Tutorials/6_RAG/6.1_RAG_From_Scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple RAG From Scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will use BGE, Faiss, and OpenAI's GPT-4o-mini to build a simple RAG system from scratch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the required packages in the environment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -U numpy faiss-cpu FlagEmbedding openai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose I'm a resident of New York Manhattan, and I want the AI bot to provide suggestion on where should I go for dinner. It's not reliable to let it recommend some random restaurant. So let's provide a bunch of our favorate restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [\n",
    "    \"Cheli: A downtown Chinese restaurant presents a distinctive dining experience with authentic and sophisticated flavors of Shanghai cuisine. Avg cost: $40-50\",\n",
    "    \"Masa: Midtown Japanese restaurant with exquisite sushi and omakase experiences crafted by renowned chef Masayoshi Takayama. The restaurant offers a luxurious dining atmosphere with a focus on the freshest ingredients and exceptional culinary artistry. Avg cost: $500-600\",\n",
    "    \"Per Se: A midtown restaurant features daily nine-course tasting menu and a nine-course vegetable tasting menu using classic French technique and the finest quality ingredients available. Avg cost: $300-400\",\n",
    "    \"Ortomare: A casual, earthy Italian restaurant locates uptown, offering wood-fired pizza, delicious pasta, wine & spirits & outdoor seating. Avg cost: $30-50\",\n",
    "    \"Banh: Relaxed, narrow restaurant in uptown, offering Vietnamese cuisine & sandwiches, famous for its pho and Vietnam sandwich. Avg cost: $20-30\",\n",
    "    \"Living Thai: An uptown typical Thai cuisine with different kinds of curry, Tom Yum, fried rice, Thai ice tea, etc. Avg cost: $20-30\",\n",
    "    \"Chick-fil-A: A Fast food restaurant with great chicken sandwich, fried chicken, fries, and salad, which can be found everywhere in New York. Avg cost: 10-20\",\n",
    "    \"Joe's Pizza: Most famous New York pizza locates midtown, serving different flavors including classic pepperoni, cheese, spinach, and also innovative pizza. Avg cost: $15-25\",\n",
    "    \"Red Lobster: In midtown, Red Lobster is a lively chain restaurant serving American seafood standards amid New England-themed decor, with fair price lobsters, shrips and crabs. Avg cost: $30-50\",\n",
    "    \"Bourbon Steak: It accomplishes all the traditions expected from a steakhouse, offering the finest cuts of premium beef and seafood complimented by wine and spirits program. Avg cost: $100-150\",\n",
    "    \"Da Long Yi: Locates in downtown, Da Long Yi is a Chinese Szechuan spicy hotpot restaurant that serves good quality meats. Avg cost: $30-50\",\n",
    "    \"Mitr Thai: An exquisite midtown Thai restaurant with traditional dishes as well as creative dishes, with a wonderful bar serving cocktails. Avg cost: $40-60\",\n",
    "    \"Yichiran Ramen: Famous Japenese ramen restaurant in both midtown and downtown, serving ramen that can be designed by customers themselves. Avg cost: $20-40\",\n",
    "    \"BCD Tofu House: Located in midtown, it's famous for its comforting and flavorful soondubu jjigae (soft tofu stew) and a variety of authentic Korean dishes. Avg cost: $30-50\",\n",
    "]\n",
    "\n",
    "user_input = \"I want some Chinese food\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to figure out a fast but powerful enough method to retrieve docs in the corpus that are most closely related to our questions. Indexing is a good choice for us.\n",
    "\n",
    "The first step is embed each document into a vector. We use bge-base-en-v1.5 as our embedding model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from FlagEmbedding import FlagModel\n",
    "\n",
    "model = FlagModel('BAAI/bge-base-en-v1.5',\n",
    "                  query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages:\",\n",
    "                  use_fp16=True)\n",
    "\n",
    "embeddings = model.encode(corpus, convert_to_numpy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 768)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, let's create a Faiss index and add all the vectors into it.\n",
    "\n",
    "If you want to know more about Faiss, refer to the tutorial of [Faiss and indexing](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials/3_Indexing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import faiss\n",
    "import numpy as np\n",
    "\n",
    "index = faiss.IndexFlatIP(embeddings.shape[1])\n",
    "\n",
    "index.add(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.ntotal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Retrieve and Generate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we come to the most exciting part. Let's first embed our query and retrieve 3 most relevant document from it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Cheli: A downtown Chinese restaurant presents a distinctive dining experience with authentic and sophisticated flavors of Shanghai cuisine. Avg cost: $40-50',\n",
       "        'Da Long Yi: Locates in downtown, Da Long Yi is a Chinese Szechuan spicy hotpot restaurant that serves good quality meats. Avg cost: $30-50',\n",
       "        'Yichiran Ramen: Famous Japenese ramen restaurant in both midtown and downtown, serving ramen that can be designed by customers themselves. Avg cost: $20-40']],\n",
       "      dtype='<U270')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_embedding = model.encode_queries([user_input], convert_to_numpy=True)\n",
    "\n",
    "D, I = index.search(q_embedding, 3)\n",
    "res = np.array(corpus)[I]\n",
    "\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then set up the prompt for the chatbot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt=\"\"\"\n",
    "You are a bot that makes recommendations for restaurants. \n",
    "Please be brief, answer in short sentences without extra information.\n",
    "\n",
    "These are the restaurants list:\n",
    "{recommended_activities}\n",
    "\n",
    "The user's preference is: {user_input}\n",
    "Provide the user with 2 recommended restaurants based on the user's preference.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill in your OpenAI API key below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let's see how the chatbot give us the answer!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "client = OpenAI()\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    messages=[\n",
    "        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": prompt.format(user_input=user_input, recommended_activities=res)\n",
    "        }\n",
    "    ]\n",
    ").choices[0].message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Cheli - Authentic Shanghai cuisine with sophisticated flavors.  \n",
      "2. Da Long Yi - Szechuan spicy hotpot with good quality meats.\n"
     ]
    }
   ],
   "source": [
    "print(response.content)"
   ]
  }
 ],
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