OpenAI Embeddings and Vector Databases Crash Course
TLDRThis video crash course delves into the world of OpenAI embeddings and vector databases, essential for AI product development. It explains the concept of embeddings, which are numerical representations of words, and vector databases, where these embeddings are stored. The tutorial covers the creation of embeddings using OpenAI's API, their storage in a cloud database, and how to perform semantic searches. By leveraging these technologies, one can create long-term memory for AI applications or conduct complex searches through vast databases of information.
Takeaways
- ๐ Embeddings and vector databases are crucial for building AI products, as they help represent data in a numerical form to measure similarity and relationships.
- ๐ Embeddings are a way to convert words into vectors, which are arrays of numbers representing patterns and relationships between the words.
- ๐ A vector database is a database that stores these embeddings, allowing for various applications such as searching, clustering, and recommendations based on similarity.
- ๐ OpenAI provides an AI model to create embeddings but does not offer a way to store them, necessitating the use of a cloud database.
- ๐ง Postman is a useful tool for making API requests and can be used to interact with OpenAI's API to create embeddings.
- ๐ To use OpenAI's API, an API key is required for authorization, which should be kept private and secure.
- ๐ The strength of embeddings lies in their ability to handle large chunks of information, such as paragraphs or entire documents, for more nuanced and detailed searches.
- ๐๏ธ Vector databases can be set up on cloud platforms like AWS, and they allow for real-time searching and storage of embeddings.
- ๐ Searching a vector database involves creating an embedding for the search term and comparing it against existing embeddings to find the most similar results.
- ๐จโ๐ป Practical applications of embeddings and vector databases include semantic searches on large databases of PDFs or creating long-term memory for chatbots like GPT.
- ๐ Further learning resources are available, including a digital book 'Teach Me OpenAI and GPT', which covers comprehensive usage of the OpenAI API and fine-tuning techniques.
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 once created, they can be stored in a vector database. This database can then be used for various tasks like searching, clustering, and classification by comparing the similarity of these vectors in response to a query.
What is an example of a multi-word embedding?
-A multi-word embedding could be a small sentence like 'open AI vectors and embeddings are easy'. This creates a more nuanced embedding that represents the combined meaning of the words in the sentence.
How does OpenAI's API facilitate the creation of embeddings?
-OpenAI provides an API that allows users to create embeddings by sending a POST request with the model and input text. The API then returns a response containing the vector representation of the input text.
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 is roughly equivalent to 30,000 letters or characters.
How can you store embeddings without OpenAI providing a database solution?
-Since OpenAI does not provide a database for storing embeddings, users can opt for a cloud database provider like SingleStore, which allows for the creation of a vector database to store and manage these embeddings.
What type of SQL database is recommended for storing embeddings?
-A real-time unified distributed SQL database like SingleStore is recommended for storing embeddings, as it supports vector databases and offers easy cloud-based usage.
How do you perform a search in a vector database?
-To perform a search, you create an embedding for your search term, and then run a SQL query against the database to find existing embeddings with the closest similarity. The results are ranked based on the score, which represents the similarity.
What is an example of a JavaScript function to create an embedding using Node.js?
-A JavaScript function named 'createEmbedding' can be created in Node.js, which uses the fetch API to send a POST request to OpenAI's embeddings endpoint with the appropriate headers and body to generate an embedding for a given text.
How can embeddings be utilized in practice?
-Embeddings can be used for semantic searches on large databases of documents, like PDFs, or for information retrieval from websites. They can also be used to create long-term memory for chatbots or perform classification tasks based on similarity.
What is the significance of the vector's dimensions in an embedding?
-The dimensions in a vector represent the multi-dimensional relationships between words or data points. The higher the number of dimensions, the more complex and nuanced the relationships that can be captured, allowing for more accurate similarity measurements.
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 and Creating Embeddings
The speaker delves into the specifics of embeddings, describing them as data represented as vectors that capture patterns and relationships. Using the example of a 2D graph, the video explains how words like 'dog' and 'puppy' are represented close together in a vector space. The speaker then transitions intoๅฎๆ, showing how to create an embedding using OpenAI's API with the help of Postman, a GUI software for API requests. The process involves making a POST request with the model and input text to generate an embedding vector.
๐๏ธ Storing and Searching Vector Databases
This section discusses the role of vector databases in storing embeddings and their applications in searching, clustering, and classification. The speaker chooses to focus on searching and demonstrates how to store embeddings in a vector database using a cloud database provider. The process involves setting up a database, creating a table with specific columns for text and vector data, and inserting rows with the embeddings. The speaker also explains how to perform a search by creating an embedding for the search term and comparing it against the database entries.
๐ป Implementing Embeddings in Node.js
The speaker concludes the video by showing how to create a JavaScript function using Node.js to interact with embeddings. The function, named 'createEmbedding', takes a text input and fetches an embedding from OpenAI's API. The speaker emphasizes the importance of keeping API keys secure and suggests using an environmental key for production use. The video ends with a call to action to learn more about OpenAI through the speaker's book and a brief overview of the possibilities with embeddings, such as importing PDFs and performing semantic searches.
Mindmap
Keywords
๐กEmbeddings
๐กVector Databases
๐กOpenAI
๐กAPIs
๐กPostman
๐กSemantic Search
๐กChat GPT
๐กCloud Database
๐กSQL
๐กSearch Term Embeddings
Highlights
Embeddings and vector databases are essential for building AI products.
Embeddings are words converted into vectors that represent patterns of relationships.
Vectors are multi-dimensional maps that measure similarity between words or images.
Google uses vector representations for similar image searches.
A vector database is a database full of embeddings that can be used for various applications.
OpenAI provides an AI model to create embeddings but does not offer a storage solution.
Postman is a free API platform that simplifies API requests and testing.
Creating an embedding involves a simple POST request with inputs and a response.
OpenAI's text embedding uses the Ada model, with a maximum input of 8,000 tokens.
Embeddings can be used for semantic searches by comparing vector similarity.
Single store is a cloud database provider that supports vector databases.
An SQL query can be used to create a table for storing embeddings in a vector database.
Searches in a vector database 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.
The process of embedding and searching is demonstrated using 'hello world' as an example.
The video also covers creating a function in JavaScript to automate the embedding process.
The potential applications of embeddings include long-term memory for chatbots and semantic searches on large PDF databases.