Lesson 3.1 — Principles of Vectorization & Embeddings
Introduction: Translating Words into Numbers
At their core, large language models are mathematical machines. They do not understand words and sentences in the same way that humans do. For an AI agent to process and retrieve information, that information must first be translated into a language it can understand: the language of numbers.
This is the role of vectorization and embeddings. These two concepts are the foundation of modern natural language processing and the key to unlocking the power of Retrieval-Augmented Generation (RAG). This lesson will demystify these concepts and provide you with a clear understanding of how they work and why they are so important.
The good news is that raia automatically converts your files into embeddings and uploads into the vector store.
What are Embeddings?
An embedding is a numerical representation of a piece of text, such as a word, a sentence, or an entire document. It is a dense vector of floating-point numbers, where each number represents a different dimension of the text's meaning.
An embedding is a vector of floating-point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness [1].
Think of it like a map. On a map, the location of a city is represented by a pair of coordinates (latitude and longitude). In the same way, an embedding represents the "location" of a piece of text in a high-dimensional "meaning space." Words and sentences with similar meanings will have similar embedding vectors and will be located close to each other in this space.
The Process of Vectorization
Vectorization is the process of creating an embedding for a piece of text. This is done using a specialized machine learning model called an embedding model. The embedding model takes a piece of text as input and outputs a vector of numbers.
There are many different embedding models available, each with its own strengths and weaknesses. Some of the most popular models include:
Word2Vec: One of the earliest and most influential embedding models.
GloVe: Another popular model that is known for its ability to capture the global statistical properties of a language.
BERT: A powerful, context-aware model that has become the standard for many NLP tasks.
OpenAI's
text-embedding-ada-002: A widely used and powerful embedding model.
Why are Embeddings So Powerful?
The power of embeddings lies in their ability to capture the semantic meaning of a piece of text. This allows an AI agent to perform a wide range of tasks that would be impossible with traditional keyword-based search.
Semantic Search
An agent can search for information based on the meaning of a query, not just the specific keywords it contains. For example, a search for "how to fix a flat tire" could return a document that uses the phrase "repairing a punctured wheel."
Clustering
An agent can group similar documents together, even if they do not share any of the same keywords. This is useful for tasks such as topic modeling and document organization.
Classification
An agent can classify a piece of text into a predefined category based on its meaning. This is useful for tasks such as sentiment analysis and spam detection.
Conclusion: The Building Blocks of Knowledge
Vectorization and embeddings are the fundamental building blocks of a knowledgeable AI agent. By translating words and sentences into a numerical format, we can unlock a wide range of powerful capabilities that go far beyond simple keyword matching.
In our next lesson, we will explore the crucial process of data hygiene and optimization, and learn how to ensure that the data we are feeding into our embedding model is as clean and effective as possible.
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