langchain/docs/modules/indexes/retriever_examples/vectorstore-retriever.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc0db1bc",
   "metadata": {},
   "source": [
    "# VectorStore Retriever\n",
    "\n",
    "The index - and therefor the retriever - that LangChain has the most support for is a VectorStoreRetriever. As the name suggests, this retriever is backed heavily by a VectorStore.\n",
    "\n",
    "Once you construct a VectorStore, its very easy to construct a retriever. Let's walk through an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5831703b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "loader = TextLoader('../../state_of_the_union.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fbcc58f",
   "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": [
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores import Chroma\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "\n",
    "documents = loader.load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "texts = text_splitter.split_documents(documents)\n",
    "embeddings = OpenAIEmbeddings()\n",
    "db = Chroma.from_documents(texts, embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0cbfb1af",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = db.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc12700b",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}