egutierrez
c47b9474f4
- Added `text_manager.py` to handle the creation of text libraries via FastAPI. - Introduced database connection management in `conexion.py` using PostgreSQL credentials from environment variables. - Created abstract base class `EmbedderABC` in `Base_Embedder.py` for embedding models. - Developed `OpenAIEmbedder` class to generate embeddings using OpenAI's API. - Implemented `OpenAIEmbedderModel` and repository pattern for managing OpenAI embedders in `Openai_embedder_mmr.py`. - Established `Biblioteca` class for managing text libraries and their associated notes in `biblioteca.py`. - Created SQLAlchemy models and mappers for `Biblioteca` and `Nota` in `biblioteca_mmr.py` and `notas_biblioteca_mmr.py`. - Added functionality for dynamic table generation for notes associated with libraries. - Included comprehensive methods for adding, retrieving, and managing notes and libraries in their respective repositories.
144 lines
2.8 KiB
Plaintext
144 lines
2.8 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": "5206b9c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.ConexionSql.Postgres_conexion import PostgresConexion\n",
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"from src.Credenciales.postgres_credencial_mmr import PostgresCredencialRepo\n",
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"\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": "63a0b954",
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"metadata": {},
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"outputs": [],
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"source": [
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"from entrypoint.init_db import db_credencial\n",
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"conexion_admin = PostgresConexion(db_credencial)"
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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": "0575f424",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"repo_cred = PostgresCredencialRepo(conexion_admin)\n",
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"\n",
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"\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": 4,
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"id": "a5266309",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'PGCR20250510-02f3cf9610127084237'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"repo_cred.add(db_credencial)"
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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": "670c79b7",
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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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"Production_Fitz_db\n"
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]
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}
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],
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"source": [
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"modelo_cred = repo_cred.get_by_id(\"PGCR20250510-02f3cf9610127084237\")\n",
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"\n",
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"print(modelo_cred.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": 6,
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"id": "6cf421c7",
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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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"Estado de conexión: exito\n"
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]
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}
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],
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"source": [
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"# Conectar con la base de datos utilizando el modelo recuperado\n",
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"conexion_db = PostgresConexion(modelo_cred)\n",
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"# Verificar estado de conexión\n",
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"estado = \"exito\" if conexion_db.probar_conexion() else \"fallo\"\n",
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"\n",
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"print(f\"Estado de conexión: {estado}\")\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": 7,
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"id": "feba1446",
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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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"7Souw9SFD5P5RYpRWuTVvFkY7zlxATcN\n"
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]
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}
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],
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"source": [
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"print(modelo_cred.password)"
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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": ".venv",
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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": "3.11.11"
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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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