langchain/docs/versioned_docs/version-0.2.x/integrations/text_embedding/openai.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "278b6c63",
   "metadata": {},
   "source": [
    "# OpenAI\n",
    "\n",
    "Let's load the OpenAI Embedding class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ff98ff-58e9-4716-8788-227a5c3f473d",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First we install langchain-openai and set the required env vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c66c4613-6c67-40ca-b3b1-c026750d1742",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -qU langchain-openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62e3710e-55a0-44fb-ba51-2f1d520dfc38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0be1af71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import OpenAIEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c66e5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01370375",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f012c222-3fa9-470a-935c-758b2048d9af",
   "metadata": {},
   "source": [
    "## Usage\n",
    "### Embed query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfb6142c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: model not found. Using cl100k_base encoding.\n"
     ]
    }
   ],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.014380056377383358,\n",
       " -0.027191711627651764,\n",
       " -0.020042716111860304,\n",
       " 0.057301379620345545,\n",
       " -0.022267658631828974]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_result[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b733391-1e23-438b-a6bc-0d77eed9426e",
   "metadata": {},
   "source": [
    "## Embed documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0356c3b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: model not found. Using cl100k_base encoding.\n"
     ]
    }
   ],
   "source": [
    "doc_result = embeddings.embed_documents([text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.014380056377383358,\n",
       " -0.027191711627651764,\n",
       " -0.020042716111860304,\n",
       " 0.057301379620345545,\n",
       " -0.022267658631828974]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_result[0][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7dc464a-6fa2-4cff-ab2e-49a0566d819b",
   "metadata": {},
   "source": [
    "## Specify dimensions\n",
    "\n",
    "With the `text-embedding-3` class of models, you can specify the size of the embeddings you want returned. For example by default `text-embedding-3-large` returned embeddings of dimension 3072:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7be1e7b-54c6-4893-b8ad-b872e6705735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3072"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(doc_result[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33287142-0835-4958-962f-385ae4447431",
   "metadata": {},
   "source": [
    "But by passing in `dimensions=1024` we can reduce the size of our embeddings to 1024:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "854ee772-2de9-4a83-84e0-908033d98e4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings_1024 = OpenAIEmbeddings(model=\"text-embedding-3-large\", dimensions=1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b464396-8d94-478b-8329-849b56e1ae23",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: model not found. Using cl100k_base encoding.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(embeddings_1024.embed_documents([text])[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "poetry-venv",
   "language": "python",
   "name": "poetry-venv"
  },
  "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"
  },
  "vscode": {
   "interpreter": {
    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}