ChatToPDF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the required libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lang_funcs import *\n",
    "from langchain_ollama.llms import OllamaLLM\n",
    "from langchain import PromptTemplate\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the ollm and Embedding Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading orca-mini from Ollama\n",
    "llm = OllamaLLM(model=\"orca-mini\", temperature=0)\n",
    "\n",
    "# Loading the Embedding Model\n",
    "embed = load_embedding_model(model_path=\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the PDF and converting into vector database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading and splitting the documents\n",
    "docs = load_pdf_data(file_path=\"ml_book.pdf\") # Update the file path as per requirements\n",
    "documents = split_docs(documents=docs)\n",
    "\n",
    "# creating vectorstore\n",
    "vectorstore = create_embeddings(documents, embed)\n",
    "\n",
    "# converting vectorstore to a retriever\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"\n",
    "### System:\n",
    "You are an respectful and honest assistant. You have to answer the user's \\\n",
    "questions using only the context provided to you. If you don't know the answer, \\\n",
    "just say you don't know. Don't try to make up an answer.\n",
    "\n",
    "### Context:\n",
    "{context}\n",
    "\n",
    "### User:\n",
    "{question}\n",
    "\n",
    "### Response:\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the prompt from the template which we created before\n",
    "prompt = PromptTemplate.from_template(template)\n",
    "\n",
    "# Creating the chain\n",
    "chain = load_qa_chain(retriever, llm, prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Supervised learning is a type of machine learning where the training data includes both the desired\n",
      "solution (label) and the input features. The algorithm learns from this data to predict the label\n",
      "for new, unseen examples. In other words, it \"learns\" how to classify new emails as spam or ham\n",
      "based on the characteristics of the training set. This is a common task in machine learning, and\n",
      "many algorithms are trained using supervised learning techniques.\n"
     ]
    }
   ],
   "source": [
    "get_response(\"What is supervised learning ?\", chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Linear regression is a statistical method used to model the relationship between a dependent\n",
      "variable and one or more independent variables. The goal of linear regression is to find the best\n",
      "fit line that can predict the dependent variable based on the independent variables. In other words,\n",
      "it tries to find a straight line that best fits the data points and uses this line to make\n",
      "predictions about future values. Linear regression assumes that there is a linear relationship\n",
      "between the independent and dependent variables.\n"
     ]
    }
   ],
   "source": [
    "get_response(\"What is Liner Regression ?\", chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Overfitting refers to the situation where a machine learning model performs well on the training\n",
      "data but poorly on the test data. It occurs when a model has too many parameters, which allows it to\n",
      "fit the training data very closely. However, in the case of overfitting, the model's performance on\n",
      "new data is poor because it memorizes the training data instead of learning general patterns.\n",
      "Regularization techniques such as L1 regularization or early stopping can be used to prevent\n",
      "overfitting.\n"
     ]
    }
   ],
   "source": [
    "get_response(\"What is Overfitting ?\", chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "llm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}