FlagEmbedding/docs/source/tutorial/6_RAG/6.2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RAG with LangChain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LangChain is well adopted by open-source community because of its diverse functionality and clean API usage. In this tutorial we will show how to use LangChain to build an RAG pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, install all the required packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install pypdf langchain langchain-openai langchain-huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then fill the OpenAI API key below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For openai key\n",
    "import os\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BGE-M3 is a very powerful embedding model, We would like to know what does that 'M3' stands for.\n",
    "\n",
    "Let's first ask GPT the question:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M3-Embedding typically refers to a specific method or framework used in machine learning and natural language processing for creating embeddings, which are dense vector representations of data. The \"M3\" could indicate a particular model, method, or version related to embeddings, but without additional context, it's hard to provide a precise definition.\n",
      "\n",
      "If you have a specific context or source in mind where \"M3-Embedding\" is used, please provide more details, and I may be able to give a more accurate explanation!\n"
     ]
    }
   ],
   "source": [
    "from langchain_openai.chat_models import ChatOpenAI\n",
    "\n",
    "llm = ChatOpenAI(model_name=\"gpt-4o-mini\")\n",
    "\n",
    "response = llm.invoke(\"What does M3-Embedding stands for?\")\n",
    "print(response.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By quickly checking the GitHub [repo](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) of BGE-M3. Since BGE-M3 paper is not in its training dataset, GPT is not capable to give us correct answer.\n",
    "\n",
    "Now, let's use the [paper](https://arxiv.org/pdf/2402.03216) of BGE-M3 to build an RAG application to answer our question precisely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to load the pdf of our paper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import PyPDFLoader\n",
    "\n",
    "# Or download the paper and put a path to the local file instead\n",
    "loader = PyPDFLoader(\"https://arxiv.org/pdf/2402.03216\")\n",
    "docs = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'source': 'https://arxiv.org/pdf/2402.03216', 'page': 0}\n"
     ]
    }
   ],
   "source": [
    "print(docs[0].metadata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The whole paper contains 18 pages. That's a huge amount of information. Thus we split the paper into chunks to construct a corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "\n",
    "# initialize a splitter\n",
    "splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=1000,    # Maximum size of chunks to return\n",
    "    chunk_overlap=150,  # number of overlap characters between chunks\n",
    ")\n",
    "\n",
    "# use the splitter to split our paper\n",
    "corpus = splitter.split_documents(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing is one of the most important part in RAG. LangChain provides APIs for embedding models and vector databases that make things simple and straightforward.\n",
    "\n",
    "Here, we choose bge-base-en-v1.5 to embed all the chunks to vectors, and use Faiss as our vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n",
    "\n",
    "embedding_model = HuggingFaceEmbeddings(model_name=\"BAAI/bge-base-en-v1.5\", \n",
    "encode_kwargs={\"normalize_embeddings\": True})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then create a Faiss vector database given our corpus and embedding model. \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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import FAISS\n",
    "\n",
    "vectordb = FAISS.from_documents(corpus, embedding_model)\n",
    "\n",
    "# (optional) save the vector database to a local directory\n",
    "vectordb.save_local(\"vectorstore.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create retriever for later use\n",
    "retriever = vectordb.as_retriever()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Retreive and Generate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write a simple prompt template. Modify the contents to match your different use cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "template = \"\"\"\n",
    "You are a Q&A chat bot.\n",
    "Use the given context only, answer the question.\n",
    "\n",
    "<context>\n",
    "{context}\n",
    "</context>\n",
    "\n",
    "Question: {input}\n",
    "\"\"\"\n",
    "\n",
    "# Create a prompt template\n",
    "prompt = ChatPromptTemplate.from_template(template)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now everything is ready. Assemble them to a chain and let the magic happen!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
    "from langchain.chains import create_retrieval_chain\n",
    "\n",
    "doc_chain = create_stuff_documents_chain(llm, prompt)\n",
    "chain = create_retrieval_chain(retriever, doc_chain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell, we can see that the chatbot can answer the question correctly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M3-Embedding stands for a new embedding model that is distinguished for its versatility in multi-linguality, multi-functionality, and multi-granularity.\n"
     ]
    }
   ],
   "source": [
    "response = chain.invoke({\"input\": \"What does M3-Embedding stands for?\"})\n",
    "\n",
    "# print the answer only\n",
    "print(response['answer'])"
   ]
  }
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