{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b14a24db",
   "metadata": {},
   "source": [
    "# MistralAIEmbeddings\n",
    "\n",
    "This notebook explains how to use MistralAIEmbeddings, which is included in the langchain_mistralai package, to embed texts in langchain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ab948fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install -U langchain-mistralai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c637ca",
   "metadata": {},
   "source": [
    "## import the library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5709b030",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_mistralai import MistralAIEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1756b1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = MistralAIEmbeddings(mistral_api_key='your-api-key')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2a098d",
   "metadata": {},
   "source": [
    "# Using the Embedding Model\n",
    "With `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "584b9af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding.model = 'mistral-embed'  # or your preferred model if available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be18b873",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_query = embedding.embed_query(\"The test information\")\n",
    "res_document = embedding.embed_documents([\"test1\", \"another test\"])"
   ]
  }
 ],
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