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  {
   "cell_type": "markdown",
   "id": "07c1e3b9",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "\n",
    "This example showcases question answering over a vector database.\n",
    "We have chosen this as the example for getting started because it nicely combines a lot of different elements (Text splitters, embeddings, vectorstores) and then also shows how to use them in a chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82525493",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.vectorstores import Chroma\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain import OpenAI, VectorDBQA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b7adc54",
   "metadata": {},
   "source": [
    "Here we load in the documents we want to use to create our index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "611e0c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "loader = TextLoader('../state_of_the_union.txt')\n",
    "documents = loader.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fdc0fc2",
   "metadata": {},
   "source": [
    "Next, we will split the documents into chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afecb8cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "texts = text_splitter.split_documents(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bebc041",
   "metadata": {},
   "source": [
    "We will then select which embeddings we want to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9eaaa735",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24612905",
   "metadata": {},
   "source": [
    "We now create the vectorstore to use as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c7049db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Chroma using direct local API.\n",
      "Using DuckDB in-memory for database. Data will be transient.\n"
     ]
    }
   ],
   "source": [
    "db = Chroma.from_documents(texts, embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c4e5c6",
   "metadata": {},
   "source": [
    "Finally, we create a chain and use it to answer questions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3018f865",
   "metadata": {},
   "outputs": [],
   "source": [
    "qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", vectorstore=db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "032a47f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" The President said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
      ]
     },
     "execution_count": 10,
     "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": "8b403637",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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