarco documents dataset. \n",
    "\n",
    "### Text segmentattion (Chunking)\n",
    "**Semantic**\\\n",
    "While considering the semantic relationships, semantic chunking segments a text into meaningful and semantically complete chunks. It does this by storing these chunks together in vector embeddings. Here, it analyzes and groups consecutive sentences within a specific window size while making adjustments along the way. It starts with the initial sentence then compares its embeddings to the subsequent sentences, and iterates through the text until a new semantic block is detected, creating a breakpoint.\n",
    "\n",
    "**Recursive**\\\n",
    "Recursive chunking divides the input text into smaller chunks in a hierarchical and iterative manner based on an inputted set of separators. If the first separator doesn't results in the expected chunk size or structure, the method will recursively be implemented with the next separator to split the text until it outputs the correct chunk size or structure asked. Here, chunks won't be exactly the same size, but they will be close because they leverage fixed chunk sizes and overlap processes.\n",
    "\n",
    "\n",
    "Paragraph\n",
    "\n",
    "### Evaluation\n",
    "The evaluation is primarily quantitative, using RAGAs metrics for scoring our RAG answers. These metrics are scored on a merged dataset of the newly created chunks, MSMarco documents and a sample of 1000 documents of the MSMarco QA dataset. These metrics rely on answers, questions, contexts, and ground truth. Here, questions come from the QA dataset to be used with the RAG model; context comes from the various chunk implementations; answers come from a RAG pipeline that uses the different chunks; and ground truth in this case is pulled from the body of the sampled MSMarco documents to make sure the RAG pipeline gets that information.\n",
    "\n",
    "The evaluation metrics come from the RAGAs evaluator and can be split into the various parts of the RAG pipeline.\n",
    "\n",
    "##### Chunking Metrics\n",
    "- Context_Relevancy: Gauges how relevant the retrieved context is to the question. \n",
    "- Context entities recall: Looks at the probability of both context entities and ground truth entities are found in both, over the total number of ground truth entities.\n",
    "\n",
    "##### Embedding Metrics\n",
    "- Answer Correctness: Accuracy of generated answer compared to ground truth. \n",
    "- Answer semantic similarity: Assesses the semantic resemblance between the generated answer and the ground truth, I.e. alignment between the generated answer and ground truth. \n",
    "\n",
    "##### Overall Metrics\n",
    "- Faithfulness: Measures the factual consistency of the generated answer against the given context (information found in the document). \n",
    "- Answer Relevance: Measures how pertinent the generated answer is to the given prompt.  Low score for incomplete answers or those that contain redundant information, high score for better relevancy.\n",
    "- Context Recall: Measures how well the retrieved context aligns with the annotated answer (the ground truth answer). \n",
    "    - each sentence in the ground truth answer is analyzed to determine whether it can be attributed to the retrieved context or not. In an ideal scenario, all sentences in the ground truth answer should be attributable to the retrieved context.\n",
    "\n",
    "Below is the guided implementation of this pipeline.\n",
    "\n",
    "\n",
    "--\n",
    "Resources\n",
    "[Semantic Chunking for RAG](https://medium.com/the-ai-forum/semantic-chunking-for-rag-f4733025d5f5)\n",
    "[Enhancing RAG Efficiency: The Power of Semantic Chunking](https://medium.com/@sthanikamsanthosh1994/enhancing-rag-efficiency-the-power-of-semantic-chunking-844f9cfbdd0b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in the json data from MSMarco\n",
    "#data format for MSMArco docs: https://ir-datasets.com/msmarco-document-v2.html\n",
    "#data format information for MSMarco QA: https://microsoft.github.io/MSMARCO-Question-Answering/\n",
    "# Tutorial used: https://medium.com/the-ai-forum/semantic-chunking-for-rag-f4733025d5f5\n",
    "\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "# from langchain_community.embeddings.fastembed import FastEmbedEmbeddings\n",
    "from langchain_experimental.text_splitter import SemanticChunker\n",
    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
    "from langchain.chains import create_retrieval_chain\n",
    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
    "from langchain_community.vectorstores import Chroma\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain.vectorstores import FAISS\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from datasets import load_dataset, Dataset, Features, Sequence, Value\n",
    "\n",
    "from openai import OpenAI\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "# import re\n",
    "import io\n",
    "import os\n",
    "import gzip\n",
    "import json\n",
    "import openai\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# from random import sample \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  is_selected                                       passage_text  \\\n",
      "0           0            0  the cost of attending new york university is c...   \n",
      "1           1            0  DeWitt, MI 48820 Phone: (517) 669-6578 Fax: (5...   \n",
      "2           2            0  DeWitt, MI 48820 Phone: (517) 669-6576 Fax: (5...   \n",
      "3           3            0  5. If you want to create a new email address f...   \n",
      "4           4            0  4. If you want to use an existing MSN or Hotma...   \n",
      "\n",
      "                                                 url     key  \\\n",
      "0              http://cusp.nyu.edu/tuition-and-fees/  100000   \n",
      "1         http://dewitttownship.org/OurContacts.aspx  100011   \n",
      "2         http://dewitttownship.org/OurContacts.aspx  100011   \n",
      "3  http://answers.msn.com/solution.aspx?solutioni...  100012   \n",
      "4  http://answers.msn.com/solution.aspx?solutioni...  100012   \n",
      "\n",
      "                             question  query_id   query_type  \\\n",
      "0                    nyu tuition cost    467556      NUMERIC   \n",
      "1  what county is dewitt michigan in?    605123     LOCATION   \n",
      "2  what county is dewitt michigan in?    605123     LOCATION   \n",
      "3     how to create msn email address    353608  DESCRIPTION   \n",
      "4     how to create msn email address    353608  DESCRIPTION   \n",
      "\n",
      "                                              answer  \\\n",
      "0       ['$43,746 for the 2014-2015 academic year.']   \n",
      "1  ['DeWitt is located in Clinton County, Michiga...   \n",
      "2  ['DeWitt is located in Clinton County, Michiga...   \n",
      "3                             ['No Answer Present.']   \n",
      "4                             ['No Answer Present.']   \n",
      "\n",
      "                                            contexts  \\\n",
      "0  the cost of attending new york university is c...   \n",
      "1  DeWitt, MI 48820 Phone: (517) 669-6578 Fax: (5...   \n",
      "2  DeWitt, MI 48820 Phone: (517) 669-6576 Fax: (5...   \n",
      "3  5. If you want to create a new email address f...   \n",
      "4  4. If you want to use an existing MSN or Hotma...   \n",
      "\n",
      "                                               title  \\\n",
      "0  Tuition & Financial Information | NYU Center f...   \n",
      "1                                       Our Contacts   \n",
      "2                                       Our Contacts   \n",
      "3        How do I add a secondary account to my MSN?   \n",
      "4        How do I add a secondary account to my MSN?   \n",
      "\n",
      "                                            headings  \\\n",
      "0  Admitted Students\\nTuition & Financial Informa...   \n",
      "1                                                 \\n   \n",
      "2                                                 \\n   \n",
      "3      \\nHow do I add a secondary account to my MSN?   \n",
      "4      \\nHow do I add a secondary account to my MSN?   \n",
      "\n",
      "                                        ground_truth  \\\n",
      "0  Tuition & Financial Information | NYU Center f...   \n",
      "1  Our Contacts\\n\\n\\n\\n\\n\\n\\nHome\\nSite Map\\nFeed...   \n",
      "2  Our Contacts\\n\\n\\n\\n\\n\\n\\nHome\\nSite Map\\nFeed...   \n",
      "3  How do I add a secondary account to my MSN?\\n\\...   \n",
      "4  How do I add a secondary account to my MSN?\\n\\...   \n",
      "\n",
      "                            docid  \n",
      "0  msmarco_v2.1_doc_00_1274300683  \n",
      "1  msmarco_v2.1_doc_00_1371312479  \n",
      "2  msmarco_v2.1_doc_00_1371312479  \n",
      "3   msmarco_v2.1_doc_00_341905946  \n",
      "4   msmarco_v2.1_doc_00_341905946  \n",
      "313119\n"
     ]
    }
   ],
   "source": [
    "# Read in the data and review it\n",
    "# df = pd.read_csv('MSMarco_QA_Docs.csv')\n",
    "df = pd.read_csv('MSMarco_QA_Docs_sample.csv')\n",
    "print(df.head())\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    Tuition & Financial Information | NYU Center f...\n",
      "1    Our Contacts\\n\\n\\n\\n\\n\\n\\nHome\\nSite Map\\nFeed...\n",
      "Name: ground_truth, dtype: object\n",
      "313119\n"
     ]
    }
   ],
   "source": [
    "# pull out just the body of the document\n",
    "docs = df['ground_truth']\n",
    "print(docs[0])\n",
    "print(len(docs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set-up for RAG Chunking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test the semantic, naive (Recursive), and paragraph chunking splitter from the experimental package. This package uses OpenAI so be sure to have a key ready.\n",
    "\n",
    "[Semantic Chunking Documentation](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/semantic-chunker/)\\\n",
    "[Recursive](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/recursive_text_splitter/)\\\n",
    "[Paragraph Chunking](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/character_text_splitter/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Implement Open AI credemtals and set up for embedding \n",
    "# connect to AI using your credentials\n",
    "exec(open('./chunk_embeddings/configs.py').read())\n",
    "## here you create file with LAS token key\n",
    "\n",
    "#connect to the gptmodel\n",
    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
    "# the tutorial uses fast embeddings, but it's not very fast\n",
    "# embed_model = FastEmbedEmbeddings(model_name=\"BAAI/bge-base-en-v1.5\")\n",
    "embed_model = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## #implement basline segmenter\n",
    "#using the naive/standard recursive text splitter for testing\n",
    "# as well as a basic paragraph splitter\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=1000,\n",
    "    chunk_overlap=10,\n",
    "    length_function=len,\n",
    "    is_separator_regex=False\n",
    ")\n",
    "\n",
    "\n",
    "#\n",
    "###Paragraphs -- here is a custom splitter used in the extractive summarizer\n",
    "# def chunk(text):\n",
    "#         '''Split text into paragraphs on new lines.'''\n",
    "#         return re.split(r'\\n+', text)\n",
    "\n",
    "# using this to coincide with the rest of the langchain splitters\n",
    "paragraph_splitter = CharacterTextSplitter(\n",
    "    separator=\"\\n\\n\",\n",
    "    chunk_size=1000,\n",
    "    chunk_overlap=200,\n",
    "    length_function=len,\n",
    "    is_separator_regex=False,\n",
    ")\n",
    "\n",
    "#\n",
    "## Semantic\n",
    "semantic_chunker = SemanticChunker(embed_model, breakpoint_threshold_type=\"interquartile\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implement all segmenters and save their embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "naive_chunks = (text_splitter.create_documents(docs))\n",
    "naive_chunk_vectorstore = Chroma.from_documents(naive_chunks, embedding=embed_model, persist_directory=\"./naive_chunk_vectorstore_db\")\n",
    "naive_chunk_retriever = naive_chunk_vectorstore.as_retriever(search_kwargs={\"k\" : 5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(paragraph_chunks[10:15])\n",
    "paragraph_chunks = (paragraph_splitter.create_documents(docs))\n",
    "print(\"Number of paragraph_chunks: \",len(paragraph_chunks))\n",
    "paragraph_chunks_df = pd.DataFrame(paragraph_chunks)\n",
    "# paragraph_chunks_df.to_pickle(\"paragraph_chunks.pkl\")\n",
    "paragraph_chunks_vectorstore = Chroma.from_documents(paragraph_chunks, embedding=embed_model, persist_directory=\"./paragraph_chunks_vectorstore_db\")\n",
    "#here we use k = 5 to make a \"fair\" comparison\n",
    "paragraph_chunks_retriever = paragraph_chunks_vectorstore.as_retriever(search_kwargs={\"k\" : 5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<langchain_community.vectorstores.chroma.Chroma object at 0x7bad5488dcc0>\n"
     ]
    }
   ],
   "source": [
    "# Implement semantic chunker\n",
    "semantic_chunks = (semantic_chunker.create_documents(docs))\n",
    "#save vectors if necessary with param 'persist_dictionary'\n",
    "semantic_chunk_vectorstore = Chroma.from_documents(semantic_chunks, embedding=embed_model, persist_directory=\"./semantic_chunk_vectors_db\")\n",
    "#limit the semantic retriever to k=1 \n",
    "semantic_chunk_retriever = semantic_chunk_vectorstore.as_retriever(search_kwargs={\"k\" : 1})\n",
    "print(semantic_chunk_vectorstore)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's start running the RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the financial aid process?: \n",
      "\n",
      "I don't know.\n",
      "The financial aid process typically involves completing the FAFSA (Free Application for Federal Student Aid) form, which is a key step in applying for federal funding and private loans. It also includes considering payment plans, checking for tuition rates, and exploring scholarships, grants, and loan options to help cover the cost of attending the institution.\n",
      "Does the IRS prefer a child's parents?: \n",
      "\n",
      "The IRS does prefer a child's parents when it comes to claiming the child as a dependent for tax purposes.\n",
      "Yes, the IRS gives first preference to a child's parents when it comes to claiming the child as a dependent on income taxes.\n",
      "What are some speed monitoring technologies?:\n",
      "\n",
      "Breathalyzer and blood tests are some speed monitoring technologies mentioned in the context.\n"
     ]
    }
   ],
   "source": [
    "#set up the chatbot\n",
    "# setup the rag template to build the prompt\n",
    "rag_template = \"\"\"\\\n",
    "Use the following context to answer the user's query. If you cannot answer, please respond with 'I don't know'.\n",
    "\n",
    "User's Query:\n",
    "{question}\n",
    "\n",
    "Context:\n",
    "{context}\n",
    "\"\"\"\n",
    "\n",
    "rag_prompt = ChatPromptTemplate.from_template(rag_template)\n",
    "\n",
    "#now we can implement RAG\n",
    "semantic_rag_chain = (\n",
    "    {\"context\" : semantic_chunk_retriever, \"question\" : RunnablePassthrough()}\n",
    "    # {\"context\": lambda x: semantic_chunk_retriever, \"question\": RunnablePassthrough()}\n",
    "    | rag_prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")\n",
    "\n",
    "paragraph_rag_chain = (\n",
    "    {\"context\": lambda x: paragraph_chunks_retriever, \"question\": RunnablePassthrough()}\n",
    "    | rag_prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")\n",
    "\n",
    "naive_rag_chain = (\n",
    "    {\"context\": lambda x: naive_chunk_retriever, \"question\": RunnablePassthrough()}\n",
    "    | rag_prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "# semantic_rag_chain = create_stuff_documents_chain(llm, rag_prompt)\n",
    "# chain = create_retrieval_chain(semantic_chunk_retriever, semantic_rag_chain)\n",
    "# print(\"got the chat/retrieval connection setup\")\n",
    "\n",
    "#test with some questions\n",
    "print(\"What is the financial aid process?: \\n\")\n",
    "print(semantic_rag_chain.invoke(\"What is the financial aid process?\"))\n",
    "print(paragraph_rag_chain.invoke(\"CWhat is the financial aid process?\"))\n",
    "\n",
    "print(\"Does the IRS prefer a child's parents?: \\n\")\n",
    "print(semantic_rag_chain.invoke(\"Does the IRS prefer a child's parents?\"))\n",
    "print(paragraph_rag_chain.invoke(\"Does the IRS prefer a child's parents?\"))\n",
    "\n",
    "print(\"What are some speed monitoring technologies?:\\n\")\n",
    "print(naive_rag_chain.invoke(\"What are some speed monitoring technologies?\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement RAGAs Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "###let's implement RAGAs\n",
    "from ragas import evaluate\n",
    "from ragas.metrics import (\n",
    "    answer_relevancy,\n",
    "    faithfulness,\n",
    "    context_recall,\n",
    "    # context_precision,\n",
    "    # context_relevancy,\n",
    "    answer_correctness,\n",
    "    answer_similarity,\n",
    "    context_entity_recall\n",
    ")\n",
    "\n",
    "metrics=[\n",
    "    answer_relevancy,\n",
    "    # context_relevancy,\n",
    "    context_recall,\n",
    "    # context_precision,\n",
    "    context_entity_recall,\n",
    "    answer_similarity,\n",
    "    answer_correctness,\n",
    "    faithfulness]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get a subset of what you need to run through RAGAs\n",
    "df = df[['question','answer','contexts','ground_truth']]\n",
    "\n",
    "# Define the correct features for the dataset\n",
    "features = Features({\n",
    "    \"question\": Value(\"string\"),\n",
    "    \"answer\": Value(\"string\"),\n",
    "    \"contexts\": Sequence(Value(\"string\")),\n",
    "    \"ground_truth\": Value(\"string\")\n",
    "    # \"labels\": Value(\"int32\") #example\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthetic Data Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [00:39<00:00,  3.28s/it]\n"
     ]
    }
   ],
   "source": [
    "def create_synthetic_data(data_splitter, rag_chain, num_chunks):\n",
    "\n",
    "    #set up lists for column content\n",
    "    questions = []\n",
    "    ground_truths_chunker = []\n",
    "    contexts = []\n",
    "    answers = []\n",
    "\n",
    "    #use prompt to create question data\n",
    "    question_prompt = \"\"\"\\\n",
    "    You are a teacher preparing a test. Please create a question that can be answered by referencing the following context.\n",
    "\n",
    "    Context:\n",
    "    {context}\n",
    "    \"\"\"\n",
    "\n",
    "    question_prompt = ChatPromptTemplate.from_template(question_prompt)\n",
    "\n",
    "    # create ground truth data\n",
    "    ground_truth_prompt = \"\"\"\\\n",
    "    Use the following context and question to answer this question using *only* the provided context.\n",
    "\n",
    "    Question:\n",
    "    {question}\n",
    "\n",
    "    Context:\n",
    "    {context}\n",
    "    \"\"\"\n",
    "\n",
    "    ground_truth_prompt = ChatPromptTemplate.from_template(ground_truth_prompt)\n",
    "\n",
    "    #implement llm to create our data\n",
    "    question_chain = question_prompt | llm | StrOutputParser()\n",
    "    ground_truth_chain = ground_truth_prompt | llm | StrOutputParser()\n",
    "\n",
    "    # append the data to add to it\n",
    "    # note, for the paragraph_chunks I split the data on 12 chunks instead of 20\n",
    "    for chunk in tqdm(data_splitter[0:num_chunks]):\n",
    "        questions.append(question_chain.invoke({\"context\" : chunk.page_content}))\n",
    "        contexts.append([chunk.page_content])\n",
    "        ground_truths_chunker.append(ground_truth_chain.invoke({\"question\" : questions[-1], \"context\" : contexts[-1]}))\n",
    "        answers.append(rag_chain.invoke(questions[-1]))\n",
    "\n",
    "    qagc_list = []\n",
    "\n",
    "    for question, answer, context, ground_truth in zip(questions, answers, contexts, ground_truths_chunker):\n",
    "        qagc_list.append({\n",
    "            \"question\" : question,\n",
    "            \"answer\" : answer,\n",
    "            \"contexts\" : context,\n",
    "            \"ground_truth\" : ground_truth\n",
    "        })\n",
    "\n",
    "        eval_dataset = Dataset.from_list(qagc_list)\n",
    "        eval_dataset\n",
    "    return eval_dataset\n",
    "\n",
    "\n",
    "synthetic_semantic = create_synthetic_data(semantic_chunks, semantic_rag_chain, 20)\n",
    "synthetic_recursive = create_synthetic_data(naive_chunks, naive_rag_chain, 20)\n",
    "synthetic_paragraph = create_synthetic_data(paragraph_chunks, paragraph_rag_chain, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating:   0%|          | 0/120 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|██████████| 120/120 [01:22<00:00,  1.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'answer_relevancy': 0.6580, 'context_recall': 0.9500, 'context_entity_recall': 0.4756, 'answer_similarity': 0.9237, 'answer_correctness': 0.6982, 'faithfulness': 0.8000}\n",
      "                                            question  \\\n",
      "0  Question: What is the tuition rate for the 201...   \n",
      "1  Question: Which department is responsible for ...   \n",
      "2  Question: Which department is responsible for ...   \n",
      "3  Question: What are the steps to add a secondar...   \n",
      "4  Question: What are the steps to add a secondar...   \n",
      "\n",
      "                                              answer  \\\n",
      "0  The tuition rate for the 2017-2018 academic ye...   \n",
      "1                                      I don't know.   \n",
      "2                                 Treasurer's Office   \n",
      "3  To add a secondary account to MSN, follow thes...   \n",
      "4  To add a secondary account to MSN, follow thes...   \n",
      "\n",
      "                                            contexts  \\\n",
      "0  [Tuition & Financial Information | NYU Center ...   \n",
      "1  [Our Contacts\\n\\n\\n\\n\\n\\n\\nHome\\nSite Map\\nFee...   \n",
      "2  [Our Contacts\\n\\n\\n\\n\\n\\n\\nHome\\nSite Map\\nFee...   \n",
      "3  [How do I add a secondary account to my MSN? H...   \n",
      "4  [How do I add a secondary account to my MSN? H...   \n",
      "\n",
      "                                        ground_truth  answer_relevancy  \\\n",
      "0  $1,731.00 per unit, plus registration and serv...          0.975478   \n",
      "1  The Planning Department is responsible for han...          0.000000   \n",
      "2                                 Treasurer's Office          0.878115   \n",
      "3  The steps to add a secondary account to MSN ar...          0.957675   \n",
      "4  To add a secondary account to MSN, you need to...          0.956874   \n",
      "\n",
      "   context_recall  context_entity_recall  answer_similarity  \\\n",
      "0             1.0               0.000000           0.893309   \n",
      "1             1.0               0.142857           0.712001   \n",
      "2             1.0               1.000000           1.000000   \n",
      "3             1.0               0.857143           0.979267   \n",
      "4             1.0               0.666667           0.994425   \n",
      "\n",
      "   answer_correctness  faithfulness  \n",
      "0            0.723327           1.0  \n",
      "1            0.928000           0.0  \n",
      "2            1.000000           1.0  \n",
      "3            0.685993           1.0  \n",
      "4            0.862243           1.0  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "result = evaluate(\n",
    "    dataset = synthetic_semantic, \n",
    "    metrics= metrics,\n",
    "    llm = llm,\n",
    "    embeddings = embed_model,\n",
    "    raise_exceptions=False,\n",
    "    )\n",
    "print(result)\n",
    "df_semantic = result.to_pandas()\n",
    "df_semantic.to_pickle('./ragas_result_semantic.pkl')\n",
    "print(df_semantic.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating:  38%|███▊      | 45/120 [00:11<00:14,  5.13it/s]No statements were generated from the answer.\n",
      "Evaluating: 100%|██████████| 120/120 [00:38<00:00,  3.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'answer_relevancy': 0.7222, 'context_recall': 0.9500, 'context_entity_recall': 0.3099, 'answer_similarity': 0.8900, 'answer_correctness': 0.5889, 'faithfulness': 0.6254}\n",
      "                                            question  \\\n",
      "0  Question: Where can admitted students find inf...   \n",
      "1  Question:\\nWhat was the tuition rate per unit ...   \n",
      "2  Question: Where can students go to speak to a ...   \n",
      "3  Question:\\nHow can students access their eBill...   \n",
      "4  Question: What is the NYU federal school code ...   \n",
      "\n",
      "                                              answer  \\\n",
      "0  Admitted students can find information regardi...   \n",
      "1  The tuition rate per unit at New York Universi...   \n",
      "2  Students can go to the Credit Counselor area a...   \n",
      "3  To access their eBill notification and view th...   \n",
      "4  Answer: The NYU federal school code number tha...   \n",
      "\n",
      "                                            contexts  \\\n",
      "0  [Tuition & Financial Information | NYU Center ...   \n",
      "1  [The cost of attending New York University is ...   \n",
      "2  [If you would like to speak to a Bursar’s offi...   \n",
      "3  [Each time a bill is generated, students are n...   \n",
      "4  [Federal Funding and Private Loans\\nDomestics ...   \n",
      "\n",
      "                                        ground_truth  answer_relevancy  \\\n",
      "0  Admitted students can find information regardi...          0.982988   \n",
      "1                                          $1,731.00          0.977086   \n",
      "2  Students can go to the Credit Counselor area a...          0.981706   \n",
      "3  To access their eBill notification and view th...          0.982544   \n",
      "4                                             002785          0.963843   \n",
      "\n",
      "   context_recall  context_entity_recall  answer_similarity  \\\n",
      "0             1.0                    0.5           0.987953   \n",
      "1             1.0                    1.0           0.818871   \n",
      "2             1.0                    0.5           0.984769   \n",
      "3             1.0                    0.5           0.994783   \n",
      "4             1.0                    0.0           0.801194   \n",
      "\n",
      "   answer_correctness  faithfulness  \n",
      "0            0.675560          0.25  \n",
      "1            0.954273          1.00  \n",
      "2            0.496190          1.00  \n",
      "3            0.811203          1.00  \n",
      "4            0.700299          1.00  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "result = evaluate(\n",
    "    dataset = synthetic_recursive, \n",
    "    metrics= metrics,\n",
    "    llm = llm,\n",
    "    embeddings = embed_model,\n",
    "    raise_exceptions=False,\n",
    "    )\n",
    "print(result)\n",
    "df_recursive = result.to_pandas()\n",
    "df_recursive.to_pickle('./ragas_result_recursive.pkl')\n",
    "print(df_recursive.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|██████████| 72/72 [00:28<00:00,  2.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'answer_relevancy': 0.8004, 'context_recall': 0.9881, 'context_entity_recall': 0.3265, 'answer_similarity': 0.9088, 'answer_correctness': 0.6395, 'faithfulness': 0.8528}\n",
      "                                            question  \\\n",
      "0  Question:\\nWhat is the tuition rate for the 20...   \n",
      "1  Question:\\nWhat is the title of the section in...   \n",
      "2  Question: Which department oversees the assess...   \n",
      "3  Question: In the context provided, what action...   \n",
      "4  Question: What information can be found in the...   \n",
      "\n",
      "                                              answer  \\\n",
      "0  Based on the information provided, the tuition...   \n",
      "1                                      I don't know.   \n",
      "2  The Assessing Department oversees the assessme...   \n",
      "3  To gain access to their account or the system,...   \n",
      "4                                      I don't know.   \n",
      "\n",
      "                                            contexts  \\\n",
      "0  [Tuition & Financial Information | NYU Center ...   \n",
      "1                                     [Our Contacts]   \n",
      "2  [Home\\nSite Map\\nFeedback\\nFrequently Asked Qu...   \n",
      "3                                            [Login]   \n",
      "4                                     [Our Contacts]   \n",
      "\n",
      "                                        ground_truth  answer_relevancy  \\\n",
      "0  The tuition rate for the 2017-2018 academic ye...          0.957742   \n",
      "1  The title of the section in our textbook where...          0.000000   \n",
      "2                               Assessing Department          0.988188   \n",
      "3  To gain access to their account or the system,...          0.930036   \n",
      "4  The \"Our Contacts\" section provides informatio...          0.000000   \n",
      "\n",
      "   context_recall  context_entity_recall  answer_similarity  \\\n",
      "0             1.0               0.142857           0.996766   \n",
      "1             1.0               0.000000           0.759122   \n",
      "2             1.0               1.000000           0.860127   \n",
      "3             1.0               0.000000           0.877194   \n",
      "4             1.0               0.000000           0.740154   \n",
      "\n",
      "   answer_correctness  faithfulness  \n",
      "0            0.849190      1.000000  \n",
      "1            0.564772      1.000000  \n",
      "2            0.965032      0.666667  \n",
      "3            0.819298      0.500000  \n",
      "4            0.560039      1.000000  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "result = evaluate(\n",
    "    dataset = synthetic_paragraph, \n",
    "    metrics= metrics,\n",
    "    llm = llm,\n",
    "    embeddings = embed_model,\n",
    "    raise_exceptions=False,\n",
    "    )\n",
    "print(result)\n",
    "df_paragraph = result.to_pandas()\n",
    "df_paragraph.to_pickle('./ragas_result_paragraph.pkl')\n",
    "print(df_paragraph.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review RAGAs Results on Synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Question:\\nWhat is the tuition rate for the 20...</td>\n",
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       "      <td>Question: Where can residents find information...</td>\n",
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       "      <td>[Home\\nSite Map\\nFeedback\\nFrequently Asked Qu...</td>\n",
       "      <td>Residents can find information about property ...</td>\n",
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       "      <td>0.166667</td>\n",
       "      <td>0.988098</td>\n",
       "      <td>0.997025</td>\n",
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       "      <td>[How do I add a secondary account to my MSN?\\n...</td>\n",
       "      <td>The process for adding a secondary account to ...</td>\n",
       "      <td>0.980414</td>\n",
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       "      <td>0.500000</td>\n",
       "      <td>0.896622</td>\n",
       "      <td>0.224156</td>\n",
       "      <td>0.866667</td>\n",
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       "      <td>0.857143</td>\n",
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       "      <td>0.500000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>[Go To Other Settings Help Solutions\\nGo To Ot...</td>\n",
       "      <td>To add an account as a secondary on MSN, the s...</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>Below are the steps to add a secondary account...</td>\n",
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       "      <td>0.500000</td>\n",
       "      <td>0.902164</td>\n",
       "      <td>0.225552</td>\n",
       "      <td>0.200000</td>\n",
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      "text/plain": [
       "                                             question  \\\n",
       "0   Question:\\nWhat is the tuition rate for the 20...   \n",
       "1   Question:\\nWhat is the title of the section in...   \n",
       "2   Question: Which department oversees the assess...   \n",
       "3   Question: In the context provided, what action...   \n",
       "4   Question: What information can be found in the...   \n",
       "5   Question: Where can residents find information...   \n",
       "6   Question: In the context provided, what is the...   \n",
       "7   What is the process for adding a secondary acc...   \n",
       "8   Question: What steps should be followed to add...   \n",
       "9   In the context provided, where can you find in...   \n",
       "10  Question: What are the steps to add an account...   \n",
       "11  What steps do you need to follow in order to a...   \n",
       "\n",
       "                                               answer  \\\n",
       "0   Based on the information provided, the tuition...   \n",
       "1                                       I don't know.   \n",
       "2   The Assessing Department oversees the assessme...   \n",
       "3   To gain access to their account or the system,...   \n",
       "4                                       I don't know.   \n",
       "5   The residents can find information about prope...   \n",
       "6   The action that typically involves entering yo...   \n",
       "7   Below are the steps to add a secondary account...   \n",
       "8   To add an account as a secondary on MSN, you n...   \n",
       "9   You can find information on adding a secondary...   \n",
       "10  To add a secondary account to MSN, you can fol...   \n",
       "11  Below are the steps to add a secondary account...   \n",
       "\n",
       "                                             contexts  \\\n",
       "0   [Tuition & Financial Information | NYU Center ...   \n",
       "1                                      [Our Contacts]   \n",
       "2   [Home\\nSite Map\\nFeedback\\nFrequently Asked Qu...   \n",
       "3                                             [Login]   \n",
       "4                                      [Our Contacts]   \n",
       "5   [Home\\nSite Map\\nFeedback\\nFrequently Asked Qu...   \n",
       "6                                             [Login]   \n",
       "7   [How do I add a secondary account to my MSN?\\n...   \n",
       "8   [Go To Other Settings Help Solutions\\nGo To Ot...   \n",
       "9   [How do I add a secondary account to my MSN?\\n...   \n",
       "10  [Go To Other Settings Help Solutions\\nGo To Ot...   \n",
       "11  [How do I add a secondary account to my MSN?\\n...   \n",
       "\n",
       "                                         ground_truth  answer_relevancy  \\\n",
       "0   The tuition rate for the 2017-2018 academic ye...          0.957742   \n",
       "1   The title of the section in our textbook where...          0.000000   \n",
       "2                                Assessing Department          0.988188   \n",
       "3   To gain access to their account or the system,...          0.930036   \n",
       "4   The \"Our Contacts\" section provides informatio...          0.000000   \n",
       "5   Residents can find information about property ...          0.977053   \n",
       "6   The action that typically involves entering yo...          0.970075   \n",
       "7   The process for adding a secondary account to ...          0.980414   \n",
       "8   To add an account as a secondary on MSN, follo...          0.976149   \n",
       "9   You can find information on adding a secondary...          0.941741   \n",
       "10  To add an account as a secondary on MSN, the s...          0.912258   \n",
       "11  To add a secondary account to your MSN, you ne...          0.971054   \n",
       "\n",
       "    context_recall  context_entity_recall  answer_similarity  \\\n",
       "0         1.000000               0.142857           0.996766   \n",
       "1         1.000000               0.000000           0.759122   \n",
       "2         1.000000               1.000000           0.860127   \n",
       "3         1.000000               0.000000           0.877194   \n",
       "4         1.000000               0.000000           0.740154   \n",
       "5         1.000000               0.166667           0.988098   \n",
       "6         1.000000               0.333333           0.981035   \n",
       "7         1.000000               0.500000           0.896622   \n",
       "8         0.857143               0.375000           0.974785   \n",
       "9         1.000000               0.500000           0.950154   \n",
       "10        1.000000               0.400000           0.979201   \n",
       "11        1.000000               0.500000           0.902164   \n",
       "\n",
       "    answer_correctness  faithfulness  \n",
       "0             0.849190      1.000000  \n",
       "1             0.564772      1.000000  \n",
       "2             0.965032      0.666667  \n",
       "3             0.819298      0.500000  \n",
       "4             0.560039      1.000000  \n",
       "5             0.997025      1.000000  \n",
       "6             0.745259      1.000000  \n",
       "7             0.224156      0.866667  \n",
       "8             0.660363      1.000000  \n",
       "9             0.237531      1.000000  \n",
       "10            0.825445      1.000000  \n",
       "11            0.225552      0.200000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recursive_ragas = pd.read_pickle(\"ragas_result_recursive.pkl\")\n",
    "paragraph_ragas = pd.read_pickle(\"ragas_result_paragraph.pkl\")\n",
    "semantic_ragas = pd.read_pickle(\"ragas_result_semantic.pkl\")\n",
    "paragraph_ragas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analysis(df1, df2, df3):\n",
    "  sns.set_style(\"whitegrid\")\n",
    "  fig, axs = plt.subplots(2,3, figsize=(12, 5))\n",
    "  for i,col in enumerate(df1.columns):\n",
    "    sns.kdeplot(data=[df1[col].values,df2[col].values, df3[col].values],legend=False,fill=True)\n",
    "    # sns.kdeplot(data=[df1[col].values,df2[col].values, df3[col].values],legend=False,ax=axs[i],fill=True)\n",
    "    axs[i].set_title(f'{col} scores distribution')\n",
    "    axs[i].legend(labels=[\"Paragraph\", \"Recursive\", \"Semantic\"])\n",
    "  plt.tight_layout()\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x792fa6201300>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1178.12x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a column to identify the source data frame\n",
    "paragraph_ragas['source'] = 'Paragraph Chunking RAGAs Results'\n",
    "recursive_ragas['source'] = 'Recursive Chunking RAGAs Results'\n",
    "semantic_ragas['source'] = 'Semantic Chunking RAGAs Results'\n",
    "\n",
    "df_list = [paragraph_ragas[['source','context_recall', 'context_entity_recall', 'answer_similarity', 'answer_correctness', 'faithfulness']],\n",
    "    recursive_ragas[['source','context_recall', 'context_entity_recall', 'answer_similarity', 'answer_correctness', 'faithfulness']],\n",
    "    semantic_ragas[['source','context_recall', 'context_entity_recall', 'answer_similarity', 'answer_correctness', 'faithfulness']]]\n",
    "\n",
    "df_combined = pd.concat(df_list)\n",
    "\n",
    "# Melt the data frame to long format\n",
    "df_melted = pd.melt(df_combined, id_vars='source', var_name='variable', value_name='value')\n",
    "\n",
    "# Create a FacetGrid\n",
    "g = sns.FacetGrid(df_melted, col=\"variable\", col_wrap=3, sharex=False, sharey=False, hue=\"source\")\n",
    "g = g.map(sns.kdeplot, \"value\", common_norm=False)\n",
    "\n",
    "# Add legend with titles from the 'source' column\n",
    "g.add_legend(title='Semantic Chunking Outputs')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "############################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MSMarco QA Data\n",
    "--- Ran out of time after one month of project implementation in the fellowship.\n",
    "- Sad attempt to use our own data. There might be better support [here.](https://docs.ragas.io/en/latest/howtos/customisations/bring-your-own-llm-or-embs.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nyu tuition cost\n"
     ]
    }
   ],
   "source": [
    "print(df['question'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "from datasets import load_dataset, Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build the semantic dataset\n",
    "\n",
    "## implement the different chunking\n",
    "## create semantic chunk dataset for testing\n",
    "df_semantic = df\n",
    "answers = []\n",
    "# for chunk in semantic_chunks:\n",
    "#     answers.append(semantic_rag_chain.invoke(df['question'][0]))\n",
    "# print(\"Answers: \", answers)\n",
    "\n",
    "#try doing this instead\n",
    "for chunk in semantic_chunks:\n",
    "    for q in tqdm(df['question']):\n",
    "        print(\"q\", q)\n",
    "        answers.append(semantic_rag_chain.invoke(q))\n",
    "        # print(answers)\n",
    "print(\"Answers: \", answers)\n",
    "\n",
    "\n",
    "df_semantic['answer'] = answers\n",
    "df_semantic[\"contexts\"] = semantic_chunks\n",
    "# Ensure \"contexts\" is a list of lists of strings\n",
    "df_semantic[\"contexts\"] = df_semantic[\"contexts\"].apply(lambda x: list(map(str, x)))\n",
    "print(df_semantic)\n",
    "\n",
    "\n",
    "MSMarco_QA_Docs_semantic = (Dataset.from_pandas(df_semantic, features = features))\n",
    "print(\"MSMarco_QA_Docs_semantic\", type(MSMarco_QA_Docs_semantic))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df_semantic['answer'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = evaluate(\n",
    "    dataset = MSMarco_QA_Docs_semantic, \n",
    "    metrics= metrics,\n",
    "    llm = llm,\n",
    "    embeddings = embed_model,\n",
    "    raise_exceptions=False,\n",
    "    )\n",
    "print(result)\n",
    "df_semantic = result.to_pandas()\n",
    "df_semantic.to_pickle('./ragas_result_semantic.pkl')\n",
    "print(df_semantic.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### create naive chunks dataset for testing\n",
    "df_naive = df\n",
    "answers = []\n",
    "for chunk in naive_chunks:\n",
    "    for q in df['question']:\n",
    "        answers.append(naive_rag_chain.invoke(q))\n",
    "print(\"Answers: \", answers)\n",
    "\n",
    "df_naive['answer'] = answers\n",
    "df_naive['contexts'] = naive_chunks\n",
    "# Ensure \"contexts\" is a list of lists of strings\n",
    "df_naive[\"contexts\"] = df_naive[\"contexts\"].apply(lambda x: list(map(str, x)))\n",
    "print(df_naive)\n",
    "\n",
    "\n",
    "MSMarco_QA_Docs_naive = (Dataset.from_pandas(df_naive, features = features))\n",
    "print(\"MSMarco_QA_Docs_naive\", type(MSMarco_QA_Docs_naive))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_naive = evaluate(\n",
    "    dataset = MSMarco_QA_Docs_naive, \n",
    "    metrics= metrics,\n",
    "    llm = llm,\n",
    "    embeddings = embed_model,\n",
    "    raise_exceptions=False,\n",
    "    )\n",
    "print(result_naive)\n",
    "df_naive = result_naive.to_pandas()\n",
    "df_naive.to_pickle(\"./ragas_result_naive.pkl\")\n",
    "print(df_naive.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "######\n",
    "### create paragraph chunk dataset for testing\n",
    "df_parag = df\n",
    "answers = []\n",
    "for chunk in paragraph_chunks:\n",
    "    answers.append(paragraph_rag_chain.invoke(df['question'][0]))\n",
    "print(\"Answers: \", answers)\n",
    "\n",
    "df_parag['answer'] = answers\n",
    "print(df_parag)\n",
    "\n",
    "# Ensure \"contexts\" is a list of lists of strings\n",
    "df_parag[\"contexts\"] = paragraph_chunks\n",
    "df_parag[\"contexts\"] = df_parag[\"contexts\"].apply(lambda x: list(map(str, x)))\n",
    "\n",
    "\n",
    "MSMarco_QA_Docs_parag = (Dataset.from_pandas(df_parag, features = features))\n",
    "print(\"MSMarco_QA_Docs_paragraph\", type(MSMarco_QA_Docs_parag))\n",
    "\n",
    "\n",
    "result_paragraph = evaluate(\n",
    "    dataset = MSMarco_QA_Docs_parag, \n",
    "    metrics= metrics,\n",
    "    llm = llm,\n",
    "    embeddings = embed_model,\n",
    "    raise_exceptions=False,\n",
    "    )\n",
    "print(result_paragraph)\n",
    "df_paragraph = result_paragraph.to_pandas()\n",
    "df_paragraph.to_pickle(\"./ragas_result_paragraph.pkl\")\n",
    "print(df_paragraph.head())"
   ]
  }
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