{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "278b6c63",
   "metadata": {},
   "source": [
    "# LLMRails\n",
    "\n",
    "Let's load the LLMRails Embeddings class.\n",
    "\n",
    "To use LLMRails embedding you need to pass api key by argument or set it in environment with `LLM_RAILS_API_KEY` key.\n",
    "To gey API Key you need to sign up in https://console.llmrails.com/signup and then go to https://console.llmrails.com/api-keys and copy key from there after creating one key in platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0be1af71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import LLMRailsEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2c66e5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = LLMRailsEmbeddings(model='embedding-english-v1') # or embedding-multi-v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01370375",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42e4035",
   "metadata": {},
   "source": [
    "To generate embeddings, you can either query an invidivual text, or you can query a list of texts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.09996652603149414,\n",
       " 0.015568195842206478,\n",
       " 0.17670190334320068,\n",
       " 0.16521021723747253,\n",
       " 0.21193109452724457]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_result = embeddings.embed_query(text)\n",
    "query_result[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.04242777079343796,\n",
       " 0.016536075621843338,\n",
       " 0.10052520781755447,\n",
       " 0.18272875249385834,\n",
       " 0.2079043835401535]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_result = embeddings.embed_documents([text])\n",
    "doc_result[0][:5]"
   ]
  }
 ],
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