How deeply does Generative AI in Action cover vectorization?

Defines text conversion through embedding and vector management, without detailing implementation or mathematical derivation.

Vectorization

As explained in Generative AI in Action

Vectorization is the transformation of data into numerical vectors suitable for computation, comparison, or model input.

Vectorization prepares data as numerical representations for use in generative AI applications. In a grounding layer, it helps make data from varied sources and formats compatible with LLMs. section 10.4

For text, the process involves embedding and vector database management. Choose embedding techniques and vector storage with data size, origin, change rate, and scalability in mind. section 10.4.2

Account for factors that affect how text is prepared for embedding and retrieval:

Match chunking to content length, the embedding model, query complexity, and how the application uses results. section 7.7.2
Balance retrieval accuracy against performance and cost when selecting a chunking approach. section 7.7.2

How Generative AI in Action covers it

liveBook manning.com

Read Generative AI in Action on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

AI Agents and ApplicationsAn embedding model is a model that maps text or other data to numerical vectors representing semantic or contextual properties.
liveBook manning.com

Build AI-Enhanced Web AppsVectorization is the transformation of content or features into numerical vector representations for computational processing.
liveBook manning.com

AI Agents in Action, Second EditionVectorization is the transformation of documents or other data into numerical vector representations.
liveBook manning.com

Building Reliable AI SystemsA model that maps input content to numerical vectors that capture semantic properties.
liveBook manning.com

Build Python Web Apps with StreamlitA text embedding model is a machine-learning model that converts text into numerical vectors representing its semantic content.
liveBook manning.com

Fast Python: High performance techniques for large datasets
liveBook manning.com

Machine Learning Platform EngineeringAn embedding model is a machine learning model that maps inputs such as text or images to numerical vector representations.
liveBook manning.com

The Art of AI Product DevelopmentAn embedding model is a machine-learning model that maps text or other inputs to numeric representations in an embedding space.
liveBook manning.com

Data Pipelines with Apache Airflow, Second EditionVectorization is the transformation of content into numerical vectors that capture features for computational comparison or retrieval.
liveBook manning.com

Transformers in ActionAn embedding model maps inputs such as queries and documents to vector representations capturing semantic or other task-relevant relationships.
liveBook manning.com

Knowledge Graphs and LLMs in ActionFeaturization is the transformation of raw data or graph objects into numerical features suitable for machine-learning models.
liveBook manning.com

Natural Language Processing in Action, Second EditionA vectorizer is a function or component that transforms text into numerical vectors.
liveBook manning.com

MongoDB 8.0 in Action, Third EditionAn embedding model is a machine learning model that transforms text or other data into numerical vector representations.
liveBook manning.com

LLMs in ProductionVectorization is the transformation of input data into numerical vector representations.
liveBook manning.com

Deep Learning with R, Third EditionData vectorization is the conversion of input data or targets into numerical tensors suitable for neural-network processing.
liveBook manning.com

AI Agents in ActionVectorization is the transformation of data such as text into numerical vector representations.
liveBook manning.com

Deep Learning with Python, Third EditionThe process of converting data into vector form so it can be used by machine-learning code.

Compare how 18 books explain vectorization →

Related concepts

embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
knowledge graph embeddingcontrasts with · Knowledge graph embedding is a technique that represents entities and relationships as low-dimensional vectors while preserving patterns expressed by valid triples.
similarity searchused by · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
NumPyused by · NumPy is an open-source Python library for numerical computing that provides multidimensional arrays and operations on them.
vectorproduces · A vector is an ordered collection of components representing an element of a vector space.

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.