¿Cómo funcionan los embeddings?
Cómo Entender y Aplicar Embeddings en IA: De Teoría a Práctica
Introducción a One-Hot Encoding y TF-IDF en IA
Representación Vectorial de Palabras
Evaluación de Similitudes Semánticas: Métodos y Aplicaciones
Quiz: ¿Cómo funcionan los embeddings?
Creación de embeddings
Creación y entrenamiento de modelos Word2Vec con Gensim
Procesamiento y Limpieza de Datos para IA con Word2Vec y Gensim
Entrenamiento de Modelos Word2Vec con GenSim y Análisis de Similitud
Word2Vec: Entrenando IA para Comprender el Lenguaje
Quiz: Creación de embeddings
Usando embeddings preentrenados
Uso práctico de Sentence Transformers en procesamiento de textos
Análisis Semántico: Buscar Textos con Sentence Transformers
Manejo de Embeddings con OpenAI: API, Instalación y Datasets
Manejo y Visualización de Embeddings con OpenAI: Guía Práctica
Creación de un Motor de Búsqueda Semántico con Python
Transformación de Texto a Embeddings con Sentence Transformer
Quiz: Usando embeddings preentrenados
Bases de datos vectoriales
Qué es y cómo usar una base de datos vectorial
Gestión de Bases de Datos Vectoriales con ChromaDB: Instalación y Uso
Generación y manejo de embeddings en Chroma con Sentence Transformer
Consultas avanzadas y filtrado en bases de datos con Chroma
Cargar colección de Chroma previamente creada
Configuración y Uso de Pinecone: Desde la Instalación hasta la Inserción de Datos
Optimización de Ingesta de Datos en Pinecone: Procesos y Estrategias
Consultas Avanzadas en Pinecone: De Texto a Vector y Filtros
Carga de índices en Pinecone: Gestión eficiente en la nube
Carga de embeddings en Pinecone para búsqueda semántica
Creación de buscador semántico con Gradio y Sentence Transformer
Quiz: Bases de datos vectoriales
Conclusiones
Potenciando los LLMs: Integración de Embeddings y Datos Vectoriales
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How to take advantage of PyCon in the cloud?
PyCon stands out for not relying on local sessions or needing direct loading of instances into your Python environment. Moreover, being hosted in the cloud, you can close or open new sessions without losing your progress, which represents a significant advantage for your workflow. Do you want to know how to take advantage of this? Keep reading!
How to manage datasets and analysis in PyCon?
From the PyCon interface, you can manage datasets efficiently. An example is the "Movies embeddings" dataset which, although it contains a small typo, shows its versatility by being loaded with 1,000 vectors and at zero cost in its free version. This platform allows you to:
- Perform analysis at no additional cost.
- Observe and manipulate data directly from the interface.
- Execute queries and updates.
- Verify and delete fields without complications.
How to use PyCon with Python in a new session?
If you decide to start from scratch or simply continue after closing a previous session, PyCon allows you to easily integrate with Python. How do you do it? When starting a new session, simply instantiate PyCon again with your API key and environment variable. This will allow you to define any new index and work on it seamlessly.
# Example of PyCon integration with Python
# Instantiating PyCon againapi_key = 'tu_api_key'pd_config = PyCon(api_key=api_key, environment='tu_environment_variable')
# Defining a new indexindex2 = pd_config.index('Movies MVX')
# Perform a queryquery query_result = index2.query('The story of a space trip')
# Display the titles related to the topicfor result in query_result: print(result['title'])What are the expected results when running a query?
By running queries in PyCon, you will get accurate and relevant results without having to process the data again. For example, when querying for "The Story of Space Travel", the system can point to titles such as "The Right Stuff", Christopher Nolan's "Interstellar", or "Hidden Figures", all of which are related to the theme of space travel.
As you can see, PyCon greatly optimizes the workflow by handling the load and processing in the cloud, allowing you to focus on what really matters: getting accurate and efficient insights. So, don't hesitate any longer and take full advantage of PyCon's cloud capabilities to enhance your Python projects - keep learning and exploring the opportunities these tools offer you!
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