Crash Course: OpenAI Embeddings and Vector Databases

📚 Embeddings and Vector databases are essential for building AI products.

🔍 Embeddings are data converted into arrays of numbers that measure similarity.

💾 Embeddings can be stored in a vector database for searching, clustering, recommendations, and classification.

🔑 Creating API requests for embeddings using Postman.

💻 Using Postman to perform API requests for embeddings.

🔒 Generating an API key for authentication with the OpenAI endpoint.

💡 Creating the first embedding using the model and input.

📝 OpenAI embeddings allow you to generate vectors for different types of input, from single words to paragraphs or sections of documents.

💾 To store the generated embeddings, you can create a vector database using a provider like SingleStore, which offers a real-time unified distributed SQL database.

🔍 Once you have the vector database set up, you can search through the stored embeddings to retrieve relevant information.

🏢 Creating a workspace and a vector database using OpenAI Embeddings.

💻 Setting up the workspace with minimal configurations.

🗄️ Creating a table in the database and inserting data.

📋 Using Postman, the speaker demonstrates how to input and store embeddings in a vector database.

🔍 The process of searching a vector database involves creating an embedding for the search term and performing a search against existing embeddings.

💡 The speaker explains the simple steps involved in searching a vector database for embeddings and highlights the importance of adding more data to the database.

🔑 Vector databases allow for efficient searching and ranking of similar vectors.

🔍 The process of vector searching involves creating embeddings, searching for similar vectors, and ranking them based on similarity scores.

💻 A JavaScript function can be used to interact with OpenAI embeddings, fetching data from the API and creating embeddings.

OpenAI Embeddings can be obtained by sending a post request with specific parameters

The response from the server contains the embedded data, which can be stored in a database

The embedded data can be used for various purposes, such as processing PDFs or websites