OpenAI Embeddings and Vector Databases Crash Course
TLDRThis video crash course introduces embeddings and vector databases, essential tools for AI product development. It explains the theory behind embeddings, which are numerical representations of words that capture relationships, and vector databases, which store these embeddings for various applications like semantic searches and recommendations. The tutorial demonstrates how to use OpenAI's API to create embeddings and store them in a cloud database for future retrieval and searching. By leveraging these technologies, one can build systems that perform complex tasks such as long-term memory for chatbots or searching large databases of documents.
Takeaways
- 📚 Embeddings and vector databases are crucial for building AI products, allowing for semantic understanding and efficient searching.
- 📈 An embedding is a numerical representation of words or images, capturing patterns and relationships in a multi-dimensional vector space.
- 🔍 Vector databases store and manage these embeddings, enabling functions like searching, clustering, and classification based on similarity.
- 🌐 OpenAI provides APIs for creating embeddings but does not offer a storage solution, requiring developers to use external databases.
- 🛠️ Postman is a useful tool for making API requests and can simplify the process of creating and testing embeddings.
- 🔑 Authorization is required to use OpenAI's API endpoints, which can be managed through API keys kept secure and private.
- 📊 The strength of embeddings lies in their ability to represent large chunks of information, such as paragraphs or documents, for detailed analysis.
- 🗄️ Single store is an example of a cloud database provider that supports vector databases, facilitating the storage and searching of embeddings.
- 🔎 Searching a vector database involves creating an embedding for the query term and comparing it against existing embeddings to find the most similar results.
- 💡 Practical applications of embeddings include semantic searches on large databases of text or images, and creating long-term memory for AI applications like chatbots.
- 📚 Further learning resources, such as books and tutorials, are available for those interested in a deeper understanding of OpenAI and vector databases.
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 pieces of data.
How do embeddings and vector databases work together?
-Embeddings represent data as vectors, and a vector database is a database that stores these vectors. Together, they enable functions like searching, clustering, and classification based on the similarity of the vectors, which can represent words, images, or larger chunks of data.
What is an example of how embeddings can be used?
-Embeddings can be used to create long-term memory for a chatbot like GPT or to perform semantic searches based on a large database of documents, such as PDFs, by measuring the similarity between the query and the embedded data.
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 significance of the dimensionality of vectors in embeddings?
-The dimensionality of vectors in embeddings captures the complexity and richness of the relationships between words or data points. While a simple 2D example might only show similarity based on one dimension, in reality, vectors can have hundreds of dimensions, allowing for a more nuanced representation of relationships.
How can images be turned into vectors for similarity searches?
-Images can be broken down into arrays of numbers, where each number represents a specific aspect or feature of the image. These arrays of numbers can then be used to find patterns of similarity between different images, enabling similar image searches.
What is Postman and how is it used in the context of API requests?
-Postman is a software and web application that allows users to make various types of API requests easily. It provides a graphical user interface for creating, sending, and managing API requests and can be used to test and interact with APIs like OpenAI's.
How does one store embeddings in a vector database?
-Once an embedding is created, it can be stored in a vector database by creating a record with the original text and the vector as its attributes. This allows for the organization and retrieval of embeddings for later use in searching or other applications.
What is an example of a practical use for embeddings in a vector database?
-A practical use for embeddings in a vector database could be to perform semantic searches where the results are ranked by relevance to a query string. This could be used to find documents, images, or other data that closely relate to the search term.
How does one perform a search using a vector database?
-To perform a search using a vector database, one would first create an embedding for the search term, then use a query to compare this embedding with the stored embeddings in the database. The results are ranked based on the similarity score, with the most similar items being listed first.
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. This allows for the embedding of substantial amounts of text, such as sections of documents or multiple pages of a contract.
Outlines
📚 Introduction to Embeddings and Vector Databases
This paragraph introduces the concept of embeddings and vector databases as essential components in building AI products. It explains that embeddings are data, such as words, converted into numerical arrays or vectors that capture patterns and relationships. The paragraph outlines the video's structure, which includes explaining the theory, usage, and integration of embeddings and vector databases. It emphasizes the practical applications, like creating long-term memory for AI chatbots or performing semantic searches on large databases of documents.
🔍 Creating Embeddings with OpenAI's API
The second paragraph delves into the process of creating embeddings using OpenAI's API. It describes the need for a model and input text to generate an embedding and provides an example using 'ada002', OpenAI's text embedding model. The paragraph also touches on the versatility of embeddings, from single words to large chunks of text, and mentions the maximum input limit of 8,000 tokens. It demonstrates how to use Postman, an API platform, to create and store embeddings, highlighting the importance of keeping API keys private.
💾 Storing Embeddings in a Vector Database
This paragraph explains the necessity of storing embeddings in a vector database, as OpenAI does not provide storage for them. The speaker chooses a cloud database provider called 'a single store' to create a real-time, distributed SQL database. The process of setting up an account, creating a workspace and database, and designing a table within the database is detailed. It also covers how to insert a row with an embedding into the database using SQL commands, providing a step-by-step guide on populating the database with relevant data.
🔎 Searching Vector Databases for Embeddings
The fourth paragraph focuses on searching a vector database for specific embeddings. It outlines the process of identifying search terms, creating embeddings for those terms, and querying the database to find the most similar vectors. The paragraph provides a practical example of searching for 'open AI' and explains how the results are ranked based on similarity scores. It also discusses the potential for more complex searches and the adaptability of vector databases in various applications.
👨💻 Building a Function to Interact with Embeddings in Node.js
In the final paragraph, the speaker demonstrates how to create a JavaScript function in Node.js to interact with embeddings. The function, named 'createEmbedding', fetches data from the OpenAI API based on input text and logs the response. The paragraph emphasizes the importance of keeping API keys secure and suggests using an environment variable for key storage in practice. It also mentions the potential for further applications, such as importing PDFs or web content into the database for later retrieval and search.
Mindmap
Keywords
💡Embeddings
💡Vector Databases
💡Semantic Search
💡OpenAI API
💡Postman
💡SQL Database
💡AI Model
💡Search Term Embedding
💡Cloud Database Provider
💡Semantic Relationships
Highlights
Embeddings and vector databases are essential for building AI products.
An embedding is an array of numbers representing words with patterns of relationships.
Vectors with similar patterns are close together in a multi-dimensional space.
Images can also be converted into vectors for pattern recognition and similarity searches.
OpenAI provides AI models to create embeddings but not a storage solution.
Postman is a free API platform used for making API requests.
Creating an embedding involves a POST request with inputs and receiving a response.
OpenAI's API requires an API key for authorization.
Embeddings can represent single words, multi-word phrases, or large text blocks.
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 can create a table in a vector database to store embeddings.
Searches in a vector database return results ranked by similarity to the query string.
JavaScript on Node.js can interact with embeddings and vector databases.
The video demonstrates creating an embedding for 'hello world' and storing it in a database.
A search for 'open AI' retrieves embeddings with the highest similarity to the term.
The process of creating, storing, and searching embeddings is outlined for practical applications.
The video provides a foundation for further exploration of OpenAI's API and vector databases.
