Build an AI Application in Typescript
4 hours | 10 units
Master the basics of vector search and build AI applications — all in your favorite language.
Introduction
Welcome to the first part of our course: Understanding Vector Databases. In this section, we will explore the concept of vector databases, understand the differences between vector databases and vector indices, and delve into how vector databases work. Efficient processing for embeddings has become essential, especially for applications that involve large language models, generative AI, and semantic search. To manage these complex embeddings effectively, we need a specialized database designed for this purpose - a vector database. Let’s get started and learn more about what vector databases are.
Vector Embeddings
In this section, we will explore vector embeddings, a key concept in modern AI. These embeddings translate diverse data, like text or images, into numerical vectors, capturing their inherent relationships. This conversion facilitates advanced processing by machine learning algorithms. Dive in to understand the creation, properties, and applications of these transformative entities.
Vector Similarity
In this section, we dive deep into the mechanics of Vector Similarity. This is a key concept that enables the powerful capabilities of Pinecone. Vector similarity allows us to understand the relationship between data points represented as vectors, aiding the retrieval of relevant information based on the query.
Get Hands On
In this section, we explore practical applications of TypeScript and Pinecone in advanced technologies. We'll create a semantic search engine using Pinecone, tackling setup, data preprocessing, and text embeddings. Next, we'll develop a LangChain Retrieval Agent to address chatbot challenges like data freshness and domain knowledge. Finally, we'll use Pinecone's similarity search for personalized content recommendations. This combined approach showcases the power of these technologies in real-world scenarios.