OpenAI Embeddings and Vector Databases Crash Course
TLDRThis video tutorial introduces embeddings and vector databases, essential for AI product development. It covers the theory, usage, and integration with OpenAI's APIs. The video demonstrates creating embeddings using OpenAI's text embedding model, Ada, and storing them in a vector database for semantic searches and recommendations. It also showcases how to use Postman for API requests and SQL for database management, providing practical insights into embedding data and performing searches based on query strings.
Takeaways
- ๐ Embeddings and vector databases are crucial for building AI products, converting words into numerical vectors that capture patterns of relationships.
- ๐ The combination of embeddings and vector databases allows for semantic searches, where results are ranked by relevance to a query string.
- ๐ In reality, vectors have hundreds of dimensions, providing a rich, multi-dimensional representation of complex relationships between words and even images.
- ๐ Once created, embeddings can be stored in a vector database, which can be used for searching, clustering, and recommendations based on similarity.
- ๐ OpenAI provides an AI model for creating embeddings but does not offer a way to store them; hence, a cloud database is used for this purpose.
- ๐ ๏ธ Postman, an API platform, is used to create API requests for embeddings by sending a POST request with inputs and receiving a response.
- ๐๏ธ Vector databases, such as the one from SingleStore, can be set up in the cloud and are used to store and search through embeddings.
- ๐ An SQL query is used to create a table in the database to store text and corresponding vector embeddings.
- ๐ Searching a vector database involves creating an embedding for the search term and finding the closest matches in the database based on similarity scores.
- ๐ The video provides a practical guide on how to create embeddings with OpenAI, store them in a vector database, and perform semantic searches using these embeddings.
- ๐ The example demonstrates how to embed large documents, like an NDA contract, and how to insert the resulting embeddings into a vector database for later retrieval and searching.
Q & A
What are embeddings in the context of AI?
-Embeddings are data, such as words, that have been converted into an array of numbers known as a vector. These vectors contain patterns of relationships and act as a multi-dimensional map to measure similarity between different data points.
How do embeddings and vector databases work together?
-Embeddings represent data as vectors, and a vector database is a database full of these embeddings. They work together to enable searching, clustering, and classification based on the similarity of the vectors.
What is an example of a 2D graph in the context of word embeddings?
-In the context of word embeddings, a 2D graph example would be representing words like 'dog' and 'puppy' as vectors that are close together, indicating their similar usage in various situations.
How does Google perform similar image searches?
-Google performs similar image searches by turning images into vectors, which are arrays of numbers representing the image's features. These vectors allow Google to find patterns of similarity between different images.
What is the purpose of creating embeddings with OpenAI's API?
-Creating embeddings with OpenAI's API allows developers to generate vectors for words, phrases, or documents, which can then be stored in a vector database for various applications such as semantic searches, clustering, and recommendations.
How does one create an embedding using OpenAI's API?
-To create an embedding using OpenAI's API, one needs to make a POST request with the model and input text as parameters. The API then returns a response containing the vector for the embedding.
What is Postman and how is it used in this context?
-Postman is an API platform and a web app that allows users to make various API requests. In this context, it is used to send POST requests to OpenAI's API to create embeddings and to interact with the vector database.
How can embeddings be stored and used in a vector database?
-Embeddings can be stored in a vector database by creating a table with columns for the original text and the vector. The embeddings can then be inserted as rows in this table and searched, clustered, or classified based on their vectors.
What is an example of a practical application of embeddings in AI products?
-An example of a practical application of embeddings is creating long-term memory for a chatbot like GPT or performing semantic searches based on a large database of PDFs connected directly to an AI system.
How does one perform a search in a vector database?
-To perform a search in a vector database, one creates an embedding for the search term and then uses a SQL query to find the closest matching vectors in the database. The results are ranked by their similarity score.
What is the maximum input size for embeddings using Ada version 2?
-The maximum input size for embeddings using Ada version 2 is approximately 8,000 tokens, which translates to around 30,000 letters or characters.
Outlines
๐ Introduction to Embeddings and Vector Databases
This paragraph introduces the concept of embeddings and vector databases, emphasizing their importance in building AI products. It outlines a three-part plan to explain the theory, usage, and integration of these technologies with OpenAI's APIs. The speaker intends to demonstrate how to create a long-term memory for AI models like chatbots or perform semantic searches on large databases of documents.
๐ Understanding Embeddings and Their Applications
The speaker delves into the definition of embeddings, describing them as data represented as vectors that capture patterns and relationships. Using a 2D graph example, the video explains how similar words like 'dog' and 'puppy' are represented close together in vector space. The paragraph also discusses how images can be converted into vectors for similarity searches, and touches on various applications of embeddings, such as searching, clustering, and classification, with a focus on searching for this video's context.
๐ ๏ธ Creating and Storing Embeddings with OpenAI and a Database
This section walks through the process of creating an embedding using OpenAI's API and storing it in a vector database. The speaker guides the audience on how to use Postman, a GUI software for API requests, to create an embedding by sending a POST request to OpenAI's API. It also explains the need for a database to store these embeddings and introduces a cloud database provider for this purpose. The paragraph details the steps to set up a database, create a table for embeddings, and insert data into it.
๐ Searching Vector Databases and Building a Function with Node.js
The speaker demonstrates how to perform searches in a vector database by creating an embedding for a search term and comparing it with existing embeddings to find the most similar results. The paragraph then transitions to showing how to build a simple function in JavaScript using Node.js to interact with OpenAI's API for creating embeddings. The function is tested by sending a 'hello world' request, highlighting the potential for further applications such as importing and searching through PDFs or websites.
Mindmap
Keywords
๐กEmbeddings
๐กVector Databases
๐กOpenAI
๐กAPIs
๐กSemantic Searches
๐กPostman
๐กCloud Database
๐กSQL
๐กVector Similarity
๐กNode.js
Highlights
Embeddings and vector databases are essential for building AI products.
Embeddings are words converted into vectors, representing patterns of relationships.
Vectors act as multi-dimensional maps to measure similarity between words.
Images can also be turned into vectors for pattern recognition and similarity searches.
OpenAI provides an AI model to create embeddings but does not offer a storage solution.
Postman is a free API platform used for creating and sending API requests.
Creating an embedding involves a POST request with a model and input text.
OpenAI's text embedding uses the Ada002 model for creating embeddings.
Embeddings can represent single words, multi-word phrases, or large text chunks.
Ada version 2 allows for a maximum input of approximately 30,000 characters.
A vector database is a database full of embeddings that can be used for searching, clustering, and recommendations.
SingleStore is a cloud database provider that supports vector databases.
An SQL query is used to create a table in the SingleStore database for storing embeddings.
Searches in vector databases return results ranked by relevance to the query string.
JavaScript on Node.js can be used to interact with embeddings and vector databases.
The video provides a step-by-step guide on creating embeddings and setting up a vector database.