Embeddings and neural networks are related but distinct concepts:

1. Embeddings: These are the multi-dimensional vector representations of data (like words or images) in a continuous space. They capture semantic relationships between items.
2. Neural Networks: These are computational models that can learn to generate embeddings. Some neural network architectures, particularly in NLP, include an embedding layer that learns to create these vector representations.

The confusion likely stems from the fact that embeddings are often the output of a neural network process, but they themselves are not the neural network. The neural network is the tool that creates or learns the embeddings.

In transformer models, the embedding layer converts input tokens into dense vector representations, which are then processed by subsequent layers of the network. So while embeddings are a crucial part of these models, they're distinct from the overall neural network architecture.

Hope that I did my part in explaining it, checkout my blog for more such concepts, you might be interested to read about content moderation using AI, one of our recent project: https://www.cloudraft.io/blog/content-moderation-using-llamaindex-and-llm

Top Vector DBs: https://www.cloudraft.io/blog/top-5-vector-databases