Introduction
As a data scientist specializing in Natural Language Processing (NLP), Vector Databases, Embeddings, and Generative AI, understanding the intricacies of these concepts is crucial for building effective and efficient models. This tutorial will delve into the concepts of embeddings, vector databases, and various distance metrics, providing examples and code snippets along the way.
Embeddings in NLP
Embeddings are numerical representations of objects, words, or entities in a continuous vector space. In NLP, word embeddings capture semantic relationships between words, enabling algorithms to better understand the context and meaning of text.
Let’s try to understand this with an example and some visualization to give an intuition of embeddings and it’s understanding.
Example Code:
Let’s assume we have 6 sentences for which we want to create embeddings which is as given in below code snippet , please follow the comments in the snippet.
from sentence_transformers import SentenceTransformer
# Sample text embedding model
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
#Sentences we want to encode. Example:
sentence = ['The team enjoyed the hike through the meadow',
'The team…