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 with patterns of relationships, and vector databases, where these embeddings are stored for various applications like semantic searches and recommendations. The tutorial demonstrates how to create embeddings using OpenAI's API, store them in a cloud database, and perform searches to find relevant information. By showcasing the process in a step-by-step manner, the video empowers viewers to harness the power of embeddings for their AI projects.
Takeaways
- ๐ Embeddings are numerical representations of words or phrases that capture their semantic meaning and relationships.
- ๐ Vector databases store embeddings and enable efficient searching and retrieval based on semantic similarity.
- ๐ OpenAI provides an API for creating embeddings but does not offer a database service for storing them.
- ๐ง Postman is a useful tool for making API requests and can be used to interact with OpenAI's API for creating embeddings.
- ๐ To use OpenAI's API, an API key is required, which should be kept secure and not hardcoded in your applications.
- ๐ Embeddings can be created for single words, phrases, or large blocks of text, such as paragraphs or entire documents.
- ๐๏ธ A vector database can be set up using cloud services like SingleStore, which supports real-time unified distributed SQL databases.
- ๐ SQL queries can be used to create tables in the database to store text inputs and their corresponding embeddings.
- ๐ Searching a vector database involves creating an embedding for the search term and comparing it with stored embeddings to find the most similar results.
- ๐ The strength of embeddings lies in their ability to capture complex, multi-dimensional relationships between words, which is useful for semantic searches and recommendations.
- ๐จโ๐ป Practical applications of embeddings and vector databases include creating long-term memory for chatbots, semantic searches on large PDF databases, and content-based recommendations.
Q & A
What are embeddings in the context of AI?
-Embeddings are a way of representing data, such as words, as an array of numbers known as a vector. These vectors capture patterns of relationships and can be used to measure similarity in a multi-dimensional space.
How do embeddings and vector databases work together?
-Embeddings represent words or images as vectors, which 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 to a query.
What is the significance of embeddings for AI products?
-Embeddings are crucial for AI products as they allow for the creation of long-term memory, semantic searches, and the processing of complex relationships between data, which enhances the product's understanding and response to user inputs.
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.
What is the role of a vector database in AI applications?
-A vector database stores embeddings, allowing AI applications to perform tasks such as searching for relevant information based on query strings, clustering similar text strings, and making recommendations based on related text.
How can one store embeddings created by OpenAI's API?
-Since OpenAI does not provide a way to store embeddings, users can utilize cloud databases, such as SingleStore, to create their own vector databases for storing and managing embeddings.
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.
How does one perform a search using a vector database?
-To perform a search, one must create an embedding for the search term, and then query the vector database to find and rank the closest matching vectors based on their similarity to the search term.
What is the process for inserting a vector into a SingleStore database?
-First, create a table with columns for the original text and the vector. Then, use an SQL INSERT command to add a new row with the text and the vector, which must be converted to a BLOB type before insertion.
How can one use JavaScript to interact with embeddings?
-By creating a function in JavaScript that sends a fetch request to the OpenAI API to create an embedding, and then using this embedding to interact with a vector database, such as by adding it to the database or performing searches.
What are some potential applications of embeddings and vector databases?
-Embeddings and vector databases can be used for semantic searches, long-term memory in chatbots, content recommendation systems, and information retrieval from large databases of text or images.
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. The speaker mentions the intention to demonstrate how to create a long-term memory for AI models like chatbots and perform semantic searches on large databases of documents, all connected to AI.
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. An example using a 2D graph illustrates how words with similar usage, like 'dog' and 'puppy', are represented by closely located vectors. The paragraph further explains that embeddings can be used for searching, clustering, and classification. The speaker then guides the audience through the process of creating an embedding using OpenAI's API and the Postman API platform, highlighting the simplicity of the process.
๐๏ธ Storing and Searching Vector Databases
This section discusses the storage and searching capabilities of vector databases. The speaker explains that while OpenAI provides a model for creating embeddings, it does not offer a storage solution, leading to the use of a cloud database. The process of setting up a database, creating a table, and inserting embeddings is detailed. The speaker demonstrates how to insert a single word, multi-word phrases, and larger text chunks into the database, and how to perform searches by creating an embedding for the search term and comparing it against the database's stored embeddings.
๐ป Practical Application with JavaScript and Node.js
The speaker concludes the video by showing a practical application of the discussed concepts using JavaScript on Node.js. A function is created to interact with the OpenAI API to fetch embeddings for given text. The process of making a fetch request, handling the response, and logging the output is demonstrated. The speaker suggests further applications, such as importing PDFs and websites into the database for later retrieval and search. The video ends with a promotion of a digital book for those interested in learning more about OpenAI and its applications.
Mindmap
Keywords
๐กEmbeddings
๐กVector Databases
๐กOpenAI
๐กSemantic Search
๐กPostman
๐กAPI Requests
๐กCloud Database
๐กSQL
๐กJSON
๐กNode.js
Highlights
Embeddings and vector databases are essential for building AI products.
Embeddings are words converted into vectors that represent patterns of relationships.
Vectors can be used for semantic searches based on a database of PDFs connected to AI.
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 that simplifies API requests and responses.
Creating an embedding involves a simple POST request with inputs and receiving a response.
OpenAI's Ada version 2 allows for a maximum input of 8,000 tokens.
Embeddings can be used for searching, clustering, and recommendations.
Single store is a cloud database provider that supports vector databases.
A vector database can be queried using SQL to find embeddings with the highest similarity.
JavaScript on Node.js can be used to interact with embeddings and perform searches.
Embeddings can be used to index and search large documents, such as contracts or legal documents.
The process of creating and storing embeddings can be automated for scalable AI applications.
OpenAI's API can be integrated with databases and web applications for advanced AI functionality.
The video provides a comprehensive guide on using embeddings and vector databases with OpenAI's API.