Crash Course: OpenAI Embeddings and Vector Databases
Learn how to use embeddings and vector databases with OpenAI to enable long-term memory and semantic searches based on a database of PDFs connected to an AI.
00:00:00 Learn how to use embeddings and vector databases with OpenAI, enabling long-term memory for chat GPT and semantic searches based on a database of PDFs connected to an AI.
📚 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.
00:02:44 Learn how to create API requests for embeddings using Postman and OpenAI's text embedding model ada002.
🔑 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.
00:05:27 Learn how to create OpenAI embeddings and vector databases, allowing you to store and search through large amounts of information efficiently.
📝 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.
00:08:12 Learn how to create a workspace and database using OpenAI Embeddings and Vector Databases. Run SQL commands to create a table and insert data.
🏢 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.
00:10:58 Learn how to use OpenAI embeddings and vector databases to store and search for data. Add rows to the database and perform searches based on similarity.
📋 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.
00:13:42 Learn how to use OpenAI embeddings and vector databases to search for similar content. Create a JavaScript function to interact with embeddings using the OpenAI API.
🔑 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.
00:16:29 Learn how to use OpenAI embeddings and vector databases to store and retrieve data quickly. Explore the possibilities of importing PDFs, websites, and more. Check out the book 'Teach Me OpenAI and GPT' for in-depth knowledge. End with integration and web app video.
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
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