A Complete Guide to Embedding For NLP & Generative AI/LLM

Author(s): Mdabdullahalhasib

Originally published on Towards AI.

Understand the concept of vector embedding, why it is needed, and implementation with LangChain.

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Source: Image by Author (converting word into Vector)

If you want to learn something efficiently, first, you should ask questions yourself or generate questions about the topics. For example, why should I learn this topic? Why this topic has been discovered? How I can effectively use this topic? And so on. The more questions you ask, the more knowledge you get.

After reading the whole article carefully, you can answer the following questions.

What is Vector embedding and why do we need this?How Vector Embedding has been discovered?How to implement Vector Embedding in LangChain?How to visualize the embedding?

For training machine learning algorithms with datasets, Machines only understand numbers. The data type can be an image, text, audio, or tabular data, we have to convert the data into representative numerical formats.

Vector embedding is a mathematical representation of any objects/data. The main theme is that it can contain semantic and meaningful contextual information about the objects so that ML algorithms can efficiently analyze and understand the data.

Many neural network approaches have been developed to convert the data into numerical representation. Different data types are embedded in different ways. Let’s have a look at those.

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Published via Towards AI