Cómo Entender y Aplicar Embeddings en IA: De Teoría a Práctica

What are embeddings and why are they key to natural language processing?

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.

How do embeddings represent text in vector space?

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.

Practical implementation of embeddings in Python

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.

How to install 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.

How to transform text into embeddings?

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.

How to measure the similarity between sentences?

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.

What are the applications of embeddings in vector databases?

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!