langchain/docs/versioned_docs/version-0.2.x/integrations/vectorstores/duckdb.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DuckDB\n",
    "This notebook shows how to use `DuckDB` as a vector store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install duckdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "from langchain.vectorstores import DuckDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "\n",
    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
    "documents = loader.load()\n",
    "\n",
    "documents = CharacterTextSplitter().split_documents(documents)\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "docsearch = DuckDB.from_documents(documents, embeddings)\n",
    "\n",
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = docsearch.similarity_search(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(docs[0].page_content)"
   ]
  }
 ],
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