Yo opino que nos patrocinen unas API keys 🥰
¿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
Embeddings are the core of natural language processing (NLP) in artificial intelligence. They allow us to understand language and use large language models such as GPT-4, Lambda and others. They help us create advanced recommendation systems and semantic search engines, as well as enable machine translation and text classification. In essence, embeddings are numerical representations that enable Machine Learning models to understand the world we live in. They process instructions of various types, such as audio, text, video and images, bringing them into the world of numbers.
Embeddings transform text into numerical vectors, a fundamental process for understanding language. Although we can only visualize in three dimensions as humans, in reality, the representation extends to hundreds or thousands of dimensions. This is similar to the concept of digital image representation, where each pixel is identified by a number. Similarly, embeddings represent words in vector coordinates in a large dimensional space.
To take advantage of embeddings, we can use them from the Python programming language. Here's how to do it using the sentence-transformers
library.
sentence-transformers
in Python?To install the sentence-transformers
library we will use the pip package. This open-source library allows us to execute text sequences to embedding vectors.
pip install sentence-transformers
Then, we instantiate sentence-transformers
and use the utils
library to measure distances between the vectors generated by our sentences.
We start by instantiating a pre-trained model of sentence-transformers
, in this case all-MiniLM-L6-v2
, and pass a list of sentences to transform them to a vector space.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')sentences = ["The cat plays outside", "I like to play guitar", "I love pasta"]embeddings = model.encode(sentences)
Here, embeddings
stores the numerical representation of each sentence in a dimensional space.
Once the embeddings are generated, we can measure the similarity between sentences using the cosine metric, which evaluates how close the vectors are in vector space.
from sklearn.metrics.pairwise import cosine_similarity
similarity_scores = cosine_similarity(embeddings)
We create a list called pairs
that stores pairs of sentences and their similarity scores:
pairs = []for i in range(len(sentences)): for j in range(len(sentences)): if i != j: pairs.append({'sent1': sentences[i], 'sent2': sentences[j], 'score': similarity_scores[i][j]})
We sort the results in descending order to get the sentences closest to each other.
One of the most fascinating applications of embeddings is semantic search. This involves storing vector representations in a vector database that facilitates searching and querying information. This approach is used in conjunction with LLMs and chatbots, enabling more precise and contextual queries. To perform these applications, it is crucial to have basic knowledge in Python, API consumption, data manipulation and linear algebra.
Embeddings are fundamental to modern artificial intelligence, and their application offers enormous opportunities for innovation in language processing. It is an exciting and challenging path, I encourage you to join this exciting journey!
Contributions 21
Questions 4
Yo opino que nos patrocinen unas API keys 🥰
¡Les doy la bienvenida al Curso de Embeddings! 🤓💚
Recuerden traer su cuenta de OpenAI y su API Key y conocer el hub de Hugging Face para usar sus modelos de embeddings pre-entrenados más adelante.
Excelente , estoy emocionado por este curso , Gracias
Genial que estén generando cursos más avanzados en estas rutas!
When express the use of LLM, What does he mean ? Because when I use ChatGPT I never use an Embeddings only put my text into in the input text box and appear to answer the question that appear at that moment. The question that appears is, What does he mean with say the use of LLM.
Termine el curso de redes neuronales convolucionales y rapidamente comencé este emocionante
What are the dimension that he talks ?
I understood when he says like models of machine learning understand the word, the reality, the language, but I think is not the correct form to express the idea, because the word "understand" express mental metaphysic and epistemology conceptions that no appear into the LLM models, In my opinion is necessary to explain that the LLM process the information in such a way that the answer is coherent to our consciousness and is necessary the Embeddings to convert words space meaning into the vector number meaning
What are the Embeddings ? Numerical representations of the words, because the machine learning system only can process numbers no text words.
What is a semantic search ?
When I read more about Embeddings, appear these functionalities many times "recommended advanced systems" but How does it work ?, What are the process to implement? What is the role of Embeddings in the "recommended advanced systems" ?
He express "understanding of the language", but I think that is a illusion, the LLM models or any machine learning model only process numbers with a vector operations, when he express "understanding of the model", the complex question is what is understand ?
What are the embeddings, Why are necessary the embeddings to create a vector database ?
Wow!!! 🤯
¡Lo esperé hace mucho! 😌
Algunos ejemplos comunes de embeddings incluyen:
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