220 lines
72 KiB
Plaintext
220 lines
72 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "3c2edde7",
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.ApiKeys.openai_apikey import OpenAICredencial\n",
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"from src.ApiKeys.openai_apikey_mmr import OpenAICredencialRepo # Ajusta si está en otro módulo\n",
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"from src.ConexionSql.Postgres_conexion import PostgresConexion\n",
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"\n",
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"# 1. Crear instancia de conexión (asegúrate de configurar bien tu conexión en Base_conexion)\n",
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"from entrypoint.init_db import db_credencial\n",
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"conexion_admin = PostgresConexion(db_credencial)\n",
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"\n",
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"# 3. Guardar la credencial en la base de datos\n",
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"repo = OpenAICredencialRepo(conexion_admin)\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": 2,
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"id": "7a24ff10",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✅ Credencial: Credencial_enmanuel_gpt\n"
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]
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}
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],
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"source": [
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"credencial_openai = repo.get_by_id('OPAK20250510-ac2cea8af3110632314')\n",
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"print(f\"✅ Credencial: {credencial_openai.titulo}\")"
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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": 3,
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"id": "5ae61fe2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.Llms.Embedders.Openai_embedder import OpenAIEmbedder\n",
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"\n",
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"embedder = OpenAIEmbedder(credencial_openai, model=\"text-embedding-3-large\")"
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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": 4,
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"id": "42cb5e7e",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector1 = embedder.encoder(\"Hola, ¿cómo estás?\")"
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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": 5,
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"id": "13d34622",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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-0.015784094110131264, -0.017001541331410408, -0.019116055220365524, 0.022725677117705345, -0.01053518895059824, 0.005230216775089502, -0.007229926530271769, -0.008110973984003067, -0.04404168203473091, 0.03242253512144089, 0.011533708311617374, -0.011053137481212616, 0.0014830961590632796, 0.007010999601334333, -0.002102498896420002, -0.0014871008461341262, 0.007518269587308168, -0.008522129617631435, -0.007662440650165081, -0.008602224290370941, 0.011587105691432953, 0.0023334401194006205, -0.008143012411892414, -0.002288052812218666, 0.012857949361205101, -0.004544067662209272, -0.003153081052005291, -0.02291790582239628, -0.00569743849337101]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(f\"Vector 1: {vector1}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "9d3b1fa6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"3072\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(embedder.dimension_number())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "008ca87d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from src.TextManager.biblioteca import Biblioteca\n",
|
|
"from src.TextManager.biblioteca_mmr import BibliotecaRepo\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"biblioteca = Biblioteca(nombre=\"biblioteca_de_prueba\",\n",
|
|
" embedder=embedder,\n",
|
|
" )\n",
|
|
"\n",
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"\n",
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"# rerobiblio = BibliotecaRepo(conexion_admin)\n",
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"# rerobiblio.add(biblioteca)\n",
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"\n",
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"\n"
|
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "943d0deb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
|
"E:\\Fitz_Studio\\src\\TextManager\\notas_biblioteca_mmr.py:51: SAWarning: This declarative base already contains a class with the same class name and module name as src.TextManager.notas_biblioteca_mmr.NotaModel, and will be replaced in the string-lookup table.\n",
|
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" mapper_registry.map_imperatively(NotaModel, tabla)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'NOTA20250510-ec1a2f971d867643070'"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from src.base import Base\n",
|
|
"from src.TextManager.nota import Nota\n",
|
|
"from src.Llms.Embedders.Openai_embedder import OpenAIEmbedder\n",
|
|
"from src.ApiKeys.openai_apikey import OpenAICredencial\n",
|
|
"from src.TextManager.biblioteca import Biblioteca\n",
|
|
"from src.TextManager.notas_mmr import NotaRepo\n",
|
|
"\n",
|
|
"conexion = conexion_admin\n",
|
|
"embedder = OpenAIEmbedder(credencial_openai, model=\"text-embedding-3-large\")\n",
|
|
"biblioteca = Biblioteca(nombre=\"Física Cuántica\", embedder=embedder)\n",
|
|
"\n",
|
|
"# Crear tabla si no existe\n",
|
|
"NotaModel = biblioteca.generar_modelo_notas(conexion)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "f1726302",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'NOTA20250510-a1f15163f2041347124'"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"texto = \"Este es un texto de prueba\"\n",
|
|
"\n",
|
|
"\n",
|
|
"# Guardar una nota\n",
|
|
"nota = Nota(\n",
|
|
" titulo=\"Entrelazamiento cuántico\",\n",
|
|
" tags=[\"cuántica\", \"teoría\"],\n",
|
|
" conexiones=[],\n",
|
|
" texto=texto,\n",
|
|
" vector=embedder.encoder(texto)\n",
|
|
")\n",
|
|
"\n",
|
|
"repo = NotaRepo(conexion.get_session(), NotaModel)\n",
|
|
"repo.add(nota)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.11"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|