$0 Embeddings (OpenAI vs. free & open source)
TLDRThe video discusses the cheapest and best ways to generate embeddings, highlighting OpenAI's Text Embedding Ada 2 for its affordability and performance. It also explores open-source alternatives for self-hosting embedding models to avoid vendor lock-in and work offline. The video covers various embedding models, their use cases, and how to rank them, especially compared to OpenAI. It introduces the concept of multimodal embeddings, which can handle different media types like text and images in the same vector space, showcasing the potential for future AI applications.
Takeaways
- π° OpenAI's text embedding model, Ada 2, is cost-effective at $0.0001 per 1000 tokens but there are other open source models worth considering.
- π€ The best embedding model depends on the specific use case, including input size limits, dimension size, and the type of tasks the model is designed for.
- π Hugging Face's massive text embedding Benchmark (mteb) provides a comprehensive evaluation of various embedding models across different tasks.
- π When selecting an embedding model, consider the model's performance, speed, and size, as well as the nature of the data and the requirements of the application.
- π Hugging Face offers an API for generating embeddings, which can be used for free for development purposes but requires a dedicated instance for production use.
- π οΈ Transformers.js allows for running state-of-the-art machine learning models in the browser or on a server using JavaScript, providing an alternative to using APIs.
- π Understanding the different tasks that embeddings can be used for, such as search, clustering, classification, and summarization, can help in choosing the right model.
- π The process of generating embeddings involves tokenization, where text is broken down into tokens, and models likeBERT and MPNet use different tokenization strategies.
- π Multimodal embeddings, which can represent different media types like images and text in the same vector space, are an exciting development for the future of AI and machine learning.
- π The future of embeddings may involve more focus on multimodal spaces and the ability to generate embeddings that work across different media types, opening up new possibilities for AI applications.
Q & A
What is the main topic of the video?
-The main topic of the video is the comparison of different methods to generate embeddings, focusing on open AI and open source alternatives.
What is the cost of OpenAI's text embedding as of June 13th, 2023?
-As of June 13th, 2023, OpenAI's text embedding costs 0.0001 per 1000 tokens.
What are some advantages of using open source embedding models over OpenAI's model?
-Open source embedding models allow for self-hosting, avoiding vendor lock-in, working completely offline, and potentially better performance for specific use cases.
What is the purpose of embeddings in AI and machine learning tasks?
-Embeddings are used to relate content together, such as determining the similarity between two pieces of text, images, or other data types.
How do multimodal embeddings differ from traditional embeddings?
-Multimodal embeddings can represent different types of media, such as images and text, in the same vector space, allowing for comparisons and similarities to be found across different media types.
What is the role of the Hugging Face hub in the AI community?
-The Hugging Face hub serves as a central platform for machine learning models, datasets, and tooling, allowing users to store, share, and use various models for different AI tasks.
What is the main benefit of using the transformers.js library for generating embeddings?
-The transformers.js library allows for the generation of embeddings directly in the browser or on a server using Node.js, providing flexibility and the ability to run models offline.
How does the MTEB (Massive Text Embedding Benchmark) project help users choose the best embedding model for their needs?
-The MTEB project evaluates and ranks embedding models based on their performance across diverse tasks, providing a leaderboard that serves as a reference for users to select the most suitable model for their specific use case.
What is the significance of the sentence-transformers library in the context of generating embeddings?
-The sentence-transformers library is a framework for generating sentence embeddings, which are used for tasks like semantic similarity comparison and clustering.
How does the video demonstrate the process of generating embeddings using the Hugging Face API?
-The video shows the process of installing the Hugging Face inference API, setting up the environment variables, and using the API to generate embeddings with a chosen model, such as the E5 small V2.
What are some potential future developments in the field of embeddings?
-Future developments in embeddings may focus on improving multimodal embeddings, allowing for more efficient and cost-effective generation of co-embeddings in shared vector spaces across different media types.
Outlines
π‘ Introduction to Text Embeddings and Open AI
The paragraph discusses the popularity of Open AI's text embeddings, particularly the Ada 2 model due to its affordability. It raises the question of whether there are better, open-source alternatives for generating embeddings, especially for those who wish to avoid vendor lock-in or work offline. The paragraph introduces the video's aim to explore different models, including self-hosted options, and to compare their performance with Open AI's offerings.
π Exploring Open Source Embedding Models
This section delves into the world of open source embedding models, highlighting the existence of models like Sentence-BERT (SBERT) and their capabilities. It emphasizes the importance of understanding the different use cases for embeddings, such as input size limits, dimension size, and task types. The paragraph also touches on the versatility of embeddings beyond text, including images and audio, and mentions the functionality of Google's reverse image search as an example of image embeddings in action.
π οΈ Building a Node.js App for Embeddings
The paragraph describes the process of building a Node.js application to work with embeddings, focusing on the choice of TypeScript due to its popularity among JavaScript developers and its compatibility with AI concepts. It outlines the basic structure of the project, including the package.json file and the index.ts entry point. The paragraph also discusses the importance of understanding embeddings as a way to relate content and the potential applications of embeddings in areas like search, clustering, and re-ranking.
π Understanding Embedding Models and Their Specializations
This section provides an overview of various embedding models, their specializations, and their performance benchmarks. It discusses the all-Dash models for general purposes, models specialized for search tasks, and multilingual models for bitext mining. The paragraph also mentions multimodal models that handle both text and images, emphasizing the potential of comparing dissimilar media types within the same vector space.
π Hugging Face's MTEB Leaderboard for Embedding Models
The paragraph introduces Hugging Face's Massive Text Embedding Benchmark (MTEB) as a valuable resource for evaluating text embedding models. It highlights the importance of the leaderboard for comparing models and understanding their performance across different tasks. The section also discusses the significance of input sequence length and embedding dimensions, as well as the potential benefits of using models with fewer dimensions for faster computation and lower memory usage.
π Building with Hugging Face's Inference API
This part of the script discusses two approaches for generating embeddings: using an API or running the model locally. It focuses on Hugging Face's Inference API, which allows running various models through a unified API. The paragraph explains the process of installing the necessary packages and using the API to generate embeddings, including the need for an access token. It also briefly touches on the pricing and infrastructure considerations for using the API in production environments.
π§ Deep Dive into Tokenization and Embedding Generation
The paragraph provides an in-depth look at tokenization, explaining how it works in the context of embedding generation. It discusses the process of breaking down text into tokens, which are then used by embedding models. The section also covers different tokenization algorithms like byte pair encoding and wordpiece tokenization, and how they affect the generation of embeddings. The paragraph emphasizes the importance of understanding tokenization for effectively working with embeddings.
π€ Implementing Embeddings Locally with Transformers.js
This section explores the option of generating embeddings locally using Transformers.js, a JavaScript library that enables running machine learning models in the browser or on a server. The paragraph explains the installation process, the creation of a pipeline for feature extraction, and the generation of embeddings using the library. It also discusses the use of the Onyx runtime, the need for models in the Onyx format, and the potential for quantizing models to reduce size and improve performance.
π The Future of Embeddings: Multimodal Models
The final part of the script looks at the future of embeddings, particularly focusing on multimodal models like CLIP that can handle different media types within the same vector space. The paragraph discusses the significance of being able to compare dissimilar media types and the potential applications this technology could enable. It mentions the importance of understanding and working with multimodal spaces as a key area of development in the AI field.
Mindmap
Keywords
π‘Embeddings
π‘OpenAI
π‘Self-hosting
π‘Vendor lock-in
π‘Open-source
π‘Sentence-transformers
π‘Hugging Face
π‘Text embedding Ada 2
π‘Multimodal models
π‘Zero-shot learning
Highlights
Exploring the cheapest and best ways to generate embeddings, with a focus on open AI and open source alternatives.
Open AI's text embedding Ada 2 is highly cost-effective at $0.0001 per 1000 tokens, but there may be better options.
Considering self-hosting and working offline with embedding models to avoid vendor lock-in and external API dependencies.
Introduction to popular open source embeddings that can be self-hosted and run directly in the browser.
Understanding the different use cases for embeddings, such as search, clustering, classification, re-ranking, and retrieval.
Evaluating the performance of various embedding models and their suitability for specific tasks and input sizes.
The importance of choosing the right embedding model based on the task requirements and the limitations of the model.
Using TypeScript for the demonstration of embedding generation, offering a different perspective from Python.
Exploring the potential of image embeddings for tasks like reverse image search and comparing image similarities.
Discussing the future of embeddings, including exciting developments in multimodal models that integrate text and images.
Comparing Open AI's offerings with other models on the hugging face inference API and the benefits of using APIs for embedding generation.
The role of databases like Postgres with PG Vector extension in storing and managing embeddings for search and retrieval tasks.
Understanding the tokenization process and how it affects the input sequence length and embedding generation.
The significance of the mteb leaderboard from hugging face for benchmarking and selecting the best embedding models.
Practical demonstration of generating embeddings using the hugging face inference API and transformers.js library.
Addressing the challenges of working with large embedding dimensions and the advantages of smaller models like E5 small V2.
The potential of using multimodal models for zero-shot image classification and captioning, expanding the capabilities of AI systems.