langchain/docs/getting_started/data_augmented_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ba0decc",
   "metadata": {},
   "source": [
    "# Data Augmented Generation\n",
    "\n",
    "This notebook covers getting started with some key concepts of data augmented generation, specifically Documents, Embeddings, and Vectorstores.\n",
    "\n",
    "After that, we will cover how to use these concepts to do question/answering over select documents.\n",
    "\n",
    "For a more conceptual explanation of what Data Augmented Generation is, see [this](../explanation/combine_docs.md) documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b37c3e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
    "from langchain.vectorstores.faiss import FAISS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c13318",
   "metadata": {},
   "source": [
    "First, let's load in our private data that we want to use in conjunction with an LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91d307ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../examples/state_of_the_union.txt') as f:\n",
    "    state_of_the_union = f.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f8bc8f",
   "metadata": {},
   "source": [
    "Now, we need to create smaller chunks of text from this one large document. We want to do this because we cannot (and do not want to) pass this whole large text into the language model in one go - rather, we want to split it up, select the relevant parts, and then pass those into the language model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10a93bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "texts = text_splitter.split_text(state_of_the_union)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f8c006",
   "metadata": {},
   "source": [
    "We could work with ALL these documents directly, but often we only want to find only the most relevant ones. One common way to do that is create embeddings for each document, store them in a vector database, and then query that database with an incoming query to select the most relevant documents for that query.\n",
    "\n",
    "In this example, we use OpenAI embeddings, and a FAISS vector store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa0f3066",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()\n",
    "docsearch = FAISS.from_texts(texts, embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c6ce83f",
   "metadata": {},
   "source": [
    "Now let's give it a go!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8465b4b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = docsearch.similarity_search(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "611be801",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n",
      "\n",
      "We cannot let this happen. \n",
      "\n",
      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
      "\n",
      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
      "\n",
      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
      "\n",
      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n"
     ]
    }
   ],
   "source": [
    "print(docs[0].page_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6a48e5",
   "metadata": {},
   "source": [
    "So we now have a way of selecting the most relevant documents - now what? We can plug this vectorstore into a chain, where we first select these documents, and then send them (along with the original question) to get a final answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6255b02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import OpenAI, VectorDBQA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec4eacad",
   "metadata": {},
   "outputs": [],
   "source": [
    "qa = VectorDBQA.from_llm(llm=OpenAI(), vectorstore=docsearch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59c7508d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and will continue Justice Breyer's legacy of excellence.\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "qa.run(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b192c91c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}