117 lines
3.0 KiB
Plaintext
117 lines
3.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "096e6224",
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"metadata": {},
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"source": [
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"# Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b1970d59",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_index.core import download_loader\n",
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"from ragas.testset.evolutions import simple, reasoning, multi_context\n",
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"from ragas.testset.generator import TestsetGenerator\n",
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"from langchain_openai import ChatOpenAI\n",
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"from ragas.embeddings import OpenAIEmbeddings\n",
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"import openai\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6bfe1ca0",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import re\n",
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"import uuid\n",
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"from pathlib import Path\n",
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"from typing import Any, Protocol\n",
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"from datasets import load_dataset\n",
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"from langchain_core.documents import Document\n",
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"from langchain_elasticsearch import ElasticsearchStore\n",
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"import torch\n",
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"import torch.nn.functional as F\n",
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"from loguru import logger\n",
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"from langchain_ollama import OllamaEmbeddings\n",
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"from ragas.metrics.collections import SemanticSimilarity \n",
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"from transformers import AutoTokenizer, AutoModel, AutoConfig\n",
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"from elasticsearch import Elasticsearch\n",
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"from langchain_elasticsearch import ElasticsearchStore\n",
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"import nltk\n",
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"from nltk.tokenize import sent_tokenize\n",
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"nltk.download(\"punkt\", quiet=True)\n",
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"\n",
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"ES_URL = os.getenv(\"ELASTICSEARCH_LOCAL_URL\")\n",
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"ES_INDEX_NAME = os.getenv(\"ELASTICSEARCH_INDEX\")\n",
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"HF_EMBEDDING_MODEL_NAME = os.getenv(\"HF_EMBEDDING_MODEL_NAME\")\n",
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"BASE_URL = os.getenv(\"LLM_BASE_LOCAL_URL\")\n",
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"MODEL_NAME = os.getenv(\"OLLAMA_MODEL_NAME\")\n",
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"\n",
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"config = AutoConfig.from_pretrained(HF_EMBEDDING_MODEL_NAME)\n",
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"embedding_dim = config.hidden_size"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ea41ce0f",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = OllamaEmbeddings(base_url=BASE_URL, model=MODEL_NAME)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8eee9390",
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"metadata": {},
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"source": [
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"# Similitud Aleatoria"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d7b150e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"ds = load_dataset(\"sentence-transformers/natural-questions\")\n",
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"\n",
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"metric = SemanticSimilarity(embeddings=embeddings) \n",
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"result = await metric.ascore(reference=pregunta, response=respuesta)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "assistance-engine",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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