{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "096e6224",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1970d59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import download_loader\n",
    "from ragas.testset.evolutions import simple, reasoning, multi_context\n",
    "from ragas.testset.generator import TestsetGenerator\n",
    "from langchain_openai import ChatOpenAI\n",
    "from ragas.embeddings import OpenAIEmbeddings\n",
    "import openai\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bfe1ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import uuid\n",
    "from pathlib import Path\n",
    "from typing import Any, Protocol\n",
    "from datasets import load_dataset\n",
    "from langchain_core.documents import Document\n",
    "from langchain_elasticsearch import ElasticsearchStore\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from loguru import logger\n",
    "from langchain_ollama import OllamaEmbeddings\n",
    "from ragas.metrics.collections import SemanticSimilarity  \n",
    "from transformers import AutoTokenizer, AutoModel, AutoConfig\n",
    "from elasticsearch import Elasticsearch\n",
    "from langchain_elasticsearch import ElasticsearchStore\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "nltk.download(\"punkt\", quiet=True)\n",
    "\n",
    "ES_URL = os.getenv(\"ELASTICSEARCH_LOCAL_URL\")\n",
    "ES_INDEX_NAME = os.getenv(\"ELASTICSEARCH_INDEX\")\n",
    "HF_EMBEDDING_MODEL_NAME = os.getenv(\"HF_EMBEDDING_MODEL_NAME\")\n",
    "BASE_URL = os.getenv(\"LLM_BASE_LOCAL_URL\")\n",
    "MODEL_NAME = os.getenv(\"OLLAMA_MODEL_NAME\")\n",
    "\n",
    "config = AutoConfig.from_pretrained(HF_EMBEDDING_MODEL_NAME)\n",
    "embedding_dim = config.hidden_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea41ce0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OllamaEmbeddings(base_url=BASE_URL, model=MODEL_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eee9390",
   "metadata": {},
   "source": [
    "# Similitud Aleatoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7b150e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = load_dataset(\"sentence-transformers/natural-questions\")\n",
    "\n",
    "metric = SemanticSimilarity(embeddings=embeddings)  \n",
    "result = await metric.ascore(reference=pregunta, response=respuesta)"
   ]
  }
 ],
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   "file_extension": ".py",
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