Vector Embeddings Tutorial – Code Your Own AI Assistant with GPT-4 API + LangChain + NLP

This paragraph introduces the concept of vector embeddings, which are numerical representations of rich data like words or images that capture their essence. The course, led by Anya Kubo, aims to help learners understand the significance of text embeddings, their diverse applications, and how to generate their own with Open AI. It also covers integrating vectors with databases and building an AI assistant using these powerful representations.

This section delves into what vector embeddings are and their uses in machine learning and natural language processing. It explains how text embeddings can provide more information about words, such as their meaning, in a format that computers can understand. The paragraph also discusses the use of cosine similarity to calculate the similarity between vectors and provides examples of how vector embeddings can be applied in various AI tasks, including recommendation systems, anomaly detection, transfer learning, visualizations, and information retrieval.

This paragraph discusses the wide range of applications for vector embeddings, beyond just text. It highlights the ability to vectorize sentences, documents, images, and even facial recognition data. The section covers the use of embeddings in tasks such as document classification, semantic search, social network analysis, and more. It emphasizes the core advantage of vector embeddings in transforming complex, multi-dimensional data into a lower-dimensional space that captures semantic or structural relationships.

This part of the script provides a practical guide on how to generate vector embeddings using Open AI's Create Embedding API. It walks through the process of logging into Open AI, obtaining an API key, and using the API to generate embeddings for a given text. The example demonstrates how to represent a sentence with an array of numbers and how to use different models to create text embeddings, showcasing the versatility of vector embeddings in capturing the meaning behind words.

This paragraph discusses the importance of storing vector embeddings in databases designed for AI workloads. It explains the need for a purpose-built database, like Data Stacks or AstroDB, which can handle the storage and access of these embeddings efficiently. The script then guides the user through setting up a vector database, creating a keyspace, and preparing for the creation of an AI assistant by storing and accessing vector embeddings.

This section focuses on the technical steps required to connect to the created database and Open AI from an external source. It covers obtaining an application token and a secure connect bundle from Data Stacks, as well as creating a new API key from Open AI. The paragraph provides instructions on setting up a Python script with the necessary packages and configurations to interact with the database and Open AI's API for generating embeddings.

This paragraph details the process of building an AI assistant capable of performing vector searches on a database. It explains how to use the Lang Chain package to connect various AI models and data sources, and how to use Cassandra and Open AI embeddings to create an index and search for similar text. The script demonstrates inserting data into the database, performing vectorized searches, and returning relevant documents based on the query.

In this final section, the AI assistant's ability to search for and return relevant documents based on user queries is demonstrated. The assistant uses vector search to find documents similar to the user's question from a dataset, and presents the results with a relevance score. The example shows how the AI assistant can handle different types of questions and provide appropriate responses by searching through vectorized data.